As consumer data becomes more voluminous inside organizations, so, too, does the pressure to use this information to drive business outcomes. As a result, analytics departments are challenged with leveraging “hard” (behavioral and quantitative) data to uncover substantive knowledge about consumers and their pathways. They often use data as a tool to create business opportunities using descriptive, predictive, and prescriptive modeling. Yet decisions based on hard data are not enough. Organizations must also understand and incorporate the “why” behind the data they have at their disposal.
This was increasingly important for one health-care organization whose core values drive the ethos of the consumer as central to its decision-making process.
Historically, acquisition marketing targeted consumers through mass media (broadcast, digital, and print), and was largely supported by modeling efforts and self-reported data. Our client recognized the need to integrate hard measurement of behavior (correlation) with softer motivations (causation). Yet putting this into practice would take time, since transactional and usage data is collected in near-real-time, becoming a go-to option for frontline decision makers. Furthermore, while this data can be an efficient way to drive tactical and short-term strategies, it can often impart a false sense of security in the decision-making process.
To infuse this thinking into a blueprint for the future, our initiative went well beyond integrating analytical toolboxes and market research functions. It needed to encourage teams to continue to partner in informing approaches that will more closely emulate the consumer journey.
A one-month audit of current analytic models and market research work identified the degree of integration between these nearly parallel work streams, and stakeholder interviews determined current and desired future states. An added layer of immersion meant delving into their processes and bodies of work — from examining existing propensity, response, and attribution modeling efforts, to exploring shopper/buyer, customer satisfaction, and path-to-purchase studies. From there, a framework was developed to operationalize how each discipline could inform the other in an ongoing and cyclical way.
Next, modeling initiatives that proved to be most impactful for driving conversion were identified and prioritized, and in this case, sales attribution models became the center of focus. To demonstrate feasibility, proof-of-concept work included running cluster analyses and profiling consumer subgroups to create algorithms to fine-tune existing analytics models by closing the gap between self-reported and modeled outcomes. Lastly, refining models, data collection, and analytics initiatives spurred a conversation around marketing and conversion processes, better aligning one-to-one marketing strategies with the consumer path.
With the organization rated one of the “World’s Most Admired Companies” in its category in 2018, this work underscores the impact of understanding both correlation and causation — and, better yet, putting systems into place that reinforce its commitment to the consumer being its true north.
