I recently wrote about the two D’s of Big Data: Disintermediation and Disruption. As I stated in that blog:

Across multiple industries, startups are coupling new big data technologies and new sources of data with advanced analytics (data science) to uncover new customer, product, operational and market insights in order to disintermediate existing customer relationships and disrupt existing business models (see Figure 1).

Figure 1: Business Model Disintermediation and Disruption

Customer relationship disintermediation is becoming the business norm, and successful customer relationship disintermediation is being driven by big data. Companies like Uber, AirBnB, Mint.com and others are successfully disintermediating existing customer relationships by leveraging superior customer insights to insert themselves between companies and their customers. Let’s just look at Mint.com, who provides a service to aggregate all of your financial data (credit cards, banking, brokerage, IRA, 401K, property, etc.) into a single location from which they can not only monitor your financial status, but can make recommendations for a wide variety of financial decisions including credit cards, auto insurance, life insurance, 401K rollovers, and others (see Figure 2).

Figure 2: Mint.com Financial Recommendations

I can tell you that the financial services companies with whom I talk are very concerned about startups like Mint who are trying to “steal away their customers.”

And now the big boy jumps into the fray…Amazon. The Amazon Echo (see Figure 3), which innocently sits in the middle of your house taking orders like a dutiful servant, holds the potential to be the biggest disintermediator of all. And don’t think that fact is lost on the other big data masters such as Google and Apple. Yep, a new battleground is forming up and that battleground is your home.

Figure 3: Amazon Echo

A Day in the Life with Amazon Echo

So imagine in the not so distant future, the Amazon Echo sitting in your house. Throughout the day, you are barking orders at it:

“We need to get more toilet paper!”

“We are almost out of milk.”

“Dang it, I need to get my car serviced.”

“Honey, I’m hungry. Do we want Chinese or Mexican tonight?”

“Where’s – my – super – suit?”

The Echo hears all of these commands and leverages insights about your preferences, tendencies, propensities and behaviors to:

• Compile a shopping list by finding the best deals on your favorite brands regardless of the retailer
• Automatically schedules an appointment for your car looking at your prior experience with different service shops and their Yelp ratings and social media sentiment
• Provides options on the “best” Chinese and Mexican restaurants and can either schedule a reservation (interfacing into OpenTable) or have the food scheduled for delivery (interfacing with DoorDash).

And as Amazon has done with its Amazon.com shopping experience, it is going to learn what you and others like you like so that it can make recommendations.

How are retailers, restaurants, service providers, banks, insurance companies and other business-to-consumer industries supposed to combat these giants of big data and data science who are looking to turn your customers into their customers? The answer: superior customer loyalty analytics.

The Failure of Customer Loyalty Programs

Customer Loyalty programs are nothing new. Heck, many of them have been around for decades. Just look into your wallet or your desk drawer at home and you will see an almost countless number of loyalty programs to which you probably belong: Walgreens, CVS, Safeway, Sports Authority, Foot Locker, Best Buy, United Airlines, Virgin America Airlines, Delta Airlines, Marriott Hotels, Hilton Hotels, National Car Rental, Hertz, Starbucks, etc. In fact, if you are like me, you can show up at almost any random hotel or grocery store and discover that you are already a member of their loyalty program.

What is my experience with this loyalty programs? I am not impressed. Sure, they give me a discount or free airline miles when I use their programs. And I suspect that these organizations are using my purchase data for marketing purposes. But other than saving money and getting miles that I can’t use when I want, I get nothing else. These programs have all this detailed information about my buying and product preferences – what I buy, how much I buy, when I buy, where I buy, what I buy together, what coupons or discounts that I use, etc. – and use little if any of that data to create a more compelling, more relevant customer experience. #Fail

For example, let’s look at the customer loyalty challenge from the perspective of one my favorite restaurants: Chipotle (surprise, surprise). Chipotle does not even have a customer loyalty program. So with the recent e-coli incidents, Chipotle cannot answer some very fundamental business questions such as:

• Who are my most important and loyal customers?
• Where do these important and loyal customers tend to eat?
• Which of these loyal customers have not come back to the restaurants after the incidents?
• What can I offer them to get them to come back?
• How do I even reach my most important and loyal customers?

IMHO, the lasting impact of Chipotle’s e-coli incidents will have less to do with their supply chain issues and more to do with their inability to identify and engage with their most loyal and important customers. #Fail

It is these weak customer loyalty experiences that will allow companies like Amazon, Google, Apple, Mint and others to successfully disintermediate existing customer relationships and relegate existing companies to a very low margin, transaction-based customer relationship. #Fail

Answer: Customer Loyalty Driven By Data Science

Let’s consider a fictitious company to brainstorm how it could combine customer loyalty with data science to create such a compelling and engaging customer relationship that disintermediators won’t stand a chance.

Let’s say that I am the head of Customer Relationship Marketing at Cool Pets. Cool Pets is a nation-wide, big box retailer that focuses on selling to the pet market. Cool Pet has had a customer loyalty program for a decade now, but really has done very little with that data other than to create zip code specific mailers. Let’s apply parts of our “thinking like a data scientist” process to see what I could do to create a stickier, more compelling customer engagement.

Step 1: Identify Key Business Initiatives. For purposes of this example, we’re going to go with the “Create a more compelling, stickier customer relationship.”

Step 2: Develop Stakeholder Personas. I’d probably want to segment my customers into similar clusters (dog lovers, cat lovers, bird lovers, etc.) and create a persona for each of these segments, but for this exercise, let’s just go with a simple generic “pet owner” segment.

Step 3: Determine Stakeholders’ Key Decisions. For many families (mine included), pets are a part of the family and we are making many decisions every day about the care of our pets, including:

• What is the best food to feed my pet given what type of pet I have, the age of my pet, and my pet’s current health/weight situation?
• How much exercise does my pet need and where are some local areas where I can take my pet for exercise?
• When does my pet need to be groomed and who are the best groomers in my area for my type of pet?
• What is the current health condition of my pet?
• When should my pet see a veterinarian and who are the best vets in my area for my type of pet?
• When do I need to restock my pet care items such as flea and tick control medicine?

Step 4: Identify and Collect the Supporting Data. By conducting an envisioning exercise on the decisions captured in step 1, we would uncover a multitude of data sources that might yield better predictors of performance.

For the decision “What is the current health condition of my pet?” the following data sources might be useful:

• Purchase data (to see what medication and types of food have been purchased)
• Veterinarian records (especially if our vets are performing the service) and veterinarian notes
• Groomer notes
• Social media (people like to post photos of their pets and we could monitor those photos to judge the pet’s wealth and wellness conditions such as weight)
• Consumer comments and email conversations
• Location of pet walking parks within close proximity of customer’s home (https://www.google.com/search?q=dog+parks+in+palo+alto%2C+CA&ie=utf-8&oe=utf-8 )
• General pet health and sickness trends (from the U.S. Humane Society, American Pet Products Association and others)
• Clinical studies on pet health concerns
• Google trends on immediate pet health issues and disease outbreaks

And of course there are many more data sources that might help to provide a better predictor on the health and care of our pets.

Step 5: Put Analytics Into Action. Though I have skipped the very important “Create Actionable Scores” step (I have to leave something for a homework assignment), we can now identify how to leverage all of this customer and pet data to create a more compelling, stickier customer engagement with ideas such as:

• Pet health, wellness and medicine recommendations
• Pet exercise recommendations
• Pet food recommendations
• Pet grooming recommendations

Yes, there are many recommendations that Cool Pet could make to their pet owners, but let’s step out of the box a bit. How about the creation of a Pet fitness tracker? That fitness tracker not only tracks the whereabouts of lost pets, but more importantly captures important pet health and exercise data that can be used to monitor the pet’s overall health and wellness. Advanced customer analytics could be applied to individual pets based upon the pet’s detailed exercise and wellness data that can be used to deliver very personalized recommendations and offers to the pet’s owners. #Success!

Superior Customer Analytics

In the end, the only way to stave off the disintermediators is to provide such a compelling and relevant customer relationship, that your customers will not allow someone coming between them and their favorite retailers, restaurants and service providers. But so far today, I’d say that most organizations have failed to provide something relevant and compelling out of their customer loyalty programs. The customer loyalty programs are failing because they have not leveraged all the customer data to make their customers’ lives better by not helping to support the key decisions that these customer needs to make. Without a focus on superior customer analytics (and the ability to act on those superior customer insights), you are setting yourself up to be disintermediated. Hey, it’s your choice!

By the way, to learn more about our “Thinking Like A Data Scientist” process, check out this infographic.

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