A few weeks ago Pragmatic Pricing talked about having the quoting and invoicing systems necessary to execute price segmentation strategies. Another fundamental capability you need is systematically collecting the right data. In this era of “big data” this shouldn’t come as a surprise, but unless someone has consciously sat down to determine what data to collect, it’s very difficult to use this information for price segmentation.
Each of the four techniques for price segmentation requires a different set of data.
Customer characteristics – In order to charge different customers different prices, you need to know the customer types. This information is best collected in whatever sales automation tool you are using and stored with your customer master. The types of data you want to collect are things like region (geography), size of company, industry(ies) they serve, and type (commercial, government, non-profit, educational). This is certainly not an exhaustive list. You want to gather data for any characteristics of your customers that you think may indicate their willingness to pay.
Transaction characteristics – We can charge different customers different prices based on what we know at the time of the transaction. Of course we can only do this and analyze it if we systematically collect the information. The best place to gather this information is in your quoting and/or order entry system since you need to capture this for every transaction. The types of data you want to collect are things like requested lead time, season, weather, budget cycle and many more. Anything you can think of that may help indicate how much your customer is willing to pay you should collect.
Behaviors – This type of price segmentation is when you put a hurdle in place to force people to show you they are price sensitive. The typical method is a coupon, but you can consider periodic short term price discounts, or discounts for other reasons where your buyers may expect them to happen in the future. The data you need to collect is the performance of the pricing tactic. For example, if you are using coupons, did sales spike? Can you determine if the customers who used the coupons were new customers or existing ones that likely would have paid full price? The better you can decipher what happened the better decisions you can make in the future. Can you learn how individual buyers made their decisions?
Products – Of course we can use products to drive price segmentation. The classic technique is good, better, best. The data you should be collecting is what are the customer and transaction characteristics of those that purchase good and best. What you may find is there are some market segments who purchase best more than the rest of your customers. This likely indicates the need to create a new set of products targeted at and priced for that specific market segment.
Once you have data, you can analyze it to help determine which attributes indicate how much different buyers are willing to pay. You can then use these attributes to drive your pricing models. However, if you don’t have the data, you’re just guessing. In today’s world of “big data” does it really make sense to guess?