People-based measurement is the new black

Ad tech and CRM are converging, and columnist Jordan Elkind believes the need for people-based measurement has never been greater.

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Ad tech and CRM (customer relationship management) used to run in very different circles.

Think of ad tech — performance marketing channels like display and search — as the cool kid of the marketing world. Big budgets, splashy campaigns, a constant stream of glamorous new advertising products and formats, exciting metrics like “share of attention.”

Ad tech was once largely focused on reaching pools of anonymous users, identified by cookie or device IDs. True, the rise of programmatic display has given marketers more control over the types of audiences they target, driven by the ecosystem of second- and third-party data. And retargeting enables advertisers to deliver highly targeted lower-funnel calls to action. But at its heart, ad tech is a world long motivated by the need for performance and scale.

CRM, in contrast, has always been… well, more of the boy or girl next door type. The domain of CRM has traditionally been known users, particularly those who have transacted with the brand. Working with largely enterprise IT systems and limited budgets, CRM professionals have long focused more on optimizing touch points with known customers through channels like direct mail and email.

But the rise of targetable ad technology — the ability to reach individual, known users on the open web — has broken down the once-clear distinction between these two worlds.

First came Facebook Custom Audiences, a revolutionary product that brought the intimacy and personalization of the email inbox to display advertisements on Facebook. Google’s highly successful remarketing lists for search ads (RLSA), and now Customer Match, enable marketers to enable dynamic bid optimization strategies around known customer segments. And data onboarding services like LiveRamp now empower marketers to onboard sophisticated CRM segments and reach them — or audiences that look like them — across dozens of display networks and DMPs (data management platforms).

The convergence of ad tech and CRM

These worlds are colliding — and this is a good thing. The result is more relevant, higher-impact marketing across all channels.

Imagine that a customer just bought a handbag. The retailer predicts, based on dozens of data points about her, that she’s likely to be a high price-point fashionista. In addition to powering a personalized post-purchase email series focused on the brand’s couture bona fides, the retailer now has the ability to tell her stories about complementary items on Facebook, through display advertisements on relevant web properties, and to show up at the top of search results when she’s back in market for her next high-fashion purchase. So far, so good.

But there’s a gaping hole in the way that most retailers approach cross-channel orchestration in the age of the ad tech-CRM convergence. Most retailers still rely on channel-based measurement. Display tools optimize around reach and CPM. Search tools optimize around last-click ROAS. Email optimizes around open and click rate.

At one point, when CRM and ad tech efforts were still worlds apart, this represented a legitimate attempt at quantifying difficult-to-measure impact, especially of upper-funnel activities. But now that the power and relevance of CRM targeting has extended to a world that once trafficked exclusively in anonymous audiences, there’s a new path available to data-driven marketers: people-based measurement.

People-based measurement refers to the practice of measuring the incremental impact of marketing efforts in terms of customer value. In contrast to questions like “what was the view-through ROAS of my holiday display campaign?,” a people-based measurement system will attempt to answer questions like:

  • How did my holiday display campaign impact my rate of conversion of one-time buyers to repeat customers?
  • How much did the campaign impact the customer lifetime value (CLV) of the customers to whom it was exposed?
  • How much value did this campaign add, incrementally, on top of the five other channel touch points that were part of a coordinated multi-channel campaign?

The opportunity to communicate directly with customers through more stages of the customer journey means more opportunity than ever. But it also places a premium on knowing the impact of each and every touch point.

Without a full-fledged multi-touch attribution system in place, it can be quite difficult to measure the full customer impact of upper-funnel activities like non-branded paid search and display. And even for those with attribution buttoned up — savvy retailers are realizing that optimizing for short-term gain is a race to the bottom.

Understanding marketing actions in terms of long-term impact on customer behavior and loyalty is the only way to drive sustainable long-term growth. That’s where people-based marketing comes in.

Leveraging people-based measurement

So how does a retailer just beginning to flex the joint power of ad tech and CRM take action around people-based measurement?

1. Consolidate your data: The foundation of people-based measurement is a single view of each customer, powered by aggregated data from across siloes — online and in-store purchase data, email engagement, web history and more. That data needs to be shaped around individual customers rather than aggregate channel engagement metrics, so that a clear picture of each user can begin to emerge.

2. Know your customers: In order to build meaningful customer journeys or customized experiences, raw data isn’t enough; marketers must be able to identify and extract insights on their most important customer segments. Examples include new customers with high-CLV potential, customers who are beginning to show signs of fading away, or those with a propensity to shop certain types of merchandise sale.

With such a rich opportunity landscape of potential actions, a key for marketers is being able to prioritize which segments to take action on — and the content, message and creative that are likely to resonate with each segment. Customer analytics and segmentation platforms can provide prescriptive guidance about where to focus and how to craft strategies around key segments for maximum impact.

3. Run controlled experiments: Once target segments have been identified, the marketing team should run a holdout experiment. This means setting aside a random portion of the relevant segment as a control group — a subset that won’t actually receive the communication, ad impression or search boost in question — to serve as a benchmark for measuring incrementality.

One simple example might include a multi-channel winback campaign for lapsing customers. The control group might receive nothing; test group (1) might receive just an email; test group (2) might receive an email and Facebook Custom Audiences ad; and test group (3) might receive an email, Facebook ad and personalized site experience. This would enable the marketer to systematically measure the amplification impact of a multi-pronged winback strategy.

4. Measure and optimize: With a control group identified, marketers can run campaigns and instantly benchmark the incrementality of each test group against the performance of the control group.

While many BI software systems can measure short-term conversion and revenue lift, sophisticated customer analytics tools can measure the impact on the customer lifecycle (e.g., the “reactivation” rate of lapsed customers) and predict the lifetime value impact of each customer touch point.

Ultimately, people-based measurement isn’t just a competitive advantage for brands focused on long-term growth. It’s a necessity for retailers who want to realize the full potential of advanced CRM insights — with the impact of reaching customers across channels, at every stage of the life cycle.

Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.

About the author

Jordan Elkind
Jordan Elkind heads the product team at Custora, an advanced customer analytics platform for ecommerce retailers. Prior to joining Custora, he earned an MBA from Wharton and worked in marketing analytics at Citi Cards.

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