Customer Lifetime Value (CLTV) for a Digital Wallet Business

Customer Lifetime Value (CLTV) is a metric that measures the total revenue/profit that a customer is expected to bring over the course of their lifetime with the business. It is an important metric that calculates the worth of all customers. CLTV models can then help address issues such as

“We have lost this high-value customer, how much can I afford to spend to win them back?” or

“Which customers should I be targeting to maximize my return on investment?”

Here are some reasons why understanding and knowing CLTV is essential to a business:

1. It helps in targeting profitable customers: When the CLTV of each customer is known, it helps in identifying customers who need to be retained or targeted for more products/services and which customers can be let go if there is a risk of churn.

2. It helps in understanding the kind of customers a business is acquiring: It is always desirable that a business acquires as many as high-value customers, so to evaluate this, calculating the CLTV of new customers relative to existing customers is important. If the CLTV of new customers is higher than that of existing customers, it is a positive for the business as this indicates that the new customers are of high value and profitable customers.

3. Campaign Effectiveness: CLTV can also be used as a proxy to determine the effectiveness of campaigns where the purpose is to increase the value or revenue from customers. For instance, if a cross-sell or up-sell campaign is being done on a set of customers, and if the CLTV of those customers goes up in the near term, then we can reasonably say that the campaign has been successful.

4. Business performance: CLTV can also be used as a business performance metric since it essentially tells us the value of all customers, which in turn tells us the value of the business. So, if CLTV increases over time, it is a positive sign for the business.

5. ROI on Customer acquisition cost (CAC): For a business, (CLTV: CAC) ratio is a critical business metric. This tells us how much value the customer is bringing to the business for every dollar spent on acquiring the customer. This ratio tells you how profitable a customer will be over their lifetime. CLTV: CAC ratio can also yield insights into how efficiently the sales and marketing team are spending money to acquire customers.

Calculating Customer Lifetime Value

There are different ways to calculate based on different business models. Here, we will look at calculating CLTV for a digital wallet business.

CLTV is calculated as follows:

CLTV = ((T*AOV) AGM)) ALT

There are four components when it comes to calculating CLTV:

Out of these 4 components, Customer lifespan is somewhat difficult to calculate. If customer churn data is available for a longer period, i.e., 8-10 years, then we can arrive at customer lifespan using 1/ (average churn rate).

Our Methodology

We at Subex built a CLTV solution for a digital wallet company as part of our campaign intelligence offering.

Here is the process we followed:

• We took Active 90 subscribers as our base for this solution and calculated T, AVPT, and AGM at the monthly level using the last 90 days of data.
• To calculate Average customer lifespan (ALT), we relied upon our churn probability prediction model, which was already integrated with our campaign intelligence solution, and thus, we arrived at customer lifespan. Since we wanted to reduce skewness in churn probability, we decided to create customer segments using RFM and then took the median of churn probability for each segment and used that to calculate the average customer lifespan.
• We calculated Recency, Frequency, and Monetary scores and created a composite score by assigning weights to each score.

RFM Composite Score: 60%(Monetary) +20%(Frequency) + 20%(Recency)

• Using RFM composite scores, we created RFM segments based on percentiles with the following logic. For example, a very low segment contains customers with RFM scores between 0 and 15th percentile.

• For each of these segments, median churn probability scores were calculated, which were then used to calculate the average customer lifespan. This gave us all the components for the calculation of CLTV.

Integrating Customer Retention Module

We also built a customer retention module basis CLTV scores of customers. As mentioned earlier, CLTV provides insights on which set of customers’ needs to be prioritized for retention.

The process of building this module was as follows:

• We first created risk buckets based on the churn probabilities.

So, a customer having a churn probability of less than 50% will belong to the Low-Risk bucket.

• Using our RFM segmentation and risk buckets, we created a matrix to prioritize customers for retention.

For prioritization, we created the order as follows: P1>P2>P3>P4>P5

• Since a customer belonging to either high-risk or very high-risk segment and high, very high, and Elite RFM segment should be prioritized first in a retention campaign, we tagged them as P1. We then continued to move down the prioritization order for customers with lower risk and lower value.
• When spending on campaign promotions to retain customers, the strategy should be to spend more on highly valuable customers first and then decrease the spending amount as we go down the prioritization order. We created a spending band for each of the priority groups as follows.

So, suppose a retention campaign is being run on customers in the P1 category. In that case, the maximum amount we can spend on the campaign is 25% of their respective CLTV, so even if we can successfully retain any of the customers, we will still make 3-4 times of our campaign spends.

Thus, by using CLTV, we created an end-to-end campaign management solution.

It is also essential to know some of the pitfalls of CLTV because at the end of the day, CLTV is a prediction; therefore, caution is necessary when using it as a guide for making decisions.

Pitfalls of CLTV:

1. CLTV cannot be used to justify campaign expenditure, and it is only good as its assumptions.
2. CLTV is not immune to changes in the macro environment. If inflation is high or there is some geo-political risk, CLTV cannot reflect it immediately.

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Vidyasagar

Vidyasagar is an analytics professional with around 3.5 years of experience. He has primarily worked in Retail sector and is currently part of the analytics team in Subex where he is engaged with Insurance and Fintech domains.