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A customer lifetime value (LTV) analysis is the best way to figure out which type of customers provide the most value to your business.

An LTV analysis attempts to predict the value that a customer brings to your business over their whole customer life cycle. That is, how long will they remain a loyal customer, and when are they likely to churn? How much money will they spend with your business, and how often? What are the acquisition costs associated with this customer?

We can use both traditional statistics and financial techniques in order to answer these questions, but once you have  this information, how will you take action on it?

What’s Next: Targeted marketing

After you have LTV estimates for your customers, you can find the customer traits which have the highest value for your business by using machine learning or statistics to segment and cluster your customers further.Depending on the data that is available, this could be demographic information (age, gender, income), behavioral information (number website visits, time of purchase), or marketing channel information (first touch channel, last touch channel, most frequent channel). With this information, you could:

  • Serve targeted advertising to high LTV demographics to try finding new high LTV customers from that same demographic. For example, if 50-60 year old females had a high LTV, you could use display advertising to serve ads targeted  specifically to this demographic.
  • Adjust your creative design to appeal to your highest LTV demographics. For example, if 30-40 year old males had a high LTV, you could use images that resonate well with this demographic.
  • Use behavioral information such as frequency of website visits, purchase type, etc., to set up retargeting lists for your campaigns.You could remind your low LTV behavior segments about your company in order to try to increase their LTV; you could also serve additional advertising when someone enters a high LTV behavior segment.
  • Decide whether you want to explicitly target low LTV segments. Should you create new marketing campaigns to appeal to them in new ways to decrease churn or increase purchase amounts? Market research groups could lead insight into why these customers have low or high LTV, and help you learn how to enhance their experience.

Personalize and Optimize Experience

  • Use Optimizely, Optimize 360, or a similar personalization platform to optimize the user experience. Test changes for high and low LTV segments to see if you can increase their value.
    • For high LTV demographics and behaviors, can you personalize their experience to ensure that they remain high LTV customers? Can you serve them a better website experience to reward them for being a top customer? Can you provide them with discounts? Can you display their favorite products first?
    • For low LTV demographics and behaviors, can you change the website experience to increase their LTV? Can you show them different suggested products to increase their chances of purchase?

Budget Allocation

  • If particular marketing channels bring in high or low LTV customers, you can use this to determine your budgets for each channel.
    • Channels which may have appeared undervalued before based on only the first customer purchase may be bringing in customers who have the most repeat purchases.
    • Rather than relying on the first purchase, take LTV into account when determining where to spend your marketing budget.

These are just a few suggestions for getting started with your LTV analysis. Depending on the data you had available from the start, there may be other ways you can use the results. No matter which type of results you get, make sure you are prepared and setup to start using them through marketing, budget allocation, and personalization to get the most value from your analysis.

We can take the complexity out of predictive analytics and data science to help you understand who your best, most valuable customers are. Download our Customer Lifetime Value Analysis solution sheet and contact us to learn more!