Measurement

Measuring the impact of your advertising efforts should be an integral part of your creative development and media buying. Our vision is that ad effectiveness measurement, aka Marketing Science, should be equally accessible to small and mid-size businesses, as it is for the biggest advertisers who are managed by the biggest agencies with dedivated teams of Marketing Scientists.

We know the best practices for measuring ad effectiveness. Heck, as former Marketing Science Partners at Meta, we created some of them and educated the entire North American sales force as well as thousands of mid-size and big brands.

We have unique mastery over Meta's entire measurement stack - from AB testing, to Brand and Conversion Lift, to Marketing Mix Modeling. We also have expertise in the leading independent measurement solutions from Nielsen, Millward Brown, and other established measurement vendors.

We don't rest with standard attribution solutions. We test, calibrate, optimize.

 

A/B Testing

Ever wondered how to run a proper AB Test to test out Business as Usual vs. New Creative? Why do you often see the BAU creative win over the new creative? Let us run Creative Tests using the most rigorous way in the industry which we ourselves have come to define through our experience running hundreds of such tests.

Optimizations with A/B Tests are endless. Besides creative tests, here are a few common optimizations that can be informed through A/B Testing.

  • Objective / Delivery Optimization
  • Bidding Strategy
  • Placement
  • Targeting
  • Budget
  • Campaign Flighting etc.

 

Conversion Lift Testing

Do your ad campaigns drive incremental sales? Does your next advertising dollar acquire new sales that wouldn't have happened had it not been for it? Let us run the most rigorous solution in the industry, Meta's Conversion Lift study to quantify your cost per incremental conversion. 

Do you want to understand whether one strategy is driving greater incrementality over another one? Let us run a multicell Conversion Lift study to understand which strategy is driving lower cost per incremental conversion.

Have you ever struggled to prove the value of your prospecting campaigns amid lower cost per conversion vs. retargeting campaigns? Let us run a Conversion Lift study to tease apart the value of Cost per Conversion vs. Cost per Incremental Conversion.

In a conversion lift study, we’ll split your account audience into two groups, a large exposed group and a small holdout/control group that won’t see ads, and we’ll measure the difference or lift in the primary conversion event between the two groups. We'll also diagnose the lift across the funnel and understand at which point in your purchase funnel you are losing incrementality.

With this study you will address common challenges across different Meta marketing strategies, such as:

  • Lack of confidence in source of truth reporting of Meta’s true impact
  • Inaccurate attribution methods
  • Inefficient budget allocation and campaign decision-making

 

Brand Lift Testing

Do your ad dollars drive incremental awareness and consideration? Are you building brand equity over time? Does your advertising capture brand equity share from competitors? Let track track your brand equity over time and help you chip away brand equity share from your immediate competitors.

Meta's brand lift methodology is as regorous as medical Randomized Control Trials. Meta randomizes and splits your audience into test and control groups who have similar characteristics and are probabilistically equivalent. The test or exposed group has the opportunity to see ads for your brand where the control group doesn't see any ads. 

 

Attribution

Why do different Attribution tools credit platforms differently? Do you struggle to understand which touchpoints along the path to conversion matter for real business outcomes? How do you deal with discrepancies in reporting between Google and Meta? How can you calibrate mainstream attribution solutions? What is your conversion cycle? Which attribution setting should you use in Meta Ads Manager or in other mainstream attribution solutions?

These are some of the questions you and many other Direct Response advertisers are struggling with. As Subject Matter Experts on the Meta attribution solutions throughout the past 5 years, we have a unique understanding and come close to answering these questions (whoever answers them perfectly will have solved the biggest advertising problem).

When you advertise with us, you will have a piece of mind that we've dealt with and educated thousands of advertisers how to work with attribution for their unique case and how to calibrate and optimize the results of any mainstream attribution solution.

 

MMM (Marketing Mix Modeling)

Do you want to understand which part of your marketing mix is having the biggest effect on sales? Let us run the most rigororous and transparent/opensource MMM, Robyn, created by Marketing Science Partners at Meta, to understand which one of your marketing channels are being over or undervalued and to allocate budget based on true channel attribution to sales.

What is MMM? Marketing mix modeling (MMM) is a privacy-friendly, highly resilient, data-driven statistical analysis that quantifies the incremental sales impact and ROI of marketing and non-marketing activities.

MMM is an econometric model that aims to quantify the incremental impact of marketing and non-marketing activities on a pre-defined KPI (like sales or website visits). This is a holistic model used to understand how to allocate a marketing budget across marketing channels, products and regions and can help forecast the impact of future events or campaigns:

(Coming soon....)