How to Forecast and Grow Customer Lifetime Value

AI & Machine Learning|[wtr-time]

How to Forecast and Grow Customer Lifetime Value

Practically every business professional today knows the importance of predicting customer lifetime value (CLV). But not every company has the ideal foundation in place — that is, the right people, processes, and technology — to forecast and improve customers’ CLV scores.

CMO Survey Director Christine Moorman noted many organizations are investing in solutions to help elevate their marketing efforts. For instance, they’re using tools that help them acquire new customers, track customer journeys, and improve customer experience.

But too many companies don’t invest in the right resources to gain granular, real-time insights into customers’ long-term value to their businesses, according to Moorman:

  • “Profits and sales growth tell you about what a customer is worth today, but what really matters is a customer’s value to your future growth.”

If your organization doesn’t have the personnel and technology to easily and efficiently predict and calculate customer lifetime value scores and use that data to grow CLV scores over time, it’s time to update your marketing approach — as well as your tech stack.

customer lifetime value

How leading companies predict and increase customer lifetime value today

Calculating revenue your organization can expect to generate from an individual over an extensive time period based on purchase frequency, churn rates for target segments, and other data points is vital to your business’s long-term growth.

All in all, CLV represents one of the most important metrics for companies across all industries today. And it’s one one that requires constant monitoring to improve over time.

Simply put, the more attention paid to improving customer profitability — and, therefore, determining how to boost profit margins in the long run — the better for your business.

A 2019 Econsultancy survey found companies utilize a number of tactics to grow CLV: from dynamic audience segmentation and new customer experience initiatives to increased investment in technologies that help them better manage customer relationships.

One such solution many businesses use to grow CLV is a customer data platform (CDP).

The role of artificial intelligence (AI) in marketing today continues to grow at a remarkable pace. It’s easy to see why the tech (i.e., machine learning) is being used more often.

It helps everyday marketers and other growth-focused professionals (customer experience, analytics, etc.) immediately, efficiently, and automatically calculate predictive scores like customer lifetime value based on a vast volume of user data.

In BlueConic’s pure-play CDP, for instance, you can easily calculate and attach CLV scores to an individual level profile to build segments based on high- or low-CLV customers.

With data unified in BlueConic’s persistently updated customer profiles — data that dynamically updates existing customer segments as individuals’ behavior and engagement changes — you don’t have to worry about pulling manual lists before building CLV models.

With AI Workbench, BlueConic users have access to out-of-the-box, machine learning models that tell them buyers’ propensity to purchase, when they might churn, and CLV.

By incorporating a customer’s historical transaction data, AI Workbench constructs a predictive model that forecasts customer lifetime value based on:

  • Probability a customer will buy again: With historical purchase information and behavioral data (e.g., frequency, recency, and intensity of digital activity) incorporated in your CLV model, you can accurately gauge which customers or clients are likely to buy or subscribe again down the line.
  • Likely number of purchases made in the prediction period based on past buying behavior: Similarly, the machine learning predictive models deployed in AI Workbench can use the same data sets to determine how many times an individual or segment of customers are likely to buy over time.

BlueConic customers can then get a CLV score they can attach to individual-level profiles.

By enriching profiles with predictive scores, those who use our CDP can get the customer insights they need to not only improve their customer retention rate, but also cross-sell and upsell individuals with personalized and one-to-one offers across lifecycle stages.

customer lifetime value

What your organization can do to increase customers’ CLV scores and brand loyalty

Now, how you elevate the average CLV score for all customers — and, in particular, those for your highest-value buyers — will certainly differ from the strategy other companies employ.

If a furniture retailer aims to increase customer lifetime value for those who order $1,000-plus of home furnishings once every 12-24 months, its predictive CLV model for those buyers (e.g., customer loyalty program members) will be different than one for a retail business whose customers buy lower-cost items (e.g., those less than $50) more often.

Why is this the case?

Because the former company isn’t likely to get business from an individual making a sizable furniture purchase more than once a year, whereas the latter organization may see a repeat customer spend again within weeks or even days of their most recent purchase.

Factoring in your business model with your CLV formula

As BlueConic Sr. Customer Success Manager Ali Paradiso noted on our AI marketing capabilities webinar, you can tweak your organization’s unique customer lifetime value model to match your sales cycle and products’ and services’ price points:

  • If your brand has a typically long sales cycle and low-frequency products, your predictive customer lifetime value model will likely require a longer timeframe in order to capture data necessary to make an accurate calculation. For example, you might want to use purchases over weeks or months instead of days for your model, then ensure the chosen observation period is large enough to observe multiple purchases in order to deliver the most accurate calculation.
  • Conversely, if your business has a generally short sales cycle and high frequency of products sold, you could use daily or weekly purchases to more accurately calculate CLV.

The more you understand your business model, the better your machine learning predictive modeling efforts will provide relevant, detailed, and timely insights about your entire customer base you can use to continuously enhance your overall customer lifetime value.

Creating new customer segments based on CLV models

Once you have a wealth of info about your customers’ value to your organization — order frequency, purchase price points, propensity to buy, etc. — you can develop dynamic customer segments based on CLV scores and other key data points to help with cross-channel personalization.

It’s no secret there are far greater marketing ROI opportunities by targeting or creating premium experiences your most valuable customers — repeat buyers, long-time subscribers, and luxury-product purchasers – than other customers, like one-time buyers who’ve shown little intent to buy again (or at least in the near future).

With CLV scores that update dynamically in BlueConic, you always have a clear understanding of your customers and can efficiently market to each one accordingly.

It’s these adjustments based on customers’ lifetime value scores that can not only improve day-to-day marketing output, but also drive more revenue for your business.

In recent research, Salesforce discovered retail shoppers who receive personalized recommendations from companies from whom they’ve purchases products or services in the past accounted for only 7% of their overall website call-to-action clicks.

However, those recommendations drove a quarter of their orders and revenue.

If you know which customers are most and least likely to buy again and/or renew their subscriptions, you can deliver bespoke, timely, custom-tailored experiences to each one.

Prioritizing advanced customer lifetime value modeling with a pure-play CDP

With a CDP like BlueConic that helps you forecast CLV with ease, you gain actionable insights about the most prized customers in your database and, in turn, can easily discern which individuals and segments deserve most of your immediate marketing attention.

More specifically, BlueConic can help you extract more value from existing customers and interact with them more intelligently in real time as their behaviors change.

Simply put, leveraging the machine learning functionality in our customer data platform enables you to up-level your marketing and, in the long run, accelerate business growth.

Get more insights into how the predictive CLV model and other AI marketing capabilities in our CDP can accelerate your marketing efforts in our webinar.

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See what BlueConic can do for you.

Whether you’re looking for operational efficiencies or improved marketing effectiveness through data activation, our customer data platform can help.