There’s a lot of talk about modernizing hospitality marketing – and most of it is dependent on finally cracking the nut between online and offline guest data and bringing predictive analytics to play. But there is a fundamental problem hampering our ability to do this and it starts with how hospitality manages web analytics. This problem is not new to Suneel Grover, senior solutions architect for digital intelligence at SAS, since he started his marketing analytics career building predictive models for online marketing campaigns.

“I really wanted to use web interaction data in my models, to more accurately predict the outcomes of these online marketing campaigns” he told me. “When I went to the web analysts to get access to the underlying data, it fast became apparent that they didn’t have it.” Most web analytics data is being hosted in the cloud, then aggregated to produce the kinds of reports that tell you how many people came to your website last week, how many came from one search engine versus another, what version of the operating system or browser they were using, etc. These reports can be very helpful, but do not give you the type of data you need for a deeper analysis.

Where this impacts hospitality firms the most is when it comes to personalization and targeting. Without a more granular level of data, your segmentation will remain at a very macro level. You also need the underlying data for building predictive models. “And if you cannot do predictive analysis on digital data, then your personalization efforts will plateau very quickly,” Suneel explained.

“Basically, web analytics is still functioning at the descriptive level,” Suneel said, “and few web analysts have ever built predictive models, or are familiar with predictive techniques.” That makes it rather difficult for there to be common ground between the marketing analyst and his/her web analytics counterpart. And these two need to be able to work together to meet the demands of the industry today. “To achieve effective personalization and targeting, we need to be able to work with both online and offline data in an integrated format,” explained Suneel. “This is extremely important – if hospitality companies are going to be relevant and engaging as they interact with their consumers across online devices and offline interactions - these two functions need to come together.”

Up to recently, many organizations could not handle detailed digital data because it amassed at such a quick rate. “Digital data has always been a classic big data problem,” Suneel told me. “Aggregating digital data for reports is much easier to manage,” he said, “but this has very limited analytic value.” Now that we are able to play with very large digital data sources, the possibilities really open up. “You can quickly start to move from descriptive analytics – describing things that happened in the past, to diagnostic analytics – statistical validation that analyzes why something happened,” he explained. “Predictive analytics allows you to be more proactive, because you are analyzing what will happen next. But most important is prescriptive analytics, which provides data-driven decision recommendations prioritized for individuals (not mass groups) in outbound and inbound consumer interactions.”

Predictive and prescriptive analytics are needed today by hospitality marketing organizations to answer the questions they have, such as how can we double website visitation? Should we invest more advertising dollars into Paid Search, Social, Email, or Display? How much more should invest within each of these channel? Is there a way to arrive to an optimized allocation to support our digital media mix business goals? “With detailed digital data we can answer these questions,” said Suneel. “Using optimization to identify how much money I should spend in each media channel in a given time period to get the highest return on an overall business objective is the queen bee of analytics, and we can do this by re-thinking how we collect, store, and apply advanced analytics to digital data.”

How is your organization thinking about digital data? Are you stuck at the aggregated level? Or do you already have a plan for collecting, storing and using detailed digital data?