It’s time for sales to catch up to our ongoing workplace evolution, using automation, coaching and experimentation.
September 21, 2018 6 min read
Opinions expressed by Entrepreneur contributors are their own.
Over the past few decades, workplaces have adopted new technologies that have drastically changed the way we work. With the integration of computers, robotics and now advanced machine-learning and AI capabilities, teams today are operating in ways we’ve never seen before — except when it comes to sales.
Many of us see selling more as an old-school artform than a structured process. Veteran sales reps tend to focus on subjective, “tried-and-true” methods based on brute force, intuition and mental toughness.
But it’s time for sales to catch up to our ongoing workplace evolution. Introducing data science into the sales industry on a widespread basis could open organizations up to more efficient and effective customer interactions and a whole new way of approaching sales.
In particular, there are three areas that could be optimized through data science, enabling sales teams to transform their selling process from an art to a science. Here are those areas:
Despite the common misconception that automation is a job-killer, its truest purpose is to make our jobs easier and more effective. Automation allows you to spend more time on what you’re good at and waste less time on manual, boring, low-reward tasks.
Today, sales reps spend an average of 64 percent of their time focused on non-selling tasks, according to a Salesforce blog. That’s a lot of wasted time, which annoys the heck out of sales reps and results in missed opportunities. Unanimously, this is the part reps hate most.
Whether they’re poring over spreadsheets, sorting through an unorganized inbox or managing prospect information, they’re stuck doing administrative tasks instead of building long-lasting relationships. The key to solving this problem? Automation.
While some manual tasks — like scheduling, emailing or logging phone calls — can be automated in a straightforward way, many others require data science techniques, such as natural language understanding and machine-learning. One way data science can help here is keeping prospect information up to date.
According to a study by ZoomInfo, 10 percent to 25 percent of B2B database contacts contain critical errors, such as missing or incorrect phone numbers. Through machine-learning, this could become a problem of the past. For example, I recently worked on a model that automatically analyzes prospects’ email replies, identifies their signature and extracts updated information, such as new phone numbers. Not only were we able to save reps valuable time — the model often found new information that reps miss.
Automation takes care of tedious, yet essential tasks and allows sales reps to devote more time to what they do best: selling and focusing on their prospects, ultimately driving their company’s bottom line.
In addition to automating non-selling tasks, data science can help reps be more effective in selling, by providing real-time guidance and identifying coaching opportunities. Research reported on the Salesforce blog has found that high-performing sales teams are 2.3 times more likely to use guided selling than under-performing teams.
One example of this is the move to help reps better handle objections. Salesforce’s State of Sales report showed that leaving sales objections unaddressed often leads to deals falling through. In contrast, reps who handle negative emails well have the best performance metrics.
Learning how to handle objections well, however, takes time and practice. While experienced reps slowly get better at it, new reps typically do poorly. I’ve found opportunities to solve this problem by
- using machine-learning to automatically identify emails with specific types of objections
- collecting responses from top-performing reps able to turn those objections around
- automatically presenting the best-performing template to the rep facing that specific type of objection.
Being able to automatically identify objections also helps to more accurately measure the performance of email templates. Typically, the reply-rate metric is used as the main measure. However, if most of the replies are “unsubscribe” requests, this metric can be misleading. Counting only non-unsubscribe replies, and further categorizing them into positive replies or objections, provides a more complete way to evaluate the performance of email templates, helping select better-performing ones.
Data-driven features give sales reps clear next steps for tough situations and provide real-time suggestions for an improved response that can result in more closed deals.
While each sales rep has his or her own flair and personality, every sales team follows a specific message that best represents its company. In order to share this message, organizations typically put together a playbook with best practices and key talking points. These “best practices,” however, are often based on anecdotes and gut feeling — not on evidence from data. So, how can sales managers accurately measure the success of their guidelines and understand how to improve on them, leading to increased performance of their team?
The answer is experimentation, aka A/B testing, which can reveal the precise relationship between the idea being evaluated and changes in key metrics we care about, such as positive replies, scheduled meetings and qualified opportunities.
In one related experiment, my team wanted to know whether adding a video link to an email helped improve the reply rate. We designed an experiment where we compared two email templates. Both were short follow-ups where the prospect did not reply.
The first template had a video link; the second template did not. When we asked sales reps which template they thought was more effective, the opinions were split, with more reps voting for the template with the video link. However, experiment results showed that the template without the video link had double the reply rate of that with the video link, a clear and highly statistically significant result.
The lesson from this example is that, while well-intentioned, reliance on intuition can often be harmful to a team’s performance. By opening up your organization to experiments, your teams will be able to rely on scientific knowledge when evaluating competing ideas. This will encourage innovation and create a culture of continuous performance improvements.
In an industry as crowded as sales, teams are searching for options that drive their organization’s goals across the finish line. By investing time and resources into data science, sales teams will find themselves crushing their sales goals and developing long-lasting client relationships.