Part 2: Three Ways to Improve Sales Performance

November 20, 2017 Barrett Thompson

In this two-part blog series, Zilliant General Manager of Commercial Excellence Barrett Thompson shares his insights on how to improve sales performance in three areas: on-boarding new reps, up-leveling current reps and empowering all reps to maximize customer lifetime value.

In the last post, we discussed two main areas ripe for sales performance improvement: when a new sales rep is hired and up-leveling the performance of all your sales reps. While it can be difficult to turn your mid- to low-level performers into sales stars, you can mine the pricing intuition of the very best sales reps using artificial intelligence. Their good decisions are baked into past deals. A scientific, analytical approach to pricing can coax out that intelligence and disseminate it to the team and increase their overall performance.

Looking at Data: Identify Opportunities to Price Smarter

At this point, you may be asking yourself, “Using artificial intelligence to give my reps price guidance sounds nice, but does my team really need it?” It’s a fair question, and a natural one. To that I respond, “Let’s look at the data!” What we observe in the data, again and again, are two key pricing phenomena: misalignment and groove pricing.

Example 1: Price Misalignment

Take a peek at the graph below. It’s a classic example of misalignment. More often than not, when sales reps are out in the field pricing off-the-cuff using gut feel and intuition, the actual prices that customers pay don’t actually match up to what you might expect. Here you can see that small customers have received lower pricing than large customers.

This phenomena isn’t unique to one customer or product segment. We see this across the board in massively complex industrial B2B companies with thousands of products and customers. With so many moving parts, it’s alarmingly common for small customers to pay less than large customers, or for premium products to sell for less than lower-tier products.

price misalignment


Example 2: Rounding Prices 

Another example is the tendency of reps to quote in increments of five points of margin when using a “cost plus” pricing approach. The graph below shows this well. It’s human tendency to round up and down when quoting customers, but it’s not very accurate or profitable.

It also points to another unsavory habit:  Sales reps gaming the system; discounting just below levels that require a high-level approval so as not to trigger a review. In the specific example below, the company had a rule that discounts of more than 18 percent required manager approval. So, reps would give customers a 17 percent discount, knowing it would be automatically approved.  

Companies stand to quickly improve the margin performance of all sales reps (new reps and lower-level performers alike) with advanced pricing science. It uses a company’s own set of transaction data, applies artificial intelligence-driven algorithms to it and delivers market-aligned pricing to sales reps in the tools they are used to, including, CRM, ERP, homegrown or otherwise. Taking this approach removes the subjectivity and tribal knowledge perfected by your best reps and infuses it as smarter price guidance for all reps. 

Read the Zilliant Artificial Intelligence B2B Benchmark Report to discover more pockets of uncaptured revenue and profit. 

About the Author

Barrett Thompson

Barrett leads the Business Solutions Consultant team, aligning Zilliant solutions to customer needs and promoting pricing and sales best practices among our customers. Over the past two decades, Barrett has built and delivered optimization and pricing solutions to Fortune 500 businesses in diverse vertical industries including: building materials manufacturing and distribution, industrial components manufacturing, semiconductor manufacturing, office-supply distribution, hardware-software distribution, pharmaceutical and medical-device distribution, telecommunications, and multiple travel & transportation verticals. Barrett received both his Bachelor of Science in Applied Mathematics and his Master of Science in Operations Research from the Georgia Institute of Technology.

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