[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"cms-page:\u002Fabout\u002Four-team\u002Fharry-brace":3},{"name":4,"url":5,"description":6,"avatar_urls":7,"acf":9,"social":193,"head_links":195,"breadcrumbs":181,"posts":199,"pages":176,"head":1460},"Harry Brace","","I'm the Director, Head of Solutions at Impression. I spend my time debugging and implementing tracking using Google Tag Manager, managing data warehousing platforms and building Looker Studio reports!",{"24":8,"48":8,"96":8},"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress&fit=fit&fm=jpg&h=96&q=90&w=96",{"full_bio":10,"job_title":11,"specialist_service_links":12,"photo":49,"page_content_modules":105,"hero":177},"\u003Cp>Director, Head of Solutions. Joined Impression in November 2017.\u003C\u002Fp>\n\u003Cp>I gained 4 years of experience as a web developer before moving to Impression as a digital account manager in 2017. I moved across to the Analytics department in 2019 and I’ve been strengthening and growing our Analytics offering ever since.\u003C\u002Fp>\n\u003Cp>Day to day, I spend my time debugging and implementing tracking using Google Tag Manager, managing data warehousing platforms and building Looker Studio reports!\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Something that I’m proud of:\u003C\u002Fstrong>\u003Cbr \u002F>\nBuilding the analytics department over the last couple of years with the help of Aaron and now Alex H.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Outside of work:\u003C\u002Fstrong>\u003Cbr \u002F>\nFind me socialising, skiing or gaming.\u003C\u002Fp>\n","Director, Head of Solutions",[13,30,40],{"page":14,"link_text":29},{"ID":15,"post_author":16,"post_date":17,"post_date_gmt":17,"post_content":5,"post_title":18,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":21,"to_ping":5,"pinged":5,"post_modified":22,"post_modified_gmt":22,"post_content_filtered":5,"post_parent":23,"guid":24,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":28},374,"98","2021-01-11 16:25:32","Analytics","publish","closed","analytics","2026-09-25 09:11:08",0,"\u002F?page_id=374","page","0","raw","\u002Fanalytics\u002F","Web Analytics",{"page":31,"link_text":34},{"ID":32,"post_author":16,"post_date":33,"post_date_gmt":33,"post_content":5,"post_title":34,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":35,"to_ping":5,"pinged":5,"post_modified":36,"post_modified_gmt":36,"post_content_filtered":5,"post_parent":37,"guid":38,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":39},17916,"2022-06-20 08:56:55","Tag Management","tag-management","2026-09-24 15:15:21",17552,"\u002F?page_id=17916","\u002Fmedia-solutions\u002Ftag-management\u002F",{"page":41,"link_text":44},{"ID":42,"post_author":16,"post_date":43,"post_date_gmt":43,"post_content":5,"post_title":44,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":45,"to_ping":5,"pinged":5,"post_modified":46,"post_modified_gmt":46,"post_content_filtered":5,"post_parent":37,"guid":47,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":48},17140,"2022-05-27 14:22:19","Google Marketing Platform","google-marketing-platform","2026-09-25 10:43:13","\u002F?page_id=17140","\u002Fmedia-solutions\u002Fgoogle-marketing-platform\u002F",{"ID":50,"id":50,"title":51,"filename":52,"filesize":53,"url":54,"link":55,"alt":5,"author":56,"description":5,"caption":5,"name":57,"status":58,"uploaded_to":23,"date":59,"modified":59,"menu_order":23,"mime_type":60,"type":61,"subtype":62,"icon":63,"width":64,"height":65,"sizes":66},25135,"Harry-Brace2","Harry-Brace2.png",306919,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&q=90","\u002Fharry-brace2\u002F","12","harry-brace2","inherit","2023-10-03 16:03:03","image\u002Fpng","image","png","\u002Fwp\u002Fwp-includes\u002Fimages\u002Fmedia\u002Fdefault.png",696,600,{"thumbnail":67,"thumbnail-width":68,"thumbnail-height":68,"medium":69,"medium-width":70,"medium-height":71,"medium_large":72,"medium_large-width":73,"medium_large-height":74,"large":75,"large-width":76,"large-height":77,"1536x1536":78,"1536x1536-width":79,"1536x1536-height":80,"2048x2048":81,"2048x2048-width":82,"2048x2048-height":83,"post-thumbnail":84,"post-thumbnail-width":85,"post-thumbnail-height":86,"12_col":87,"12_col-width":88,"12_col-height":89,"default_landscape":90,"default_landscape-width":91,"default_landscape-height":65,"default_portrait":92,"default_portrait-width":65,"default_portrait-height":91,"6_col_and_margin_short":93,"6_col_and_margin_short-width":85,"6_col_and_margin_short-height":89,"11_col_and_margin":94,"11_col_and_margin-width":95,"11_col_and_margin-height":89,"9_col_rectangle":96,"9_col_rectangle-width":97,"9_col_rectangle-height":98,"11_col":99,"11_col-width":100,"11_col-height":89,"staff_image_696_600":101,"staff_image_696_600-width":64,"staff_image_696_600-height":65,"rectangular_thumbnail":102,"rectangular_thumbnail-width":103,"rectangular_thumbnail-height":104},"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640","640","https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=scale&h=172&q=90&w=200",200,172,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=scale&h=662&q=90&w=768",768,662,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=1000&q=90&w=1892","1892","1000","https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=scale&h=1324&q=90&w=1536",1536,1324,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=scale&h=1766&q=90&w=2048",2048,1766,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=1180&q=90&w=1860",1860,1180,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2880",2880,870,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=600&q=90&w=800",800,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=800&q=90&w=600","https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=870&q=90&w=1860","https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=870&q=90&w=3080",3080,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=1400&q=90&w=2150",2150,1400,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2640",2640,"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=600&q=90&w=696","https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress%2Cformat&fit=crop&h=300&q=90&w=750",750,300,[106],{"acf_fc_layout":107,"reduced":108,"top_overlap":-1,"bottom_underlap":-1,"disable_observer":-1,"title":109,"posts":110},"author_posts",true,"Harry Brace’s posts",{"data":111,"pages":176},[112,126,138,148,157,166],{"type_key":113,"title":114,"excerpt":115,"to":116,"postedAt":117,"readingTime":118,"image":119,"authors":120,"category":123},"post","Which Marketing Mix Modelling platform should you use?","\u003Cp>If you’ve decided to build a Marketing Mix Model (MMM), an important first step is to choose what platform you want to use. There are quite a few options, ranging from simple to complex, with different benefits to each You’ve probably come across these libraries already: Google’s Meridian, Meta’s Robyn, PyMC Marketing and a handful […]\u003C\u002Fp>\n","\u002Fblog\u002Fwhich-marketing-mix-modelling-platform-should-you-use\u002F","2026-06-10T11:28:01",6,"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F06\u002Fvaleria-nikitina-ie2R2t9fC9Q-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[121],{"name":4,"image":122},"https:\u002F\u002Fimages.impression.co.uk\u002F2023\u002F10\u002FHarry-Brace2.png?auto=compress&fit=crop&fm=jpg&h=150&q=80&w=150&bg=CFE6F9",{"to":124,"name":125},"\u002Fblog\u002Fcategory\u002Fdata-engineering\u002F","Data Engineering",{"type_key":113,"title":127,"excerpt":128,"to":129,"postedAt":130,"readingTime":131,"image":132,"authors":133,"category":135},"The future of Marketing Mix Modeling is Bayesian: Here’s why","\u003Cp>For decades, the Frequentist approach to Marketing Mix Modeling (MMM) was the industry standard. But as marketing journey became more complex and fragmented, it started to show its cracks. Here’s why a Bayesian approach to Marketing Mix Modeling has emerged as the stronger approach. At a high level, Frequentist statistics treats probability as purely data-driven. […]\u003C\u002Fp>\n","\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F","2026-03-18T15:19:38",3,"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[134],{"name":4,"image":122},{"to":136,"name":137},"\u002Fblog\u002Fcategory\u002Fmeasurement\u002F","Measurement",{"type_key":113,"title":139,"excerpt":140,"to":141,"postedAt":142,"readingTime":143,"image":144,"authors":145,"category":147},"Diminishing returns & Saturation curves: Is your ad spend working for you or against you?","\u003Cp>Why does the first £1,000 you spend on a marketing channel sometimes work harder than the last £10,000? The answer lies in saturation curves, and they’ll tell you exactly how effective each pound you spend really is. What exactly is a saturation curve? Saturation curves illustrate how the effectiveness of a marketing effort diminishes over […]\u003C\u002Fp>\n","\u002Fblog\u002Fsaturation-curves\u002F","2026-05-18T13:24:54",5,"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F02\u002Fjack-cohen-7m-Dt6NwCnI-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[146],{"name":4,"image":122},{"to":136,"name":137},{"type_key":113,"title":149,"excerpt":150,"to":151,"postedAt":152,"readingTime":131,"image":153,"authors":154,"category":156},"The baseline in Marketing Mix Modelling","\u003Cp>When presenting results of a Marketing Mix Model (MMM), the go-to graph to display is a waterfall chart, representing the contributions from all media channels. However, there is always the question. What is the baseline? Firstly, a quick recap on MMM. Marketing Mix Modelling is a statistical technique that measures the impact of marketing activities. […]\u003C\u002Fp>\n","\u002Fblog\u002Fthe-baseline-in-marketing-mix-modelling\u002F","2025-12-11T09:22:45","https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[155],{"name":4,"image":122},{"to":136,"name":137},{"type_key":113,"title":158,"excerpt":159,"to":160,"postedAt":161,"readingTime":143,"image":162,"authors":163,"category":165},"“Adstock” and the Long and Short of Advertising","\u003Cp>One of the most overlooked phenomena in marketing is the adstock effect. The adstock effect describes the lingering or delayed impacts of advertising, where marketing efforts hold “stock” in the mind of consumers for weeks on end. Neglecting this dynamic could lead to potentially halting campaigns which are in fact leaving a lasting effect on […]\u003C\u002Fp>\n","\u002Fblog\u002Fadstock-effect\u002F","2025-07-21T14:56:56","https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[164],{"name":4,"image":122},{"to":136,"name":137},{"type_key":113,"title":167,"excerpt":168,"to":169,"postedAt":170,"readingTime":171,"image":172,"authors":173,"category":175},"Meridian: Google’s Answer to Marketing Mix Modelling","\u003Cp>Ever since Google announced that it would be releasing an open-source Media Mix Modelling (MMM) package, which adds to and improves upon its predecessor, LightweightMMM, data scientists and marketers have been eagerly waiting for access to the platform. Most were left waiting until Google announced they would release it to the world on the 29th […]\u003C\u002Fp>\n","\u002Fblog\u002Fmeridian-googles-marketing-mix-modelling\u002F","2025-04-16T15:37:06",4,"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F04\u002Fkamran-abdullayev-wth383_dXjw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=640&q=90&w=640",[174],{"name":4,"image":122},{"to":124,"name":125},"6",{"type":178,"style":179,"title":180,"heading":4,"intro":10,"services":12,"breadcrumbs":181,"photo":67,"social":193},"author","dark","Meet the Team",[182,185,188,191],{"path":183,"name":184},"\u002F","Home",{"path":186,"name":187},"\u002Fabout\u002F","About Us",{"path":189,"name":190},"\u002Fabout\u002Four-team\u002F","Meet the team",{"path":192,"name":4},null,{"twitter":5,"linkedin":194},"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fharrison-brace-137b8482\u002F",[196],{"rel":197,"href":198},"canonical","https:\u002F\u002Fwww.impressiondigital.com\u002Fabout\u002Four-team\u002Fharry-brace\u002F",[200,522,710,898,1085,1271],{"id":201,"date":202,"date_gmt":202,"guid":203,"modified":117,"modified_gmt":117,"slug":205,"status":19,"type":113,"link":116,"title":206,"content":207,"excerpt":210,"author":211,"featured_media":212,"comment_status":20,"ping_status":20,"sticky":209,"template":5,"format":213,"meta":214,"categories":217,"collections":220,"coauthors":221,"class_list":223,"acf":232,"head_links":233,"breadcrumbs":234,"url_path":116,"search_description":216,"featured_image_urls":241,"authors":276,"type_nicename":289,"reading_minutes":118,"category":290,"yoast_title":215,"yoast_meta":293,"yoast_json_ld":349,"_links":473},39214,"2026-06-10T11:27:59",{"rendered":204},"\u002F?p=39214","which-marketing-mix-modelling-platform-should-you-use",{"rendered":114},{"rendered":208,"protected":209},"\n\u003Cp>If you’ve decided to build a Marketing Mix Model (MMM), an important first step is to choose what platform you want to use. There are quite a few options, ranging from simple to complex, with different benefits to each\u003C\u002Fp>\n\n\n\n\u003Cp>You’ve probably come across these libraries already: Google’s Meridian, Meta’s Robyn, PyMC Marketing and a handful of others.\u003C\u002Fp>\n\n\n\n\u003Cp>This post walks through these main contenders, what they do well, where they fall short, and factors you should consider before choosing.\u003C\u002Fp>\n\n\n\n\u003Cdiv class=\"table-of-contents\">\u003Cul>\u003Cli class=\"heading-h2\">\u003Ca href=\"#what-is-open-source-mmm\">What is open-source MMM?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h2\">\u003Ca href=\"#the-main-platforms\">The main platforms\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#googles-meridian\">Google’s Meridian\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#metas-robyn\">Meta’s Robyn\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#pymc-marketing\">PyMC Marketing\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h2\">\u003Ca href=\"#platform-comparison\">Platform comparison\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h2\">\u003Ca href=\"#how-we-choose-a-platform-for-our-clients\">How we choose a platform for our clients\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fdiv>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F03\u002FMeasurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27690\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Chr class=\"wp-block-separator has-alpha-channel-opacity\"\u002F>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-is-open-source-mmm\">\u003Cstrong>What is open-source MMM?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Before we get into the platforms, a quick clarification on what we mean by an open-source Marketing Mix Model. These are coding libraries and frameworks. You write the model yourself (or adapt one), run it on your own infrastructure, and retain full control over the inputs, assumptions, and outputs. This is categorically different from self-serve tools, where the model runs behind the scenes, and you interact through a dashboard.\u003C\u002Fp>\n\n\n\n\u003Cp>Open-source gives you transparency. You can inspect every prior, every transformation, every assumption baked into the model. That transparency matters when you’re trying to convince stakeholders that your channel attribution is credible, or when a client’s data has intricacies that a one-size-fits-all solution would paper over.\u003C\u002Fp>\n\n\n\n\u003Cp>The trade-off is that it requires statistical knowledge to implement well and is more time-intensive. But done right, open-source MMM tends to be more reliable than its automated rival, as you can create something truly bespoke for your needs.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"the-main-platforms\">\u003Cstrong>The main platforms\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"googles-meridian\">\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fdevelopers.google.com\u002Fmeridian\">\u003Cstrong>Google’s Meridian\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fh3>\n\n\n\n\u003Cp>Google publicly launched \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fmeridian-googles-marketing-mix-modelling\u002F\">Meridian\u003C\u002Fa> in early 2025, and it is a fully Bayesian framework.\u003C\u002Fp>\n\n\n\n\u003Cp>Meridian’s standout feature set includes geo-level hierarchical modelling, reach and frequency inputs, ROI priors, and a time-varying intercept to capture gradual shifts in \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fthe-baseline-in-marketing-mix-modelling\u002F\">baseline\u003C\u002Fa>. These are all genuinely useful capabilities.\u003C\u002Fp>\n\n\n\n\u003Cp>It also comes with an impressive dashboard output and benefits from Google’s documentation and support infrastructure.\u003C\u002Fp>\n\n\n\n\u003Cp>Meridian’s limitations include a lack of support for time-varying media parameters, and prior customisation is constrained to a fully flexible Bayesian system. For businesses with unusual structures or a strong prior reason to model a channel differently, those constraints can be frustrating.\u003Cbr>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>When to use Meridian: \u003C\u002Fstrong>Choose Meridian when you want a fully Bayesian framework with clear dashboard outputs. It is ideal if you need features like reach and frequency inputs or geo-level hierarchical modelling, but do not require time-varying media parameters or highly flexible custom priors\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"metas-robyn\">\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ffacebookexperimental.github.io\u002FRobyn\u002F\">\u003Cstrong>Meta’s Robyn\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fh3>\n\n\n\n\u003Cp>Meta’s Robyn has built up a large community of users since its launch. It was one of the first widely adopted open-source MMM packages and, for many organisations, it remains the default starting point.\u003C\u002Fp>\n\n\n\n\u003Cp>Robyn uses the \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F\">simpler Frequentist method instead of a more advanced Bayesian approach\u003C\u002Fa>. It automates the complex process of tuning a model by quickly cycling through thousands of configurations (such as adstock decay and saturation) and automatically selecting a set of good models. This is very helpful when you need a quick readout of your ad performance and don’t have the time for manual tuning.\u003C\u002Fp>\n\n\n\n\u003Cp>Robyn also produces clean one-page output summaries, including contributions, saturation curves, and a budget allocator, which are designed with clients in mind rather than a data science team.\u003C\u002Fp>\n\n\n\n\u003Cp>Robyn’s trade-offs are well-known. It uses Ridge regression rather than a Bayesian approach, which gives you rougher uncertainty estimates. For ranking channels, this often isn’t a problem, but it is less ideal for justifying budget decisions. It can also calibrate against experimental results, but only as a point estimate that nudges the model in the right direction. It does not bake the result directly into the model itself.\u003Cbr>\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>When to use Robyn:\u003C\u002Fstrong> Robyn is best when you need a quick readout of ad performance and lack the time for manual tuning. It is a great starting point for teams with lower statistical expertise who benefit from automated model selection and clean, client-ready summary outputs designed for stakeholders rather than data scientists.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"pymc-marketing\">\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.pymc-marketing.io\u002Fen\u002Fstable\u002F\">\u003Cstrong>PyMC Marketing\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fh3>\n\n\n\n\u003Cp>PyMC Marketing is the MMM module built on top of PyMC, a mature probabilistic programming library for Python. It utilises a fully Bayesian model, allowing for advanced capabilities.\u003C\u002Fp>\n\n\n\n\u003Cp>For example, you can define prior beliefs about your channel effects, run MCMC (\u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fmachinelearningmastery.com\u002Fmarkov-chain-monte-carlo-for-probability\u002F\">Markov Chain Monte Carlo\u003C\u002Fa>) sampling to get posterior distributions, and end up with uncertainty estimates on everything, including contributions, ROAS, \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fsaturation-curves\u002F\">saturation curves\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fadstock-effect\u002F\">adstock decay\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\n\u003Cp>The key strength of PyMC Marketing is flexibility. Unlike some other platforms, it doesn’t impose a fixed model structure. You can define your own adstock transformations, build hierarchical structures across geographies or product lines, incorporate incrementality test results into the model and extend the model however your client’s business logic demands. There are a few limitations.\u003C\u002Fp>\n\n\n\n\u003Cp>And if PyMC Marketing’s built-in API still doesn’t fit, you can step outside it entirely and build a fully custom PyMC model from scratch, defining your own model structure with no constraints at all. Neither Robyn nor Meridian offer anything comparable.\u003C\u002Fp>\n\n\n\n\u003Cp>It does require more statistical literacy than some alternatives, but the PyMC Marketing website offers detailed documentation and a growing library of examples, making it increasingly accessible.\u003C\u002Fp>\n\n\n\n\u003Cp>PyMC Marketing has no ties to any advertising platform. For clients who are sensitive to the idea of their MMM being built by the same company running their paid search, that independence matters.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>When to use PyMC: \u003C\u002Fstrong>When you need model flexibility, and you have the statistical literacy and Python knowledge to make use of the flexibility of PyMC. It can build completely bespoke models that, with enough time investment, will give you the best indication of marketing performance possible.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"platform-comparison\">\u003Cstrong>Platform comparison\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cfigure class=\"wp-block-table\">\u003Ctable class=\"has-fixed-layout\">\u003Cthead>\u003Ctr>\u003Cth>\u003C\u002Fth>\u003Cth>PyMC Marketing\u003C\u002Fth>\u003Cth>Meta Robyn\u003C\u002Fth>\u003Cth>Google Meridian\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Language\u003C\u002Ftd>\u003Ctd>Python\u003C\u002Ftd>\u003Ctd>R\u003C\u002Ftd>\u003Ctd>Python\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Inference method\u003C\u002Ftd>\u003Ctd>Full Bayesian\u003C\u002Ftd>\u003Ctd>Ridge regression + optimisation\u003C\u002Ftd>\u003Ctd>Full Bayesian\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Uncertainty quantification\u003C\u002Ftd>\u003Ctd>Full posterior distributions\u003C\u002Ftd>\u003Ctd>Confidence intervals\u003C\u002Ftd>\u003Ctd>Full posterior distributions\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Customisable priors\u003C\u002Ftd>\u003Ctd>Highly flexible\u003C\u002Ftd>\u003Ctd>N\u002FA\u003C\u002Ftd>\u003Ctd>Limited\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Geo-level modelling\u003C\u002Ftd>\u003Ctd>Yes \u003C\u002Ftd>\u003Ctd>No\u003C\u002Ftd>\u003Ctd>Yes \u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Time-variant media parameters\u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003Ctd>No\u003C\u002Ftd>\u003Ctd>No\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Incrementality Test Calibration\u003C\u002Ftd>\u003Ctd>Yes – custom priors or through likelihood \u003C\u002Ftd>\u003Ctd>Yes – through optimisation\u003C\u002Ftd>\u003Ctd>Yes – ROI priors\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Budget optimisation\u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003Ctd>Yes \u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Model flexibility\u003C\u002Ftd>\u003Ctd>Maximum\u003C\u002Ftd>\u003Ctd>Moderate\u003C\u002Ftd>\u003Ctd>Moderate\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Active development\u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003Ctd>Yes\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Statistical expertise required\u003C\u002Ftd>\u003Ctd>High\u003C\u002Ftd>\u003Ctd>Low–moderate\u003C\u002Ftd>\u003Ctd>Moderate\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Ffigure>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"how-we-choose-a-platform-for-our-clients\">\u003Cstrong>How we choose a platform for our clients\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>We’re platform-agnostic. When we start working with a client, we work together to define the objectives of the MMM projects and identify the channels to be considered. Using this information, we discuss with all stakeholders and decide the preferred route forward.\u003C\u002Fp>\n\n\n\n\u003Cp>If most of a client’s budget is in the Google ecosystem and they are keen to know how reach & frequency affect results, then Meridian might be the best option.\u003C\u002Fp>\n\n\n\n\u003Cp>If they have many different channels and plenty of test results to feed into a model, then PyMC might be best, as we can create something flexible enough and truly bespoke.\u003C\u002Fp>\n\n\n\n\u003Cp>If the business is newer and has few channels, then Robyn can be appropriate if a quick readout is desired and there is no need for the more advanced features.\u003C\u002Fp>\n\n\n\n\u003Chr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\u002F>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">\u003Cimg loading=\"lazy\" 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But as marketing journey became more complex and fragmented, it started to show its cracks. Here’s why a Bayesian approach to Marketing Mix Modeling has emerged as the stronger approach.\u003C\u002Fp>\n\n\n\n\u003Cp>At a high level, Frequentist statistics treats probability as purely data-driven. No assumptions, no prior knowledge, just the data. It outputs point estimates, quantified using confidence intervals. Bayesian statistics, on the other hand, treats probability as a belief that gets updated as new data arrives. \u003Cbr>At Impression, we consider many factors before choosing the best approach for all client needs, but Bayesian is almost always our approach of choice. Let me explain why.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"frequentist-methodology-rigid-straightforward-confined\">Frequentist methodology: Rigid, straightforward, confined\u003C\u002Fh2>\n\n\n\n\u003Cp>The core limitation of the traditional Frequentist approach to MMM is its rigidity. Coefficients are treated as static, unknown values. The model’s understanding is strictly confined to the data it is specifically provided with.\u003C\u002Fp>\n\n\n\n\u003Cp>For a marketer, this creates some familiar headaches. If you scale media spend across multiple channels at the same time, the model gets confused. We call this \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.ibm.com\u002Fthink\u002Ftopics\u002Fmulticollinearity\">multicollinearity\u003C\u002Fa>. The model ends up assigning contribution to the wrong channel because it can’t determine which channel drove the increase in revenue.\u003C\u002Fp>\n\n\n\n\u003Cp>And if you’ve just run an incrementality test proving your channel delivers a ROAS of 3? The model doesn’t care. It starts fresh every time, with no memory of what you already know.\u003C\u002Fp>\n\n\n\n\u003Cp>Despite its issues, the Frequentist approach still has its uses. If you are looking for a straightforward analysis and have a large, clean dataset, it could be all you need. It requires much less setup and tuning to get to your results.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"bayesian-methodology-flexibility-probability-continuous\">Bayesian methodology: Flexibility, probability, continuous\u003C\u002Fh2>\n\n\n\n\u003Cp>The Bayesian approach treats model parameters as probability distributions rather than fixed values. Rather than a single answer, the model holds a range of possibilities, with some possibilities more probable than others. These distributions are called priors. We aim for priors to be weakly informative, meaning they nudge the model towards a reasonable starting point, while staying flexible enough to move where the data tells it to.\u003C\u002Fp>\n\n\n\n\u003Cp>When the model runs, it constantly updates the prior using the data it is given. The result is an updated probability distribution, called the posterior. The better defined the prior, the more accurate the estimate of the parameter. The data still influences the outcome either way, but a good prior gives it less room to produce an implausible result. \u003C\u002Fp>\n\n\n\n\u003Cp>A well-defined prior is one thing, but incorporating external evidence is where Bayesian modelling really comes into its own.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"calibration-considerations-for-bayesian\">Calibration considerations for Bayesian\u003C\u002Fh2>\n\n\n\n\u003Cp>When you conduct an \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fguide-to-media-effectiveness-measurement\u002F\">incrementality test\u003C\u002Fa>, you are learning something concrete about a specific channel. So, why wouldn’t your model learn from it too?\u003C\u002Fp>\n\n\n\n\u003Cp>That’s exactly what calibration allows. Instead of your Marketing Mix Model and your experiments sitting in separate reports that occasionally contradict each other, the test results feed directly into the prior, and the model updates accordingly. It fills in the gaps for all the weeks you weren’t running a test.\u003C\u002Fp>\n\n\n\n\u003Cp>The result is a model that gets smarter over time. Every test you run, every result you feed in, makes the next iteration more informed. That’s something a Frequentist model simply cannot do.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"limiting-uncertainty-with-bayesian\">Limiting uncertainty with Bayesian\u003C\u002Fh2>\n\n\n\n\u003Cp>Frequentist models give you a single number. Bayesian models don’t just give you an estimate; they tell you how much to trust that estimate.\u003C\u002Fp>\n\n\n\n\u003Cp>Take two channels sitting on your media plan:\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-table\">\u003Ctable class=\"has-fixed-layout\">\u003Cthead>\u003Ctr>\u003Cth>\u003C\u002Fth>\u003Cth>\u003Cstrong>Average ROAS\u003C\u002Fstrong>\u003C\u002Fth>\u003Cth>\u003Cstrong>Credible Interval\u003C\u002Fstrong>\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>\u003Cstrong>Channel A\u003C\u002Fstrong>\u003C\u002Ftd>\u003Ctd>3.0\u003C\u002Ftd>\u003Ctd>1.0 – 5.0\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Cstrong>Channel B\u003C\u002Fstrong>\u003C\u002Ftd>\u003Ctd>2.8\u003C\u002Ftd>\u003Ctd>2.7 – 2.9\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Ffigure>\n\n\n\n\u003Cp>\u003Cstrong>Channel A \u003C\u002Fstrong>looks better on paper. But \u003Cstrong>Channel B\u003C\u002Fstrong> is the safer investment. Its credible interval is tighter. You know roughly what you’re going to get. Channel A’s range is far wider, meaning the model is far less certain. It might be a ROAS of 5. It also might be burning money. Which channel would you choose?\u003C\u002Fp>\n\n\n\n\u003Cp>As the world of \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fgoogle-consent-mode\u002F\">online privacy grows\u003C\u002Fa>, attribution measurement becomes increasingly unreliable. This is why \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">Marketing Mix Modeling\u003C\u002Fa> is emerging as the primary methodology to measure your marketing efforts. At Impression, we specialise in building robust, bespoke MMMs, tailored to each client. If you’d like to explore what that could look like for your business, \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fus\u002Fget-in-touch\u002F\">get in touch\u003C\u002Fa>.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fus\u002Fmedia-solutions\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F03\u002FMeasurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27690\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\u003C\u002Fdiv>",{"rendered":128,"protected":209},37643,{"_yoast_wpseo_focuskw":5,"_yoast_wpseo_title":5,"_yoast_wpseo_metadesc":5,"inline_featured_image":209,"footnotes":5},[219],[],[222],[538,113,225,226,227,228,229,231],"post-37628",[],[],[542,543,544,545],{"path":183,"name":184},{"path":237,"name":238},{"path":136,"name":137},{"path":192,"name":127},"For decades, the Frequentist approach to Marketing Mix Modeling (MMM) was the industry standard. But as marketing journey became more complex and fragmented, it started to show its cracks. Here’s why a Bayesian approach to Marketing Mix Modeling has emerged as the stronger approach. At a high level, Frequentist statistics treats probability as purely data-driven. […]",{"thumbnail":548,"medium":549,"medium_large":551,"large":553,"1536x1536":555,"2048x2048":557,"post-thumbnail":559,"12_col":561,"default_landscape":563,"default_portrait":565,"6_col_and_margin_short":567,"11_col_and_margin":569,"9_col_rectangle":571,"11_col":573,"staff_image_696_600":575,"rectangular_thumbnail":577},{"url":132,"width":243,"height":243},{"url":550,"width":70,"height":70},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=125&q=90&w=200",{"url":552,"width":73,"height":23},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=480&q=90&w=768",{"url":554,"width":250,"height":251},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1000&q=90&w=1892",{"url":556,"width":79,"height":79},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=960&q=90&w=1536",{"url":558,"width":82,"height":82},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=1280&q=90&w=2048",{"url":560,"width":85,"height":86},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1180&q=90&w=1860",{"url":562,"width":88,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2880",{"url":564,"width":91,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=800",{"url":566,"width":65,"height":91},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=800&q=90&w=600",{"url":568,"width":85,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=1860",{"url":570,"width":95,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=3080",{"url":572,"width":97,"height":98},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1400&q=90&w=2150",{"url":574,"width":100,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2640",{"url":576,"width":64,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=696",{"url":578,"width":103,"height":104},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=300&q=90&w=750",[580],{"id":211,"path":278,"display_name":4,"job_title":11,"description":6,"avatar_url":122,"acf":581},{"full_bio":10,"job_title":11,"specialist_service_links":582,"photo":589},[583,585,587],{"page":584,"link_text":29},{"ID":15,"post_author":16,"post_date":17,"post_date_gmt":17,"post_content":5,"post_title":18,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":21,"to_ping":5,"pinged":5,"post_modified":22,"post_modified_gmt":22,"post_content_filtered":5,"post_parent":23,"guid":24,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":28},{"page":586,"link_text":34},{"ID":32,"post_author":16,"post_date":33,"post_date_gmt":33,"post_content":5,"post_title":34,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":35,"to_ping":5,"pinged":5,"post_modified":36,"post_modified_gmt":36,"post_content_filtered":5,"post_parent":37,"guid":38,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":39},{"page":588,"link_text":44},{"ID":42,"post_author":16,"post_date":43,"post_date_gmt":43,"post_content":5,"post_title":44,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":45,"to_ping":5,"pinged":5,"post_modified":46,"post_modified_gmt":46,"post_content_filtered":5,"post_parent":37,"guid":47,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":48},{"ID":50,"id":50,"title":51,"filename":52,"filesize":53,"url":54,"link":55,"alt":5,"author":56,"description":5,"caption":5,"name":57,"status":58,"uploaded_to":23,"date":59,"modified":59,"menu_order":23,"mime_type":60,"type":61,"subtype":62,"icon":63,"width":64,"height":65,"sizes":590},{"thumbnail":67,"thumbnail-width":68,"thumbnail-height":68,"medium":69,"medium-width":70,"medium-height":71,"medium_large":72,"medium_large-width":73,"medium_large-height":74,"large":75,"large-width":76,"large-height":77,"1536x1536":78,"1536x1536-width":79,"1536x1536-height":80,"2048x2048":81,"2048x2048-width":82,"2048x2048-height":83,"post-thumbnail":84,"post-thumbnail-width":85,"post-thumbnail-height":86,"12_col":87,"12_col-width":88,"12_col-height":89,"default_landscape":90,"default_landscape-width":91,"default_landscape-height":65,"default_portrait":92,"default_portrait-width":65,"default_portrait-height":91,"6_col_and_margin_short":93,"6_col_and_margin_short-width":85,"6_col_and_margin_short-height":89,"11_col_and_margin":94,"11_col_and_margin-width":95,"11_col_and_margin-height":89,"9_col_rectangle":96,"9_col_rectangle-width":97,"9_col_rectangle-height":98,"11_col":99,"11_col-width":100,"11_col-height":89,"staff_image_696_600":101,"staff_image_696_600-width":64,"staff_image_696_600-height":65,"rectangular_thumbnail":102,"rectangular_thumbnail-width":103,"rectangular_thumbnail-height":104},[592],{"id":219,"name":137,"path":136},"The future of Marketing Mix Modeling is Bayesian: Here's why - Impression",[595,596,597,599,600,601,602,603,604,605,606,608,610,612,613,614,615,616,617,618,619],{"property":295,"content":593},{"name":299,"content":300},{"property":197,"content":598},"https:\u002F\u002Fwww.impressiondigital.com\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F",{"name":304,"content":305},{"property":307,"content":308},{"property":310,"content":593},{"property":312,"content":546},{"property":314,"content":598},{"property":316,"content":317},{"property":319,"content":320},{"property":322,"content":607},"2026-03-18T15:19:35+00:00",{"property":325,"content":609},"2026-03-18T15:19:38+00:00",{"property":328,"content":611},"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fopen-graph-bayesian-modeling.png?auto=compress%2Cformat&q=90",{"property":331,"content":332},{"property":334,"content":335},{"property":337,"content":60},{"property":178,"content":4},{"property":340,"content":341},{"property":343,"content":611},{"property":345,"content":346},{"property":348,"content":346},{"@context":350,"@graph":621},[622,633,639,641,647,651,669],{"@type":289,"@id":623,"isPartOf":624,"author":625,"headline":127,"datePublished":607,"dateModified":609,"mainEntityOfPage":626,"wordCount":627,"publisher":628,"image":629,"thumbnailUrl":631,"articleSection":632,"inLanguage":305},"https:\u002F\u002Fwww.impressiondigital.com\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F#article",{"@id":598},{"name":4,"@id":356},{"@id":598},723,{"@id":360},{"@id":630},"https:\u002F\u002Fwww.impressiondigital.com\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F#primaryimage","https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F03\u002Fubaid-e-alyafizi-A0tB3FNyjK8-unsplash.jpg?auto=compress%2Cformat&q=90",[137],{"@type":366,"@id":598,"url":598,"name":593,"isPartOf":634,"primaryImageOfPage":635,"image":636,"thumbnailUrl":631,"datePublished":607,"dateModified":609,"breadcrumb":637,"inLanguage":373},{"@id":368},{"@id":630},{"@id":630},{"@id":638},"https:\u002F\u002Fwww.impressiondigital.com\u002Fblog\u002Fthe-future-of-marketing-mix-modeling-is-bayesian-heres-why\u002F#breadcrumb",{"@type":375,"inLanguage":305,"@id":630,"url":631,"contentUrl":631,"width":376,"height":640},1500,{"@type":379,"@id":638,"itemListElement":642},[643,644,645,646],{"@type":382,"position":383,"name":184,"item":384},{"@type":382,"position":386,"name":238,"item":387},{"@type":382,"position":131,"name":137,"item":389},{"@type":382,"position":171,"name":127},{"@type":392,"@id":368,"url":384,"name":317,"description":5,"publisher":648,"inLanguage":305,"potentialAction":649},{"@id":394},{"@type":396,"target":650,"query-input":400},{"@type":398,"urlTemplate":399},{"@type":402,"@id":394,"name":317,"url":384,"logo":652,"image":408,"sameAs":653,"@context":413,"email":414,"foundingDate":415,"legalName":416,"vatID":417,"description":418,"telephone":419,"address":654,"location":655,"areaServed":665},{"@type":375,"inLanguage":305,"@id":404,"url":405,"contentUrl":405,"width":406,"height":407,"caption":317},[410,411,412],{"@type":421,"streetAddress":422,"addressLocality":423,"addressRegion":424,"postalCode":425,"addressCountry":426},[656,659,662],{"@type":429,"name":430,"geo":657,"address":658,"photo":408,"telephone":436},{"@type":432,"latitude":433,"longitude":434},{"@type":421,"streetAddress":422,"addressLocality":423,"addressRegion":424,"postalCode":425,"addressCountry":426},{"@type":429,"name":438,"geo":660,"address":661,"photo":447,"telephone":448},{"@type":432,"latitude":440,"longitude":441},{"@type":421,"streetAddress":443,"addressLocality":444,"addressRegion":445,"postalCode":446,"addressCountry":426},{"@type":429,"name":450,"geo":663,"address":664},{"@type":432,"latitude":452,"longitude":453},{"@type":421,"streetAddress":455,"addressLocality":456,"addressRegion":457,"postalCode":458,"addressCountry":459},[666,667,668],{"@type":462,"name":463},{"@type":462,"name":465},{"@type":462,"name":459},{"@type":468,"@id":469,"name":4,"image":670,"description":6,"sameAs":671,"url":198},{"@type":375,"inLanguage":305,"@id":471,"url":8,"contentUrl":8,"caption":4},[194],{"self":673,"collection":678,"about":680,"author":682,"replies":684,"version-history":687,"predecessor-version":691,"wp:featuredmedia":695,"wp:attachment":698,"wp:term":701,"curies":708},[674],{"href":675,"targetHints":676},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F37628",{"allow":677},[479,480,481,482,483],[679],{"href":486},[681],{"href":489},[683],{"embeddable":108,"href":492},[685],{"embeddable":108,"href":686},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fcomments?post=37628",[688],{"count":689,"href":690},8,"\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F37628\u002Frevisions",[692],{"id":693,"href":694},37642,"\u002Fwp-json\u002Fwp\u002Fv2\u002Fposts\u002F37628\u002Frevisions\u002F37642",[696],{"embeddable":108,"href":697},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia\u002F37643",[699],{"href":700},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fmedia?parent=37628",[702,704,706],{"taxonomy":511,"embeddable":108,"href":703},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fcategories?post=37628",{"taxonomy":514,"embeddable":108,"href":705},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fcollections?post=37628",{"taxonomy":178,"embeddable":108,"href":707},"\u002Fwp-json\u002Fwp\u002Fv2\u002Fcoauthors?post=37628",[709],{"name":520,"href":521,"templated":108},{"id":711,"date":712,"date_gmt":712,"guid":713,"modified":142,"modified_gmt":142,"slug":715,"status":19,"type":113,"link":141,"title":716,"content":717,"excerpt":719,"author":211,"featured_media":720,"comment_status":20,"ping_status":20,"sticky":209,"template":5,"format":213,"meta":721,"categories":723,"collections":724,"coauthors":725,"class_list":726,"acf":728,"head_links":729,"breadcrumbs":730,"url_path":141,"search_description":736,"featured_image_urls":737,"authors":769,"type_nicename":289,"reading_minutes":143,"category":781,"yoast_title":722,"yoast_meta":783,"yoast_json_ld":809,"_links":861},37308,"2026-02-04T17:42:37",{"rendered":714},"\u002F?p=37308","saturation-curves",{"rendered":139},{"rendered":718,"protected":209},"\n\u003Cp>\u003Cstrong>Why does the first £1,000 you spend on a marketing channel sometimes work harder than the last £10,000? The answer lies in saturation curves, and they’ll tell you exactly how effective each pound you spend really is.\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-exactly-is-a-saturation-curve\">\u003Cstrong>What exactly is a saturation curve?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Saturation curves illustrate how the effectiveness of a marketing effort diminishes over time. They’re a great visual way to represent the principle of diminishing returns in marketing.\u003C\u002Fp>\n\n\n\n\u003Cp>If we plot spend on the x-axis, and revenue (or other chosen KPI) on the y-axis, we’ll end up with something like this.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"987\" height=\"587\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F02\u002Fcurve-by-stage-2.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-37316\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>The ROAS is equal to the gradient of the line. The steeper the line, the more efficient the marketing activity is.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"428\" height=\"71\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F02\u002Froas-gradiant.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-37310\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>We can split the curve into three sections.\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>Accelerated Phase\u003C\u002Fstrong>: At the beginning, a small investment yields a return at a highly efficient rate. This is often when you’re first reaching a new audience on a new channel.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Linear Phase\u003C\u002Fstrong>: As you increase your investment, the returns continue to grow, but the rate of growth begins to slow down. You’re still getting good results, but any additional budget isn’t quite as impactful as before.\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>Plateau Phase\u003C\u002Fstrong>: Eventually, you reach a point where additional spend provides very little, if any, new return. You’ve saturated the market or audience for that specific channel. Pushing more budget into it at this point becomes inefficient and wasteful, and would be best served on a different channel.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"why-marketers-need-to-know-about-saturation-curves\">\u003Cstrong>Why marketers need to know about saturation curves\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Understanding saturation curves is crucial for optimising marketing spend. Instead of blindly increasing a budget, marketers can use saturation curves to make data-driven decisions.\u003C\u002Fp>\n\n\n\n\u003Cp>If your saturation curve suggests that one of your channels is in the accelerated phase, you can be confident that allocating extra budget here will guarantee you a good return.\u003C\u002Fp>\n\n\n\n\u003Cp>Conversely, if you are spending £20,000 for a return of £60,000, but the curve suggests you could get £55,000 by spending just £10,000, it becomes clear that your final £10,000 is working incredibly hard for very little reward. The extra £10,000 budget would be much better served by being reallocated to a different channel that is still in its Accelerated Phase.\u003C\u002Fp>\n\n\n\n\u003Cp>Ultimately, saturation curves help marketers avoid overspending and guide them toward a more diversified and efficient strategy that maximises overall ROAS.\u003C\u002Fp>\n\n\n\n\u003Cp>If you know how much you are going to spend, you can then use the charts to predict the revenue that would be generated.\u003C\u002Fp>\n\n\n\n\u003Cp>Alternatively, if you know the ROAS you need to achieve, you can identify the optimum spend.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"comparing-channels\">\u003Cstrong>Comparing Channels\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>While every business is unique, saturation curves generally fall into two categories based on where they sit in the marketing funnel: Bottom of the Funnel (BOF) and Top of the Funnel (TOF).\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"987\" height=\"587\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2026\u002F02\u002Fcurve-by-stage.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-37314\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"bottom-of-the-funnel\">\u003Cstrong>Bottom of the Funnel \u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>Think of activities like Google brand search or Meta conversion activity. These campaigns target a small audience, but these are people who are already looking for you.\u003C\u002Fp>\n\n\n\n\u003Cp>The bottom of the funnel campaigns have a very steep initial gradient in the accelerated phase, because these users are ready to buy, often delivering your highest ROAS. However, because of the small audience, these curves flatten rapidly, and you hit the plateau phase quickly. Once you’ve captured everyone searching for your brand, spending more won’t create more customers; it just makes the ones you have more expensive.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"top-of-the-funnel\">\u003Cstrong>Top of the Funnel\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>These are awareness campaigns like video ads or social prospecting. They reach a much wider audience who may not know your brand yet.\u003C\u002Fp>\n\n\n\n\u003Cp>These campaigns have a gentler slope. The accelerated phase is less steep. The ROAS might not look as impressive as BOF at low spend levels because you are introducing yourself rather than closing a sale. The saturation curve stretches much higher due to the wider audience. These curves saturate slowly. While the initial efficiency is lower, the total potential for scale is much higher. \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"how-do-we-actually-create-saturation-curves\">\u003Cstrong>How do we actually create saturation curves?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>We have spoken about how we interpret saturation curves, but how do we actually create them? \u003C\u002Fp>\n\n\n\n\u003Cp>The answer is \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fmedia-mix-modelling\u002F\">\u003Cstrong>Marketing Mix Modeling\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fp>\n\n\n\n\u003Cp>While you might spot saturation patterns by manually tracking your channel performance over time, MMM is the statistical method that builds these curves with precision and reliability.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-marketing-mix-modeling-does\">\u003Cstrong>What Marketing Mix Modeling Does\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>Marketing Mix Modeling (MMM) analyses your historical marketing data, spend, revenue, impressions and conversions across all channels simultaneously. But unlike platform analytics, MMM accounts for:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Cstrong>External factors:\u003C\u002Fstrong> Seasonality, competitor activity, economic shifts, promotions\u003C\u002Fli>\n\n\n\n\u003Cli>\u003Cstrong>True incrementality:\u003C\u002Fstrong> What revenue each channel actually drives, not just what it claims credit for through last click attribution\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-marketing-mix-modeling-outputs\">\u003Cstrong>What \u003Cstrong>Marketing Mix Modeling\u003C\u002Fstrong> outputs\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>MMM quantifies the exact relationship between spend and return for each channel, showing you precisely where the accelerated phase ends and the plateau begins.\u003C\u002Fp>\n\n\n\n\u003Cp>Instead of guessing where your channels saturate, you get statistically calculated curves that reveal where each channel has room to scale or where you’re overspending.\u003C\u002Fp>\n\n\n\n\u003Cp>Saturation curves provide a clear roadmap for budget optimisation by showing exactly where your marketing spend stops driving growth and starts hitting a plateau. By identifying which channels are still in their high-efficiency Accelerated Phase, you can move away from guesswork and ensure every pound is invested in its highest-yielding tactic.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"tldr-the-bottom-line\">TL;DR: The bottom line\u003C\u002Fh2>\n\n\n\n\u003Cp>Not every pound you spend on marketing works equally hard. Saturation curves reveal the “Goldilocks Zone” of your marketing budget:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>The Accelerated Phase: Low spend, high efficiency. You’re reaching the low-hanging fruit.\u003C\u002Fli>\n\n\n\n\u003Cli>The Linear Phase: Growth is steady, but the “easy” wins are gone. Efficiency starts to dip.\u003C\u002Fli>\n\n\n\n\u003Cli>The Plateau Phase: You’ve hit the ceiling. Adding more budget at this stage doesn’t buy more customers, it just makes your existing ones more expensive.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>To maximise your total ROAS, stop over-investing in “Bottom of the Funnel” channels once they plateau. Instead, use Marketing Mix Modeling (MMM) to find the next channel still in its high-efficiency “Accelerated” phase.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F03\u002FMeasurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27690\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\u003C\u002Fdiv>",{"rendered":140,"protected":209},37321,{"_yoast_wpseo_focuskw":5,"_yoast_wpseo_title":722,"_yoast_wpseo_metadesc":5,"inline_featured_image":209,"footnotes":5},"Diminishing returns & Saturation curves: Is your ad spend working for you or against you? | Impression",[219],[],[222],[727,113,225,226,227,228,229,231],"post-37308",[],[],[731,732,733,734],{"path":183,"name":184},{"path":237,"name":238},{"path":136,"name":137},{"path":192,"name":735},"Diminishing returns & Saturation curves: Is your ad spend working for you or against you?","Why does the first £1,000 you spend on a marketing channel sometimes work harder than the last £10,000? The answer lies in saturation curves, and they’ll tell you exactly how effective each pound you spend really is. What exactly is a saturation curve? Saturation curves illustrate how the effectiveness of a marketing effort diminishes over 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presenting results of a Marketing Mix Model (MMM), the go-to graph to display is a waterfall chart, representing the contributions from all media channels. However, there is always the question. What is the baseline?\u003C\u002Fp>\n\n\n\n\u003Cp>Firstly, a quick recap on MMM. Marketing Mix Modelling is a statistical technique that measures the impact of marketing activities. Simply put, the equation looks like this:\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"991\" height=\"97\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Fbaseline-mmm-components.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-36715\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"988\" height=\"589\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Fbaseline-mmm-waterfall-graph.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-36717\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>\u003Cstrong>Paid Media\u003C\u002Fstrong> (i.e. Google Ads, Display & Video, Paid Social), \u003Cstrong>Organic Media\u003C\u002Fstrong> (i.e. SEO, Digital PR, Organic Social) and \u003Cstrong>Controls\u003C\u002Fstrong> (i.e. potential non-marketing factors, such as seasonality, and macroeconomic factors) all make sense to stakeholders as an input into MMM. However, the \u003Cstrong>Baseline\u003C\u002Fstrong> is the input that often needs an explanation.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-does-a-baseline-in-an-mmm-represent\">\u003Cstrong>What does a baseline in an MMM represent?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The \u003Cstrong>Baseline\u003C\u002Fstrong> in MMM represents the sales for the business that would happen if all marketing efforts stopped and all organic activity ceased. If this happened, we would still expect sales for the business. This is due to:\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Brand loyalty & reputation:\u003C\u002Fstrong> The cumulative effect of brand building over previous years.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Customer retention:\u003C\u002Fstrong> Repeat purchases from your existing customer base that happen without a marketing nudge.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Cstrong>Word of mouth:\u003C\u002Fstrong> Organic recommendations that aren’t tied to a specific recent campaign.\u003C\u002Fp>\n\n\n\n\u003Cp>Put simply, it is the revenue the business captures solely through its presence in the market, rather than its recent marketing activity.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-level-of-baseline-is-normal\">\u003Cstrong>What level of baseline is normal?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>This is a common question from stakeholders. There is no specific answer to this, but based on the type of business and its market share, we can give a rough guide.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"high-baseline\">\u003Cstrong>High baseline\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Established household goods, utilities, and mature subscription services where sales are driven mainly by habit, contracts, or being available in every store.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"medium-baseline\">\u003Cstrong>Medium baseline\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Retail stores, car manufacturers, and fast food businesses are examples where people are familiar with the brand, but advertising is needed to prompt a purchase.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"low-baseline\">\u003Cstrong>Low baseline\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>New online-only brands and mobile apps, where few people know the product, so revenue naturally relies almost entirely on paid advertisements to find customers.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"is-a-high-baseline-bad\">\u003Cstrong>Is a high baseline bad?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>Again, this question has differing answers depending on the context. Many stakeholders will see a high baseline and think their marketing activity is not performing well, but this isn’t always the case. There can be many reasons for a high baseline.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"low-marketing-spend\">\u003Cstrong>Low marketing spend\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>If marketing spend is already low, we cannot expect paid media to be a large contributor to sales\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"low-variance-in-spend\">\u003Cstrong>Low variance in spend\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>When a channel has a consistent spend, it’s hard for the model to notice a relationship between it and revenue, and it will therefore under-contribute.\u003C\u002Fli>\n\n\n\n\u003Cli>This leads to the baseline being falsely inflated, explaining the gap that this phenomenon creates.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"high-brand-equity\">\u003Cstrong>High Brand Equity\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Most of the sales for the business come because the brand is well established, and all marketing efforts are just to convert those final few who might be on the fence.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>The baseline isn’t just a number that fills in the gap; it represents the success of brand building and reputation. Without it, you risk overcrediting your channels using sales that would have happened anyway.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F03\u002FMeasurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27690\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n",{"rendered":150,"protected":209},36720,{"_yoast_wpseo_focuskw":5,"_yoast_wpseo_title":910,"_yoast_wpseo_metadesc":911,"inline_featured_image":209,"footnotes":5},"The baseline in Marketing Mix Modelling | Impression","What is The baseline in Marketing Mix Modelling? Understand the crucial non-marketing sales driver in MMM and why it matters for accurate ROI. Learn more.",[219],[],[222],[916,113,225,226,227,228,229,231],"post-36710",[],[],[920,921,922,923],{"path":183,"name":184},{"path":237,"name":238},{"path":136,"name":137},{"path":192,"name":149},{"thumbnail":925,"medium":926,"medium_large":928,"large":930,"1536x1536":932,"2048x2048":934,"post-thumbnail":936,"12_col":938,"default_landscape":940,"default_portrait":942,"6_col_and_margin_short":944,"11_col_and_margin":946,"9_col_rectangle":948,"11_col":950,"staff_image_696_600":952,"rectangular_thumbnail":954},{"url":153,"width":243,"height":243},{"url":927,"width":70,"height":70},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=133&q=90&w=200",{"url":929,"width":73,"height":23},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=512&q=90&w=768",{"url":931,"width":250,"height":251},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1000&q=90&w=1892",{"url":933,"width":79,"height":79},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=1024&q=90&w=1536",{"url":935,"width":82,"height":82},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=1365&q=90&w=2048",{"url":937,"width":85,"height":86},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1180&q=90&w=1860",{"url":939,"width":88,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2880",{"url":941,"width":91,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=800",{"url":943,"width":65,"height":91},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=800&q=90&w=600",{"url":945,"width":85,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=1860",{"url":947,"width":95,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=3080",{"url":949,"width":97,"height":98},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1400&q=90&w=2150",{"url":951,"width":100,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2640",{"url":953,"width":64,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=696",{"url":955,"width":103,"height":104},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F12\u002Firham-setyaki-tfdff8Poebw-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=300&q=90&w=750",[957],{"id":211,"path":278,"display_name":4,"job_title":11,"description":6,"avatar_url":122,"acf":958},{"full_bio":10,"job_title":11,"specialist_service_links":959,"photo":966},[960,962,964],{"page":961,"link_text":29},{"ID":15,"post_author":16,"post_date":17,"post_date_gmt":17,"post_content":5,"post_title":18,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":21,"to_ping":5,"pinged":5,"post_modified":22,"post_modified_gmt":22,"post_content_filtered":5,"post_parent":23,"guid":24,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":28},{"page":963,"link_text":34},{"ID":32,"post_author":16,"post_date":33,"post_date_gmt":33,"post_content":5,"post_title":34,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":35,"to_ping":5,"pinged":5,"post_modified":36,"post_modified_gmt":36,"post_content_filtered":5,"post_parent":37,"guid":38,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":39},{"page":965,"link_text":44},{"ID":42,"post_author":16,"post_date":43,"post_date_gmt":43,"post_content":5,"post_title":44,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":45,"to_ping":5,"pinged":5,"post_modified":46,"post_modified_gmt":46,"post_content_filtered":5,"post_parent":37,"guid":47,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":48},{"ID":50,"id":50,"title":51,"filename":52,"filesize":53,"url":54,"link":55,"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of the most overlooked phenomena in marketing is the adstock effect. The adstock effect describes the lingering or delayed impacts of advertising, where marketing efforts hold “stock” in the mind of consumers for weeks on end. Neglecting this dynamic could lead to potentially halting campaigns which are in fact leaving a lasting effect on the consumer, forfeiting long-term incremental gains.\u003C\u002Fp>\n\n\n\n\u003Cp>For example, the impact of a TV advert is likely to have a lasting influence weeks after it is shown, however a \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fpaid-media\u002Fppc\u002Fgoogle-ads-management\u002F\">Google Search ad\u003C\u002Fa> is likely to be forgotten about soon after it is seen, therefore having less of a long term effect.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"what-exactly-is-the-adstock-effect\">\u003Cstrong>What exactly is the Adstock Effect?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>At its core, the adstock is just a transformation based on a decay model. The transformation captures how much of an advertising campaign’s impact persists from one week to the next. The most critical variable is the decay rate, which determines the speed of the decline. A high decay rate means the effect lasts longer, while a low decay rate indicates a quick drop-off.\u003C\u002Fp>\n\n\n\n\u003Cp>The graph below titled “Geometric Adstock Effect” illustrates the most common concept of adstock. The \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.pymc-marketing.io\u002Fen\u002Fstable\u002Fapi\u002Fgenerated\u002Fpymc_marketing.mmm.components.adstock.GeometricAdstock.html#pymc_marketing.mmm.components.adstock.GeometricAdstock\">Geometric Adstock\u003C\u002Fa> assumes a constant decay of the spend\u002Fimpressions over time. In this example, with a single £1,000 spend in the first week, the adstock value immediately peaks at £1,000. Since, in this case, there is no new spend in subsequent weeks, the graph is a pure decay curve. The ad’s effect diminishes at a fixed rate each week, representing the carryover. This illustrates the long-lasting but gradually fading impact of a single advertising campaign. \u003C\u002Fp>\n\n\n\n\u003Cp>Limits are set on adstock, otherwise the effect would carry on forever. This limit can vary depending on the type of business, but a recommended limit is around 12 weeks.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full is-resized\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1189\" height=\"690\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Fgeometric-adstock-effect.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-33980\" style=\"width:743px;height:auto\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>In this example, α is set at 0.7, meaning the adstock the following week is 70% of the adstock the week before. \u003C\u002Fp>\n\n\n\n\u003Cp>The formula for the geometric adstock effect is given here:\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"291\" height=\"49\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Fgeometric-adstock-formula.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-33979\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>Where A\u003Csub>t\u003C\u002Fsub> is the Adstock in week t, X\u003Csub>t\u003C\u002Fsub> is the ad spend\u002Fsessions on week t, and ɑ is the decay rate, and is between 0 and 1. A\u003Csub>0 \u003C\u002Fsub>= X\u003Csub>0 \u003C\u002Fsub>in all cases, meaning the adstock of the initial week is equal to the spend\u002Fsessions in the initial week. \u003C\u002Fp>\n\n\n\n\u003Cp>A\u003Csub>t  \u003C\u002Fsub>is essentially the “effective spend” on week t (in the context of ad spend). In other words, the adstock in a particular week is equal to the spend that week plus the adstock from the previous week multiplied by the decay rate. \u003C\u002Fp>\n\n\n\n\u003Cp>The geometric adstock is the most widely used adstock transformation, because it assumes that the peak of its effectiveness comes at the point of exposure to marketing activity. In some cases, it is unwise to model the effect like this (this is discussed later).\u003C\u002Fp>\n\n\n\n\u003Cp>For \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fmedia-mix-modelling\u002F\">Marketing Mix Modelling\u003C\u002Fa> (MMM), the adstock transform is vital. It estimates the true, lasting impact of advertising per channel. This is particularly important because MMM is typically conducted with weekly data, meaning a significant portion of advertising’s incremental revenue is observed in the subsequent weeks. A high decay rate (close to 1) means the campaign continues to be effective for a long period of time, maximising the incremental impact. The decay rate is influenced by multiple different factors.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"influencing-adstock\">\u003Cstrong>Influencing Adstock\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>The impact of advertising is never uniform. The adstock effect can differ considerably in both its duration and intensity, for several reasons:\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"creative-quality-memorability\">\u003Cstrong>Creative Quality & Memorability\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>Highly creative, emotionally resonant, or distinctive advertising tends to stick in consumers’ minds longer, resulting in a slower decay rate and a more enduring adstock. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"brand-category-product-type\">\u003Cstrong>Brand Category & Product Type\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>Fast-moving consumer goods might have a different adstock half-life compared to high-consideration purchases like cars or enterprise software. For high-consideration purchases, the peak of sales may well come weeks after the customer sees the advert. In these cases, the graph of the adstock effect looks slightly different, as seen below in the graph titled “Delayed Adstock Effect”. In the \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.pymc-marketing.io\u002Fen\u002Fstable\u002Fapi\u002Fgenerated\u002Fpymc_marketing.mmm.components.adstock.DelayedAdstock.html\">Delayed Adstock Effect\u003C\u002Fa>, the effective spend actually increases for the first couple of weeks before it starts to lose its effectiveness. Think about the purchase of a car, this is never an impulse buy, the purchase length for this can take months to consider.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full is-resized\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"1189\" height=\"690\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Fdelayed-adstock-effect.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-33981\" style=\"width:628px;height:auto\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>In this example, α is set to 0.7, and the delay of the peak of sales is set at 2 weeks, hence the peak of sales coming in week 2.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"competitor-marketing-efforts\">\u003Cstrong>Competitor Marketing Efforts\u003C\u002Fstrong>\u003C\u002Fh3>\n\n\n\n\u003Cp>In a crowded market with constant messaging from competitors, you might find your ad struggling to make an impact on users, leading to a quicker adstock decay due to the sheer volume of noise.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"measuring-adstock\">\u003Cstrong>Measuring Adstock\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In the world of marketing measurement, factoring in the ad stock effect is crucial. When overlooked, the mistake of misattributing revenue is a distinct possibility, leading to premature budget cuts and inaccurate ROAS calculations.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"marketing-mix-modelling\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fmodern-measurement-series-6-a-deeper-dive-into-media-mix-modelling\u002F\">\u003Cstrong>Marketing Mix Modelling\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fh3>\n\n\n\n\u003Cp>This remains the primary framework for estimating adstock effects. By analysing historical sales and media data, pricing, promotions, and external market factors, MMM can statistically estimate the decay rate of your advertising. The output provides invaluable insights into the long-term, incremental impact of each channel and informs optimal budget allocation. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" class=\"wp-block-heading\" id=\"incrementality-testing\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fincrementality-testing\u002F\">\u003Cstrong>Incrementality Testing\u003C\u002Fstrong>\u003C\u002Fa>\u003C\u002Fh3>\n\n\n\n\u003Cp>This shows the ad stock effect directly when starting to run a new campaign. Just isolate ads from one region of a country, and by using a synthetic control to predict what would have happened, we can measure the impact of the campaign. The adstock effect is visible in the first few weeks after the campaign starts, as the uplift between the true revenue and the counterfactual increases week on week, and then visible after a test finishes as the effect of advertising wears off.\u003C\u002Fp>\n\n\n\n\u003Cp>Now that we have quantified the effect of adstock, this leads to more precise budget allocation and a clearer understanding of true marketing ROAS. This knowledge also facilitates more effective campaign planning and forecasting, allowing for better timing of investments around key sales periods. Ultimately, these insights allow you to optimise your marketing mix, leading to incremental sales and revenue.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F03\u002FMeasurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27690\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n",{"rendered":159,"protected":209},33984,{"_yoast_wpseo_focuskw":5,"_yoast_wpseo_title":1097,"_yoast_wpseo_metadesc":5,"inline_featured_image":209,"footnotes":5},"“Adstock” and the Long and Short of Advertising | Impression",[219],[],[222],[1102,113,225,226,227,228,229,231],"post-33970",[],[],[1106,1107,1108,1109],{"path":183,"name":184},{"path":237,"name":238},{"path":136,"name":137},{"path":192,"name":158},"One of the most overlooked phenomena in marketing is the adstock effect. The adstock effect describes the lingering or delayed impacts of advertising, where marketing efforts hold “stock” in the mind of consumers for weeks on end. Neglecting this dynamic could lead to potentially halting campaigns which are in fact leaving a lasting effect on […]",{"thumbnail":1112,"medium":1113,"medium_large":1115,"large":1117,"1536x1536":1119,"2048x2048":1121,"post-thumbnail":1123,"12_col":1125,"default_landscape":1127,"default_portrait":1129,"6_col_and_margin_short":1131,"11_col_and_margin":1133,"9_col_rectangle":1135,"11_col":1137,"staff_image_696_600":1139,"rectangular_thumbnail":1141},{"url":162,"width":243,"height":243},{"url":1114,"width":70,"height":70},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=113&q=90&w=200",{"url":1116,"width":73,"height":23},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=432&q=90&w=768",{"url":1118,"width":250,"height":251},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1000&q=90&w=1892",{"url":1120,"width":79,"height":79},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=864&q=90&w=1536",{"url":1122,"width":82,"height":82},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=scale&h=1152&q=90&w=2048",{"url":1124,"width":85,"height":86},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1180&q=90&w=1860",{"url":1126,"width":88,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2880",{"url":1128,"width":91,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=800",{"url":1130,"width":65,"height":91},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=800&q=90&w=600",{"url":1132,"width":85,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=1860",{"url":1134,"width":95,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=3080",{"url":1136,"width":97,"height":98},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=1400&q=90&w=2150",{"url":1138,"width":100,"height":89},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=870&q=90&w=2640",{"url":1140,"width":64,"height":65},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=600&q=90&w=696",{"url":1142,"width":103,"height":104},"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F07\u002Faakash-dhage-cjFNckgXd8U-unsplash.jpg?auto=compress%2Cformat&fit=crop&h=300&q=90&w=750",[1144],{"id":211,"path":278,"display_name":4,"job_title":11,"description":6,"avatar_url":122,"acf":1145},{"full_bio":10,"job_title":11,"specialist_service_links":1146,"photo":1153},[1147,1149,1151],{"page":1148,"link_text":29},{"ID":15,"post_author":16,"post_date":17,"post_date_gmt":17,"post_content":5,"post_title":18,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":21,"to_ping":5,"pinged":5,"post_modified":22,"post_modified_gmt":22,"post_content_filtered":5,"post_parent":23,"guid":24,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":28},{"page":1150,"link_text":34},{"ID":32,"post_author":16,"post_date":33,"post_date_gmt":33,"post_content":5,"post_title":34,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":35,"to_ping":5,"pinged":5,"post_modified":36,"post_modified_gmt":36,"post_content_filtered":5,"post_parent":37,"guid":38,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":39},{"page":1152,"link_text":44},{"ID":42,"post_author":16,"post_date":43,"post_date_gmt":43,"post_content":5,"post_title":44,"post_excerpt":5,"post_status":19,"comment_status":20,"ping_status":20,"post_password":5,"post_name":45,"to_ping":5,"pinged":5,"post_modified":46,"post_modified_gmt":46,"post_content_filtered":5,"post_parent":37,"guid":47,"menu_order":23,"post_type":25,"post_mime_type":5,"comment_count":26,"filter":27,"url_path":48},{"ID":50,"id":50,"title":51,"filename":52,"filesize":53,"url":54,"link":55,"alt":5,"author":56,"description":5,"caption":5,"name":57,"status":58,"uploaded_to":23,"date":59,"modified":59,"menu_order":23,"mime_type":60,"type":61,"subtype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since Google announced that it would be releasing an open-source Media Mix Modelling (MMM) package, which adds to and improves upon its predecessor, LightweightMMM, data scientists and marketers have been eagerly waiting for access to the platform. Most were left waiting until \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fblog.google\u002Fproducts\u002Fads-commerce\u002Fmeridian-marketing-mix-model-open-to-everyone\u002F\">Google announced\u003C\u002Fa> they would release it to the world on the 29th of January 2025.\u003C\u002Fp>\n\n\n\n\u003Chr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\u002F>\n\n\n\n\u003Cdiv class=\"table-of-contents\">\u003Cul>\u003Cli class=\"heading-h2\">\u003Ca href=\"#what-did-we-already-know\">What did we already know?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h2\">\u003Ca href=\"#what-google-meridian-gets-right\">What Google Meridian gets right\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#a-fully-functional-bayesian-mmm-model\">A fully functional Bayesian MMM Model\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#built-in-priors-reduce-the-learning-curve\">Built-in priors reduce the learning curve\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#effective-visual-reporting\">Effective visual reporting\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h2\">\u003Ca href=\"#what-meridian-could-improve\">What Meridian could improve\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#meridian-lacks-flexibility-for-advanced-users\">Meridian lacks flexibility for advanced users\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"heading-h3\">\u003Ca href=\"#marketing-mix-modelling-will-always-require-expertise\">Marketing Mix Modelling will always require expertise\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fdiv>\n\n\n\n\u003Chr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\"\u002F>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"what-did-we-already-know\">\u003Cstrong>What did we already know?\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Cp>In January, just before the spontaneous release of Google Meridian, \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fgoogle-meridian-what-you-need-to-know\u002F\">we published a blog\u003C\u002Fa> outlining everything Meridian. Our conclusion at the time was that, while Meridian provides a platform built on similar foundations to current MMM tools, it lacked the flexibility we needed at Impression. Based on what we knew then, we felt Meridian didn’t support features like channel hierarchical modelling and time-variant media channels, which we could implement using \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fwww.pymc.io\u002Fwelcome.html\">PyMC\u003C\u002Fa>. The ability to customise models fully, in our opinion, is vital in building an MMM, because every business is different. Despite this, we must constantly try to improve our services, and if Meridian is the answer to that, we would be missing an opportunity if we dismissed it before it is even released! \u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"what-google-meridian-gets-right\">\u003Cstrong>What Google Meridian gets right\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"a-fully-functional-bayesian-mmm-model\">A fully functional Bayesian MMM Model\u003C\u002Fh3>\n\n\n\n\u003Cp>\u003Cstrong>Out of the box, Meridian includes:\u003C\u002Fstrong>\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fbayesian-statistics-in-media-mix-modelling-a-real-world-analogy\u002F\">Bayesian\u003C\u002Fa> inference for uncertainty estimation.\u003C\u002Fli>\n\n\n\n\u003Cli>Adstock transformation to model carryover effects.\u003C\u002Fli>\n\n\n\n\u003Cli>Saturation functions to capture diminishing returns.\u003C\u002Fli>\n\n\n\n\u003Cli>Hierarchical geo-modelling, which is useful for regional insights.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>It’s a solid implementation of modern MMM best practices.\u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"built-in-priors-reduce-the-learning-curve\">Built-in priors reduce the learning curve\u003C\u002Fh3>\n\n\n\n\u003Cp>One advantage of Meridian is that Google has pre-configured priors and hyperparameters, parameters that control the learning process of a model, based on industry knowledge. This means you don’t have to spend as much time tuning the model, which is a plus if you’re not familiar with Bayesian statistics. \u003C\u002Fp>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"effective-visual-reporting\">Effective visual reporting\u003C\u002Fh3>\n\n\n\n\u003Cp>After running your MMM, Meridian has the option to output all of your data into a two-page HTML report. In my opinion, this report is an excellent way to present your model outputs, offering immediate insights into channel performance and ROI. The waterfall chart (see below) effectively breaks down sales contributions, while the response curves provide a visual understanding of saturation points and diminishing returns for each paid media channel. This allows for \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fgranularity-marketing-mix-modelling\u002F\">quick identification of high-performing areas\u003C\u002Fa> and potential optimisation opportunities.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"665\" height=\"386\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F04\u002Fcontribution-by-baseline-and-marketing-channels.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-32923\"\u002F>\u003Cfigcaption class=\"wp-element-caption\">Source: \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fdevelopers.google.com\u002Fmeridian\u002Fnotebook\u002Fmeridian-getting-started\">Getting Started: Google Meridian\u003C\u002Fa>\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Cp>\u003Cstrong>But here’s the problem\u003C\u002Fstrong>: MMM is not a one-size-fits-all problem. And that’s exactly where Meridian falls short.\u003C\u002Fp>\n\n\n\n\u003Ch2 class=\"wp-block-heading\" id=\"what-meridian-could-improve\">\u003Cstrong>What Meridian could improve\u003C\u002Fstrong>\u003C\u002Fh2>\n\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"meridian-lacks-flexibility-for-advanced-users\">Meridian lacks flexibility for advanced users\u003C\u002Fh3>\n\n\n\n\u003Cp>Meridian provides a solid foundation for Marketing Mix Modelling, but it does have some limitations when it comes to customisation, especially from the perspective of marketing data scientists.\u003C\u002Fp>\n\n\n\n\u003Cp>If you need to use different adstock or saturation functions, you’re \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fquestions-to-ask-marketing-mix-modelling-vendors\u002F\">limited to what Meridian offers\u003C\u002Fa>. Custom likelihood functions aren’t easily adjustable, and if your business has unique constraints, making the necessary changes could be a challenge.\u003C\u002Fp>\n\n\n\n\u003Cp>\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fnavigating-mmm\u002F\">MMM isn’t a one-size-fits-all approach\u003C\u002Fa>. Different industries, marketing channels, and regions require different modelling choices, and Meridian’s structured framework might not work for everyone. For example, the figure below shows the built-in adstock effect (in blue), but Meridian doesn’t support the delayed adstock effect (in orange), which is useful for businesses with longer customer decision cycles, like buying a car or planning a wedding.\u003C\u002Fp>\n\n\n\u003Cdiv class=\"wp-block-image\">\n\u003Cfigure class=\"aligncenter size-full\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"846\" height=\"393\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2025\u002F04\u002Fgeometric-adstock-vs-delayed-adstock.png?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-32924\"\u002F>\u003C\u002Ffigure>\u003C\u002Fdiv>\n\n\n\u003Ch3 class=\"wp-block-heading\" id=\"marketing-mix-modelling-will-always-require-expertise\">Marketing Mix Modelling will always require expertise\u003C\u002Fh3>\n\n\n\n\u003Cp>One of Google’s main selling points is that Meridian makes MMM more accessible. But the reality is:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Model \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fdata-requirements-for-marketing-mix-modelling\u002F\">selection and validation\u003C\u002Fa> require deep statistical expertise. Even with built-in priors, interpreting results and making adjustments still require knowledge of Bayesian statistics.\u003C\u002Fli>\n\n\n\n\u003Cli>Business context cannot be automated. A pre-built model, no matter how well-designed, will never fully capture the nuances of every business’s marketing reality.\u003C\u002Fli>\n\n\n\n\u003Cli>Debugging Bayesian models is not always easy. Even in structured frameworks like Meridian, properly diagnosing model fit issues or adjusting priors requires technical skill.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>So, who is Meridian really for? If a company has the expertise to run MMM at a high level, it may still prefer a more flexible, customisable solution. But for businesses looking for an easier entry point into MMM, Meridian provides a structured approach with industry best practices.\u003C\u002Fp>\n\n\n\n\u003Cfigure class=\"wp-block-image size-full\">\u003Ca rel=\"noreferrer noopener\" href=\"\u002Fmedia-solutions\u002Fmeasurement\u002F\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"3840\" height=\"540\" src=\"https:\u002F\u002Fimages.impression.co.uk\u002F2024\u002F04\u002Ff0sKYLsk-Measurement_Banner_01.jpg?auto=compress%2Cformat&q=90\" alt=\"\" class=\"wp-image-27925\"\u002F>\u003C\u002Fa>\u003C\u002Ffigure>\n\n\n\n\u003Cp>At its core, MMM is not a problem that can be fully automated. Every business has unique challenges, and pre-built solutions like \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Fdevelopers.google.com\u002Fmeridian\">Meridian\u003C\u002Fa> and \u003Ca rel=\"noreferrer noopener\" href=\"https:\u002F\u002Ffacebookexperimental.github.io\u002FRobyn\u002F\">Robyn\u003C\u002Fa>, Meta’s open-source MMM, will always have some limitations.\u003C\u002Fp>\n\n\n\n\u003Cp>For teams that prioritise full control, customisation, and advanced modelling techniques, PyMC remains our preferred choice. It allows for:\u003C\u002Fp>\n\n\n\n\u003Cul class=\"wp-block-list\">\n\u003Cli>Defining custom priors, likelihoods, and transformations based on business needs.\u003C\u002Fli>\n\n\n\n\u003Cli>Adapting the model for any industry, geography, or business constraint.\u003C\u002Fli>\n\n\n\n\u003Cli>Scaling efficiently with modern Bayesian inference techniques.\u003C\u002Fli>\n\u003C\u002Ful>\n\n\n\n\u003Cp>However, for teams that want a more standardised approach with less manual tuning, Meridian offers practical alternatives. The key is understanding your business needs and selecting the right tool accordingly.\u003C\u002Fp>\n\n\n\n\u003Cp>At the end of the day, \u003Ca rel=\"noreferrer noopener\" href=\"\u002Fblog\u002Fmedia-mix-modelling\u002F\">Marketing Mix Modelling is about understanding marketing effectiveness\u003C\u002Fa> at a deep level, and that requires careful model selection, validation, and interpretation, regardless of the platform used.\u003C\u002Fp>\n",{"rendered":168,"protected":209},32936,{"_yoast_wpseo_focuskw":5,"_yoast_wpseo_title":1283,"_yoast_wpseo_metadesc":5,"inline_featured_image":209,"footnotes":5},"%%title%% %%page%%| Impression",[218,219],[],[222],[1288,113,225,226,227,228,229,230,231],"post-32917",[],[],[1292,1293,1294,1295],{"path":183,"name":184},{"path":237,"name":238},{"path":136,"name":137},{"path":192,"name":167},"Ever since Google announced that it would be releasing an open-source Media Mix Modelling (MMM) package, which adds to and improves upon its predecessor, LightweightMMM, data scientists and marketers have been eagerly waiting for access to the platform. Most were left waiting until Google announced they would release it to the world on the 29th 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