6 Ways to Profit from Data Analytics
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“Store everything” and “analyze everything” are popular IT mantras, leading companies to build data lakes and use data analytics software. But that begs an important question. What kinds of business benefits can you gain from a business intelligence project?
More specifically, how are real businesses benefiting from data analytics, and how can you follow their lead?
Garter data analytics team global manager Ian Bertram gave a number of good examples at the recent Gartner Business Intelligence & Analytics Summit in London.
Data Analytics to Drive Mass Customization
The tyranny of choice is one of the great paradoxes of the modern world, Bertram said. Customers are offered a bewildering range of competing products and left to figure out for themselves which one is best for them.
Data analytics can help by enabling you to better understand your customers, and then use that understanding to customize your products to better suit individual potential customers.
For example, auto insurance companies that use “black box” telematics devices to capture data about each of their customer's driving habits can analyze it and use the data to provide insurance coverage customized (by price) to that customer. Instead of assuming a customer has the typical driving characteristics of a driver that shares similar demographic characteristics, the customer pays the appropriate premium for his or her exact driving style and habits.
Other examples include online music services which provide customized music channels based on customers' tastes and even soda machines like Coca Cola's Freestyle. The machines allow consumers to create their own drink from various standard ingredients using an app, and then buy that drink from a Freestyle machine which formulates it from the combination of ingredients specified in the app.
The idea here is that some of the insights that your company obtains through data analytics could be valuable to your customers, Bertram said. By sharing these insights with your customers, you can make your products or services more valuable.
For example, video giant Netflix provides customers with information about the speed that Netflix traffic travels over different ISPs' networks. That means that customers who are unhappy with the quality of the Netflix service can see how their ISP compares to others, and which ISP they might consider switching to in order to get better Netflix service.
Another example is Delta Airlines, which provides data about the punctuality of each of its flights to help customers choose a flight which meets their needs and expectations.
Data Analytics to Change Customer Behavior
Most marketers have heard of A/B testing; using data analytics it's possible to do something more like A/B/C/D/E testing.
A good example of this is online lingerie retailer Adore Me, which uses models with different hair colors in different positions (seated, standing, reclining) to test which combination is best suited to attract customers to buy different individual products. For instance, it can establish that customer X is most likely to purchase a red bikini if the Web page she visits displays a seated model with brown hair wearing the bikini, and then ensure that that is what is displayed to her when she browses red bikinis.
Data analytics can also be used to change the way your sales staff interact with customers based on customers' previous buying behavior. For example, facial recognition (or iBeacons or other technology) can be used to alert sales staff when high-spending customers revisit a store.
Retail systems can then bring up their purchase history, allowing sales staff to greet a returning customer by name and suggest products they may be interested in that complement previous purchases.
Data Analytics to Boost Job Satisfaction
No-one enjoys talking to unfriendly call center staff, and call center staff who are dissatisfied in their work are likely to leave their jobs.
Some companies have discovered they can use data analytics to create personality profiles of customers and call center staff, Bertram said. Matching customers to call center staff with complementary personalities increases satisfaction levels in both customers and call center agents. Happier call center agents stay longer in the job, reducing recruitment and training costs.
Data Analytics to Find New Business
There's an old joke about a shopkeeper telling a customer who is asking for a product that “there's no demand for it; you're the 10th person I've told that to today.”
There's a serious side to this joke though. Your customers' inquiries are a valuable source of information, so if you can capture and analyze them there's a business benefit to be realized.
That's what General Mills did when data analytics showed that customers frequently asked whether its Rice Chex cereal was suitable for people on a gluten-free diet, Bertram said. Its customer service reps had to reply that it wasn't, as it was made on the same production line as its Corn Chex product, which contains gluten.
After analyzing its contact center data, General Mills decided to rearrange its manufacturing systems so that Rice Chex could be made in a separate gluten-free location. That opened up the product to a significant new market of potential buyers.
Data Analytics to Improve Service
No-one likes waiting around for service, and clever data analytics can ensure that waits are kept to a minimum, Bertram said.
For example, a fast food business has installed cameras at its drive-through lanes to detect when long lines are forming during busy periods. It links the information to smart menu boards which remove certain items from the display when lines are long, ensuring that only items with short preparation times or ones that have already been prepared are available for customers to order.
As the lines shorten to acceptable levels, the menu items that have been removed are returned to the display and made available to customers again.
These examples show that while data analytics is often based on fairly nebulous concepts like “better understanding of customers' behavior,” companies can act on these data-derived insights in practical ways to produce valuable business benefits.
That, after all, is the point of any data analytics project.
Paul Rubens has been covering enterprise technology for over 20 years. In that time he has written for leading UK and international publications including The Economist, The Times, Financial Times, the BBC, Computing and ServerWatch.
Paul Ferrill has been writing for over 15 years about computers and network technology. He holds a BS in Electrical Engineering as well as a MS in Electrical Engineering. He is a regular contributor to the computer trade press. He has a specialization in complex data analysis and storage. He has written hundreds of articles and two books for various outlets over the years. His articles have appeared in Enterprise Apps Today and InfoWorld, Network World, PC Magazine, Forbes, and many other publications.
