Big Data Doesn't Work if You Ignore the Small Things that Matter
Ever waited hours, in vain, for a repair service to arrive at your home? Of course you have. We all have. Chances are you’ve also shifted your allegiance away from a company that made you wait like that.
So why do companies spend millions on big data and big-data-based market research while continuing to ignore the simple things that make customers happy? Why do they buy huge proprietary databases yet fail to use plain old scheduling software to tell you precisely when a technician is going to arrive?
For that matter, why do they send trucks with the wrong inventory on board? Why do they impose unfathomable password requirements? Create complex online registration processes? Shut down their customer service on weekends? Hound you with robocalls until you answer a survey?
Big data is today’s panacea, the great new hope for unlocking the mysteries of marketing. To avoid being left behind, companies are rushing to cash in on the information they glean from customers, and vendors are stepping up to help. The venture capital community has taken note of this trend: Over the past year, investors have poured hundreds of millions of dollars into start-ups that promise to exploit caches like Facebook’s reported 100 petabytes of data.
But in the meantime, most companies haven’t done much to improve the customer experience. Negative interactions with companies are as common as ever. A reported 86% of consumers haveswitched companies after bad customer experiences.
Companies would do better at satisfying and retaining customers if they spent less time worrying about big data and more time making good use of “small data” — already-available information from simple technology solutions — to become more flexible, informative, and helpful.
Miami-Dade Transit allows customers to monitor its system so they can know when to arrive at a stop in time to catch a particular bus. That’s not so complicated; why can’t commercial firms invest in allowing customers to monitor the location of service or delivery techs? The UK grocery chain Tesco gives customers a choice of delivery times that come with different prices (deliveries are cheaper during off-peak hours). That’s not complicated either; why do so many companies give you no choice at all about deliveries? Cemex, the concrete maker, uses scheduling software to give customers a 20-minute window for deliveries. Comcast promises a $20 credit or three months of a free premium channel if techs are late. After a recent credit-card mixup, Apple responded immediately to my email and solved my problem. Why do so many other companies mishandle customer service and leave you to fend for yourself? Many innovative customer-service solutions like these are simple and inexpensive, and they have high payback.
Big data, by contrast, is far from inexpensive, and the payback is often iffy. Last year the UK government pulled the plug on an expensive and unwieldy patient-record database that was to have been used for clinical research and improved business processes. The government shifted its focus to creating smaller projects on more well-defined deliverables such as on-time patient appointments.
CXOs with long memories may recall prior panaceas that underdelivered over the long run. Think of “expert systems,” which promised to deliver expert levels of performance but were unable to provide explanations for why the recommended courses of action should be taken, a failing that limited their usefulness (expert systems are becoming fashionable again through IBM’s Watson).
I’m not saying that all big-data projects are useless. Far from it. Manchester City Football Club, the English Premier League Champion, has opened up part of its “on-ball events” database in the hope that people in the open-data community will find patterns and trends that could give the club an edge. That’s a simple way to make use of a big trove of data, and the upside could be substantial.
But don’t expect an easy payoff. To avoid being caught spending vast sums on half-vast results, CXOs would be wise to link a big-data project to the development of a rigorous metrics program — something like the Balanced Scorecard, which is more likely than CapEx or financial ROI to capture the full results of big-data applications downstream and across multiple process groups. Let’s say, for example, that you’re seeing a pattern of strong store sales for a group of products that were previously perceived as unrelated. It might take a scorecard approach for you to figure out that the sales peak coincided with a particular phase in the staff-training schedule. A scorecard that links financials with learning initiatives and other operations would serve as a cross-check for managers.
And in the frenzy to capitalize on big data, don’t forget what it’s like to be a data point — an individual customer dealing with your company. If you’re not making your data points happy, they’ll gladly move into someone else’s database, just as you did after the repair service failed to show up.
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