June 7, 2011
While getting beaten yet again at my Thursday night poker evening I started thinking about how the latter stages of a poker hand are really a series of negotiation rounds between two parties who are trying to maximise the outcome with limited information. In other words, similar to the pricing negotiations which occur in a B2B sale.
In Poker games, there is often much emphasis on reading the other players’ expressions and mannerisms, these are called the “tells”: this features heavily in Casino Royale. Players try to control their expressions and mannerisms as they look at their cards or bluff on a pair of fours; often they mask their features with hats, sunglasses and even beards so that ESPN’s late night poker show looks like a ZZTop convention.
However, there is a lot more information potentially available than just trying to watch for the twitch of an eyebrow. Players reveal their intentions by their bets, when they raise, when they stay and when they fold. This is empirical, objective information. As I watch more hands from the same player I can assess what they are likely to be holding based on their betting activity in that hand.
I accumulate knowledge by then aggregating that over a series of hands. And this, my fellow pricers, is what we should be doing with the B2B negotiations which occur every day in our sales teams.
In B2B, within a price setting cycle, negotiation takes place over a series of cycles. The customer doesn’t usually pay the list price or RFP price, and we don’t usually accept his initial request for discount. During the cycle more information is uncovered by both sides (equivalent to the “flop” being revealed in poker) and the deal is either won or lost (“see” or “fold”). If we could systematically mine this information through price analytics we could learn about our sales people and our customers negotiation styles, leading to more favourable outcomes.
If we look at most companies ERP assets, they are generally weak in this area, one may know the list price and the final price. But what happened in between? CRM users may fare somewhat better as they may record the intermediate prices (customer’s offer, our response, price after approval) and the other terms of the deal which may have been traded (payment terms, delivery, freight). If this data could be assembled and mined, we could start to generate analysis of the following metrics:
What is the “gradient” of the discounts offered over the sales cycle: steep followed by shallow, or the reverse
What discount is offered by different members of my sales hierarchy, and does this affect deal outcome (win/loss)
For example, focusing on the former, I could come up with a simple two-by-two classification of my customers and make this available to my sales team to assist them in their negotiation strategy.
An example from my poker nights: I suspect that Dan often folds immediately on receipt of two bad cards, but once he is committed he seldom folds. In a similar vein, I want to advise my sales team that a customer is “shallow-steep”( (i.e. small discounts early in the sales cycle followed by steep discount to close the deal) , so that the sales team knows to hold price longer than usual, so that the customer’s VP of Supply Chain can come in and be the hero. Even a simple indicator such as this provides an additional insight and may positively affect my margin or win/rate. Now if I could just stop the salesman’s eyebrow from twitching.