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What Defines Your 'Most Valuable' Customers? | @BigDataExpo #BI #BigData #Analytics

What are the characteristics and/or behaviors that make a customer 'most valuable'?

I hear it often; I need to acquire more customers like my “most valuable” customers. But what makes a customer “most valuable”? What are the characteristics and/or behaviors that make a customer “most valuable”? This simple question is anything but simple. Let me explain through an exercise.

Let’s say that you work for an airline (note: this exercise works equally well with other customer-centric industries such as hospitality, gaming, entertainment, retail, credit cards, financial services, telco and insurance). You have been asked to identify the airline’s “most valuable” customers. This information will be used to prioritize marketing, sales, support and product development efforts. As part of your analysis, you identify three different customer scenarios and need to determine which of these customer scenarios is “most valuable”:

  • Customer A is a long-time member of your top Platinum frequent flyer rewards program. Customer A consistently flies over 250,000 miles a year with your airline no matter how awful the airline treats them. Unfortunately, they are active on social media, have a large following on social media (about 1,500 followers), and enjoy broadcasting each and every imperfection of your airline’s performance (delayed flights, broken Wi-Fi, uncomfortable seats, grouchy flight attendants, old planes, etc.). They are relentless on their assault on your airline’s reputation.
  • Customer B is a long-term Gold frequent flyer (second highest level), flying between 70,000 to 90,000 miles a year on your airline. They seem happy with your airline (no registered complaints). Customer B has a very large following on social media (about 3,000 followers) and frequently posts positive comments and reviews about your airline and their trips on social media.
  • Customer D is another long-term Gold frequent flyer, flying between 70,000 to 90,000 miles a year on your airline. Customer D is very active on social media (well north of 10,000 followers), and loves to share stories and photos from where they most recently traveled. You know courtesy of their social media pasts that they fly other airlines. For example, Customer C recently took a flight from SFO to Phoenix on your airline, and you later determined that Customer C flew a different airline to Austin, and then a third airline back to SFO, posting stories and photos on Facebook, Twitter and Instagram along each stop.

So, which customer is “most valuable”?

As any good consultant would tell you, the answer is “It depends.” “It depends” on the airline’s targeted business initiative. For example:

  • If your key business initiative is about “increasing positive social sentiment, likelihood to recommend and advocacy”, then Customer B is your “most valuable” customer.
  • If your business initiative is “customer retention”, then Customer A is your “most valuable” because they are spending considerable amount of money with your airline and you do not want to lose them to a competitor.
  • If your business initiative is “increase cross-sell/up-sell” or “increase share of wallet”, then Customer C is your “most valuable” customer because they are spending significant travel dollars with your competitors.

In order to make the determination as to which customers are “most valuable,” it’s critical to know what’s important to the business. And while most business leaders will tell you that all three types of customers are “most valuable,” in reality organizations have limited sales, marketing, service, support and product development money and resources that they can invest in their customers. So in order to get the most “bang for the buck”, organizations need to start by identifying what’s “most important” to the business so that the analytics team can prioritize who is “most valuable.”

Customer Lifetime Value (LTV) Calculation Journey
I just completed a Big Data Vision Workshop with a client where we had a very interesting conversation about how to determine an organization’s “most valuable” customers. We spent a considerable amount of the workshop contemplating a customer Lifetime value score around the organization’s “most valuable” customers that could be used to prioritize sales, marketing, services, support and product development efforts.

The client originally thought of “Create Customer LTV score” as a single use case. However, through the envisioning process they came to realize that creating a “Customer LTV” score was not a single “use case” as much as it is a journey. Each customer-centric use case (customer acquisition, customer retention, customer satisfaction, customer cross-sell, customer advocacy, fraud, etc.) subsequently gathers more data and more analytic insights that can be used to continuously build towards a more refined, more accurate, more actionable “Customer LTV” score.

For example, you may want to leverage historical purchases, returns, market baskets, product margins and sales and support engagement data to create a current “Customer LTV” score. This score is a great starting point for optimizing the organization’s financial and people resources around the organization’s “most valuable” customers (see Figure 1).

Figure 1: Determining Current Customer Lifetime Value

However, there is a significant business opportunity to create more predictive customer use cases; that is, identify those variables and metrics that might be better predictors of customers’ behaviors and performance. Sample predictive use cases could include:

  • Create a “Customer Behavioral Segments” and “Customer Demographic Segments” that can be used to predict the highest-potential customer acquisition targets
  • Create a “Customer At-risk” score that can be used to predict which customers are likely to attrite or leave
  • Create a “Fraud Propensity” score to predict which customers have a higher-than-normal probability for fraudulent transactions or behaviors
  • Create a “Product Propensity” score for each of the organization’s products that can be used to predict which customers might have the highest propensity for cross-selling and up-selling additional products and services

As we address each of these predictive, customer-centric use cases, we capture more data and create more analytic scores. Each use case adds more data to the data lake, and more analytic scores and predictive indicators to the Analytic Profiles[1]. In the end, we’ll end up transitioning our customer lifetime value calculation from being a score of that measures what customers did, to creating a predictive customer LTV score that predicts which customers might become “most valuable.” In the end, this “Predictive Customer LTV” score will be more effective in helping organizations optimize their sales, marketing, service, support and product development resources (see Figure 2).

Figure 2: Predictive Customer Lifetime Value Score

Think about the potential applicability of Figure 2. Using the traditional “Customer LTV” score – which is based upon historical transactions and engagements – the organization would make marketing, sales and support decisions based upon the blue bars. In the case, the company would focus their sales and marketing investments on customers like Customers 101 and 102, as they are the “most valuable” customers based upon a historical perspective.

However moving towards a “Predictive Customer LTV” score highlights the following:

  • Customers 101 and 102 are tapped out and probably should not be the focus of heavy sales and marketing efforts (you don’t want to ignore Customers 101 and 102, but they should not be the primary focus of future sales and marketing initiatives)
  • On the other hand, Customers 103 and 104 have significant untapped potential; that these are the types of customer upon which to target sales, marketing, support and product development investments in order to capture that untapped potential.

I understand that this chart can take some time to appreciate, and as we explained in the first part of this blog, the application of this type of analytic insights is dependent upon the organization’s key business initiatives. However with that said, the concept behind predicting “Customer LTV” can be staggering in prioritizing financial investments and driving financial value.

The Chipotle Grand Failure
As many of you know, I use Chipotle as the case study for the “Big Data MBA” class that I teach at the University of San Francisco School of Management. It’s a great case study because 1) all of my students have been to a Chipotle and 2) I love Chipotle. Recently Chipotle has been plagued with food quality problems, which has hammered their business. Chipotle’s business objective of using organic locally sourced foods means that supplier, logistics and product quality analytics are critical to returning Chipotle to their glory days.

However, I think Chipotle’s more devastating consequence to their business is that they do not know who their customers are. Oh, I’m sure that their marketing folks know the demographic and behavioral characteristics of their customers through surveys and monitoring social media, but they don’t know the individual customer and they do not know their “most valuable” customers. They know very little about my propensities, inclinations, tendencies, preferences, interests, passions, associations and affiliations because they do not have a customer loyalty program.

Think about the insights that Chipotle would gain about every individual customer such as me if they had a loyalty program. They’d know the frequency of my visits, the recency of my visits, what products I buy, what products I buy in combination, what ingredients I prefer, which stores I visit, if I buy for others, what days of the week I tend to visit, and what times of the day I tend to visit, etc. They would have enough insights into my tendencies, behaviors, preferences, patterns and inclinations to form a baseline of the names (not just the demographic or behavioral categories) of their “most valuable” customers. Then when problems such as the food quality problems hit (and other problems that may hit in the future), they would know the names of the individual customers to whom they need to reach out – to offer assurances of their renewed focus on quality and maybe use some coupons and offers to entice these people back to the stores.

In the end, the demise of Chipotle will likely have more to do with their lack of insights into their “most valuable” customers at the individual level than it will their food quality problems. And as a Chipotle shareholder and one of their most loyal customers, this makes me sad.

“Most Valuable” Customer Summary
How an organization answers the question: “Who are my most valuable customers?” depends upon the organization’s key business initiatives and the supporting use cases. As organizations apply their superior customer and product insights from use case to use case, they will start gaining deeper and wider insights into each individual customer that sets the stage of moving from identifying your most valuable customers to predicting your most valuable customers.

I just hope that someone can get the word to Chipotle before it’s too late…

[1] Analytic Profiles are structures that standardize the collection, application and re-use of the analytic insights for the key business entities at the level of the individual (human or physical object). We build Analytic Profiles for each individual business entity. For example, the Analytic Profiles for The Disney Company could include guests, talent, rides, shows, attractions and operators.

The post What Defines Your “Most Valuable” Customers? appeared first on InFocus Blog | Dell EMC Services.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.