A Direct Mail Attribution Guide for DTC Marketers

Today’s marketing landscape is growing increasingly diverse and complex. With channels and touchpoints more interconnected than ever, the ability to demonstrate a clear, quantifiable return has never been more challenging – or necessary.

With many brands tightening up budgets due to COVID-19, CMOs are honing in on data to drive marketing strategy and optimize spend. Therefore, it is critical for marketers to bridge the gap between channel-specific performance and channel agnostic ROI.

For direct mail attribution, a comprehensive view of performance is best. Ensuring reliable processes for tracking results, calculating incrementality and influencing attribution is key to realizing and optimizing the full potential of this high-performing channel.

How to track direct mail performance

To genuinely understand direct mail’s role in a multifaceted marketing mix, you must first establish a reliable signal for its impact. This can be done via direct mail matchback, a process where untracked sales or orders are matched back to the campaign’s mail file, with a match indicating at least some level of credit for the sale. Matchbacks are important when determining campaign performance because only a fraction of direct mail responses will come through a directly attributable element (URL, TFN, promo code, etc.). This is especially true if your offer is not unique or materially different from what can be found through a branded search or direct hit on your website, as your attributable element may not be used and campaign analytics would fail to recognize the channel that drove the sale. Without a matchback, you are not able to see the complete view of your direct mail performance.

Tactically, most matchback logics leverage an alpha-numeric matchkey to link your sales file to the mail file, which is often a derivation of name and address information. Additional business rules are layered onto the process to only allow for matches after the campaign in-home window and only for as long as the read period. As a tangible and long form of advertising, mail has an average lifespan of 17 days, and pieces are often shared among multiple members of the household, or even multiple households. As a result, a direct mail read period often lasts 60-90+ days, depending on the product or service.

Once a match is established, it serves as a signal that direct mail has had an impact on that sale and merits attribution. The matched record also carries over information about the list, offer, and creative cell it came from, which helps marketers optimize strategy and performance within the mail channel moving forward.  

Ultimately, while directly attributable results can provide an early indication of performance, patience is key when tracking direct mail, as the matchback result provides the most comprehensive baseline for direct mail’s potential impact.

How to use holdouts to establish incrementality

While directly attributable performance does not fully account for direct mail’s impact, matching sales back to the mail file for +/- 90 days inevitably overstates it. Therefore, to fully understand which matchback sales are incremental and deserving of credit, direct marketers can implement a holdout strategy. This best practice allows marketers to track the lift that mail produced over no mail exposure to determine if direct mail has successfully contributed to prospect conversions and to establish program incrementality.

Direct mail holdouts act as a campaign’s control group. Chosen at random from the targeted prospect list, holdouts are comprised of a group of comparable individuals who are intentionally not mailed to and instead withheld for measurement against the mailed group.

During campaign analysis, sales files are matched to the holdout records via the same process and matchkey methodology that is used on the mailed group. With a matchback complete on both groups, you can calculate an incremental sales rate and lift for direct mail. In most mature campaigns, holdouts perform anywhere from 25-40% as well as the mailed group, meaning 60-75% of matchback sales from the mail group are incremental.

It’s worth noting that although mail is withheld from the holdout group, they are still exposed to your brand’s other influencing channels (digital, TV, radio, etc.), which tethers direct mail incrementality results to outside factors, creating limitations in its accuracy.

The other drawback of using holdouts to infer incrementality is that they fail to provide an individualized credit decision for each sale. A holdout only quantifies the likely impact of mail in aggregate, triggering the need for an attribution approach.

How to attribute credit across multichannel campaigns (or at least influence attribution)

Traditionally, attribution models fall into two methods: single-touch or multi-touch.

Single-touch attribution is generally categorized as first-touch or last-touch. The first-touch model gives credit to the first marketing channel that touched a prospect, naturally over-emphasizing top-of-the-funnel marketing tactics and overlooking down-funnel channels that successfully nurture conversions. Alternatively, last-touch attribution gives credit to the last touchpoint the customer engaged with before purchase. Although very easy to implement, single-touch attribution does not consider the broader customer journey and the role each marketing channel has played in the conversion.

When it comes to tracking direct mail campaigns, multi-touch attribution is ideal. Multi-touch attribution recognizes each touchpoint that the consumer has engaged with along the purchase path – providing greater insight into marketing channel optimization. As a more complex form of attribution, marketers must have the capability to track individual consumers across multiple channels. Machine learning or data-driven models that use historical data to distribute credit to each touchpoint are most effective, but they are rarely implemented effectively. Other weighted, rules-based methods that credit channels based on factors like their distance from the final touchpoint, are more commonly used and still provide practical and sufficient conversion data.

Ultimately, there is no perfect attribution method given today’s staggering number of conversion paths. Still, effective attribution models should provide insight into the buyer journey including the messages and channels the converting consumer was exposed to and engaged with along the way. Many of these attribution solutions live online, so you must influence the outcome and accuracy of those models by feeding in your offline direct mail matchback data.

A path forward

Without a silver bullet to direct mail attribution, it is recommended that direct mail performance is measured from several competing viewpoints. As a best practice, consider performance to be bookended by directly attributable (bare minimum) and full matchback (maximum credit) results. Then calculate several additional performance scenarios to project reality somewhere in-between:

• Incremental lift over holdout
• Time-decayed version of matchback (more credit given the closer the matched sale is to the in-home mail dates)
• Fractional attribution based on the last-touch attribution model (more credit given to a direct mail match if the last touch was branded search than if it was non-branded)

Perfecting multi-touch attribution may be a long shot but proving direct mail’s incrementality and role in the marketing mix does not have to be. A comprehensive matchback and holdout strategy paired with multi-touch attribution equips direct mail marketers with valuable data to optimize their direct mail programs and highlights the genuine strength of this traditional channel.

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Featured in BRAND United on 6.30.2020 and Target Marketing on 7.9.2020