Customer LTV in retail and e-commerce: business value and prediction methods

There are lots of factors that have an impact on the business environment. It depends on your business industry, but the main aspects are economic, leadership, political and legal, social and cultural, technological. Usually, we keep our focus on obviously fundamental things, but sometimes even small details can play a significant role in the company's growth.

Recently we described the value of inventory management, now we would like to talk about Lifetime Value (LTV).

What is LTV in business?

Lifetime Value, or simply LTV in E-commerce is an estimated revenue that you can have within your future relationship with current customers. It also can be defined as the monetary value of a customer relationship, based on the present value. One of the most crucial contributions had been made by Dr. Peter Feder with his book "Customer Centricity" where he helps businesses radically rethink how they relate to customers, describes his research on lifetime value and explains how to calculate it. Different industries have various data for LTV analytics, but it's obviously important for any kind of business. With the help of LTV management, you can simply predict your profit, optimize your marketing budget, identify your target audience and, as a result, increase your profits.

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What's the needed data for LTV calculation?

The simplest prediction you can get by calculating average purchase value, and then multiply that number by the average purchase frequency rate to determine customer value. When you have calculated average customer lifespan, you can multiply that by customer value to determine customer lifetime value. But we suggest diving deeper and take into account more details. Many companies predict LTVs only by looking at the total monetary amount of sales, without understanding of the wider context. For example, a customer who makes one big order might be less valuable than another customer who buys multiple times, but in smaller amounts. Lifetime modeling can help you better understand the buying profile of your customers and help you value your business more accurately. We also suggest taking into account the kind of business setting that you're in, the demography and gender of your customers.

We can define three most important inputs into LTV models: recency, frequency, and monetary value.

Recency means last time when was the customer's order. Frequency shows us how often do customers make purchases. By monetary value we imply the amount of money that they spend.

How to calculate

As a metric, LTVs calculation in principle is simple. But in practice, it is rarely that easy. LTV can keep changing forever. It's not easy to keep track of LTV across campaigns and channels if one is managing a moderately large marketing effort. Individual modeling of the behavior of every single LTV curve is possible to be done in tools like Excel, but it can eventually become very time-consuming. Different software can simplify your LTV calculation, such as AI platforms, that deal great with complex algorithms and Machine Learning models for analytics.

You can use different models and ways to calculate your LTV. In most cases, you will follow the next steps, regardless of the method you've chosen:

Based on this data you can manage your marketing campaigns. With LTV management, companies can plan campaigns and predict impact on revenue substantially better. Choose the marketing channel, time period, budget, and then analyze the simulated results.

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Lifetime value platforms can also help you with loyal customer identification and increase your retention rate. Apply different loyalty programs and measure different effects based on key results. LTV management helps with: Churn Rate, increasing net revenue, marketing budget optimization, retention level improving, defining customer acquisition cost.

Business value

LTV can help easily discover audience potential, optimize revenue forecasts, identify the highest value customers and calculate their worth. WIth LTV companies can easily define relevant advertising budget, divide marketing costs for promo campaigns by focusing on the most valuable groups of customers. Besides profit prediction, company can plan sales and marketing workflows. LTV in E-commerce helps with demand forecasting and goods retention, historical data analytics. You can get the hyper-accurate forecasting based on external market changes and customer behavior.

Datrics team is working on a data science end-to-end platform. Our aim is to help companies with challenges in their business domains and make life easier with optimization and automation. Wondering if there is a way to optimize your business? Contact us to create a custom pipeline.

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