Have you ever wondered why the same food you make at home looks so much better in the commercial on TV?  Or why the burger you get doesn’t measure up to the picture on the menu? Delivering the perfect single image requires creating a meal with ingredients that you would never actually consume.  That immaculate bowl of cereal?  The milk is likely glue.  Syrup gently dripping over a stack of scrumptious pancakes?  You can bet it’s motor oil.  

Creating the ideal form at the total expense of function is as prevalent in the measurement of brand campaigns as it is in food photography – and just like photography tricks, you may not even be aware of it.

The most common scenario is when a company promises a “holistic” view of campaign performance across all channels from digital to TV to OOH and more.  As we’ll see, combining those components into a single brand lift metric will do you as much good as a plate of pancakes and Pennzoil.  

The Pitfalls of “Holistic” Measurement

When reporting brand lift, it is critical to not corrupt the results by combining incompatible measurement methodologies into a single metric.  Typically this occurs when a measurement partner attempts to stitch together deterministic datasets from digital channels with probabilistic OTS measurement for linear TV or OOH. 

As we have previously explored, using OTS, or “Opportunity to See,” is in-and-of-itself a problematic, outdated methodology for measuring the effectiveness of brand campaigns that suffers from delayed, imprecise, and inaccurate results. For one, OTS relies on human memory as a signal of exposure. While memory is a helpful tool for determining attitudes, it should not be used to determine exposure in advertising. This reliance on memory makes OTS very susceptible to cognitive biases and has a direct but unmeasurable impact on research design. It’s an inferior methodology that is rife with false positives and artificially deflates KPIs.

Deterministic results from digital channels may be accurate in their own right but are corrupted when combined with OTS measurement such that the entire dataset becomes unreliable.

The Right Approach
True holistic measurement requires a common dataset with common methodology, not just a single metric that one measurement company provides by mixing incompatible methodologies between channels.

For an accurate picture of multi-channel campaign performance, cross-media measurement should be limited to channels where deterministic measurement is possible, while other channels (such as OOH or some social platforms) should rely on MMM. In our commitment to arm brands and their media partners with truly holistic – and accurate – measurement, Upwave unifies multiple channels on a single cross-media dataset. Upwave never uses OTS for linear, for instance, because it’s incompatible with deterministic OTT measurement and would corrupt campaign-level results.

Consider the following cross-channel campaign that is measured with incompatible methodologies:

  • Linear TV (Probabilistic OTS): 4% Lift
  • Display (Deterministic): 8% Lift
  • OOH (Probabilistic Proximity Data): 3% Lift
  • Total Campaign: 5% Lift

What is wrong with these results? First, due to the control contamination of probabilistic exposure, the lift from Linear and OOH is artificially reduced, thus reducing the lift of the overall campaign. Second, this same control contamination makes cross-media comparisons impossible. Should budget be moved from Linear and OOH to display in subsequent campaigns? Probably not. Third, it’s impossible to determine the boost effect from exposure to multiple channels, because the exposure data is incompatible across channels.

The reality is that some channels, like OOH, are not measurable using bottoms-up deterministic data. In these cases, we recommend that brands work with best-in-class MMM partners on OOH measurement, rather than mixing methodologies and rendering an unreliable end result.

While it might be alluring to find one company that provides a single-source view of campaign performance, mixing OTS with deterministic datasets inevitably corrupts and deflates campaign-level results and clouds your ability to make informed campaign investment decisions. The next time you find yourself longing for the single PowerPoint slide with one simple “holistic” measurement metric, remember: behind the curtain, this approach is as contaminated as an oil-covered hot stack.