see if everything added up. This was a major drain on time and motivation. This level of automation was also a game-changer for sales leaders. They could easily analyze data across teams, channels, territories, and more to see who was excelling, who needed ad- ditional coaching, or how bundles and SPIFs could drive short- and long-term goals. As the system easily integrated with complementary sales systems, such as CRM and CPQ, sales reps and leaders alike had a clear view into their customers, from the initial point of contact through to the final sales. During our first 10 years in business, we amassed hundreds of custom- ers from all over the globe – many outside the traditional sales realm. Our customers began using vari- able incentives to drive performance across everything – from truck drivers and bank tellers, to wedding planners and retail workers.
But then a funny thing happened… The Rise of Big Data
Our customers had come to rely on our technology to automate and optimize their compensation initia- tives. But we were sitting on top of a valuable asset that we had not yet considered: Huge amounts of data, amassed right under our feet. In 2014, we began to analyze the multiple terabytes of information that had been accumulating in our SaaS systems for the better part of a decade. We’re talking about billions of incentive transactions each month, and billions of dollars in incentive compensation payments. What we learned from this data is astonishing. For example, we saw that 79 percent of SaaS sales representa- tives were missing their quota – but why? One correlation is that many companies have been making incen- tives too complicated, with too many targets. These complications can confuse and distract sales reps. We could also see how the mix of base pay and variable compensation impacted rep performance and reten- tion. This is all based on real-world data from real sales reps – not the “gut
“ We purchased Xactly Insights to enhance our executive reporting and validate our strategy with benchmarking to assure that we could recruit, retain and properly reward top sales talent.” – LEEANN BENAVIDEZ, VP SALES OPS – AMERICAS, HYATT
check” theory or non-empirical survey data on which our industry has relied. By considering these factors (and countless more), customers were able to create a quota and pay mix that not only better engaged reps, but drove them to reach their full potential. We have always recognized the
relationship between a strategically motivated sales team and business success, especially in an environment where the pace of disruption con- tinues to accelerate. What we never knew or had, until now, was empirical proof of compensation best practices and how they can transform a sales organization. Sales compensation has certainly evolved over time, but we finally had the revolution we des- perately needed.
For companies hoping to compete and win in this market, there is simply no turning back.
Big data holds the answers, but
an archaic approach to compensa- tion, managed in spreadsheets and siloed homegrown systems, has made that data impossible to reach. Today, advancements in analytics and cloud- based, multitenant systems have finally opened new avenues for companies to inform, measure, and benchmark their spend and performance against similar companies to identify new inefficien- cies and opportunities. To put this all into perspective, one of our first customer engagements leveraging this new big data intel- ligence was a Fortune 100 SaaS com- pany. Using the data, we compared the company’s compensation plans against other organizations of similar size and industry. The result? We were able to pinpoint significant problems in the company’s incentive compensa- tion model that resulted in savings of seven figures annually. Seven figures. Using 10 years of real-world compensation data, we are able to
anonymously compare what actually works and doesn’t work to drive per- formance across organizations. We can also identify what best-in-class companies are doing differently to create true behavioral changes and results. For the first time, sales lead- ers can ask pointed questions about their sales compensation initiatives, and get real, empirical answers to queries, including: • How does my compensation spend stack up against other companies my size?
• How much is being spent in base pay versus variable compensation?
• How are my top performers deliv- ering against the top performers of other companies in my industry?
• What number of plan metrics drives the best sales success? Equally as important, the data is able to “red flag” risks that not only help companies reduce costly errors and overspending, but also help them understand how to tweak incentive compensation to inspire a new level of engagement and productivity from their sales teams. Let’s put this into dollars and cents. A 10 percent lift in quota at- tainment for an average SaaS rep is around $46,481.52. If you adjust your compensation levers to save just 10 percent of that cost, it equates to $120,000. That’s just one rep. Spread that over 1,000 enterprise sales reps and that 10 percent translates into $120 million dollars in savings. Imagine how you would rethink your sales compensation program if you had access to similar industry data. What adjustments could you make to improve morale, retention, and perfor- mance? What are similar companies in your space considering that you are not? The answers are out there. As a sales leader, you simply cannot afford to ignore them anymore.
SELLING POWER MAY 2016 | 27 © 2016 SELLING POWER. CALL 1-800-752-7355 FOR REPRINT PERMISSION.
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32