By Matt Pigott - April 14th, 2017
Marketing Attribution 2017-Part 3: How leading brands use it while others are losing it
Take a look at Airbnb. When founders Brian Chesky and Joe Gebbia discovered they couldn’t pay the rent, they resorted to turning part of their loft space into a bed and breakfast. And while managing a few guests didn’t require a great deal of number crunching, the company’s current tally of two-million listings in 34 cities and 191 did. From penury to early market positioning to global domination, their online marketplace and homestay network now needs sophisticated algorithms to break down the huge, relentless flow of data, make sense of it, and use it.
LTV - a heavyweight acronym
Traveling through a high-level tech stack, it’s this data and these insights that, when effectively attributed, give Airbnb analysts and marketers the granular detail they need to improve every customer experience. The better the experience the longer the engagement. And longevity, it turns out, is one of the most overlooked aspects of modern business development. If the quality of a brand can be judged by a single metric, it’s customer lifetime value (LTV). And, here’s the rub: LTV is the lifeblood of reliable marketing attribution. How so?
Because it’s within the customer journey that attribution happens, and a truly valuable customer journey takes place over time. Stories and narratives that inform business decisions can be drawn from this journey, individually and in aggregate, once they have been verified as significant through the attribution model—as defined by the data scientist through his or her tech stack. The longer the brand-consumer relationship is nurtured, the more customer data can be gathered through that stack, and the more accurate and refined the subsequent data will be. From many time-stamped interactions, no matter how fickle and sporadic they are, meaning can be extracted, and as technology and understanding continue to improve, the possibilities for using it are endless.
The point is, engagement over time provides a deep well of rich data that can be transformed into powerful signposts for future marketing and business strategies, which is why it must be harnessed well and used wisely. Currently, there is an over-reliance on one-off conversions, which is short-sighted, not least of all because it leads to upstream clicks losing their value. Put another way: acquiring customers is one thing, holding onto them—which in actual fact means them holding onto you—requires not just looking at the start and end points of a journey, but every touch point in between.
But a shocking statistic, according to Raab Associates, is that only 3% of companies turning over $5million or less use any notable form of marketing automation, let alone marketing attribution. By contrast, 60% of companies turning over around $500million use automation. A chasm exists between what large and small companies are doing here. According to AdRoll last year, while 84% of marketers believe that attribution plays an important role in successful marketing, less than half of marketers use any sort of multi-touchpoint attribution model. Even fewer use a custom model. On the upside, that’s still twice the number of the year before, indicating that attribution will feature high up on the list of marketing priorities this year.
The power of a call
Today, many brands, particularly those that receive a lot of inbound inquiries, are also bringing the phone call into the marketing mix and using marketing attribution to weight various elements of the decision-making process along the customer journey that led to the call. Due to phone calls being high intent actions, and usually occurring after a customer has done 90 percent of their research online, being able to pinpoint what they were looking at before dialing is useful for contact center agents who then handle the call.
Knowing, for example, that a customer was looking at red sofas and chairs in a particular range, at a particular price point, empowers the agent to give relevant advice, to talk about any promotions relating to the customer’s browsing history and, if appropriate, to upsell. Algorithmic attribution that makes sense of the customer’s cookie-tracked historic journey the moment a call is made, and weights the value of a PPC campaign, or a specific web page that did a good job of moving a customer along the sales funnel—or of converting—is key to revising existing and devising new strategies for business growth.
Over the next few years, finding new and innovative ways to bring offline activities into attribution modeling are likely to gain traction. As NFRs move into high street retail outlets, for example, and credit card and store card data gathering gets smarter, working out how web-based behavior interfaces with ‘real world’ behavior will help brands narrow the data gap, bringing them closer to their customers, helping them to identify buying triggers, and leading them closer to realizing the dream of predictive selling.
All of which means that training up execution-based marketers to understand the output of data science, or training the data scientists to communicate in ways a typical marketer can understand will be vital moving forward.
When tech firms such as Oracle, Deloitte, and IBM start buying agencies, it’s clear that a big shift is happening in the marketing space. Increasingly data driven—citing combinations of CRM systems, bid management systems, and attribution analytics tools as their big guns—these kinds of moves suggest that the next few years will be data, attribution, and programmatic driven. This will enable creative teams working alongside data teams to deliver better all round outcomes for their customers.
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