Media Effectiveness Measurement Services

In marketing, incrementality testing is used to assess the impact of a marketing campaign or channel. Two distinct groups are created: a test group and a control group. Marketers can accurately measure the incremental effect of marketing activities by comparing behaviors and outcomes between these groups. Using this approach allows you to isolate the true impact of the marketing effort from other variables, which allows you to refine strategies and make informed decisions about marketing investments.

Media Mix Modelling (MMM) determines the most efficient allocation of resources, such as spend, across various marketing channels. Using historical data on spend and ROI, it identifies correlations between channels and performance. By identifying the optimal mix of these channels, MMM helps you optimise marketing strategies, predict future performance, and ultimately enhance overall marketing effectiveness.

Both methods are similar but are different in scope and purpose. Incrementality testing evaluates the impact of a specific marketing campaign by comparing a group exposed to the campaign (test group) with a group not exposed (control group). It isolates and measures the incremental effect of the marketing activity. A/B testing compares two or more versions of a marketing asset to determine which version performs better. While A/B testing helps optimise specific elements, incrementality testing assesses the broader impact of the entire marketing campaign, helping to inform overall marketing strategies.

Incrementality testing is essential when there's a need to accurately measure the true impact of a marketing campaign or activity. It helps determine if the desired outcomes, such as increased sales, customer engagement, or conversions, are a direct result of the marketing effort itself, rather than other external factors. This methodology is particularly valuable when making decisions about budget allocation, channels, and building marketing strategies to achieve optimal ROI. Incrementality testing can also be used when evaluating the performance of new marketing approaches or assessing the effectiveness of existing ones for continuous improvement.

Media mix refers to the specific combination of channels (e.g., paid search, social media, DOOH, programmatic display) a company uses to reach and covert its target audience effectively. On the other hand, the marketing mix encompasses a broader set of elements which includes not only media but also product design, pricing strategies, distribution methods, and promotional activities.

Media mix modelling should be used when a business wants to fully understand the incremental contributions of their media impact to business goals such as leads, conversions or revenue. Media mix modelling is best suited to businesses that have a mix of digital and traditional marketing channels and businesses with KPI’s that fluctuate by season and/or on particular events. Businesses should invest in media mix modelling if they wish to gain insights into budget allocation and ROI forecasts to inform decision making.

Media mix modelling allows businesses to balance and identify growth areas in current or past media spend investments. Media mix modelling provides a way to maximise ROI with respect to marketing spend and hence produce forecasted budget allocations. Businesses can also use media mix modelling to view how ROI changes as marketing investment changes, hence allowing businesses to understand what is required to achieve target ROI.

Media mix modelling requires 2-3 years of historical time series data on digital marketing spend, any traditional marketing spend, performance metrics such as sales or conversions , and other relevant variables. Data on different media channels, advertising expenditures, and market conditions are essential.

Incrementality testing requires 2-3 years of historical time series data concerning the variable we wish to measure across the media channel we wish to infer on. Additionally data concerning the variable we wish to measure across different locations is essential. Other relevant variables that may have a large influence over the KPI in question can be included but aren't essential.