THE PROBLEM

When organizations want to derive an optimal price for a specific product, they are heavily reliant on utilizing the traditional pricing methodologies, namely, cost-based, value-based, and competition-based; thus experiencing internal struggles usually inflicted by sales managers, brand or marketing managers, and finance managers; these individuals believe that the optimal price should be based on what suits their internal departmental targets, therefore ignoring the bigger picture.

WHY IT MATTERS

Like most companies, this company relied on historical data to categorize their clients; and while history can be a predictor of the future, classifying clients based on this miss out on the importance of client potential. Analyzing the previous purchasing habits of a client is important, but it is not as important as assessing potential. When you offer the best price, terms, limits, and attention to a client who has reached their maximum potential, you leave money on the table in the form of opportunity cost. Taking credit limits for example, the company’s risk and credit departments set a fixed sum for all the credit the company can offer. Then, this sum is divided across clients based on their classification; the higher the classification, the higher the credit limit offered. This means that smaller and/or newer clients receive much less credit support from the company. Which forces clients to either remain unable to compete or to switch suppliers, both of which are unfavorable for the company.

On the other hand, figuring out how to fairly and properly assess and reward clients will establish the company as a trusted partner to clients. In return, this will lead to an increase in brand loyalty, which will allow both partners to scale up together. Therefore, we set out to validate our hypothesis. If it were to turn out valid, the company would be facing many challenges, some of which are highlighted below:

THE SOLUTION

To rectify this, HEED decided to rethink the current client segmentation by profiling accounts based on an evaluation scorecard that allows the company to categorize and prioritize clients quickly and accurately. To begin, we grouped the factors that are most important to both the manufacturing company and its clients, and weighted each factor as follows:

Revenue Figures

This category depicts the revenue, in dollars, each client generates to the company, in addition to the payment habits of clients. Clients who regularly settled dues in an orderly and timely manner (based on daily sales outstanding) received a higher score.

Product Basket

This category evaluates the types and variety of products purchased by a client. Clients who regularly purchase value-added products (usually with higher profit margins) with larger quantities and higher frequencies received higher scores.

Client Loyalty

In a “traditional trade” manufacturing company, relationships are crucial, and those who have regularly stuck by and promoted the company were rewarded. This category includes tenure with the company (calculated from the 1st to the last order purchased), relationship (determined subjectively by the account manager and refers to the overall attitude the company has towards a specific client), and the degree to which a specific client is an ambassador for the company (determined by ranking clients on the scale of 1 to 4, 1 being an approved vendor and 4 being a trusted partner).

Independent Client Situation

This factor is determined and controlled by the clients rather than the company; and refers to the current reputation the client has and their current size in the market, regardless of their volume of business with the company. This is a factor based on a client’s potential for success with the company.

Once the business requirements were clearly defined, we collected the required set of data from the various available sources and integrated our methodology with the company’s ERP system to calculate the results. Ideally, we wanted to categorize the clients into 4 groups while also adhering to the 80/20 rule; then, we wanted to determine how often a sales executive should visit a client; thirdly, we needed to determine different price points and credit terms offered to reward clients for their loyalty; and finally, we needed to determine a formula upon which credit limits were set. Therefore, we categorized clients into 4 classes, “A” through “D” as follows:

As for the credit limit formula, the limit clients should have received is based on their class as well
as their purchase behavior and is calculated by the following formula:

THE BENEFIT

Implementing these changes took time, however, once the dust settled the new system proved itself worthy by creating a ripple effect that touched on a multitude of issues including, but not limited to:

• An increase in

Revenue per Customer

Total revenue

Total number of units sold

Customer lifetime value

Client retention

Client loyalty

• Fairly rewarding customers according to their commitment, behavior, and potential.
• Providing better clarity of the current standings of accounts, their order and payment
  schedules, and their likelihood of defaulting on credit.
• Enhanced forecasting due to the ability to predict the timing and sizing of a client’s next
  order.
• A more accurate routing strategy for sales representatives.
• A better division of the salesforce with a clear succession plan that rewards better-performing salespeople with a higher level of clients.