Buyer Analytics

I was recently forwarded an interesting newsletter from Decision Path Consulting. The article focused on identifying the “cost to serve” major customers.

From a sales perspective the article asked these questions, do you have customers who:

• Place "rush" orders with less than your stated lead time?
• Change or cancel their orders after you've already started to pick them?
• Require special pallet configurations that force you to build their pallets manually?
• Refuse to accept your delivery of their order, if you show up five minutes late for your delivery appointment?
• Take unsubstantiated deductions?

Does this sound like your company? If buyers and sellers work together they could identify the cost to serve and remove waste. Decision Path offered some advice on how to get this done:

Some or all of the data necessary to determine cost to serve - order lead times, order changes and when they occur, arrival time versus delivery appointment, returns by reason code, and so on - resides in your transactional systems.  The challenge lies in transforming that raw data into the cost to serve each customer and, ultimately, into profitability by customer.  Business Intelligence can contribute toward cost-to-serve and customer profitability in multiple ways:

• Integration of data from disparate internal systems and external sources
• History and trending - the pattern of exceptions and special requests - to provide context
• Holistic view of the customer across all products, all its distribution centers/stores, and all its divisions
• Comparison across customers and to cost-to-serve and profitability targets