Using Matchback Data to Help Improve Digital Campaign Analysis

Matchback data

Digital media has transformed the way customers arrive at a purchase. As marketers, we’ve got more ways of reaching buyers than ever before — which is good news and bad news when it’s time to accurately evaluate the performance of specific channels.

In the digital marketing arena, there are many marketing analytics tools and techniques in use today, from setting cookies and placing pixels to tracking codes. But when customers browse and buy across multiple channels, understanding what worked and what didn’t in prompting them to act can quickly get complicated. That’s where matchback data can give you a big advantage.

The art and science of the matchback  

The matchback technique analyzes your untracked orders — regardless of the source — and, just like it sounds, matches them back to the data source you used to reach them. This allows you to allocate the conversion or sale to the correct digital marketing campaign source.

And accurate allocation is a top priority for nearly 65% of marketers this year, according to the latest benchmarking survey from the Interactive Advertising Bureau and Winterberry Group. That’s one reason why U.S. companies spent more than $20 billion on data and related solutions in support of their advertising, marketing and audience engagement efforts in 2017.

The distinction between matchback and attribution

Though you may hear the terms used interchangeably, matchback and attribution are in reality two very different marketing analytics.

Matchback is the process of using a source file, either from your own house file or from a third-party data provider, to target and then identify the originating source of a conversion or sale. In comparison, attribution is a much more ambitious goal: Measuring the amount of influence each advertising impression has on a consumer’s decision to buy. This is typically calculated in one of three ways:d

  1. Single-touch attribution — Using the first or last touch as the driver to buy.
  2. Fractional attribution — Assigning a set value to each touchpoint in the buyer’s journey.
  3. Probabilistic attribution — Creating a statistical model that measures the probability of conversion across all touchpoints.

Pre-planning for matchback success

Matchback isn’t an ad hoc decision you can make on the fly — you must plan to use it while you’re designing your campaign and choosing the right type of data so you can set parameters like these to ensure consistency in measurement:

  • Testing — Hold back a sample so you can accurately measure lift compared to a control group.
  • Counting responses — Will multiple responses be credited back to a single campaign?
  • Overlapping campaigns — Determine whether the last touchpoint will be credited for the conversion or pro-rated across all successful touchpoints.
  • Pass-alongs — When a communication converts someone other than the original addressee, will it be counted?

The matchback process can work for many different types of campaigns — from CRM remarketing to display banners to third-party email prospecting. Keep in mind, there are always going to be orders that can’t be credited to a specific source — even with matchback data available. Trusted matchback reporting can help reduce the number of mystery conversions so you can apply the learning to your next campaign.

Solving the conversion puzzle

Thinking of giving matchbook analysis a go? Consider working with a reputable offline data compiler that can onboard your data and provide accurate matchback services.

At SMS, we use matchback data to track and analyze how consumers respond to the content our clients put in front of them. This process gives us the essential information to help determine which channels and touchpoints were most effective during the consumer’s buying journey — and the best way to make your next campaign drive even stronger results.

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