Wouldn’t it be nice if there was a crystal ball for customer loyalty? InfoTrellis, an unparalleled leader in customer analytics specializing in creating algorithms that match customer identities with peoples’ online identities, recently conducted a study on customer data in hospitality and developed a predictive analytics model for sustained customer engagement.
To understand what a specific hotel chain’s customers were going to do next, the analytics team decided to take a holistic approach to the study – drawing on not only its own customer knowledge, but external data from competitor stays. The impact was instant, as this new influx of data painted a complete consumer profile, and allowed the hotel chain to prioritize customer value by potential for growth. Additionally, these newfound numbers provided insight on travel patterns, loyalty based on geography, and relative share of wallet – all of which contributed to holistic customer profiles to predicate future enhancements to the customer experience.
While the challenges of integrating collected internal data alone can be daunting, drawing insight from external data sources as well is another beast altogether. To accomplish this, the company looked to a qualified vendor that would possess the necessary analytic traits: Collect big data, Prepare big data, Match big data, and Enrich big data. Beginning with internal customer data, the solution was able to use “Social Listening” to weed through external data from social media, retaining only the most relevant and meaningful information to pair with existing customer data assets.
While compiling a rich trove of holistic customer data is a tremendous feat in itself, it did not yet achieve the hotel chain’s goal: To determine the true value of each customer. A base of enriched customer profiles necessitated a more sophisticated, individualized targeting process. Thus, the responsibility to build a more personalized relationship with customers became that much more important. Through individualized messaging and an added knowledge of competitor insight, the hotel chain was able to communicate at an appropriate level with both the most frequent loyalty members as well as those more likely to stray.
With the creation of specific loyalty program member profiles, the company hopes to extend its efforts to all guests. The analytics team looks forward to creating “personas” for regular hotel patrons not associated with the loyalty program. New qualifications will aim to predict things like free Wi-Fi, specific menu preferences, and propensity to travel to local attractions.