Harness Customer Profitability Analysis to Boost Profits

David Haertzen

Customer Profitability Analysis (CPA) is an analytic approach that determines the profitability of individual  customers or segments of customers by identifying revenue and cost patterns associated with those customers.  This includes identifying the most profitable customers (angels) as well as unprofitable customers (devils).

Dr. Robert Kaplan of the Harvard Business School has conducted research that has contributed greatly to CPA.  He wrote “.. The most profitable 20% of customers generate between 150 – 300% of total profits.  The middle 70% of customers about break even, and the least profitable customers lose 50 – 200% of total profits, leaving the company with its 100% of total profits.  Often some of the largest customers turnout to be the most unprofitable.”

Best Buy Case Study

Best Buy was able to build profiles of best and worst customers.   Patterns of customer behavior were identified by an analysis of company databases.  It’s research showed:

• 20% of customers are angels and result in bulk of profit
• 20% of customers are devils and reduce profits by 20%
• 60% of customers are breakeven
• Profitable and unprofitable customers show patterns of behavior.

Based on the research results Best Buy was able to make changes to improve its profitabilty.  The company attracts the most profitable customers by promotions, stocking desired products and providing best service.  In addition, the company avoids unprofitable customers by dropping them from promotion lists, stopping loss-leader promotions and charging fees for restocking.

How to Analyze Customer Profitability

Determining the profitability of individual customers is a challenge.  Traditional accounting does not identify the profitability of individual customers, so Dr. Kaplan recommends the use of Time Drive Activity Based Costing (TDABC).  This approach identifies the true Cost To Serve (CTS) individual customers by adding the service interactions with each customer such as: customer service telephone calls and product returns.  This analysis should consider revenues, expenses, opportunites and risks:

Revenues	Revenues generated may include:  Initial sale of goods and services Follow on sale of goods and services Service plans Fees
Expenses	Expenses incurred may include:  Cost of goods sold Discounts and coupons Customer service calls Returns Warranty repairs Mailing invoices and statements Collecting overdue receivables
Opportunites	Opportunities generated may include:  Customer recommendations and testimonials Feedback to improve products
Risks	Risks incurred may include:  Customer complaints harm reputation Company focuses on unprofitable customers

Detailed, time-phased data that has been integrated at the individual customer level is required.
Time-phasing is needed to determine behavior sequence.
For example, a customer may return an item and then buy back the same item at a lower cost
when it has been restocked.

 

Profiles of Best and Worst Customers

After determining the profitability of individual customers, customer segmentation analysis can be used to determine behavior patterns of best and worst customers.  The segementation may look like this:

Best Customers	Worst Customers
Orders standard products Orders standard handling Orders via web or ecommerce Makes short service calls Almost never returns goods Orders large volume Pays on time Praises company on social media	Orders custom products Orders via call centers Often makes lengthy service calls Frequently returns goods Orders special handling Orders small volume Requires low cost – price match Pays late – requires collection Complains on social media Buys only on sale or with coupons

You may find that some of the largest volume customers are the least profitable due to the low prices that they negotiate and the costly service that they require.

 References

Analyzing Customers, Best Buy Decides Not All Are Welcome : Wall Street Journal, 2004

Time-Driven Activity Based Costing : Dr. Robert Kaplan – Harvard Business School

Motivation-Need Theories and Consumer Behavior : W. Fred Van Raaij

The Analytical Puzzle – Profitable Data Warehousing, Business Intelligence and Analytics : David Haertzen

David Haertzen, September 2019

Would you like to better understand your customers?   Customer knowledge can both help to improve revenues, reduce losses  and avoid risks.   A customer is broadly described as a party (person or organization)  who is of interest to the enterprise.   A customer model that identifies characteristics of your customers  is a great way to achieve a level of understanding and achieve  company goals.  This article is the first one in a series about customer modeling.

There are two broad categories of customer models:   data models and analytical models.  Data Models are representations, usually through graphical means, of facts,  statistics, or items of  information. Data models may be used to:  understand data, communicate the structure of data and design data structures.   In contrast, Analytical Models are representations of reality coupled with
algorithms that produce results such as:  classifications, predictions, optimizations or recommendations.

Customer Data Model

This information can be organized into a high level data model for  better understanding and easier management. This diagram shows  the Shrewd Data high level Customer Data Model.

Examples of data for each topic in the customer data model include:

• Customer Identifiers: Account numbers, tax id numbers, drivers license numbers
• Demographics: Birthdate, age, gender, marital status, education level
• Measures: Networth, income, revenue, credit score, Lifetime Customer Value (LCV)
• Behaviors: Social media, driving record, address changes
• Locations: Geo location (country, state, city, zip), district, telephone
• Psychographic: Opinions, values, sentiments, preferences, risk tolerance
• Transactions: Purchases, returns, payments, deposits, withdrawals
• Interactions: Service calls, web visits, abandoned cart, response to offers
• Products: Current products owned, products under consideration

These topics are the “tip of the iceberg”.  A full 360 degree view of customer will be comprised of multiple data stores with multiple data sources.

Customer Analytical Models

Analytical models apply algorithms to data to: enable better understanding, make predictions or recommend decisions.  Examples of customer analytical models include:

• Acquisition Model: A model that predicts the probability that a prospect will buy the company’s products or services.
• Cross-sell Model: A model that predicts the probability that an existing customer will buy additional products or services of a different type than currently bought. Goods are at the same level.
• Up-sell Model: A model that predicts the probability that an existing customer will buy an upgraded product or service.
• Attrition Model: A model that predicts the probability that an existing customer will stop purchasing the company’s products or services. This also known as a churn model.
• Value Model: A model that predicts a numeric value such as customer lifetime value (CLV) or value resulting from the sale of a specific product to a customer.
• Tone-Of_Voice Model: A model that identifies the most effective message for each targeted customer.
• Risk Model: A model that predicts potential negative activities by customers such as: fraud, loan defaults, or excess service costs.
• Customer Segmentation Model: A model that assigns customers to groups with similar characteristics.
• Recommendation Engine: A model that provides advice on a near real-time basis – such as advice about offers that should be made to a customer or additional products to show to a customer.
• Look-alike Model: A model where the target-marketed group (e.g. for a marketing campaign, product offering etc.) is an expanded list of parties whose profiles look like the selected party.

Customer Model Conclusion

In conclusion, customer models are powerful tools that have the potential to impact an organization’s bottom line.  You can learn more about this topic by studying additional articles and white papers provided on the Shrewd Data and other websites.