Increase your income in Ecommerce using LTV prediction from Datrics

Definitely, there are lots of specific factors that influence the business environment. But the key aspects remain the same for different kinds of businesses like politics and law, economics and leadership. We are used to paying attention to significant factors but sometimes even small ones can play a vital 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 LTV means for business

LTV (lifetime value) can be defined as the monetary value of a customer relationship, based on the current value. It’s an estimated income you can earn within your future relationship with existing clients. Dr. Peter Feder with his book “Customer Centricity” made a significant contribution to lifetime value, LTV calculations, and customer relations.

Each industry has specific data for LTV analytics, which is obviously important for any kind of business. Using LTV predictions you will be able to optimize your marketing funds, identify the target audience and, as a result, increase the revenue.

Required data for LTV prediction

To determine the customer lifetime value you will need to calculate the average customer lifespan and multiply received numbers by customer value.

Numerous companies predict LTVs by taking into account the total monetary amount of sales, without understanding context. We suggest diving deeper for getting more in-depth analytical data. For example, a customer who makes one big order can be less valuable than another customer who buys multiple times in smaller amounts. Also, we suggest taking into account the type of business, the demography, and the gender of your customers.

If you want to understand the buying profile of your customers and value your business more accurately you shall try CLV modeling.

The most important inputs into LTV models are recency, frequency, and monetary value.

Recency means last time when was the customer’s order. Frequency shows us how often customers buy. By monetary we suggest the amount that they spend.

Steps to calculate LTV

LTV calculation can be simple and difficult at the same time. Sometimes it’s not easy to control 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 do in Excel, but it can take a lot of time. AI platforms can simplify LTV calculations by using

complex parameters and ML models for analytics.

You can try different models and ways to calculate your LTV. In most cases, regardless of the method you’ve chosen, you will do as follows:

1. Determine your target audience with all the specifications as gender, demography, etc.

2. Purchase Frequency Curve based on your transactional data.

3. Get Customized Lifetime Value Forecast.

You will be able to manage your marketing campaigns using this data. With LTV forecast, companies can plan campaigns and predict the impact on revenue. To do that just choose the marketing channel, time period, budget, and analyze the results.

Lifetime value platforms can also help you to increase your retention rate. Identify your loyal customers, apply for different loyalty programs and measure different effects based on key results. LTV helps with: Churn Rate, Revenue, Marketing Budget, NPS, Retention Level, Customer Acquisition Cost.

LTV value for business

LTV can clearly identify audience potential, optimize forecast revenues, recognize the highest value customers and calculate their worth. Companies can easily determine appropriate advertising budget, separate marketing costs for promo campaigns focusing on the most valuable groups of customers using LTV. In Ecommerce, it helps with demand forecasting and goods retention, historical data analytics. Besides company revenue prediction, you can plan and manage sales and marketing workflows. You can get highly accurate forecasting based on external market changes and customer behavior.

Now Datrics team is working on custom pipelines. The full version of the article and demo is available on datrics.ai.