How Dynamic B2B CLV Outperforms Traditional CLV In Optimizing The Sales Efforts

A Leading Technology Company Specializing In B2B Products For More Than 1200+ Customers.



Calculated accurate financial projection empowering client to make intelligent business decisions regarding: margins, profitability, historical data, and future needs.


Identified high-risk/high-value customers, resulting in the retention of millions of dollars in lost sales.


Devised an easy and intuitive cloud-based tool that was integrated with existing systems. Tool is now used by the marketing and sales team to personalize offers.


Accurately define the Customer Lifetime Value, as well as provide insights into key focus areas that will influence and encourage customers to explore more options.


Empower independent decision making, by training internal team to model source code, functionality, and model refresh procedures.


The client offers 350+ technology products to business customers. While sales volume was modestly on the rise, they could foresee many new challenges; namely discount e-commerce that stood to threaten their margins and profitability-even if they could maintain or increase sales. The combined modest sales growth, competitive price points, and budget reductions compelled the client to:

1. Reassess the potential lifetime value of their business customers.

2. Align sales efforts and take a more strategic approach to client acquisition.

3. Optimize the pricing strategy to balance value and quality.

Misclassifying consumers, put our client at risk for millions of dollars of financial loss. The client was searching for a dynamic and proactive analytics tool-that could be easily integrated with their current systems.


Realizing the importance and impact of the project, Dextro Analytics kick-started the engagement with a 2-day workshop focused on building a structured and strategic problem-solving framework-with the goal of rapidly understanding existing models, data, and technology-and then identifying knowledge gaps and challenges. Dextro discovered that client was using static and traditional way of calculating CLV – keep customers who show positive NPV and ignore the ones who don't.

1. Due to high variations among business customers, decision and data engineers segmented the business customers based on their profile, behavior, firmographics, and crucial web and survey data.

2. Built in-depth consumer profiles, detailing the varying needs at all stages of the consumer/business life-cycle.

3. Being that B2B transactional data occurs less frequently, and is generally less detailed than typical B2C data-traditional regression-based techniques often over-estimate earning potential. Considering the unique type of data, decisions engineers adopted a Markov-Based Modeling approach to accurately estimate the potential earnings from each customer at both the overall and segment levels.

Expected profit from a customer at time “T” in the future was predicted by hypothesizing a Markov model. Discounted cash flow models were then used to calculate the Net Present Value for each customer.

4. The model also integrated data around willingness to pay, consistency of requirements, volume, ARPU, margin, profits, and tenure.

5. Additionally, decisions and data engineers built an eco-system of adaptive self-learning. Consumer transactions, and ongoing input from sales team would automatically refine the model to reflect the most current insights and analytics.


• Predicted CLTV scores along with NPV and focus areas across 98% business customers.
• Provided an Excel and Cloud-based CLV tool for the marketing and sales teams.
• Identified potential $1.2 million in new revenues from currently under-served target audiences.
• Improved the accuracy of existing models by 300%.

“Best-in-class methodology to estimate the CLV. Highly innovative”

- - Chief Marketing Officer


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