Understanding the ‘Decision Funnel’ Using Unstructured Data

As recent store closures by popular brands such as bebe and Payless attest, without a better understanding of data, retailers risk losing touch with their market.

Retailers of all sizes can gather data via the millions of customer interactions that occur through the in-store experience or online via websites and apps. Beyond that, companies can tap nearly unlimited volumes of unstructured data generated by users on personal devices as well as how they use text, images and videos on social media.

Often, this is the data that holds the greatest business value, but isn’t utilized as much as internal data.

Unstructured data comes from many channels and sources, both conventional and emerging. Conventional data sources often include survey data, web browsing data, product purchase data and focus groups. Emerging sources can include social media data, connected devices/wearables, mobile data and customer service data.

Combining the information from all of these sources gives a company a critical view of how customers perceive its brand and how the business is stacking up against its competition. In fact, applying a custom analytics model to unstructured data with millions of data points can reveal deep insight into what drives a customer’s purchasing decisions.

The 6 Stages of the Decision Funnel

The average customer commonly moves through a process that's described as the “purchase decision funnel.”

The upper funnel includes:

• Brand awareness: share of voice (SOV) compared to competitors
• Brand perception: how closely a brand is associated with key features for an offering (e.g., performance, reliability, cost, etc.)
• Digital engagement: how effectively a brand engages with potential customers across the digital channels of web, apps, social media.

While the lower funnel includes:

• Brand consideration: how frequently is a brand present in multiple conversations
• Buying experience: how satisfied is the customer at the time of purchase vs. competitor brands
• Owner engagement: post-purchase customer advocacy.

Intelligence gathered from unstructured data analytics can help brands adapt their businesses to address gaps in these critical areas. For instance, a brand can invest resources into improving its online SOV to influence consumers at the upper part of the decision funnel, or it can invest more in the customer experience process post-purchase to create greater loyalty and drive engagement at the lower end.

The influence that higher SOV can bring to a brand is at times a double-edged sword. Consider the case of compact SUVs offered by Toyota and Honda, the RAV4 and the CR-V, respectively. An analysis of publicly available data from 2015 shows Toyota’s RAV4 had a three times higher SOV compared to Honda's CRV, yet the vehicles were roughly equal in terms of sales volume. At the time, the Toyota RAV4 was plagued by a seat belt recall issue which dominated the social conversations and potentially impacted consumers’ purchase decisions.

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In an analysis of frequently compared car brands, Mazda showed strong SOV for comfort and design among consumers. While it’s also a fuel efficient vehicle, consumer perception doesn’t reflect this. Mazda’s service sentiment is also low, and service levels were on decline between 2010 to 2015. Therefore, future campaigns could possibly address these issues and enhance the number of Mazda brand advocates.

                                                                                                                                     Source: LatentView Analytics

In a one-year SOV comparison between Honda CR-V, Mazda CX5 and the Toyota RAV4, it was also observed that Mazda was lagging in the digital consumer experience.

                                                                                                                                     Source: LatentView Analytics

And in the lower funnel, the data showed that Mazda appeared less frequently in the brand consideration set. While Mazda was rated high when it came to sales satisfaction, it scored low in prospect satisfaction, suggesting potential lapses in prospective customer experience at dealerships.

Brands could use data analyses such as these to identify individual store locations that need improvement as well as those that are performing well, and enable cross-learning opportunities that can increase overall performance.

The decision funnel can reveal insights into brand perception to help senior management compare desired and actual perception for their brands. This multifaceted analysis of all available data allows adjustments in strategy and messaging that can sustain a brand over time.

Through advanced analytics, retailers can gain insights that drive decision making about everything from marketing and customer support to accelerating product innovation. To strengthen a brand, mapping out the decision funnel needs to be your starting point.

Krishnan Venkata is a vice president at LatentView Analytics, a decision science and marketing technology firm.