what is CVO
Customer Experience, Customer Retention, Customer Value Optimization, eCommerce Growth, NPS, Retention Rate Optimization, RFM

The Ultimate CVO Guide – Customer Value Optimization Methodology Part 1

In the latest chapter, we dived into the concept of CLV or Customer Lifetime Value, a crucial element in the evolution of an eCommerce business, if calculated correctly.

What comes next is a three-part inspection of the Customer Value Optimization methodology.


What is Customer Value Optimization

Customer Value Optimization is the process of improving Customer Lifetime Value through Customer Experience, Acquisition and Retention strategies. 

It’s an ongoing process that starts from analyzing the customer database and ends with improving acquisition strategies, branding, messaging and website performance. 

The Customer Value Optimization Methodology

The Customer Value methodology will completely change the way you look at your business. However, it pays off in the long-term. 

Step 1. Change the way your company defines “success”

Each company has its own ways in which they measure “success” but the key here is to realize that growth or success does not mean checking growth generating factors on a daily basis. 

Your revenue might look fantastic in the profit and loss statement, but if you do not pay close attention to your customers’ feedback by doing qualitative research and to your customer retention KPIs through quantitative research, you are missing out on opportunities that can benefit your business in the long run.

One important aspect to remember is that customer value is a company-wide measure of success and not a marketing KPI.

If you want to fully understand customer value, you need to determine the impact of customer retention on your business and teach its importance to your employees as well (try an interactive workshop, for example). After all, they are the ones dealing with the customers and they should always be on board with the changes in company culture.

Step 2. Monitor CLV and the KPIs that are affecting it

Customer value optimization is a process that starts first by analyzing the customers’ performance and coming up with insights to improve the entire Customer Journey. 

The Customer Journey consists of several stages:

  1. Awareness
  2. Consideration
  3. Purchase & Reception
  4. Retention
  5. Advocacy

Online businesses monitor the first step of the funnel too much and too little the bottom of the funnel. 

When in fact, if you focus on retention and advocacy, you can easily improve the other stages as well and create an organic flow. 

Customer Lifetime Value is the metric that monitors the performance of the last stages: Retention & Advocacy, but not only. It also reflects the health of the business and the performance of the other stages. 

Customer (Lifetime) Value is of course influenced by some factors that will become your KPIs:

  • Customer Experience (including here product reviews, the Net Promoter Score or the  Customer Effort Score) 
  • Customer Retention Rate
  • The RFM distribution
  • The Margin
  • Cohort stickiness
  • New Customer Stickiness 

In no particular order, let’s analyze them a bit.

RFM distribution

It’s essential to segment your customers to discover your valuable customers and how they transform over time. The result is building and targeting specific clusters of customers with more relevance to their particular behavior. In conclusion, you can create increased loyalty (which means better customer lifetime value) and, of course, higher rates of response.

Based on the Average Days Between Transactions and the customer distribution for Recency, Frequency & Monetary values, you adjust and decide over the number, score levels (from 1-5) and components of the RFM Groups.

The RFM segmentation can split your customers into: 

  1. Soulmates (Recency 5; Frequency 5; Monetary Value 5): these are your most valuable customers. They bought the most often and have spent the most in your online shop. Their latest order has been recently placed.
  2. Lovers (Recency 4-5; Frequency 3-5; Monetary Value 3-5): these are active customers who have placed more than a couple of high value orders, the last one being placed recently. 
  3. New Passion (Recency 5; Frequency 1-4; Monetary Value 4-5): these customers placed 1-2 orders very recently and had an average monetary value 
  4. Flirting (Recency 4; Frequency 1-3; Monetary Value 3-5): these customers are active on and off. They’ve placed a couple of high-value orders, but are inconsistent.   
  5. Potential Lovers (Recency 3-5; Frequency 3-5; Monetary Value 2-5): they’re active customers who placed a couple of high-value orders. They have the potential to become Lovers.   
  6. Platonic Friend (Recency 3-5; Frequency 1-5; Monetary Value 1-5: these customers are active but placing very few orders of small or medium value.    
  7. About to Dump You (Recency 2-3; Frequency 1-5; Monetary Value 1-5): these are inactive customers, having placed their latest order more than 6 months ago. 
  8. Don Juan (Recency 1; Frequency 1; Monetary Value 4-5): these customers have placed only one high-value order. You need to decide if it’s worth going after them or if they will just run away again. 
  9. Ex-lovers (Recency 1; Frequency 1-5; Monetary Value 3-5): these are your former True Lovers or Soulmates, who have abandoned your store and are now inactive. You probably don’t know why they stopped buying but getting them back could be very profitable.
  10. Apprentice (Recency 4-5; Frequency 1-3; Monetary Value 1-2): these are the new customers who just placed their first order/orders
  11. Break-up (Recency 1; Frequency 1-5; Monetary Value 1-2): these customers are inactive, but they weren’t your best customers to begin with. They might be promotion hunters, hitting your margin. They used to place low-value orders and with a relatively low frequency.

RFM segmentation use case 

You are currently looking at your customers as a big group that has the same characteristics and you are treating them like this. Imagine you have a group of customers who are about to forget your brand, but still, you have the last chance to reengage them. RFM helps you to determine this group of customers and ultimately to send them a re-engagement campaign. 

Margin

Moving even further, an RFM analysis reveals customer anomalies that will allow eCommerce managers to understand the most important groups of customers when they balance the customer acquisition cost with the margin they generate.

Many eCommerce owners realize after applying RFM segmentation that the Pareto Principle applies to their customer distribution: 20% of their repeat customers bring 80% of their margin which makes you wonder what happened to the other customers?

Customer Experience

At first glance, customer experience might seem difficult to be correctly measured. However, there are some KPIs that can provide you enough clarity over your store performance. 

  • Product Reviews – Keep an eye on what people say about your products and create a habit for monitoring this.
  • Product Return – Watch out for a high product return rate, as this can be translated as a problem with your products or insufficient information about them.
  • Resolution rate – This determines how effective your support in resolving customer problems is.
  • Net Promoter Score – Deliver an NPS survey pre- and post-delivery and you’ll be able to determine whether your customers are satisfied or not with your product or service. 
  • Customer Effort Score – This score will show how hard it is for a customer to use the product, find information or solve a problem.

Customer Retention Rate 

The customer retention rate calculates the percentage of customers with more than 1 order out of the total customer base. Calculating your retention rate periodically will always tell you if you are doing a good job of nurturing your customers and targeting the right people in your campaigns.

Customer retention rate use case

This graph specifically shows you an increase over time of the Retention Rate from September 2019 to September 2020. During this period, customers returned and were convinced to place at least 1 more order.

New Customers stickiness 

By analyzing your cohorts, you will discover what campaigns brought you repeat customers over time or just one-time purchases. It’s an essential report for customer acquisition that every eCommerce should monitor.

Cohort Analysis use case

Cohort analysis is an ideal report to figure out which type of campaigns are most profitable. Taking the graph above as an example, you can see that a campaign launched in September 2019 managed to retain only 0.05% of the customers, meaning that only 0,05% of people who bought thanks to that campaign returned to your store to buy again during 1 year. Comparing this campaign to the one launched in December 2019, you will see a greater retention rate (0.08%) in September 2020. 

The take from this is to analyze the targeting strategies you used in those campaigns and go for those who brought you more repeat customers. 


Stay tuned for our next chapter where we will continue our discussion about the CVO methodology!

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