Asya Tikhomolova, the head of digital strategy at E-Promo Agency
explains how calculating customer lifetime value will help optimize business processes. And what to do if there is not enough data.
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CLV (customer lifetime value) is one of the main metrics in marketing and e-commerce. It shows your total profit between your customer’s first and last purchase.
To work with CLV, in addition to data, you need two formulas — simple and predictive.
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Today, digital companies have to put up with the ever-increasing competition. The only way to confidently grow your business is to incorporate data-driven marketing into your team’s work. Why? Let me make an analogy:
When decisions are made intuitively it may often feel like looking for a black cat in a black room. However, decision-making that is backed up by data brings more clarity to the process of finding growth points for your business.
Analyzing data to better understand the customer sounds simple enough, yet in fact, it becomes a stumbling block for many companies. Either they don’t know how to interpret the results correctly, or they don’t understand which metrics are important for a particular situation.
Our team has been successfully using various data-driven approaches for several years and has learned how to select the right tools for working with clients. Today we will focus on CLV, a metric that helps manage customer behavior, identify the most valuable customer groups, and adjust business processes accordingly.
To do this, you need to multiply the two indicators:
- Lifetime — a metric that shows how long a person remains an active user of a product (the cycle from their first to their last purchase).
- ARPU (average revenue per user) — the average profit per customer for a period. To calculate it you need to divide the regular income of the store for a certain period by the number of customers for the same period.
This is a basic formula, it shows a portrait of an average buyer and does not give a complete picture of your customers. To identify inactive or dormant customers, it is better to use a more complex formula.
The predictive CLV formula
For it we will need the following indicators:
- AOV — an average order value or an average check.
- RPR — repeat purchase rate.
- Lifetime — the duration of your relationship with the client.
We will also multiply them.
Now let’s look at an example of how it works.
The owner of a grocery delivery company came to us with a request: to conduct an in-depth analysis of their target audience. They had a rough understanding of the portrait of their consumer and consumer’s habits, but nothing more than that. For example, based on the data on the frequency of purchases, they could not understand why Group A was more profitable than Group B. But they wanted to find out how their current clients “perform” in general.
First, we requested CRM data for the past six months. We got:
- customer IDs,
- transaction IDs,
- number of items in a particular transaction,
- order values,
Then we looked at shopping carts and identified three categories of customers based on how much they usually ordered.
It turned out that 37% make purchases worth between 3,000 and 5,000 rubles, 29% — between 2,000 and 3,000 rubles, and another 16% spend from 5,000 to 7,000 rubles every time. Now it was easier for our client to decide which group to work with in the first place and to “grow”.
This is where we applied the CLV calculations.
A simple formula allowed us to look at the average portrait of the customer: we found that over six months the average person made 16 orders (two to three orders per month) and almost 99% of customers came back again. But this formula tells us nothing about the intermediate state of the buyer — how they behaved within this buying cycle.
The predictive formula helped us identify dormant and inactive customers. Our client got a useful insight: they have a group of customers with great buying potential, which is still to be stimulated. Now it is for these customers that we can develop a set of special offers or additional discounts to boost their purchase frequency.
CLV helps you win back customers
and save resources
Data analysis has allowed us not only to better understand the portrait of the customer, but also to identify the most promising customers and save resources on generating leads.
Having analyzed the frequency with which customers make transactions, we concluded that part of the target audience will return on their own in the next 30 days. They can be left alone for a while and resources can be shifted to those who are most likely to be less active.
Data based on predictive models helps adjust internal business processes
For example, set up upsale algorithms for each customer category, as we did in the case of our client. Their site was not tailored to the needs of individual customer groups. However, after the research, depending on which category a buyer fell into and what stage of the cycle they were in, the algorithm began to offer them relevant product categories.
For example, the cart of customers who have small children, more often consisted of children’s products, so the algorithm began to offer them better deals or complementary products in this product category. There is a group of customers who buy exclusively at a discount, so for them special offers, promotions and sales were selected more often.
Your business may have different audience portraits than the example above, but predictive models will be equally useful for building long-term relationships with customers.
What to do if CLV does not work
While CLV-based surveys provide valuable information about customers, they are not without their downsides.
There are no clear criteria as to what time frame to take in order to calculate the CLV better. One business may have existed for four years, when another may have existed for only three months. The CLV indicators in each case must be looked at individually, focusing on the specific history.
If we use CLV as the primary metric, we will grow old waiting for this metric to prove itself and enable us to conduct in-depth audience research. Businesses can’t wait four years, especially in the ecommerce sector, where cash flow plays a special role.
For business models in which purchases are made frequently and in large volumes, it is more useful to consider a new metric — the cash multiplier
This is a kind of payback window: revenue value of specific customer segments over limited time frames adjusted for sources and entry points.
How is it calculated?V (visitors) × CR (conversion rate) × Cash Multiplier − VC (variable costs) = $ (or margin)
The 60-day window is most often used for the calculation, as a business that has an established relationship with a client receives a 30% increase in CLV within 60 days. But you can choose a period that suits you: 30, 60, 90 days, depending on the range and the time customers need to make a decision. Unlike CLV, cash multiplier does not require large amounts of data, you can start working with the base in a couple of months.Based on CM indicators, you can adjust your marketing communications (email newsletters, special offers, advertising campaigns) to the needs of the consumer at a specific time interval, without waiting for the completion of the full consumption cycle.
For most ecommerce companies, this metric is more versatile because it helps them make situational decisions and stay flexible. Nevertheless, in order to properly implement CM, a business must have digital maturity and established internal data processes. Proper application of data and digital technologies helps make fast and correct business decisions, as well as increase relevance and effectiveness of marketing activities.Contact us
if you want to know more about data-driven marketing and business solutions for your business