TripAdvisor.com is amongst the more well-known traveling sites, providing information about vacation hotspots, cheap and affordable hotel rates, as well as other people’s reviews and comments for a particular venue.  But how do they improve from here, personalizing this aggregate of data sets?  By utilizing big data analytics.

Wikibon analyst Jeff Kelly outlines TripAdvisor’s use of predictive analytics for determining how to interact with clients and what steps they should take towards improving their marketing tactics, outlined in a recently published case study.

TripAdvisor gets its revenue from hotels who want to display a link to their own site in the travel advisory site.  Click-throughs, or CTs, are what help hotels determine whether they should renew their subscription or not, as well as pushing related bookings from TripAdvisor.

Director of Analytics Michael Barry stated that, though they have no control over how people review hotels on their site, they do have control over their client’s CT rates.

TripAdvisor’s analytics team built models using advanced big data analytics to predict how many more CTs are required for a given client to renew their subscription.  They calculate how much increased marketing activity will be required to reach the required number of CTs for that client to renew, and also determine if it makes economic sense to do so.

“For some clients, Barry and his team has found, even a significant increase in CT rates only marginally increases its likelihood of renewing,” writes Kelly. “For others, even a modest increase in CTs dramatically impacts the odds of renewing. Knowing this through advanced analytics, TripAdvisor can focus its finite marketing efforts on the latter and not the former.”

Knowing this information can help TripAdvisor focus their efforts on those customers who are likely to renew subscription, and empower customers to make more informed decisions on their marketing tactics on TripAdvisor.

Though merely a case study, the TripAdvisor example demonstrates the growing need and use case scenarios for predictive analytics in particular.  Being able to see a glimpse of the future can enable you to make smarter decisions in the present, and boost your relationships with clients and end users alike.

We can all learn from TripAdvisor’s case study, improving methods for big data analytics.  Kelly notes some key takeaways from the above example:

“First, identify which performance metrics you can control and those you can’t. Next, marketing and other resources are finite, so use analytics to prioritize customers for targeting based on relevant value metrics. Finally, don’t get locked in to one specific technology or vendor, but rather find the right mix of tools to get the job done.”