Advertising influences all consumers; it serves to increase brand awareness and affinity for both those who are in-market and everyone else who could become customers in the future. However, the effect of advertising is not constant over time.
The traditional concept of Adstock describes how the response to an advertisement changes over time. Adstock effect describes how initially as a consumer is exposed to new ads the response increases until later when the campaign ends and the response starts to wear off.
The influence that a single piece of advertising has on a person’s purchase behavior is not constant and diminishes over time. You can use adstock models to approximate the rate at which an ad’s influence diminishes over time, but adstock models are often based on preset assumptions rather than data-driven observations.
The illustration below visualizes adstock for a scenario where the effect of advertising follows a radioactive half-life decay i.e. as each day passes the effect is halved.
The problem with adstock is that it attempts to “abstract” reality instead of reflect it. This disconnect from real-world data can lead you to make sub-optimal decisions on how to spend your media and marketing budget.
Convertro’s Data-Driven Approach To Time Decay
Instead of making a single, one-size-fits-all model of time decay, Convertro creates new time decay models based on the actual response data collected from people who have been exposed to your ads. This feature represents the real-world aspects of how advertising works and hence makes results more business-relevant. Specifically, attribution of conversions is now dependent on “time since exposure”, so recent exposures could see more revenue attributed to them than not-so-recent ones.
Because Convertro knows who was exposed to an ad and how long after that exposure they converted, Convertro’s time decay models reflect the true rate at which the influence of your ads diminishes over time. This leads to better understanding of how your media touchpoints actually perform, which in turns leads to more accurate insights into how to optimize your media spend.
Converto’s time decay models are client-, event- and channel-specific
Convertro creates customized time decay models that are unique to your organization and to the data collected from your own customers. Your time decay models are further customized by:
Event type - A different time decay model is created for each event you are tracking with Convertro. We use the actual converting clicktrail data for an event type to create the time decay model for that event type.
Media channel - Convertro takes into account that different channels have different rates of decay, so it models time-decay for TV spots differently than for display ads or emails, etc. To calculate each channel’s rate of decay, we look at the “channel type” of touchpoints within converting clicktrails to determine how long each type of ad remained influential after exposure.
Compare the Convertro time decay models below to the adstock model shown earlier in this article. Because they are created from actual user clicktrails, the dropoffs and plateaus in the Convertro models reflect real-world customer behavior much more accurately than the artificially smooth curve of the adstock model. This difference enables Convertro to provide more precise attribution.
Time-decay models for display touchpoints contributing to “sale” events in February and March
Impact on attribution
The clicktrails below illustrate the impact that time decay has on attribution. In these examples, a customer is exposed to 3 display ads between August 2015 and February 2016, then makes a $90 purchase on 2/17/2016.
Before accounting for time decay, the display touchpoints, being otherwise equal, are attributed equal credit for the conversion ($90).
Attribution for a $90 sale, before accounting for time decay
When time decay is accounted for, however, the amount attributed to each touchpoint becomes dependent on “time since exposure”, as represented in the time-decay model below for the event type, channel, and time frame.
Time-decay model for display touchpoints contributing to February “sale” events
You’ll see that after accounting for time decay, Convertro adjusts the amount of credit given to each touchpoint according to the time-decay model above. In this case, revenue attributed to the touchpoints nearest and furthest from the conversion date have shifted dramatically to reflect the relative impact each touchpoint had on the conversion according to the model.
Attribution for a $90 sale, after accounting for time decay
As you can see, accurately accounting for time decay can make a huge impact on attribution, and by extension, optimization. By including data-driven time decay as part of its algorithmic attribution process, Converto also increases the accuracy of its reports, spend recommendations, and optimization insights.
This means you, the marketer, will understand the impact of recency of exposure on attribution and use those learnings to space your advertising campaigns to achieve maximum effect.
Want to know more? If you are already working with us, contact your Client Services Manager for more information. If you are new to attribution and or Convertro, please email email@example.com to get started.
This post was written by Vish Oza, Director of Product Management, Jonathan Boswell, Senior Technical Writer, and Maryam Motamedi, Senior Product Marketing Manager.