Just as there are endless permutations to a nonlinear customer journey, there are endless ways to measure a marketing campaign’s influence on customer behavior throughout the stages of a journey. How do you track the impact on revenue when combinations of triggers become astronomical?
In the not-so-distant past when customer journeys were linear, fairly predictable and limited to a few channels, it was easier for a marketer to set and track success metrics through traditional research, evaluation and purchase phases than it is today. With increasingly digital, omnichannel journeys now the norm, it’s more difficult to pin down not only which channel a customer may appear in next, but which phase of the journey they’re in and how they are best influenced. The disruption caused by coronavirus increases the difficulty; digital transformation is accelerating at a blistering pace, and customers are changing some entrenched patterns and behaviors.
Marketers have many tools at their disposal to influence consumer behavior and drive revenue, with many different metrics to gauge effectiveness. Click-through rate, time on page, revenue per sale, frequency of sales/transactions, retention rates, conversions and segment level revenue are but a few of the metrics used to determine a campaign’s influence on moving a customer – or segments of customers – forward in a customer journey.
I was reminded of the complexity involved with measuring the success of guiding a customer journey in a recent discussion with a Redpoint customer, a retail executive, who said that at any one time his company is likely to have roughly 20 active campaigns (across rewards, retention, etc.) with about 50 active offers for as many as 80 different customer segments. With an average of seven touchpoints for each campaign, that’s hundreds of thousands of different combinations or into the millions when accounting for the various channel dimensions (mobile app, SMS, email, direct mail, in-store). At first glance, it seems too unwieldy for a marketer to manage all of those combinations – identifying and maximizing the revenue part of the equation for an individual customer as it pertains to customer lifetime value (CLV).
Define Your Objectives
The complexity and uncertainty that define today’s non-sequential, non-linear customer journeys make it more difficult yet more important than ever for marketers to define which metrics will yield the intended business outcomes. To maximize revenue while minimizing costs, marketers must have clearly defined goals in mind. If the aim is for the organization to lift revenue for top decile segments, that will require different metrics than a goal to improve retention across the entire customer base even though both are customer-centric approaches.
Whatever the overarching goal, an urgent need for visibility underscores whichever metric or metrics a marketer chooses to focus on. Visibility into the different levers used to guide a customer journey, and visibility into the results, are essential to optimize customer experiences. The emerging customer journey analytics market is crowded with point solutions that focus on a narrow competence, thus providing limited visibility only as it pertains to a compatibility for a pre-determined objective – such as optimizing an individual touchpoint.
Complete visibility and a holistic understanding of customer journey KPIs requires knowing not just how a metric influenced a customer or segment for one campaign or one journey, but rather over time and in relation to every other journey. A closed-loop process, granularity to the customer level and journey stage tracking across channels (i.e. not limited to channel-specific metrics) provides marketers with the insights they need to understand what is effective in creating a superior customer experience. This is only possible by tracking customer, offer and response data (those millions of combinations) at a detailed level.
Tame Complexity with a Digital Experience Platform
To handle the complexity of both the data and the journeys, organizations need a platform that provides a single point of control for the customer experience with built-in automated machine learning. The retail customer who highlighted the complexity of having to manage the myriad combinations said that he chose the Redpoint rg1 solution in large part because of its proven capability to build and track dozens of segments, using several dozen attributes, while easily managing all types of customer data from multiple sources.
With automated machine learning models guaranteeing the delivery of a next-best action at an individual customer level – with no duplicate or conflicting offers – the platform’s reporting templates provide marketers with unmatched granularity into the effectiveness of specific campaigns. Being able to analyze what’s most impactful on revenue from an individual customer standpoint gives marketers a powerful tool for testing and optimizing customer journeys at scale. The closed-loop process provides marketers with peace of mind knowing that, whatever their objective, they’ll never have to rely on guesswork for guiding a customer through an impactful customer journey that drives revenue.
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