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AttributionMedia OptimizationTV Measurement

TV Effectiveness Analysis is Incomplete Without Ad-stock Consideration

By November 15, 2016 September 11th, 2019 No Comments

This was originally published in MarTech Advisor

Your media team negotiated a great deal for prime time spots in major channels. But you are not a direct response marketer with a “1800 number, order now” call to action, nor can you just include it in your branding efforts with less stringent ROI expectations. It would be great to have numbers to share with the SEM team to demonstrate that TV is also responsible for the spike in search. Those numbers don’t exist within your BI tool, website analytics tool, marketing automation/analytics tool or within silo-ed channel performance tools. The question your left with is how do I create accountability for my TV spend, understand channel lift and TV’s short and longer term impact on my business?

That causal channel lift number comes into the picture with ad-stock. Ad-stock quantifies build-up of awareness in the minds of the consumers. It accounts for exposure to advertising and the influence it has on purchase behavior. Wikipeadia says “Each new exposure to an advertisement builds awareness and it will be higher if there have been recent exposures and lower if not. In the absence of further exposures ad stock eventually deteriorates to negligible levels”.

Five aspects to keep in mind to understand how ad-stock effects marketing effectiveness:

1. Decay or lagged effect – The stacking effect of TV advertising and how repeat exposure increases awareness or absence of exposure decreases awareness and thus impact of the advertisement over time, is measured by the decay effect. The decay window is based on a number of different factors and the rate of decay as well as window (days, weeks or month) are very unique to each business.

2. Diminishing returns effect – There is such a thing as too much advertising. TV is a very expensive medium to advertise. Identifying and cutting wasted dollars before the point of diminishing returns is a priority for marketers. With overlapping decay curves for consumer awareness, there is a saturation level a marketer should be aware of, to cut spend.

3. Cumulative effect of marketing – TV measurement is a more complex medium to measure effectiveness, than digital. Obvious reasons include non-availability of user-level data and the challenge to build customer journeys to conversion with TV in it’s path. With ad-stock the residual effect a TV spot has when aired provides realistic unified view of which channel is working and which is not. But, more importantly if TV influences or drives customers down the funnel and/or complements ROI from other channels.

4. Ad-stock in not specific to TV advertising – Advertising on any medium influences consumer awareness and purchase intent, well that is after all the whole point of doing it. So there is a build-up of awareness created by every channel and there is a decay associated with it. For digital channels, we can encode recency into each stimulation in the user path and determine the effect organically.

5. Ad-stock in not specific to MMM – Ad-stock gets mainly associated with Marketing Mix Models (MMM) or Marketing Mix Optimization(MMO). MMM is the methodology that provides aggregate level insights, mainly for planning and budgeting. Within MMM or MMO, ability to predict the decay rates and window for presence of consumer awareness is used to simulate scenarios and predict the ROI from X amount of spend. But it’s applicability extends to multi-touch attribution. Multi-touch attribution needs the ability to quantify and give credit where it’s due. Accurate attribution provides a realistic picture of each channel’s contribution to returns and enables efficiency gains. External factors like seasonality, geography can be modeled within ad-stock for accurate attribution as well as to identify areas of waste and simulate scenarios for optimal budget allocation.

Ad-stock is the basis of accurate TV effectiveness measurement, whether within attribution, MMM or a unified approach. It’s not the only number that’s important, but has a big impact in accurate analysis.

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