When I read that only 57.1% of sales reps met their quota in 2015, according to CSO Insights, I knew that the industry was ready for a revolution. Because, let's face it. 57% just isn't good--barely better than a coin toss. It isn't good enough in today's world where the intense pressure to drive revenue performance, shareholder value, and competitive advantage is higher than ever before--for fast-growth emerging companies, large public ones, and all the businesses in between.
If we all did 57% percent of our jobs, then we would all be fired--which is often the case with salespeople who fail to meet their numbers. But is it really all their fault? Is there something that can be done to increase those odds and create more efficiency and predictability in terms of individual performance and the overall sales forecast? Despite all of the investments made in CRM systems, business intelligence (BI) tools, and other sales productivity tools, the number of reps meeting their quota has only risen 5.3%, from 51.8% in 2009. After 7 years of waiting for these technologies to live up to their promise, I think we can safely say that they have failed to deliver markedly better business performance.
Businesses not only need measurement tools to accurately assess the effectiveness of their sales organizations, but they also need tools that can improve and optimize sales performance and have a direct impact on business outcomes.
So, what's the one thing that sales leaders are missing? Predictive analytics.
Get Out of a Blockbuster Mentality
Think about it this way. Remember when we used to go to Blockbuster and choose a film to watch? We walked the aisles looking for a movie that looked appealing, had an actor that we liked, or heard was good. Maybe we asked the store clerk for advice. Sometimes it worked out and sometimes it didn't. Another coin toss. Then, Netflix came along online and at first, they were a catalog of 100,000 titles (kind of like library card catalogs) and you still had to browse through, hunting and pecking to find what you were looking for. What really changed the game was Netflix Recommendations, which leverages machine learning and statistical techniques to find relevant patterns and to make your search far more efficient, useful, and relevant to your particular tastes. I don't always watch what Netflix recommends, but I pay attention to what they recommend and it's certainly far better than what I had before. (Now, the not so good effect is I am watching more content, instead of throwing up my hands, giving up on video, and picking up a good book, but I digress.) Now, imagine a world where we went back to the card catalog? Hard to do, right?
Sales is living in that world today--a world before Netflix Recommendations. Yes, your pipeline is online in a CRM and you browse and inspect it via drill down reports and dashboards. But, you don't have predictive analytics--a powerful recommendation engine that can discern patterns in your data that are hard to see and surface meaningful insights that let you focus you on which deals will really make a difference, which are at risk, and which need an extra push over the finish line. Instead, you spend your time in the sales equivalent of a Blockbuster, inspecting every deal in the pipeline, asking 20 questions of your reps trying to get clarity on what is real--by matching the current deal attributes with others from your past experience or with similar deals in the pipe.
Embrace Netflix Recommendations: Predictive Sales Analytics
Predictive analytics, or advanced big data technologies that reliably describe what is likely to happen, gives organizations the ability to take full advantage of everything we have learnt, of patterns that develop by luck or by skill. For example, when Netflix recommends a movie to watch, or Amazon recommends a product, these are examples of machine learning over a big data stream. Uber, AirBnB, Google, Facebook, LinkedIn, Amazon, Apple are all revolutionizing various industries and driving unprecedented economic benefits by leveraging big data technologies. It stands to reason that business operations can be transformed similarly.
Why aren't more sales organizations employing these powerful tools? Some say that corporate sales leaders fear accurate measurement of corporate sales productivity investments may reveal program weakness. Isn't that the exact reason to find a better way to measure effectiveness? If not, then sales interventions are lost opportunities to strengthen the organization. Other barriers include not knowing where to begin or fear of a lengthy and costly implementation and ramp-up period. My advice, push past it and discover what you don't know, what you need to know, what your competitors know.
The data already exists and it's just a matter of tapping into it, such as knowing exactly what sales teams are doing at any given moment during a sales cycle, how they spread themselves too thin across too many deals, how much time is spent dissecting the deal instead of on the right activities and on the right deals.
Predictive Sales Analytics Changes the Game
How do you assess what predictive analytics can do? The technology behind sales forecasting platforms leverage all the activity data around sales from CRM systems as well as from email, calendar, social media, and other sources. That data is harvested and used to build a sales organization's big data stream. Then, based on that data, analysis is performed using proprietary machine-learning algorithms and patterns emerge about prospects, reps, products, channels, and geographies. These patterns are then turned into recommendations (like Netflix ) which take into account the skill, experience, and judgement of the salesperson and helps prioritize the right deals to work on.
Just like Netflix does not guarantee that you will watch the movie it recommends, but saves you a ton of time and effort in going through a vast catalog; similar techniques can save a lot of time consuming and tedious conversations in forecast and pipeline review calls intended to dissect the sales pipeline and figure out what deals are the ones that matter. These techniques scale easily and are personalized all the way to the individual salesperson. This saves time, effort, and focuses the conversation on the needle movers.
So, what makes predictive sales analytics platforms better than any other BI tool or other analytics tool out there? How do we empower the other 43% of sales reps to meet or exceed their number?
Very simple. Let them focus on what they do best--selling and leverage machine learning to free them from those tedious forecast and pipeline review calls so they have more time to make and exceed their quotas. And, by the way, don't be surprised if the 'other' 57% blow out their quarter too.
K. V. Rao is the founder and CEO of Aviso, producers of sales analytics software that helps sales teams optimize their performance and exceed revenue goals.