We recently hosted a webinar featuring Forrester Principal Analyst Brandon Purcell and Julie Schmidt, SVP of Analytics and Insights at Allant Group, on the topic “Customer Lifetime Value for Intelligent Marketing Decisions.” In this informational session, Brandon and Julie talked about the importance of CLV as a key customer metric that has several applications across the customer lifecycle. Watch a recording of the webinar here.
On the webinar, Brandon talked about how companies are using customer lifetime value to win, serve, and retain customers. He also presented best practices for overcoming the data and organizational challenges typically associated with lifetime value. Finally, he revealed on how to get started with lifetime value.
Julie positions CLV as the Gold Standard among customer metrics. Brands (clients) should not let perfect get in the way of the good when it comes to getting a metric in place that is actionable. She outlined a process for creating a CLV metric starting with data, calculating the current value and forecasting the future value, and lastly driving towards operationalizing the metrics to make it actionable.
Following our webinar presentation, we sat down with Brandon to discuss further the importance of CLV and its role in meeting the demands of today’s marketers. Brandon recommended to aim for progress not perfection (CLV may be precise but it is approximate) and to use CLV to guide customer journeys.
Below, we share some more of Brandon’s insights from our interview:
Allant: What do you think are people’s biggest challenges when it comes to calculating CLTV?
BP: The challenges most organizations encounter with customer lifetime value stem from an expectation of perfection. As a metric, CLV is precise in that the output is a number in hard currency. Unfortunately, people tend to mistake precision for accuracy, but it is impossible to develop a CLV calculation that is 100% accurate for every customer over the tenure of their relationship with your brand. As a probabilistic prediction, this just isn’t possible.
The biggest manifestation of this precision vs accuracy tension comes up with the issue of costs: “How do we incorporate costs in our CLV calculation?” The answer is that many organizations don’t, at least in their first iteration of a CLV model. That’s good enough for a first model because you’re basically assuming uniform costs across customers. Over time, as you socialize the metric and prove its value across the organization you can start to improve it by incorporating costs. But if you wait until you have the perfect cost data from Finance and the best way to account for it, you’ll probably never start.
Allant: What are some guiding (organizational) principles of socializing LTV as an actionable metric to measure the health of the customer file?
BP: From a practical perspective, the Finance team is typically one of the biggest opponents of CLV because they are used to dealing with precise and accurate metrics. They also have access to some of data you’ll need, so it’s important to engage them early and get their buy in. Explain to them how the metric will be used. It will not replace revenue, cost of goods sold, or other GAAP standard metrics. Instead, it will be used to segment customers and identify opportunities for acquisition, retention, and customer experience improvement.
View your CLV initiative is an opportunity to align the customer insights function within your organization. According to last year’s Forrester-Burtch Works State of Customer Analytics survey, 37% of companies have decentralized analytics and measurement teams embedded in a channel or business unit. These teams are typically invaluable within their areas of focus, but they lead to inconsistent and often redundant analytics practices across the organization. CLV presents an opportunity for alignment and collaboration between distributed insights teams toward a shared goal.
Allant: Once you have all the customer data in place, how do you decide which techniques to use?
BP: The best CLV model is the one your business stakeholders accept, understand, and use. Period. Customer insights professionals will typically build multiple CLV models and back test them against historical data to determine accuracy. Since you’ll need to socialize the model, it’s important to retain a degree of transparency into how it works. Business stakeholders don’t adopt models they don’t trust or understand.
From an analytical perspective, the way you calculate CLV depends on 1) whether the relationship your customers have with you is contractual or non-contractual and 2) whether their transactions are continuous (any amount) or discrete (prescribed amount). Contractual businesses such as insurance or subscription-based service providers typically calculate CLV using a customer retention model to predict the number of periods before a customer churns. Non-contractual businesses like retailers have it more difficult and must deploy probabilistic models to predict the likelihood of each future purchase. Similarly, companies with discrete transactions of known amounts need only predict retention, while companies with continuous transactions must take a probabilistic approach to predicting each future transaction’s value.
Allant: Other than allocating marketing spend, what are the most typical use cases for CLV?
BP: Many brands use CLV across the customer lifecycle. At the beginning of the life cycle, companies use CLV to target prospects who look like current high value customers. Later, companies use it to identify cross-sell and upsell opportunities. At the end of the customer life cycle, CLV can be a key factor in determining which customers to target with retention incentives.
CLV can also help inform strategic decisions, such as whether to enter a new market or acquire another business. Instead of employing a one-size-fits-all approach to customer value, some organizations are starting to determine the effect these decisions would have on customer composition and therefore their long-term financial impact. One music subscription service has started to use CLV to determine which artists’ work to license and how much to pay for it with the goal of increasing the total CLV of its listener.
ABOUT THE AUTHOR
Tim Finnigan
CMO Allant Group
Tim Finnigan is an accomplished marketing executive with nearly 25 years of experience building brands. Tim received a marketing degree in Business Administration from the University of Dayton and a MBA in Marketing from Loyola University Chicago. Tim is also an Adjunct Professor, teaching Digital marketing at Loyola University Chicago and Marketing Strategy at University of Dayton Online MBA.
please visit https://www.allantgroup.com/.