Last month we saw how analytics is used to powerful effect in retail, healthcare, infrastructure, and controlling staff calories.

This issue is the second in a two-part series on the top 10 real world applications of analytics. As with the first part, each instance is an actual on-the-ground application which is documented – contact me if you would like the reference for any of them.

Let’s continue our foray, which encompasses casinos, credit cards, cleanliness … and hurricanes ...

6. Stocking inventories ahead of an emergency
Analytically inclined retailers create models that predict what demand for a particular product will be at a particular time of year.

US retailer Wal-Mart took that even further. In 2004, as Hurricane Ivan approached Florida, Wal-Mart knew from analysis what to stock the shelves of hurricane-affected stores with: Strawberry Pop-Tarts.

By analysing years of sales data from just prior to other hurricanes, Wal-Mart figured out that people in hurricane-affected areas want food that doesn’t require refrigeration or cooking: Pop-Tarts fitted that bill.

This is a classic case of real analytic smarts: business savvy applied in a way that meets the needs of people in critical situations.

7. Keeping the most valuable customers
As well as being an exemplar of business experiments and ‘test-and-learn’ US credit card company Capital One has the critical business function of customer retention down to a fine art.

If a customer calls to close their account because they’ve received a better offer from a competitor company, Cap One’s system directs the call to a ‘retention specialist.’ This specialist, who knows that most customers simply want a better deal, can offer the customer a lower interest rate.

But here’s the analytics twist: before this happens, in the background Cap One’s systems run a real-time analysis of the customer’s value to the company: customers worth keeping are directed to a specialist; those who the company is happy to part ways with are directed to a voice response unit and can close their accounts using their Touch-Tone phone.

Cap One has perfected the art of intelligent call-routing (their phrase) based on extensive analysis of the principal reasons for calls, treating each in the most efficient way: if you’re ringing to find out your balance, you’re directed to an automated system that provides that information, while those calls that need to be handled by people are routed to live customer reps.

A highly efficient operational measure grounded in solid analysis.

8. Analysis-based pricing
If you thought Cap One’s approach represented hard-nosed business analytics, casino operator Caesar’s Entertainment (formerly Harrah’s) is even more so. (Readers from the public and non-profit sectors may want to cover their eyes for this bit…).

Former CEO Gary Loveman was a poster-boy of the analytics world for his creation of the massive analytically based Total Rewards loyalty scheme. (Loyalty schemes, by the way, are about gathering purchase information so that cross-sell and ‘next best offer’ offers can be made.)

But Loveman was also a devotee of pricing experiments and analyses. Let me quote the man himself:

To take perhaps the easiest and biggest opportunity in my tenure, we found that a ten-basis point movement of slot pricing toward the estimated demand curve for a given game could enhance our profitability by an eight figure amount and be unobservable to the guest.

Whatever you think, you can’t deny the effectiveness of an analytically based approach to pricing.

9. Analytics without computers: old-school direct mail tests
While analytics has been given impetus by the advent of cheap computing power, masses of web-enabled data, and digital storage, the principles of data-gathering, testing and evidence-based decision-making predate it by a long way.

To bear this out, consider the classic pre-computer exemplar of test and learn: Reader’s Digest.

Most of us associate the Digest with short (‘condensed’) articles, Increase Your Word Power, and the homespun humour of Laughter, the Best Medicine, but in the 1950s it was a powerhouse of direct mail testing.

The industry average response rate for direct mail at the time was a mere 0.5 percent, and the Digest was beating the industry figure by getting four percent on its promotions.

Through sustained testing of all its promotions however (including the original letter sent in the 1920s) the Digest was able to ‘line breed and cross breed’ until it found the best promotional mailing. In one year alone 400 different mail order tests were run and the Digest managed to increase the response rate from four percent to six percent, then to an astounding nine percent, and eventually to an extraordinary 11 percent.

It may have been pre-computer, but the Digest’s approach was rigorously evidence-based, and its results showed it.

10. Identifying the drivers of customer satisfaction with cleanliness
After all these other examples, you didn’t seriously expect not to see one of my analytical efforts, did you?

A few years ago I pulled together data from two different but compatible surveys – one on overall customer satisfaction, the other a mystery shopper survey. Both surveys included data on cleanliness: the first with customers’ satisfaction with the level of cleanliness; the second with counts of different types of litter and debris.

By bringing these hitherto disparate data sets together I was able to establish – with a surprisingly high degree of precision – which types of debris (litter, graffiti, grime etc.) were most closely associated with a one point shift in customer satisfaction with cleanliness.

Isolating the key drivers gave the organisation the capability to direct their efforts to where they had the greatest impact in enhancing customer satisfaction.

An even more sophisticated application would have taken that information, applied the costs associated with cleaning each types of debris, and then used analytics to maximise the customer satisfaction from a given cleaning budget. (Or calculated the cost of bringing customer satisfaction to a particular level.)

A simple, but powerful application of analytics … without even having to collect new data.

*    *    *

Are you making the most of your data? There are innumerable opportunities to increase customer satisfaction, improve operations, optimise maintenance spending and enhance cross-sales. Call me on 0414 383 374 and we’ll get started…

Director I Michael Carman Consulting

PO Box 686, Petersham NSW 2049 I M: 0414 383 374

© Michael Carman 2015