Excessive discounting has been wreaking havoc on software pricing as of late. A combination of pricing pressure on incumbents from disruptive startup software players, and cultural sales emphasis on closing deals at whatever cost, have contributed to an increasingly discounted B2B software landscape. But are lower prices actually driving more sales?
According to a recent NewtonX survey of 210 sales managers and executives, the answer is no, not necessarily. Contrary to sales lore, discounts are not always the strong incentives we think they are, and could often be substituted with other benefits. But telling that to a Business Development Representative trying to close as many deals as possible in pursuit of commissions is more easily said than done. Which is why, recently, advanced analytics tools have emerged to give comparison points with colleagues based on data about prospective deals such as company size, industry, and the quality of a proposed deal, in order to guide discount pricing.
Can Big Data and Analytics Rescue Falling Margins?
Historically, the solution to over discounting in sales teams has been a more rigorous review process. However, 87% of respondents to the NewtonX survey indicated that review processes are ineffective for two reasons: the first, is that they create a bottleneck in the approval process which slows down deal closing, and the second is that managers can be biased as much as their BDRs, and may not catch a deal that is out of sync with pricing for volume, market, etc.
Advanced analytics solutions that integrate with CRMs provide a solution to this problem by giving representatives relevant data a proposed deal and comparison points to their colleagues who made similar deals. The data can give a snapshot view into the quality of the deal based on the proposed percentage discount, and helps bridge the gap between corporate objectives and on-the-ground sales realities.
Most sales teams have a static version of deal scoring that guides discounts based on various factors. However, traditional approaches usually lack two things: scalability and adjusted scoring based on the introduction of new factors (a new product release, competitor activity, etc.). A senior member of the a sales team at an HR software provider put it this way: “There’s a tipping point when the sales team becomes large enough that not every member is aware of the presence of other deals in the pipeline. That’s when just having a spreadsheet or a graph becomes inadequate, and you need a smarter solution.”
The Big Problem With Using Big Data in Sales
It’s an old saying in computer science: garbage in, garbage out. In other words, if you feed your algorithm bad data, you will get inaccurate or problematic results.
Using analytics to drive internal pricing and discounts often surfaces this same problem: if you have a systemic issue with your sales team over discounting, giving individual sales representatives insight into how their peers are pricing similar deals won’t help them discontinue their inordinate discounts.
There are a few ways to fix this: one, is to bias the scoring system in favor of those who make sales with lower discounts, so that when a BDR checks their proposed deal against those of their colleagues, they see the curve moved to the side of lower discounts. One sales manager at a 67-person sales team anecdotally noted, however, that when she did this in her company’s CRM, the sales representatives caught wind of it, and began to ignore the scoring system altogether, assuming it was a motivating tool rather than a true indicator of their performance in relation to their peers.
“One can assume that the higher end of the spectrum for similar deals is what the market is prepared to pay,” explained the sales manager. “But if most of the sales team wasn’t making sales within a standard deviation of this target, the representatives didn’t buy into that being the actual market value of the product.”
Another potential pitfall of using analytics for deal scoring is the algorithm could end up being gamed by representatives. For instance, while it’s natural that there will be higher discounts toward the end of a quarter, if the algorithm uses this data to inform future guidance then a self fulfilling prophecy occurs. The BDRs could then delay closing deals until the end of the quarter when they can give greater discounts and optimize their commissions.
The Next Frontier: Hybrid Sales Combining Human And AI
When asked what the most effective way to get representatives to stop over discounting would be, 58% of respondents said “bonus incentives”, while only 12% of respondents said “prospective deal pricing” (9% said “not sure”).
For pricing guidance to impact behavior, it will need to be implemented in conjunction with monetary incentives for representatives to not give large discounts. The head of Sales at NewtonX described the interplay this way: “A good sales person only uses a discount as the final push to get a prospect over the line. But if you don’t have incentives to wait until you’ve tried everything but a discount, sales representatives will focus on hitting a sale over maximizing margins.”