Navigating Marketing Data Integration: Avoiding the Bermuda “Triangulation” Trap

Keeping with the pop culture theme of “Kleven” from my last post, a topic that has been swirling around the measurement space is “triangulation” which reminds me of Silicon Valley’s “Conjoined Triangles of Success.”

The concept of triangulation in marketing analytics

It’s a nice visual and is useful for viewing marketing results through different angles (pun intended), but putting them on the same playing field despite their wild differences in scope, accuracy, comparability and completeness seems crazy to me. I have seen countless advertisers get stuck in integration hell only to get sucked into the “Bermuda Triangulation,” where results contradict each other and there are no clear winners due to conflicting KPIs. Also with every addition to your measurement stack, your complexity increases exponentially, non-working media costs increase, and you end up building a Kafkaesque process to arrive at a decision or action. Stop.

Creating a clear path forward: Essential questions to ask

To navigate out, you need a clear path forward – a flight or float plan.

• Which tools answer which questions?
• How should you leverage the inherent advantages of the different measurement solutions?
• Where do assumptions, extrapolations, correlations, identity matches, and other fill-in-the-blanks get you from raw data to ROI?
• How would an app-only (Snap, TikTok), upper funnel type platform compare to a lower funnel, large identity graph, high-intent audience (branded Google search) show up differently based on inherent advantages and disadvantages of measurement?
• Are you measuring shorter-term outcomes (conversion) or longer-term (brand equity, top-of-mind awareness)
• What actions will you take from the results of this measurement tool – and what other actions should you take from the other measurement tools?

Higher scope vs. lower scope tests: Understanding their roles

For myself, the biggest bi-furcation is pretty simple:

• Higher scope tests: Comparability BETWEEN channels requires large and consistent measurement – variance and volumes are your friends. The changes that occur at aggregate outcomes when running geo tests, and running MMM models are about understanding how your most important investments drive your most important KPI (for most of my clients that is marginal incremental ROAS).
• Lower scope tests: Comparability IN a platform for enhancing your execution strategy requires granularity. Testing creative A or B at a market or larger level has a lot of executional overhead. In-platform testing allows for identity-level engagement and (match rate-depending) conversion analysis. While each platform might have a bias, the bias between options A and B are equal if designed correctly and can help you improve the effectiveness of the channel, which in-turn improves the effectiveness when doing higher-scoped tests.

Understanding key bifurcations in marketing measurement

Other bifurcations include:

• Identity vs. Non-Identity-Based Measurement – do you have user matching?
• Top-Down vs. Bottom-Up Approaches – Are the assumptions from splitting revenue or joining/extrapolating revenue?
• Incremental vs. Attributed Value Analysis – Are we looking at the value of the ads or how qualified the audience is?
• Marginal vs. Average Performance Metrics – Are you making investment decisions or recapping performance?
• Sales vs. Brand Building Objectives – Do you want conversions today, or market share in the future?
• Correlative vs. Causal Relationships – Did it happen at the same time or because of the ad?

Marketing data integration is complex and requires a clear strategy and understanding of the various tools and methodologies at your disposal. By asking the right questions and choosing the appropriate measurement solutions, you can avoid the Bermuda “Triangulation” and make informed decisions.

Request a demo to see how LiftLab can help you simplify your marketing data integration and drive better results.