By Patrick McDonald,
Chief Architect, Advanced Analytics at Clarity Insights. Patrick brings 23 years of experience to over 50 data science and advanced analytics projects and has delivered $4.4 billion to client bottom lines.

Professor Leonard Schlesinger of the Harvard Business School, considered one of America’s foremost experts on customer service, had one of his greatest moments of insight while waiting in a grocery checkout line. As he waited with his full basket, he watched customers breezing through the express line. He wondered, “Why is the store punishing me, making me wait for a long time with my $300 cart, while people spending $3 get the royal treatment?”

It seemed the store wasn’t analyzing customer data and didn’t know their customers. To Schlesinger, the issue had to do with a misalignment of customer service and customer prioritization. The supermarket couldn’t identify its best customers.

The Importance of Customer Data: A Brief Overview

“Know your customer” is probably one of the oldest and wisest bits of business advice ever uttered. Companies that understand what their customers want—truly want—are usually the most successful. Sometimes, knowing the customer is a matter of instinct. Apple Computer’s Steve Jobs was infamous for knowing, with his gut, what customers really needed, and then delivering it to them. For those of us who are not Steve Jobs, we must use data to figure out our customers.

Customer data encompasses anything and everything about the customer. That reality alone is part of the problem. There’s a huge range of customer data analytics sources to consider when looking at customers. What do they want to buy? How much of it will they buy, at a given price point? Does customer service affect loyalty and profitability? Which customers are the best ones to pursue, retain or fob off on unsuspecting competitors? How much should a company spend acquiring a customer? Where are the customers and will they notice and respond to a particular message? These questions, and hundreds of others just like them, drive a successful customer-facing strategy.


5 Reasons Customer Data Analytics Matter

Fortunes have been made and lost over interpretations of customer data. Today, this means applying advanced analytics to your customer data. The data is there, usually sitting in Point-of-Sale (POS)  systems, Customer Resource Management (CRM) systems, ad servers, public data sources, direct marketing management systems and so forth. It’s all there. Performing analytics on customer data represents an investment of time and money. It’s worth it for the five following reasons:

  1. Knowing how to prioritize your prospects – Who is your best prospect? Customers cost money to acquire. By the time someone walks in the door to buy something from you, you’ve sunk marketing budget into this person. If they don’t buy enough of your product or service, you’re losing money. And, you’ve missed the opportunity to spend your money on finding someone better. Customer data analytics enables you to investigate which types of customers are the most profitable so you can focus on them.
  1. Being able to engage optimally and mapping the customer journey – As Professor Schlesinger would tell you, a customer is ready to buy a product for a price in a certain place. (Marketers call this the “3 Ps”). Data analytics can show you where a customer is on his or her journey to buying your product. To take a simple example, a movie goer is ready to buy popcorn on the way into the theater, but not on the way out. Often, this type of insight is buried deeply in customer data. For instance, an analysis of customer sentiment data culled from social media posts can tell you a lot about how customers feel about buying your product at that moment or at a specific price.
  1. Making optimal ad buys by targeting customers by micro-segments – One of the reasons Facebook and Google have been so successful in the advertising field is their ability to analyze vast amounts of data about customers and present them with ads that are narrowly focused on the customer’s needs as demonstrated by their behavioral patterns (buying history), sentiment analysis, intent analysis, demographics and psychographics. In one interesting case of micro-segmentation, a large retail fashion chain was able to increase revenue (in overall terms as well as “share of customer wallet”) by analyzing point of sale data, matching it with demographics and original market research. Through this customer data analytics process, the chain realized that its optimal customer was a young mother who enjoyed shopping for children’s clothes. They decided to target this narrow segment to the exclusion of all others. In the process, they lost many low-profit customers and began engaging more deeply with their newly-discovered best class of clientele.
  1. Retaining or winning back customers – Customers are constantly tempted by competitors to find new sources for your product or service. With effective data analytics, you can identify likely customer defections, as well as the causal factors driving customer churn. For the customer that does leave, you can identify data-driven ways to win them back. For example, if analysis of customer service data reveals that a customer has stopped buying due to a negative service experience, you might be able to win them with a special service offer and reduce future churn by improving customer service.
  1. Maximizing customer lifetime value – Every customer has a lifetime value. For repeat purchases or subscription-type services, that value can be surprisingly high. The coffee shop customer who buys five $3 lattes a week for 10 years has a lifetime value of $7,800! Now you know why they smile at you at Starbucks. Data gives you the means to discover and then realize your customers’ maximum lifetime values. For example, with personalization based on data about customer preferences, you can build a sustainable relationship that keeps the customer loyal and spending with your business. The value of transforming a low-value customer to a high-value customer is one of the most powerful capabilities of customer analytics.


How to Implement Customer Data Analytics

Building an effective customer data analytics capability takes focus and effort, but it’s a highly attainable business objective. We have extensive experience working with companies on the realization of customer data analytics. Our process includes developing a strategy and roadmap for customer data analytics. If you need it, we work with you to modernize your data architecture, fleshing out competencies and tools for analytics and machine learning. Our expert data scientists can help you identify the factors under your control that are most likely to improve the customer buying experience and increase wallet share.  We then provide the means to measure performance, including the monitoring of metrics and fine-tuning your definition of success. Our goal is to manage performance and ensure your investment is returning value. Learn more about Clarity Insights’ customer data analytics offerings.  


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