Beginner's Guide to Predicting LTV and Optimizing for Profitability

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Meta recently published a white paper on predicting Life Time Value and how to use it to boost your marketing efforts. Here’s what you need to know as an ecommerce marketer:

Smart marketers are shifting to focus on customers who are likely to become repeat buyers

Customers who continue to buy from your store drive the bulk of profitability for your business. In other words, they have a high Life Time Value (LTV). Finding customers with high LTV drives profitability for store owners and marketers are making this more of a priority in their strategies. Predicted LTV (pLTV) allows you to better know early on how valuable a customer will be and use that information to improve your marketing approach:

• Focusing on LTV has positive impacts across your organization.

• This is more than a marketing strategy - it provides key inputs that your finance, product, customer service teams and more can use to ensure you are focused on finding high value customers and then keeping them happy.

• Machine Learning now makes it possible to predict Life Time Value earlier in the customer acquisition process. Until recently, the tools haven’t been available to find and target users based on being able to predict that behavior in advance. By aggregating all the signals customers give you and how previous consumers have behaved, you can train machine learning models to identify which customers are going to be the most profitable before they’ve even made their first purchase. Having that insight enables you to unlock value via better measurement, targeting and optimization.

• Unlocking the value of pLTV requires building out a big data team or partnering with companies that can run the models for you. These models can then leverage all the important signals you are capturing in the background to understand how valuable a new or potential customer will be. Angler AI makes these tools available for combining your data with our know-how to make pLTV data available and accessible for any size business.

• Not all pLTV models are created equal. Based on the data available and the tools used, models can focus on your entire customer base, segment by cohort, or analyze at the individual user level. They also vary in terms of how quickly results can be delivered and leveraged. Angler’s tools provide analysis at the user level at the moment of purchase (or earlier), which is ideal for growth marketing and gives merchants the tools to take full advantage of this improved marketing approach.

• What to do with predicted LTV data

• Once you have access to pLTV, it can be applied to platforms like Meta to improve:

  • Measurement: Enrich your reporting by adding new metrics around long-term ROAS, LTV:CAC and other views that will tell you how your campaigns are performing in driving high-value customers so that you can make better decisions on where to invest
  • Targeting: Leverage the feedback around which customers are most valuable to your business in the long term to build better lookalike audiences and exclude customers who buy once and never return
  • Optimization: By generating a pLTV metric immediately after purchase and feeding that data back to your marketing platform, you can train the advertising algorithms in near real-time and improve campaign performance on the fly

• Verifying Results

• Impact to the business from embracing pLTV can be easily measured

While predicted LTV will immediately see a boost, measuring results over a period of 60-120 days should demonstrate lift in retention and repurchase rates, revenue growth, improved LTV:CAC ratio and any other metrics you use to measure the quality of customers and long-term health of the business

How do I get started?

Angler has been built to make it incredibly easy for merchants to implement pLTV modeling into your existing marketing flows. These models are applied at the individual user level, which gives you the highest impact and greatest fidelity, all in a self-serve platform.

Take a closer look and start putting the power of machine learning behind your targeting efforts today.

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