July Newsletter - Customer segmentation and the pricing cube

Executive Summary

In certain B2B markets such as construction materials and electrical supplies, customers buy a mix of products from suppliers. Improving the revenue achieved in such situations calls for a slightly different approach, as laid out here.

• Segment your customer base based on customer need
• Understand and measure all the tangible and intangible benefits provided to each customer segment on a ‘average basket or invoice basis’
• Tune your list price structure and discount structures to improve pricing

Introduction

Suppliers of material to industrial and construction customers either supply for projects or for maintenance and repair work. There is a slowdown in manufacturing and in new construction projects (certainly in Europe), and the competition between firms in those industries is very high. Buyers in those industries are changing buying habits. Some are using internet price comparison tools and reverse auctions. Some are using buying agents in different countries to exploit price differences of the same essential supply in different countries. All these issues have affected the average revenue achieved on every deal.

The marketing and sales teams in supplier companies need to develop an integrated deal based and individual customer approach to tackle such issues. This newsletter outlines an approach of how one might go about it.

Customer segmentation

There isn’t something extremely novel here, except that customer research and segmentation should be done not just per country but over entire geographical areas (perhaps even globally). By doing this, you will get an idea of critical buying traits and customer needs across the customers over the entire region.
Use external sources to get qualitative information on

• How other companies supplying to the same industries as you do perform customer segmentation across countries?
• How do they track and measure customer traits etc.
• How do they manage their pricing policies and structures?
• How much of revenue do they share with their channel partners?

Use your internal sources to verify and adapt these methods after comparing them with your own internal processes. This way you can benchmark and evolve your customer segmentation.

Now talk to your sales force in the geographic area to see

• What needs to their customers have in each customer segment from us? Are there any niche customer groups that we can explore?
• How are these needs different among various groups?
• What is the minimum discount that needs to be offered off the current list price

The ‘basket’

A basket can be seen as the most commonly sold set of products. If you were to look at your invoices – preferably in your data warehouse, you’d find that most invoices had more than one product. You could create the basket in several ways

• Take composition of the set of products that appears most often in the invoices
• Calculate the average composition of the set of products that appear in your high value invoices, mid value invoices and low value invoices
• Choose a basket composition that suits your business

Now ask your sales force across all geographic areas: Given three baskets of products – One having a lot of items, one having a less number of items, and one having very few items:

• What would be the itemized price for each of the baskets that they would submit?
• If they were allowed a range of prices, at what prices would they have a 20%, 40%, 60%, 80% and 100% chance of success of closing the sale?

Build a regression model of these responses – the price at which ‘mathematically’ the basket has a 0% chance of being sold, is the value of the basket.

Tuning the list price and discount price structures

Now that you have calculated the value of the basket, you need to find a way of distributing the value across the different products in the basket. You could use any way to do this. One way could be comparison – compare the different baskets to see differences in value ascribed to different product sets. Another way could be similar to that described in the previous newsletter (Linked here) –

• Figure out the demand curve of the product as compared with a competing commodity product. Build a regression model.
• The price at which there is no uptake of the product vis-à-vis the competing commodity product is the perceived value of the product.
• Add that perceived value to the price of the competing commodity product to get the value of the product.

Using information about list price structures in external industries, current list prices, the minimum discount required and sales manager inputs about the itemized prices of products in the different baskets, we can set up a list price structure according to geographic area, type of deal and customer segment.

Now we look at the data warehouse where you have the actual spread in prices of the different products in each invoice – which we can use as a “basket”. We find invoices that resemble our theoretical basket, and use the same “value distribution” method to distribute the price of the invoice. Thus we have a list price of the product and a range of “achieved prices” of the product. Now we use our judgement to set up a discount structure.

Eventually this gives rise to a pricing cube for each type of deal: Each cell of the pricing cube should ideally have its own list price and discount structures, but we could group cells together if necessary.

Here is a graphical representation of the pricing cube


To get a pdf version of this document, please click on this link here.