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Use customer relationship management software to measure CLV in B2B industries.
Companies spend significant money and time acquiring new customers, often dedicating entire sales and marketing budgets to customer acquisition efforts. But not all customers are the same. Some buy from you because you’re offering the best deal at the moment. Others are long-term, loyal customers who will do business with you repeatedly.
Often, it costs the same to attract both customer types, even though long-term customers are preferred. For B2B businesses to truly maximize the ROI of their sales and marketing efforts – and attract loyal customers and earn repeat business – they must evaluate the customer lifetime value (CLV) metric.
The customer lifetime value metric identifies the revenue a business will generate from a particular customer over the length of the buyer-seller relationship. CLV takes acquisition and retention costs into account.
This metric can also be split into “actual” and “potential” to illustrate a relationship’s potential growth dynamics. CLV is about predicting future earnings; it’s a type of predictive data analytics that forecasts a customer relationship’s future value.
By definition, CLV can’t be a constant, hard number. And different businesses will likely adopt different variables when calculating CLV. Still, the following formula illustrates how to calculate CLV when a company’s customer retention rate and average relationship “life expectancy” are paramount:
CLV = gross contribution per customer x (yearly retention rate/1 + yearly discount rate – yearly retention rate)
CLV is widely used in business-to-consumer (B2C) industries, especially retail and telecommunications. However, the stakes are higher for B2B companies because CLVs can be worth millions per account. Additionally, CLV approaches differ in B2B and B2C industries. For example, in B2B businesses, executives’ judgment and experience are often more helpful and reliable than statistics because they have considerably fewer customer accounts. In contrast, a more statistical approach makes sense for B2C companies with numerous disparate customers.
Adding another metric to the pile of sales estimations sales teams deal with can be daunting, especially if it will affect their key performance indicators (KPIs). To get your salespeople on board, share the following ways CLV metrics can improve and benefit sales and marketing departments’ efforts.
CLV can highlight disproportional sales efforts spent on leads and current customers. When you understand various customer accounts’ comparative values, you can segment the most valuable ones and prioritize sales reps’ time accordingly.
For example, say that customer A has a $200K CLV and customer B has a $10M CLV. Knowing this, your sales department can prioritize nurturing and retention efforts more efficiently.
CLV measurements can yield true revelations for account management. You may view an account as mediocre; however, calculating its CLV can show that it’s woefully underdeveloped, with more potential than your team realized.
For example, say your team has made only a few random sales in small batches to customer A. This customer likely hasn’t made an impression on the sales department and isn’t really on the radar. However, when you calculate this client’s full potential for the expected length of the relationship, you may realize it’s actually in the top 20 percent of your business’s accounts. Knowing this, you can rethink the account’s development strategy.
CLV’s predictive nature helps you tap into a company’s market potential based on its real accounts. You gain a holistic, multidimensional picture of the company’s growth potential.
Some of the possible approaches include calculating the entire customer base’s CLV and that of strategic accounts, as well as comparing market segments by region. You can use this data in your decision-making process when a customer’s short-term value is insufficient or even misleading.
The best CRM software facilitates recording and using CLV metrics along with all the account-related facts and assumptions that can influence this figure. Flexible CRM software will allow you to create extra fields on customer account forms to record all the crucial factors you need to calculate and measure CLV.
You and your sales team must agree on your CLV measurement approach. Here are some of the most critical points to consider:
Recorded assumptions aren’t hard data. Still, they’re instrumental to CLV calculations at B2B companies because there are so many subjective factors to consider.
Relevant assumptions can cover various factors, including the following:
These assumptions are part of a sales rep’s CLV formula breakdown, complete with descriptions and quick “justifications” of the variables. With no assumptions recorded, tracking a mistake or misjudgment may be impossible.
Businesses have accounts of varying sizes. For this reason, introducing two distinct CLV calculation types makes sense:
When introducing CLV as a metric for measuring sales force performance, it’s critical to set up realistic quotas. Ideally, you’d set the quota at 80 percent of the actual CLV and 60 percent of the potential CLV. This ratio will encourage sales reps to work on accounts more willingly.
Mitigating the human factor in CLV calculations can be the hardest challenge. Consider the far-reaching implications of the following situations:
For many B2B companies, introducing the CLV metric can become a cornerstone of the corporate CRM policy. Not coincidentally, CRM software is home to CLV calculations. When you find the right CRM software, you can shape the system to store, analyze and report on this information.
CLV may have its downsides, like being heavily based on human judgment and prone to challenges in areas with high uncertainty. Still, with its potential to define individual customers’ contributions to the seller’s revenue streams, it can help you prioritize and set ambitious but realistic business goals. CLV can help sales teams identify new opportunities for account development and foster business growth.
Jennifer Dublino contributed to this article.