Marketing | April 3, 2018

How Ads Keep Generating Revenue After Campaign Ends

By: Guest Author


By Michael Anderson, data scientist, Oracle Data Cloud

Let’s say that after three months of running an ad campaign, it finally comes to a close. The numbers are in, the ads stopped running, and your targeted audience is no longer seeing your brand as part of that campaign.

Logic would say that an unfortunate side effect of a campaign ending is that revenue for the campaign ends, too.

Our data science team is here to show you how the revenue generated by an advertising campaign does not end with that campaign.

In fact, 53% of a typical campaign’s value is derived from additional consumer spending up to 12 months after a digital campaign ends.

How can this be? We’ll walk through our research, which includes some stats that might surprise even the most experienced marketer.

In the January 1978 issue of the Harvard Business Review, Nariman K. Dhalla paints advertising as an inherently long-term investment—marketers have known this forever.

Dhalla writes, “Sales revenue is not generated immediately in a lump sum”—rather, it “flows like a stream over time.”

With that in mind, let’s explore why data is the key to unlocking the potential of that statement.

What’s Lift Got to Do with It?

In marketing terms, we define “lift” as the increase in sales in response to an ad campaign.  

An ad campaign generates lift in two ways—a lift in sales or a lift in penetration.

Penetration lift during a campaign is derived from new buyers or existing buyers purchasing “out of cycle,” that is, after the campaign has ended.  

Often, sales lift is used as a primary key performance indicator (KPI), but this view alone can underestimate the full effect of penetration lift on sales.

Additional value is accrued when the change in behavior driven by the ad continues into the future, long after the campaign has run its course.

Any additional increase in sales during months 2-12 after that campaign ends is referred to as “long-term value.”

Long-term value (LTV) contributes significantly to the value of a campaign. Because LTV is defined by continuous consumer spending over time and is independent of additional ad spend, it can have a substantial impact on Return on Ad Spend (ROAS).

How Data Can Help Marketers Understand Long-Term Value

It’s true that brand loyalty decays exponentially over time—that’s a straightforward concept. Here’s what I mean:  

For an advertising example, let’s say the rate of decay is 0.7. This means that 70% of the buyers from an ad campaign will buy the advertised product again in the future, then 70% of those two-time buyers will buy for the third time, and so on.

This process continues until the effect of repeat buyers is negligible (in this example, after 12 months, only 1.3% of buyers will purchase again).

To model this behavior, Oracle Data Cloud uses a Markov model on past-purchase data to estimate two decay curves:

1.    The first curve estimates how ad-inspired/incremental buyers will buy in the year following the campaign.

2.    The second curve estimates how those households will buy if they did not see the ad.

Long-term value is calculated as the difference between those two estimated curves. (Get more in-depth with Markov chains in the below video).

In our study, we selected 107 campaigns since the implementation of long-term value measurement in early 2017 and compared short-term vs. long-term value estimates.

We discovered these three key results:

  • The full impact of long-term value is dependent on many factors, such as the purchase frequency of a product and brand loyalty.
  • A shorter purchase cycle means more opportunities for repeat sales, and brand loyalty increases the chances that the consumer will choose your brand again.
  • Advertising is positioned to influence both of these factors by reminding the customer to use your product often and by building brand loyalty.

To learn more about how your ads can drive long-term revenue, contact us on the Data Hotline.

Michael Anderson is a data scientist at Oracle Data Cloud, focusing on using data to create actionable insights and best practices for the ad-tech industry.

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