Every company has customers who are inconsistent in their buying habits. Either they buy too infrequently or they buy products that do little to move the profit needle. This wreaks havoc with inventory and revenue streams. Often, companies choose to tolerate or ignore these customers.
But what if these customers purchased higher-margin products? Would you keep them? The secret isn’t weeding out your customers. It’s about simplifying your business model and removing products that create complexity and reduce your profit.
With each new product or feature that’s introduced to keep up with trends or competitors, another level of complexity is added to the product portfolio. Sometimes this comes in the form of offering older products (soon to be classified for end of life) to customers at greater discounts, with lower profit margins. In this case, excessive overhead soon enters the equation, along with those associated costs — and the added work of managing customers and vendors.
Subtle design variations can create confusion over warranties and support, in addition to the issue of keeping track of excessive parts and components. Out-of-control variety impacts all business functions and customers. Too much product complexity ultimately compromises cost, quality, and delivery.
Analyze the data
Analytics can help you understand and address the complexity that’s generated at the intersection of customers and products — especially during sales transactions. Most companies have a wealth of data resources available to them. Using analytics allows companies to understand true product profitability and streamline their product offerings and sales processes to cut the long tail.
However, many companies fail to take advantage of this information because they lack a useable framework to analyze data for actionable insights.
The Pareto Principle — aka the 80/20 rule — suggests a strategy for handling large amounts of product and customer data. Using the Pareto distribution approach to classify products and customers based on profit and cost data lets you determine which customers and products account for the majority of your profit margin and growth. In the process, you essentially create the equivalent of an individual P&L for each product.
From there, you can use data science to extract transaction-level data. Having this data will allow you to define your options, such as where to increase prices or what products to eliminate. As you consider which products or SKUs to eliminate or replace, you can also determine where to make price adjustments to deliver additional value. The end goal is to guide the customer to an alternative product solution that will increase your profit.
An example in practice
Some car manufacturers are skilled at resolving product complexity. For example, Ford eliminated several sedan and economy models to focus on larger cars with greater demand and higher profit margins. Ford reduced its complexity by streamlining its fleet, effectively focusing on cars that are in greater demand and have higher profit margins.
Analytics can drive greater margins
Companies can use analytics to reduce product complexity. The key is in eliminating low-margin products or improving the way you manage them.
If long-tail buyers and complex product offerings are keeping you up at night, AlignAlytics can help you employ a smarter approach to managing your product offerings and pricing structures. Reach out to me below or or follow the author link.
Author: Patrick Mosimann