Why Customer Lifetime Value is Essential to Predictive Marketing

[vc_row][vc_column][vc_column_text css=".vc_custom_1509025501937{margin-bottom: 0px !important;}"]Think of predictive marketing as a tool with which you outline how your greatest customers went from interested prospect to repeat customer. If you could isolate your ideal customer profit and define the how and why of that ironclad relationship, then you could determine what current actions taken by new prospects will eventually lead to more long-lasting relationships. It is all about the data, and for today’s digital marketing professionals, data management is key.

Compiling scores of data on existing customers requires continuous data management (CDM) software capable of segmenting specific datasets while enriching and cleansing incoming data in real-time so that your marketing team can respond effectively. Data enrichment leads to a better understanding of your customers and a stronger buyer-seller relationship. A stronger relationship equals a higher customer lifetime value (CLV).

Knowing CLV Is Not Enough

Tech-savvy digital marketing professionals know that just calculating an estimated CLV is not enough. It is not just a question of taking the average order value multiplied by a customer’s purchase frequency multiplied by your average customer lifetime. Accuracy is key, and that accuracy relies upon your real-time data acquisition.

The goal for today’s digital marketing teams is to extend a customer’s life and that comes from increasing customer interaction and engagement. The more engaged your customer is, the more he or she will return. The more data you gather in the process, the more you will be able to leverage that data effectively. However, miss a beat and you will miss the boat. If your data acquisition and management platform is not up to snuff, then you will lose ground to competitors and see your market share plunge.

What Does CLV Really Mean? 

CLV is an all-encompassing metric. It is a measurement of the combined efforts of your entire organization. It defines how your organization brings value to customers. It is not a front-end metric like cost of leads and customer acquisition and it is not a back-end metric like customer retention. It is a statement the defines the effectiveness of your entire organization.

Measuring CLV Effectively 

So, how do you measure CLV effectively? Isolating that aforementioned ideal customer is the critical first step. How long has the relationship lasted? What is the average number of purchases in a month, quarter, or year? What is the average volume per order?

Your data needs to be enriched. The more data sets you use, the more accurate your CLV calculation becomes. Second, define the average customer lifetime across a given subset of customers. Is this lifetime longer or shorter based on the types of products or services provided? Are there any other differentiating variables such as geography or age group? Again, the more enriched your data, the more accurate the results.

Third, define customer retention rate. This is simply the number of customers who have placed additional orders over a given period. In this case, track retention rate by your sales cycle times. If your cycle times are a month or less, then tracking retention is easy. Sales cycle times of a month or more would have to be tracked quarterly or semi-annually.

Fourth, track your margin per sale. Ultimately, the amount of profit you generate for each sale is very important in determining the actual revenue portion of your CLV. It is the revenue total that makes CLV worth calculating.

Finally, define the value of any volume discounts provided to customers. This is critical because a discount should be removed from the revenue portion of the calculation. Rebates and reward plans go a long way to increasing customer retention so be sure to offer them to all customers.

Predictive marketingIncreasing customer engagement is the best way to increase data collection. 

CLV Calculation

There are multiple ways to calculate CLV. Ultimately, several calculations may help to average out your data. In this example, take the profit margin per customer multiplied by the customer retention rate divided by 1 plus the discount rate minus the retention rate. The calculation is summarized below.

LTV = margin per customer x (customer retention rate/1 + customer discount rate – customer retention rate)

The Importance of Accurate Data

It is obvious why the accuracy of your data is so important. However, it is not just about worrying about your profit per customer or your retention rate. Data accuracy is needed throughout the predictive marketing model. That means enriching your data across every customer interaction. Every action taken by a customer can be leveraged.

Every piece of data gathered should be used to define the longevity of the future customer relationship. Has a specific customer progressed through his or her journey in a manner similar to that of your largest customers? If so, what data points to that relationship being a long one? Is that data product-specific or tied into the type of inquiry? Backtracking your customer relationships will help you identify the markers that define successful long-term relationships. Understanding those markers and continuously enriching your data will allow you to predict future outcomes, which is the essence of predictive marketing.

Why Your Martech Stack Matters

Data gathering and enrichment are vitally important. Data management software is critical to a predictive marketing model. However, equally important is to ensure that your martech stack is up-to-date. How your customers interact and engage with your company is just as important as the data you gather. In fact, it is your martech stack that helps to facilitate that engagement. It must match the technology used by customers and common within your market. Developing a technology roadmap helps you outline existing technology uses versus any noticeable gaps.

Predictive marketing is a critical marketing method that helps you backtrack through history and pinpoint the markers that lead to your best relationships. You then take that information and duplicate your efforts.

ReachForce helps marketers increase revenue contribution by solving some of their toughest data management problems. We understand the challenges of results-driven marketers and provide solutions to make initiatives like marketing automation, personalization, and predictive marketing better. Whether you have an acute pain to solve today or prefer to grow your capabilities over time, ReachForce can unify, clean and enrich prospect and customer lifecycle data in your business, and do it at your own pace.

To learn more about how ReachForce can help you optimize demand generation and your impact on revenue, get a free data assessment and get a demo today.




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