Measurements of Media Effectiveness within Practix
Multi-touch attribution and media mix modeling have been noted as very effective tools for measuring billions of dollars in advertising spend. Both tools utilize third-party data to dive into the details and surface imperative insights. However, issues with third-party data continue to arise. For example, restrictive data privacy laws and the invasive tactics used to collect this data have made third-party data a struggle to work with. The result? The insights pulled are becoming less effective.
Faced with a future filled with increased data privacy restrictions and third-party data constraints, marketers need to find a solution to continue to measure the effectiveness of their media investments. Our solution? Creating your own first-party data and leveraging Practix, our proprietary intelligence engine, to both measure the effectiveness of media budget allocation and gain insights that drive business results. Here’s why Practix’s multi-touch attribution and media mix modeling capabilities stand out from the rest when it comes to future-proofing media effectiveness measurement.
First-party data & multi-touch attribution
Multi-touch attribution (MTA) gained popularity in the “measure everything” age as an opportunity to understand different customer touchpoints with media. Today, this advanced marketing analytics methodology determines the value of each customer touchpoint that leads to a conversion. How? MTA provides insight into which marketing channel or campaign persuaded customers to purchase a product and associates a value to that channel or campaign. The goal is to determine where future spend should be placed to acquire new customers throughout the marketing funnel — not just the last consumer interaction before the purchase.
While MTA has been an effective tool in the measurement of media effectiveness, the latest Apple update, iOS 14, threatens its success. Why? This update requires all apps to receive permission from the user to enable tracking and changes the limits on how long cookies remain active. Said another way, without opting in, users are not able to be tracked. Taking away this third-party data pool impedes MTA’s ability to provide valuable insights because it requires individual-level data. Individual-level data are what allow MTA to report what touchpoint is driving customers toward conversion. Without the individual-level data, insights become less powerful because the data are less accurate and precise. What does this mean for marketers? It’s now unclear if it was a digital ad or Google search that drove that customer to buy, which ultimately takes away the possibility of determining where future spend should be placed to acquire new customers.
Despite Apple’s iOS 14 update, Collective Measures’ multi-touch attribution capabilities via Practix are still able to report on which marketing channel or campaign persuaded customers to ultimately convert. How? Our focus on first-party data. Collective Measures’ process of setting up MTA for clients includes:
- Collecting and monitoring available data including on-site and off-site data
- Analyzing first-touch and last-touch attribution models to understand which channels may be over-credited from an attribution perspective
- Identifying opportunities to test low-performing last-click campaigns and channels
Collective Measures’ custom multi-touch attribution model algorithmically attributes conversion credit fractionally to on-site and off-site media to drive tactical optimization recommendations without the worry of third-party data. And because of our focus on first-party data, our multi-touch attribution stands out against the rest.
Our closed-loop media mix modeling approach
Media mix modeling (MMM) is another time-tested measurement tool for understanding media investment effectiveness and how this investment impacts a business’s most important KPIs. MMM runs millions of simulations on a brand’s media mix to help inform a macro view of the brand’s media strategy and drive an optimal overall media mix. However, this tool relies primarily on second and third-party data sources to process how effective the current campaign is and what is driving success.
While MMM may appear to deliver similar insights to forecasting, there are some key differences to note. MMM is different from forecasting because forecasting looks at a single variable and does not account for how two or more variables may interact. For example, forecasting is a great measurement tool to understand the effectiveness of a single channel like paid search, but MMM is a better tool to uncover what media mix is best supporting sales or other outcomes based on a number of variables affecting the outcomes.
Through Practix’s MMM capabilities, Collective Measures’ team of subject matter experts are able to uncover deeper performance insights that ultimately help markets understand where to invest media dollars. Our unique closed-loop approach gives marketers a more holistic understanding of what online and offline media channels are driving conversions. This capability strengthens marketers’ insights because it quantifies the effectiveness of channels in terms of impact, revenue, and ROI. Practix’s MMM offering is able to provide marketers with a model that is analyzed and optimized quarterly based on changes to media investment, as well as accompanied with media flow representing real-life scenarios. The unique insights Collective Measures is able to provide through Practix’s media mix modeling capabilities will propel marketers to a new level of success.
What marketers need to know
Looking at marketing data through the lens of Practix’s MTA and MMM capabilities can present a new world of opportunities. Our first-party data and closed-loop approach remove the barriers of data regulations and continue to divulge the key insights about media touchpoints a customer takes toward conversion. Now, marketers can be more informed about the effectiveness of media and where to allocate budget when powered by Practix’s insights.