Sears Could Disrupt Throwaway Tech Culture

It's funny the timing of this article.  I was just talking with my wife about Sears and how it seems they have no future, it's only a matter of time before the Sears retail store as we know it will no longer exist.  Then to read a headline about Sears disrupting?  Heck ya I'm interested.

The company has launched a Seattle office, and recruited retail tech execs to help it get a handle on the data it has amassed from the 40,000+ daily service queries its Home Services group collects on washing machines, refrigerators, and other appliances. It turns out that the industry average is that about 1 out of every 4 customers don’t get their appliance woes fixed on the first visit. 

“Each truck carries about 400 parts, yet those annual service calls require something like 168,000 different parts,” explained Arun Arora, the group’s president. “We’d have to have our 7,000 certified technicians driving semis around to anticipate them.”

"Big data" has so many applications and to see Sears trying to disrupt in a way that doesn't make headlines is impressive.  This kind of disruption, even though on the surface looks like a cost-savings initiative, can revolutionize the service of appliances.  Why does that matter?  Because loyalty is the name of the game.  If they make the experience of owning a machine better, even when it is getting old and needs some new life breathed into it, they can increase their base of loyal active customers.  

The more customers that are active with a company the more they will make.  If Sears can increase the number of loyal customers by offering a superior customer experience of ownership, they can drive more sales in other areas.  It is the process of rebuilding trust with a brand.  If I knew buying an oven will have a longer shelf-life and the company where I was buying it can make that happen, then it makes where I buy more interesting.  

So many times in the retail space it comes down to price.  Everyone sells ovens and mostly from the same manufacturers, so there is very little to differentiate.  The easiest rode to differentiation is price.  The problem is when competing on price, the business can never win.  They are not cultivating loyal customers, in fact they are probably selling to the exact wrong customer.  If a customer is only going to choose on price, they are by definition not loyal customers.  If Sears can differentiate beyond price and experience in the stores, they can grow their loyal database.  That's a big win.   

Can You See the Opportunities Staring You in the Face?

I’ve come to believe that less than 1% of the data is truly useful.

Exactly!  Most businesses are very simple if you look for the key metrics.  So many times people want to show their worth by over thinking the problem.  If I can come up with some new innovative way to look at this problem, I'll be a superstar.  But more times than not it isn't a complex problem.  Humans are fairly simple to predict.  Most humans will fall into patterns and want very straightforward things.  New data doesn't need to be introduced until you have gotten everything out of the current data you have.

Big-data initiatives are proliferating, and the information is getting more complex all the time.

There’s a lot of potential benefit for both retailers and customers.

But only if the data is well managed and well understood. Statistics literacy isn’t very high in most businesses. A few educational institutions have realized this and are making a push to turn out business graduates who know their way around a regression analysis. But for the most part, businesspeople aren’t familiar enough with statistics to use them as the basis for good decisions. If you don’t understand the numbers, you can go a long way down a bad road very quickly. That’s why every team charged with making decisions about customers should include a trusted individual who understands statistics. If that understanding isn’t between your own two ears, make sure you bring a person with that skill set onto your team.

Being able to understand what the data is telling you is more important that having a degree in statistics.  Interpreting data is really where the opportunities present themselves, not in figuring out the most optimal model.  I suggest having someone who is proficient in building statistical models and ask a lot of questions from the output.  Start to understand what the answers  of models are telling you and simplify the results into something that can be used in the future.  A model may tell you that people who buy a particular item are likely to be loyal, but is it the item that drives the loyalty or is this just a coincidence?  The better you understand your data, the better decisions you will make and you don't have to be a data scientist to do that.

The Revolutionary Way Marketers Read Your Financial Footprints

Laube, 43, Cardlytics’ president and COO, and Grimes, 51, its CEO, have since helped pioneer a data-driven advertising niche called merchant-funded rewards. It targets people based on what they buy, not who they are. “If you know where and how someone is spending money, you know lots of things about them without having to know their personally identifying information,” Laube says.

I have found that the transactions of customers is the most important predictor of future behavior in all data I have studied.  While the demo, geo and psychographics of customers is very interesting data, to maximize the revenue from known customers is to really get to know their transactions.

While I don't know how good these algorithms are, the theory is solid.  I happen to be a Bank Of America customer and the deals I receive don't seem to be any better than say my Rapid Rewards dining offers which don't seem to know my eating habits whatsoever.  If these can be perfected, I think it's something that would get me to use my card more often instead of using my AMEX.  I'll be watching because this is very intriguing.