By now, we’re all familiar with the term ‘Big Data’ – the deluge of information afforded to us by modern computers and any consumer behavioral patterns therein derived. Big Data has barely been on the main stage for a decade now and already it’s had an omnipresent effect. Organizations across the board are measuring the minutia of everything from the flow-through traffic on individual web pages to aggregated past purchase histories, all with the hope of finding methods to better market to customers.
The obligatory example that comes to mind here is Target’s mining of millions of shopper profiles to infer if a consumer is pregnant or not. But hotels cannot harness the power of Big Data in much the same way. While Target mostly deals with an elastic clientele – that is, a highly price-sensitive group with a greater likelihood of switching to a direct competitor like Walmart – hotels rely on emotional and largely inelastic purchases.
To put it another way, Big Data’s purpose is to advance an organization’s goals of maximizing revenue from consumers, using predetermined triggers to turn a buck into a buck fifty sale. This mentality can have deleterious effects for hotels because it is shortsighted. It doesn’t take into account the fact that loyalty, or adherence to one brand over another, is not cemented overnight. Building a base of dedicated consumers is best achieved by delivering an outstanding hospitality experience, and this goal becomes all the more attainable when you start to mold your experience to each person’s individual tastes. In other words, the more rapport you have with your guests prior to their arrival, the better you will serve their needs.
Enter Little Data
The newly minted offspring of Big Data, Little Data is less about how many dollars you squeeze out of your consumers and more about how you use Big Data patterns to enrich a person’s life, regardless of whether there’s an immediate call to action or sale. The reason why hoteliers would benefit more so than those in other fields is because Little Data cuts to the core of what makes for a great hospitality experience.
We are in the business of enhancing the spirits of our guests, giving them memories that will last a lifetime and, now, predict things about people that they might not even know about themselves. If you’re using the flood of electronic data to this end, then you aren’t always on the prowl for the fast sale but you’re thinking long-term by trying first to build a lasting emotional connection with your consumers.
An appropriate cross-example to introduce this concept is Amazon’s use of recommended books in its weekly personalized e-newsletters. Based upon your past purchase history (a novel let’s say) as well as what other customers have bought or looked at, Amazon will send you a list of five similar books you might be interested in purchasing. Yes, the prospect of another sale is always there, but first and foremost, these emails are informing me of other novels that I am already preconditioned to like – ones which may have been heretofore unknown to me. These e-newsletters are teaching me and making it just a tad easier to find my next casual reading project.
This is a crude example; it barely scratches the surface as to what Little Data can do for building rapport between guests and hotel brands. But it’s effective nonetheless. In the past, before the days of smartphones, we used to call this one-to-one marketing. Many used to think that one-to-one marketing was resource exhaustive as it was too personalized to be deployed en masse. But it turns out the opposite is true; it’s more effective to target a smaller group of consumers who are already primed for the sales pitch.
The cardinal point of differentiation for Little Data, however, is that it also promotes customer self-actualization. Whatever marketing or promotional efforts you develop from Big Data analyses, think first about helping your guests become more self-aware and better people overall.
Of course, like Big Data, Little Data is only as good as its underlying measurements. With the combination of cloud, mobile and social technologies, it’s easier than ever to build detailed customer profiles to mine for patterns. But you should also look to incorporate any and every opportunity available to you including onsite interactions with staff members (who are instructed to plug requests and remarks into the computer afterwards), customer feedback surveys, camera footage and direct correspondence with managers – like a doctor’s file for each guest. Essentially, the more data you capture, the more inferences you can make.
Then there are emergent data collecting vectors like remote sensing and wireless sensor networks. Along these lines, one interesting Little Data tech startup worth noting coming up from my hometown of Toronto is Turnstyle Solutions (www.getturnstyle.com). By working with hotels and event spaces to install a series of WiFi repeater nodes, Turnstyle’s software can passively detect when smartphones enter a certain instanced zone, be it the lobby, the bar, the business center, the gym or the guestroom. Its Big Data application is via heat-mapping how guests move about a space to assess where the most traffic is and if there are any underutilized areas. But from a Little Data perspective – that is, an individualistic approach – Turnstyle can monitor how each person navigates a property both spatially and temporally to identify what areas they prefer and therefore what amenities they would benefit from knowing more about.
Big Data, Little Data. All these buzz terms can get a bit confusing. By giving you a spectrum of examples, my hope is that you will not only understand the tenets of Little Data but also know how to apply them to your unique property for optimal results.
1. The Pizza Lover – Suppose you have a regular patron at your restaurant who, whether via credit card tracking or through billing meals to the room, you identify as a client who has only ever ordered pizza. The Big Data approach to this would be to mark this trend then start sending the person promotional materials related to the entire restaurant’s menu. Little Data, however, cares first about why such a diner behaves this way as well as offering a few suggested pizza toppings based on similar customer data. Via this approach, a suitable response might also be to send this person a customer feedback survey inquiring only about the pizzas and nothing else. What does he or she like most the pizzas? Any suggestions to help make them tastier? Even better than an electronic follow-up, a waiter or manager could be alerted via staff recognition, a WiFi node detecting the patron’s smartphone or a swipe of the credit card, and then deliver these questions face-to-face for a much more personable rapport.
2. The Beer Aficionado – Much like the pizza lover, suppose you pinpoint a bar regular who only ever orders beer. Not only that, when you dig deeper in the multitudes of previously acquired data, you discover that this patron belongs to a class that does not ever really go for the generic Budweiser and Heineken-style brand names. Rather, they are fans of craft beers and local microbreweries. So, when your biannual sale on Miller Light comes around, would it make sense to include this bar regular in the e-blast? Maybe, but it’s not very insightful as to who this person is and what they like. In fact, it gives off a nasty ‘Big Brother’ vibe: we have been watching you, we know what you buy and now we are going to ram pseudo-similar sales pitches down your throat. As an alternative, start the e-blast with a ‘Did You Know’ about how pale ales are made or some quirky factoids about honey browns, and then insert a marketing line like, “We noticed you like craft beers. While we aren’t having any promotions on your favorites right now, we hope you’ll join us for our upcoming event sponsored by Miller Light.”
3. The Explorer – As much as we want them to spend more time interacting with our hotels, some guests will only use their rooms for two things: sleeping and changing. They check-in, dump their bags and you don’t see them again until it’s time for some shuteye. What’s the point in trying to sell your restaurant to these people, knowing full well that there’s a 99% chance they’ll be dining off-property? The explorer will view your invitation as spam, and no one likes spam. Instead, bite the bullet and suggest some hotspots around town. Big Data will update you on the most likely attractions they’ll head to; Little Data means suggesting these places without attempting to instantly lure them back on property. Play to their preexisting interests, then over time they will remember your understanding and be more receptive your efforts to have them sample your onsite amenities.
4. The Hermit – In direct opposition to the explorer type, these are the people who spend hours on end in their guestrooms. You might identify these individuals by their hefty room service bills or by their near-constant usage of the room’s bandwidth. So, what does Big Data tell you about what they want next or what they’d appreciate? If you find yourself with a recluse, encouraging them to try your restaurant downstairs might not be the best move. More appropriate to their preferences, tell them that you value their patronage and give them a room service coupon. Then when you ask for feedback, only ask about the guestroom and how it might be improved to better suit their needs. Even better, if or when they return next, send them a quick email informing them of any room upgrades you’ve made while they were away and how you incorporated their specific comments.
5. The Casual Gym User – Perhaps they are traveling on business or perhaps they are just blowing off some steam while on vacation. Whichever way, you’re bound to have guests come to the gym who do a light medley of cardio and weights. After recognizing a member of this cluster, the first question to mind should be: how can we make their fitness experiences better? Start with something simple yet practical. Armed with large datasets compiled from remote sensors or cameras, you may have previously determined that such users who approach the gym with a casual mindset have a much higher chance of neglecting the stretching component of a workout. So, send them a brief notification with instruction on how to perform several stretches correctly. Moreover, suppose you are a roaming businessperson who has just arrived at a chain property for which you’ve previously used the gym at other brand locations. Wouldn’t it be handy to receive a warning right away about the gym hours and any upcoming closures instead of finding these facts out after you’ve gotten changed and sauntered downstairs only to discover that the gym is currently closed?
6. The Sports Enthusiast – Imagine for a minute that your property has world-class fitness facilities: full weight room, yoga studio, spin classes and squash courts. When it comes to exercise, guests might use all of these features to varying degrees or they might stick to only one. It’s their choice. But you have to be careful in your marketing follow-up. A squash guy isn’t going to want to hear about your new spin instructor and vice versa (at least not as the headline for any newsletter you send them). For squash enthusiasts, send them pro tips or anything else that will heighten their appreciation of the sport. And for chains, tell them about other hotels in your brand that also house squash courts so they will keep you in mind if they ever travel there.
7. The Avid Golfer – A subset of the sports enthusiast and only really encountered at resorts with an onsite or nearby course, these guests are easy to spot. They arrive with a particularly conspicuous golf bag! That, or they’ll rent clubs, in which case you can categorize them by their rental charges. One thing that all avid golfers have in common is that they want to improve their game. Imagine letting guests borrow wristbands with built-in gyroscopes which analyzed a player’s stroke and offered suggestions for their exact height and build. A tad sci-fi, so consider something simpler like digital score cards. By keeping it electronic, you can track a player’s improvements game by game, let them set incremental goals and inform them on any particular fairways, greens or weather conditions that other players have recently had difficulties with.
8. The Spa Fanatic – Properties that have great spa facilities should find themselves with at least a few regulars. Their credit histories at such an establishment will tell you what they prefer when they go in for a treatment or massage. And when you line that information up with a Big Data algorithm, you’ll have a pretty good idea of what such customers will want to buy next. But Big Data stops at these recommendations. Little Data delves a bit deeper, asking why these spa revelers would bother purchasing something else. Based on their purchase histories, what can you infer are their underlying rationales for visiting this amenity? Once you determine this, then you can go about explaining how a new spa product might benefit them personally.
9. The Reluctant Spa Tester – Suppose, using a wireless sensor network, you discover that quite a few guests have walked into the spa entrance, but then left within a few minutes without a sale or return visit. What deterred them? Perhaps they needed a bit more time and information to make a decision. Is there any way to judge whether such a prospective customer is worth pursuing based on their other actions and acquisitions? Just as Little Data can be harnessed to convert fans into fanatics, it can also flip curiosity into a first-time trial.
10. The Anniversary Couple – When a pair of guests proclaims themselves as an anniversary couple, a manager should be informed instantaneously. This is your chance to form a lasting memory and get customers for life. So, what do other anniversary couples enjoy which this pair might likewise enjoy? Examine year-over-year datasets to see what you’re doing right with those couples who keep coming back for more.
Notice how in all these cases, the emphasis is not on age, gender, race or geographic origin (i.e. demographics). Although those are undoubtedly factors contributing to a consumer’s purchase decisions, hobbies, interests, career choices, food preferences and personal aspirations (i.e. psychographics) are far more critical to proper Little Data interpretations and responses.
Imagine for a minute that teleportation existed. Without knowing where you are going, you are teleported into a random hotel room. Barring any tent cards or other materials with the company logo embossed in the header, how would you discern the hotel’s brand?
Every full-service three or four star property is going to have most of the same things in its guestrooms: beds, desks, televisions, cabinets, closets, bedside lamps and so on. Without a grand sense of place established by the physicality and staff demeanor in the lobby, there is little for guests to instantly distinguish one hotel brand from any other. In this era where consumers increasingly make their reservations through third-party OTAs, the creeping logic is rapidly becoming: a room is a room is a room, so just give me the cheapest one around. And this commodity mentality should be every hotelier’s worst nightmare.
Now suppose you teleport into a guestroom and connect to the hotel’s WiFi portal, only to discover that based on your past rapport with the brand, they already have several worthy suggestions for you. Moreover, as a valued customer who has stayed with them multiple times in the past and dined at their signature restaurant each time, they’ve already dispatched room service to bring up a new appetizer that they not only want you to taste but also garner feedback. Now that’s a hotel worth remembering!
The bottom line is that it’s time to personalize the hospitality experience and use the full capabilities of modern technology to enrich your guests’ lives. It’s time to start one-to-one marketing again, but employing the predictive algorithms of Big Data to increase its efficacy and reach. It’s time for hotels to starting using Little Data. We’re only starting to grasp what this technique can do for us, and the brands that figure it out first will reap massive rewards.
(Published by Larry Mogelonsky in Hotel Executive January 2, 2014)