Losing the Forest for the Trees: How Personalization Presents a New Challenge for Marketing Analysts
Remember a few years ago when marketing “personalization” hit the scene? Male return visitors saw Version A, women aged 25-34 with two kids saw Version C, and so on. It was a better solution than the previous one-size-fits-all approach, but nowhere close to the reality of true personalization consumers currently expect.
Today, marketing technology (hand-in-hand with our AI – thanks, robots!) has enabled marketers to get much closer to a point of true personalization. Creative options can be matched to dynamic, individualized copy. Links can lead to a specific product the user has previously viewed. As a result, the permutations of ads within any given campaign can grow exponentially.
And even more important? It has made consumers feel seen, known, and understood. This deep personalization shortens the purchase funnel dramatically and makes ads immediately more relevant and likely to be seen. All of these are powerful new developments in our world – marketers can now speak to individuals, not just representative audiences. But with this great power comes great (analytical) responsibility.
While creatives are going crazy writing 1000x more copy variations, those of us sitting on the marketing analytics side of the house are now faced with the challenge of having an abundance of granular data at our fingertips while still grappling with the challenge of extracting meaningful, significant results. Insights at a granular level can be powerful, but if you get too granular, they become anecdotal. So how can we maintain that valuable specificity, but also aggregate our data to a point that allows for significant insights? How do we not lose the forest for the trees?
The bad news is that there’s no silver bullet. Crafting a robust strategy for data aggregation demands a tailored approach that aligns with your unique business objectives and media strategy, not to mention specific channels and media-serving capabilities. The good news is that personalized, granular data allows for flexibility – providing your analytics team with the ability to adjust their aggregation methodology over time. As we delve deeper, it’s crucial to focus on these five strategic areas that are key to effectively harnessing and interpreting your marketing data:
1. Demographic Cohorts
This is perhaps the most fundamental level within the list, and that’s not a bad thing. Your team is likely considering demographics as a part of their media strategy already. Aggregation by age, gender, geography, income levels, or other demographic factors can provide valuable insights into broader audience trends which can help refine your strategy ahead of the next campaign.
2. Objective-Based
Objective-based aggregation takes a bit more of a strategic approach to analysis. Aggregating your data according to the overarching goals of your specific campaign, analysts can identify the effectiveness of different messaging strategies and creative elements. Understanding which themes resonate the strongest is beneficial for both your Media and Creative departments as they look to develop the next round of campaign materials.
3. Revenue/Conversion-Driven
We don’t advertise for the fun of it. Media budget comes from an expectation that it will generate more revenue than it costs, so a bottom line-focused aggregation method can be incredibly useful ahead of conversations with your leadership or client stakeholders. Which cohorts return the highest ROI? Are there any cohorts with a negative ROI?
4. Customer Lifecycle
High, medium, and low funnel. Awareness, acquisition, retention, re-engagement. Whatever your lifecycle or funnel looks like, aggregating data through this lens can provide insights into customer interactions at different stages. A nuanced understanding of the customer journey can assist in allocating funds into the stages that yield the best results, while reducing overspending in stages that may not need as much support.
5. Performance Metrics
By focusing on your media’s performance metrics like conversion rates, CTR, CPC, and web sessions, your team can clearly understand their campaign’s overall success. While some aggregation methods (demographic, lifecycle, objective) allow you to glean behavioral and audience-based insights, performance-based aggregation is similar to revenue/conversion-based aggregation in the sense that it provides more insights into marketing effectiveness.
How you can maintain the value provided from granular data while also aggregating your data to a point that allows for meaningful insights is a difficult problem to solve – further complicated by the fact that there is no single answer. Keep your business objectives and targeting capabilities in mind and consider these aggregation methods as a starting point that not only allows for refinement but begs for it. This way, you’re not only able to count the trees, but more importantly, you can grow the forest.
