What is Customer Lifetime Value?
Customer Lifetime Value (CLV or CLTV) in marketing refers to the net profit contributed to the entire future relationship with a customer. The term was introduced in 1998, from the book "Database Marketing," which describes some working examples and models. In a simple commerce example, the LTV of a business is calculated using this formula:
(Average Monthly Revenue per Customer x Gross Margin per Customer) ÷ Monthly Churn Rate = LVT
For example: $100 average monthly spend x 25% margin ÷ 5% monthly churn = $500 LTV
To increase the LTV, we need to increase the basket size (through monthly spend – buying more – cross-selling /upselling), increase the margin (Premium Brand), and retain the customer to reduce churn (Constant Engagement & Winback).
The Old Rule - Acquisition costs 5 times more than Retention. Is it true?
According to Forbes, there is an old rule suggesting the cost of acquiring a new customer is five times the cost of retaining one. Let's check if this rule actually holds in the new digital world.
As a digital full funnel integrated agency, ADA has the experience of driving both customer acquisition and engagement using paid media and owned channels. Companies can buy media, and the cost per thousand impressions (CPM) can be between $2.5 and $9. Using owned channels like email, the CPM equivalent (the users who actually opened the email) can cost between $2 and $8. With owned channels, cost can be cheaper than paid media if you know your customers already. If we take the high end of the media CPM ($9) against the low end of the owned channel ($2), the old rule of acquisition cost being five times the cost of retention is kind of true.
Source: Statista
Customer Lifecycle
To increase the Customer Lifetime Value, we could sell more, increase the brand values (or margin), and reduce the churn rate. To do that, companies need to understand their customers. In the digital world, customers leave different digital signatures on various touchpoints. The closest signatures with customers’ direct consent are the zero-party data and first-party data.
A Customer Data Platform (CDP) can capture data from various silos, transform them into a single model for segmentation analysis. Once the cohorts or segments are identified, companies can then develop specific contact strategies to engage with these customers to drive better engagement, upsell / cross-sell, and retain customers.
In the contact strategy development, we look at the 5W1H. What does it refer to?
- What to sell?
- Who to sell to?
- When to sell?
- Where to sell – eCommerce? Marketplace? Offline?
- Why to buy?
- How to sell – Online only? Online to Offline? Online Merges Offline (OMO)?
ADA typically co-creates the customer journey with companies via the design thinking workshop with multiple stakeholders. Our design thinking workshop typically takes about two months from planning to making the final deliverable presentation.
ADA Success Stories
Case 1: Quick-serving restaurant (Malaysia)
ADA engaged with a major quick-service restaurant in Malaysia. As the restaurant chain just launched a New Member Loyalty App, its User Adoption and Retention of App Loyalty Programme remained low. Though they had successfully acquired over 1 million users in the past, over 90% of them uninstalled or stopped using the app, leading to a high churn rate.
To tackle this challenge, ADA used a Customer Data Platform (CDP) to collect the users' behaviours, transaction information via the App and (Point of Sale) POS. we then analysed the data in the CDP and developed ten cohorts for different engagement and retention tactics. As a result, the restaurant chain was able to retain 50% of the users instead of just 10% of the users. The monthly transactions also jumped from 14,000 to 110,000.
Case 2: eCommerce Company specialised in health and beauty (India)
In another case, ADA partnered with a leading eCommerce company specialising in beauty and wellness products in India. ADA undertook an analysis of their vast customer base comprising 35 million individuals, with the goal of segmentising its audience. By applying the K-Means clustering approach, ADA successfully identified four clusters of consumer personas based on their level of engagement, interactions, and spending history with the brand.
The company leveraged the resulting customer insights to launch targeted marketing campaigns aimed at each of the four distinct audience segments, which, in turn, led to a 5% increase in sales and saved 30% of their advertising spend after optimisation.
