Data for Business: The Art and Science of Customer Value Management

Data for Business: The Art and Science of Customer Value Management

Background

I’m writing this piece to make business leaders and companies stay conscious and keep track of their customer’s data, a huge goldmine that they can use to create competitive advantage and make smart decisions. Over the years, CEOs have only had to pay attention to their financial data. They’d read a one-page summary of key financial metrics and make informed decisions for the companies’ benefit. Data from various departments is aggregated to cost, revenue and expenditure by the finance team which is further analysed and reported as an indicator of the state of the business. In short, the financial data appears to be the most important data business leaders are interested in as it involves money. Is it really the most important data business leaders should be concerned with? Before you answer that question, stop for a minute to think of this quote: “customers are the lifeblood of any business”. It is therefore safe to say no customers; no financial report/data. Hence, customer data and report should be getting the same if not more, level of attention and sensitivity from the management.

Enters the concept of Customer Value Management.

So, what exactly is Customer Value Management (CVM)? CVM is the measure of the lifetime worth of a customer to a business, using a set of best practices that measures the drivers of purchasing behaviour and the impact these have upon market share, profit and loss, recommendation and return-on-investment. CVM would allow a business to systematically identify customers that are valuable to acquire and retain. The importance of CVM becomes evident when businesses realise that acquiring customers cost more than retaining them. For instance, a monthly 5% increase in customer retention after being compounded can result in a 100% profit increase at the end of a financial year. With CVM in place, businesses can develop tailored products and services to their customers. As customers are flushed with various choices, it is really important that they are not perceived as just numbers and records to be bombarded with products or services that in most cases are irrelevant to them. This is why CVM uses a hypothesis and data driven approach to capture detailed customer data, analyse the data and seek for behaviours and patterns with the aim of directing customers to the most profitable offerings so that businesses can get the most out of them.

Now that we have a basic understanding and importance of CVM, how do we approach it?

The below figure gives an overview of the four phases on how to approach CVM:

Phases of Customer Value Management (CVM).

Phase 1 — Business KPIs & Objectives: This first phase of a CVM seeks to understand the business objectives and determine the Key Performance Indicators (KPIs) to measure their operations. Here, questions include but not limited to: Who are our most valuable customers? What makes them valuable? How do we segment our customers? What product(s) appeal to each segment of customer? To what segment of customers should we target our new products? How do we decrease customer churn rate? How should we allocate marketing budgets across customers? What are our competitors offering? Answers to these questions are key to smart decision making that will increase the customer’s life-time value and grow your business.

Phase 2 — Data Analytics: I dare say the most interesting phase and my favourite. In this phase, customer attributes are properly tracked, analysed and reported in alignment with the business objectives discussed in Phase 1. Sometimes, these answers are gotten by asking more questions and using different tools for deep dive. These tools are used by analysts across the world to get insight and make predictions on customer behaviour. It is a known fact that analytics is important and a team to carry this out is needed to be armed with knowledge on the following: Machine Learning, Database Management, Statistics, Programming and Data Visualization. Popular tools are Microsoft Excel, Google Analytics, SAS, R, SQL, Python, Tableau & Power BI. This technical phase requires data experts (data analysts and data scientists) to execute because the core CVM depends on the insight gotten here.

Phase 3 — Action: Insights derived from the analytics (Phase 2) drives the business action(s) to be taken. For example, if the analysis shows a high churn rate and predicts further increase in the future, then steps must be taken to win back the customers and prevent further churning. This phase involves an active participation of the marketing, sales and strategy teams to brainstorm and execute initiatives that solves the problem and take actions in the right direction.

Phase 4 — Evaluation: After the data-driven actions have been taken, they should be evaluated as a measure of the actions taken based on the data insights. This phase is crucial for measuring your actions against the set objectives to demonstrate its effectiveness or success. Key metrics like response rate, conversion rate, week-on-week/month-on-month reduction in churn rate are tracked. How customers are responding to the initiatives, what was supposed measure of growth was recorded, what different results was found amongst various customer segments and why this is so, are a good way to evaluate actions.

Effective customer value management brings tangible benefits to a business. Although not entirely a new concept, most companies share the vision and mission statements about creating value for customers and making them their focus. However, there is an emerging art and science of customer value management that focuses of capturing and using customer data with the same kind of discipline, passion and understanding not only to ultimately increase the market share of the companies, but also the value perception of the customers they seek to serve.

The implementation of data-driven strategies calls for a mind-set change at the top as they need to understand what customers want and how they can be reached on a personal level through data.

To paraphrase a quote from Michael Lebouef: A growing number of returning satisfied customers is the best business strategy of all.

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