A key to predicting and effectively using customer lifetime value (LTV) is to take a long-term view of your data and not just rely on the first month or even first few days. Many marketers will draw conclusions about a new product launch, a new feature or a unique customer cohort based on the initial data they generate. While you cannot wait months or years to make crucial business decisions, understand that these predictions are less reliable and thus making decisions based on this data is problematic.
The challenges
While intuitively more data is always better, there are challenges involved in looking back over a long period. First among these challenges is customer attribution. If you are determining the value of a specific growth channel, do you credit the lifetime spend of a user to the channel you used to acquire them initially or do you attribute the revenue to a channel (Facebook feed, email, A2U notification, etc.,) that brought the user back after a long period of inactivity.
The second issue is the sheer quantity of data. If you have millions of customers or players and years of data, it becomes quite a challenge to process all of that data. You may have multiple interactions with that user every day, literally for years. Think of how you interact with Amazon and consider they track all the products you look at, how often you visit, what you purchase, what you purchase instead, etc. You need the software, data warehousing and systems so that you can actually analyze this data quickly.
Map out the customer journey
To overcome these challenges, companies are using analytics to track the customer journey for at least six months and how it affects lifetime value (and thus where they can impact it). A customer journey map is a very simple idea: a diagram that illustrates the steps your customer(s) go through in engaging with your company, whether it be a product, an online experience, retail experience, or a service, or any combination. By modeling the customer journey, you can understand when and where users return. Understand the recipient’s journey from the time they first enter your app, play your game or visit your store to the final desired action. This action is not opening an e-mail or clicking on a link but it is potentially installing an app, making a purchase, etc.
Applying the data and winning
HotelClub, an online hotel booking website, implemented such an approach and saw strong benefits, according to the article “Using Big Data Analytics to power customer lifetime value.” Through its longer-term customer journey data analysis, the company discovered that some channels, such as affiliate websites, have a lower repeat rate and cost more of the marketing team’s money over time. This fact wasn’t apparent using traditional 30-day attribution metrics. The data also showed HotelClub that customers who first purchased a lower margin product have a higher value than originally anticipated. These multi-dimensional insights are now assisting HotelClub to make more strategic investments in communication channels and are helping improve that customer lifetime value, according to Nicholas Chu, HotelClub’s President.
By analyzing months or years of data, you can then better predict which customers at an early stage are likely to have a high LTV. With this understanding, you can ensure the early user experience caters to those users or players who are most likely to have a high LTV. For example, you might show them less advertising that can pull them off of your site than your would show other customers. You also can flag them with your customer service team to ensure they get special, white glove treatment if they have an issue. As I wrote in another post, you may want to send a personal thank you note to people you expect to become your best customers.
Do not take short cuts
The key lesson is that you should not rely on the first few days or even month of data to make your LTV calculations. While you have to with a new product, it is crucial to always go back and incorporate historical data to refine your lifetime value model. This data will then help you optimize your product, growth and CRM strategy to increase LTV and focus your efforts to users who will become your best customers.
Key takeaways
- It is critical to use at least six months of data to understand the lifetime value of your customer cohorts and optimize your product to maximize LTV.
- The best way to look at lifetime value of a long period is to understand it in relation to the customer journey, particularly so you can see what channels are driving your best users to your product.
- Once you understand long-term LTV, you should optimize everything to focusing on users with the highest LTV and increasing that value.
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