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Overview:
Tatari measures the effect of media in two buckets – cost per visitor (CPV) and cost per acquisition (CPA). Typically, Direct Response campaigns rely on effective media to drive an efficient CPV and CPA. DR buys often tend to be many spots at preemptible rates, in essence, a bid, where the cost of a unit is low enough to achieve efficiency. Brand buys on the other hand often have the goal of creating mass awareness, and therefore rely on purchasing higher reaching, more expensive networks or specific programming having a higher cache, which may not have an immediate impact on CPV conversion rates or CPA.
When a Direct Response and Brand Media campaign run in tandem, we can expect the reach
that the Brand campaign achieves to have a positive effect on the efficiency of the Direct
Response campaign. While CPA and CPV may not immediately decrease for the Brand Media
campaign, we can expect both performance indicators to steadily improve for the Direct
Response campaign over time as the Brand media will impact down funnel performance.
Essentially, the Brand Media campaign will have a halo effect on the Direct Response media and
will create an efficient and holistic down funnel marketing strategy.
How can we measure this increased conversion rate from the Brand Media spots on Direct Response spots?
It is difficult to isolate the effect of the Brand Media spots on the Direct Response spots as it will be one of many factors that contribute to the decrease in CPA and CPV and there can be substantial noise that impacts the precision of that impact. Tatari is building brand evaluation tools that can help identify the impact of brand but these are in the development stage.
What threshold of spend allows for statistical significance?
Statistical significance is achieved by having one of two factors:
1. A large enough sample size to
measure from and
2. A large enough delta between baseline and lift. In order to achieve the first
factor a spend of over $250,000 generally achieves a large enough pool of spots aired to achieve
statistical significance. That said, should there not be a large enough delta between the baseline
and lift metrics, the results will still not likely been statistically significant at a level (>85%) that
would allow for a definitive conclusion that the media being evaluated drove this lift.
Case Study:
Client X utilized a pulsing strategy for reach and efficiency in order to drive new customer acquisition.
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January and February: CPV Optimization – limiting to most efficient network rotations (high frequency)
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March: Frequency Optimized – reduced frequency to optimal levels at current CPV caps
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April through June: Growth Strategy – increased spend focused on reach and maintaining optimum reach and frequency levels
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June through July: Reach and Efficiency Pulsing – capitalize on halo from high reach weeks
Throughout the high reach months (April through June), New Customer Acquisition began to grow in proportion to CPA; this is because while high reaching networks are more expensive, therefore raising the CPA, they also provide a medium to get Client X’s messaging in front of the largest possible audience.
During the Reach and Efficiency pulsing months (June through July), the halo effect of the high reach networks coupled with the lower price point efficiency networks help drive CPA down while New Customer Acquisition begins to grow.
In this case study, we can see that the high reaching weeks (Brand) positively affect the efficiency weeks (DR), leading to a holistic media strategy where CPA declines and New Customer Acquisition grows.