When people talk about using customer analytics, they tend to cite the same common obstacles: They don’t have enough data, they don’t have the right data or the data they do have isn’t clean enough to be actionable.
While these may be valid concerns, there’s no reason they should stop you from analyzing your customer data to develop an effective pricing strategy.
Here are three common myths about using customer analytics — is your company currently treating them as facts?
Myth No. 1: Your data isn’t good enough (yet). The pursuit of perfect data often leads to paralysis, and prevents companies from using serviceable data to achieve useful insights. Achieving some visibility is better than nothing when developing your pricing strategy.
If your company currently has an effective system for sending out an invoice to a customer and receiving payment for it, you have enough data to start pricing analysis. Just by invoicing and collecting, you have information on price and purchase history, purchase frequency, channel, payment terms, product mix, etc. You might want or need additional data fields to do more sophisticated analysis, but that doesn’t mean that you can’t get started analyzing and segmenting your customer base today and develop a roadmap to collect more data in the future.
Myth No. 2: More analytics and more data are always the way to go. Many companies start a customer analytics project wanting to create charts for everything, using all of their data. They soon realize that this approach is like trying to boil the ocean. Instead, focus on the top 2 to 3 problems that you want to solve as opposed to the charts that you want to see. This will help companies prioritize what analytics are most important and enable them to link value to those charts.
For example, let’s say your goal is to find out which customers are underperforming (in terms of price or margin) when compared to their peers. You will want to develop scatter plots displaying margin and volume to uncover differences in profitability among customers driving similar volumes.
You can than focus on a single customer and deep dive on the root cause of margin loss by looking at the customer’s profitability waterfall. This view will uncover discounts, rebates, special services and other factors that may be leading to profitability loss. With this approach, you are finding opportunities to capture value using just two charts instead of creating a laundry list of visualization aids.
To avoid drowning your employees in data, develop an issues roadmap and identify the type of charts and reports you want to you to solve these issues over time. This allows you to expand your use of customer data over time as your team becomes accustomed to using a problem-solving approach to analytics and report development.
Myth No. 3: Your price waterfall must be defined from Day One. The price waterfall is one of the main analytics used in price optimization. As it takes you from the price build to the list price for a product and down to the pocket margin, it shows where the company is making and losing money on a customer. Analyzing the price waterfall might show you when you’re spending too much on freight for a specific customer, for example, or why an account costs a lot to serve due to customer care, rush orders, etc.
Even if your data isn’t perfect, it’s important to get started with your price waterfall. The best approach is to begin with a handful of benchmarks: starting price, discount amount, variable cost and fixed cost. From there, you’re able to build out that waterfall over time. For example, in the beginning, you might use standardized costs, but then switch to customer-specific costs for greater accuracy as data systems become more adept at mapping those costs back to the customer.
It’s easy to get overwhelmed by customer data and let these myths about analytics get in the way of developing a great pricing strategy. But the key is to get started where you are now with what you have. Getting started pushes you to set and prioritize business goals as well as identify the analytics needed to gain insight to achieve those goals.
As you refine your price waterfall and become more sophisticated over time, these issue roadmaps and analytics inform your data strategy, helping you identify areas to improve and prioritize data work.