If you are like most organizations, your sales team consists of primarily “B” and “C” players along with a handful of “A” performers, or those reps that consistently meet or exceed quota and do so profitably. Imagine for a moment that all of the B and C players could sell profitably at the same level as the A players. What impact would this have on your top and bottom line? No doubt it would be huge, but can it be done?
The key to achieving this kind of impact is enabling all reps to know which customers to call on, which products to talk about and what price to quote through the use of predictive guidance. Many B2B sales reps sell from a catalog of tens or even hundreds of thousands of products, and it’s not uncommon for them to each manage 100 or more accounts. The most experienced sales reps are likely to have a better idea of which products make sense to cross-sell into a given account and what price to quote, but likely only for less than the top 20 percent of products and their accounts. New and less experienced reps are overwhelmed with by the sheer volume and complexity of the decisions, making it difficult to get up to speed.
The fact is that in many B2B industries, a significant percentage of experienced sales reps will retire in the next several years, creating a big knowledge gap. The “tribal knowledge” sales teams have relied on to hit their numbers will begin to disappear. However, by delivering predictive guidance directly to sales reps, companies can provide the answers sales reps need without requiring tribal knowledge, effectively leveling the playing field.
Two of those questions reps need answers to are which customers to call on and what products to talk about. Given their large books of business and limited amount of time, sales reps need to spend their time with the customers most likely to buy more from them in order to hit and exceed quota. New and less experienced reps are largely flying blind when it comes to this given the sheer volume of products and accounts. What if each rep, whether experienced or brand new, knew the accounts with the highest probability to buy more from them?
Using a company’s transaction data, predictive models are able to determine what “ideal customers” look like in terms of spend, mix and breadth of footprint across the product catalog, then match all other customers to the appropriate purchase pattern profile. This reveals where whitespace exists, which is precisely where sales reps have the opportunity to sell more.
Once a sales opportunity is identified, whether or not the sale is profitable hinges on the sales rep’s price decision. Consider this: If every one of the B and C performers could take every deal [Read more...]