As Loyalty Withers, Lifetime Value Needs to Be Redefined
Customer lifetime value (CLV) is a metric that indicates just how much revenue companies can expect from each customer over the span of their business relationships. The frequency of orders and the dollar amount of orders are other essential CLV inputs.
It’s an important metric for companies, but as customers today have become fickle and are willing to abandon their favorite brands over the smallest customer service gaffes, calculating CLV is more difficult. But that’s no reason to abandon CLV as a metric.
CLV is one of the more important measurements companies have at their disposal, though many overlook it, says Chris Pennington, chief customer officer of SugarCRM. “I really love customer lifetime value as a metric. I think it is a really strong metric, [but] it’s not incorporated as much as it should be.”
Knowing customers’ lifetime value can help companies avoid wasting a considerable amount of time chasing them if they won’t yield much profitability over time, according to Pennington.
The calculation can include many components, says Yan Yan, a data scientist at Amperity, a customer data platform provider. “The data foundation in place must be accurate,” he says, pointing out that “many brands are still struggling to unify their offline transactions with digital interactions, and most are misidentifying as much as a quarter of their customers, meaning calculations will be inaccurate.”
Additionally, the calculation itself has evolved over the years. Banks once put a high priority on capturing customers early in life, with the idea that the lightly profitable checking or savings account would be the first piece in a long customer relationship that would grow to include auto loans, credit cards, mortgages, and other products, resulting in a significant customer lifetime value. But that was before competition increased from non-bank financial services providers that have picked off many of those more profitable products and services.
Similarly, auto manufacturers at one time spent a good deal of their marketing to convince consumers to be GM buyers or Ford buyers for life, with the idea that consumers would stay loyal with each new car purchase.
In both instances, customer lifetime value calculations could include decades-long commitments to the brand, but that is no longer the case, Pennington says. “Today’s consumer is significantly more fickle. The first thing to recognize with CLV is that the word ‘lifetime’ needs to be recast or rethought to consider the lifetime of the offer, not the individual.”
Yan agrees. “People first focus on residual customer lifetime value. Second, they put a forecasting window to it. Usually, the standard is a year.”
Therefore, “lifetime” has become somewhat of a misnomer in today’s economy, Yan says. Even a year is too long for some industries, such as quick-service restaurants or consumer packaged goods, where changes in pricing or customer tastes can influence purchases from month to month or even from week to week.
Some luxury brands might be able to extend their lifetime definition to two years or longer, Yan maintains, but he cautions that the longer the time frame, the less accurate and less actionable the CLV calculation could become.
To determine the best time frame to use, review the accuracy of past CLV calculations and adjust the formulas, if necessary, Yan recommends. “That way you are validating your model. Validation is important.”
CLV HELPS WITH SEGMENTATION
The CLV calculation can be paired with other standards, like the ideal customer profile (ICP), which Pennington says should “go together so that you can focus on those customers who will provide the best returns for you.”
Many companies fail to compute CLV and ICP together because those calculations are handled separately and by disparate teams, Pennington says. “Marketing and sales are chasing the bright, shiny ball. One of the things that you would want to weave into the sales mind is where you want to invest your time and which customers you want to invest your time in so that you get the right return.”
CLV calculations can also reveal how much different customer segments are likely to spend, agrees Andrew Kokes, executive vice president and head of global marketing at Hinduja Global Solutions, a digital customer experience services and solutions provider. “Investing in customers at key moments of truth during the customer life cycle and knowing precisely when they are deciding to buy from you again or go elsewhere are keys to maximizing CLV.”
Kokes adds that many companies look at CLV within the context of customer experience management, using CLV to help rationalize continued investment in CX and similar programs.
Once companies win customers’ initial business, the calculation should move from basic CLV to enhanced CLV, Pennington says, noting that the latter also considers churn and gross retention. Those are particularly important components in computing profitability for subscription businesses. When paired with historical, individual, purchase, revenue, and profitability data, enhanced CLV also helps determine how much time and resources should be put into customer retention moving forward. It can influence, for example, the decision about whether to offer a discount for renewals, he adds.
A hotelier, for example, can use data such as loyalty members’ transactional history for room stays, on-site meals, and spa treatments to understand which upsell offers are most likely to have the best result, says Andy Hermo, chief commercial officer of iSeatz, a provider of digital commerce and loyalty tech solutions for travel and lifestyle companies.
MAJOR CLV CHALLENGES
Companies have struggled with CLV calculations because the numbers can vary widely and mean different things depending on the types of products and services they offer, but the larger problem might be the inherent weakness of the formulas they use, according to Pennington.
Many CLV calculations focus on hard, tangible factors, such as revenue and purchase frequency, when there are many intangibles that also have to be considered, Pennington says. “One of the things that has evolved enormously, particularly in B2C, is that we live in a world of influencers and disruptors. If you look at how things can be disrupted, you [also] have to look at intangibles. The two biggest intangibles are reputation and brand influence.”
The time to acquire and retain a customer is another important intangible, according to Pennington. One customer might produce more revenue than another, for example, but the first customer might still have less lifetime value if the company has to spend more to acquire and retain him, he explains.
“We try to balance the lifetime value with a total cost to serve our customers. In the retail space, for example, if we can understand the average purchase and the number of times people purchase over a given period of time, then we can make recommendations based on that,” Kokes adds.
“There are two ways to look at time,” Pennington says. First is the time horizon of the customer relationship. In the banking and auto examples above, the time horizon was several decades, but in today’s economy, three to five years is a very long time. Using a long time horizon can make sense for very stable industries and stable markets. But if the market or product destabilizes quickly, it becomes much more difficult to compute a meaningful CLV, according to Pennington.
“The other element of time to consider is the time invested in the customer to achieve the desired outcome,” Pennington says. “One of the hardest things to do today is to maximize the efficiency of our time.”
Pennington cites these other missteps that companies sometimes make with CLV calculations:
• focusing only on short-term revenue and ignoring the long-term potential of customer relationships;
• ignoring the impact of customer satisfaction and loyalty on CLV;
• using a one-size-fits-all approach to calculate CLV rather than tailoring the calculation to the specific business and industry; and
• over-relying on CLV as the sole metric for measuring customer value, without considering other important factors, such as customer acquisition costs and customer churn.
Kokes adds that some companies fall into the trap of being overly optimistic or too pessimistic with their calculations; either extreme makes CLV less reliable and less useful as a determinant of future investment to retain certain customers.
Yan adds that sometimes with current CLV models companies fail to adjust for the errors made in previous models. For example, if a company last year projected an average revenue of $400 per customer but the average revenue was only $300, that $100 difference per customer is not only significant in its own right but can also indicate errors in other CLV calculations, Yan says. “That’s why back-testing is so important.”
Hermo adds that companies could also miscalculate CLV by looking only at purchases rather than considering engagement. The latter can uncover opportunities for additional purchases and, therefore, improved CLV.
Failing to understand the inputs can also lead to erroneous CLV calculations. Different factors, such as changing customer preferences, economic conditions, supply chain disruptions, government regulations, or market disruption, can flow in over time, and companies will need to account for them in their calculations. If a company rigidly sticks to its CLV formula without recognizing the shift of key elements, the results will be wrong, Pennington says. “Companies need to recognize that CLV needs to be dynamic; it will shift over time.”
“You need to recognize that your model will evolve. If you’ve been rigid in your definition of CLV and yet your customer base evolves, or you enter into new markets, or you introduce new products, then very quickly your formula is out of whack with your business practices,” he continues.
Customers are constantly changing, and stale data isn’t going to help ensure that CLV calculations are accurate, Yan agrees. “Customers and brands are unique, so we cannot treat all of their behavior the same.”
And while some companies leave customer acquisition costs out of their equations, others might get too complex with their CLV calculations. They try to capture everything, spending so much time calculating the CLV formula that there is very little additional value from what a simpler CLV calculation would provide, Pennington warns. “Don’t add metrics simply for the sake of adding metrics.”
“We need more streamlined ways of being able to assess the value of the customer and particularly the impact on customer experience,” Kokes says, adding that companies need easier ways to connect the dots between customer churn, loyalty, Net Promoter Scores, and other underlying data to compute an accurate CLV.
“If you start to see a wobble or a trend, how do you make investments that are going to protect that base and potentially then help you grow and extrapolate more value out of that base by protecting them?” Kokes asks.
Further complicating the CLV model is the fact that even simple CLV calculations require the right talent and technology to be performed correctly, according to Kokes. “While AI, automation, and analytics technologies can provide valuable insights into customer behavior, they require skilled professionals to implement, analyze, and interpret the data. By balancing technology and people, organizations are better able to innovate, optimize, and grow.”
Choosing the right technology can be confusing, Kokes concedes. “By paying close attention to the impact of technology on different areas, businesses can make more informed decisions that align with their requirements.”
One of those critical technology elements is the customer data platform, which Kokes says can be “easily integrated into any business’s technology stack to support CLV calculations.”
But flexibility is the key today, experts agree.
Pennington recommends that companies occasionally review their CLV calculations to ensure that they are using the right components in their formulas.
“Using an adaptive, ensemble approach allows brands to find the right models or combination of models that are the most accurate,” Yan agrees.
And finally, it’s important to keep the customer in the customer lifetime value equation. “Revenue growth is critical for all businesses. Revenue-generating strategies that aren’t customer-aligned are unsustainable,” Kokes concludes.
Phillip Britt is a freelance writer based in the Chicago area. He can be reached at email@example.com.