Applying Machine Learning to Sales

Applying Machine Learning to Sales

machine learning and sales

Organizations are beginning to see the benefits that machine learning and advanced analytics can bring to businesses, especially in relation to sales. According to MITSloan, “Executives at 168 companies with at least $500 million in annual revenue, 76% of respondents said targeting higher sales growth with machine learning, the kind of artificial intelligence software that continuously learns from big data and optimizes recommendations in real-time to sales staff.” Not only that, but the same study suggests that two out of five companies have already implemented machine learning in sales and marketing.

So what does the future of sales look like, and how does machine learning play into this future? Let’s first start with a definition of machine learning:

“Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don’t have to be explicitly programmed but can change and improve their algorithms by themselves.”

Today when most people think about machine learning they think of things like the suggested selection on Netflix, but machine learning isn’t new. In fact, there is a lengthy history of the development of machine learning. The difference today is how machine learning has broadened and become more commonplace because of its application to different industries. The expansion and use of machine learning have become more commonplace because of several factors such as more data collection than ever before, processing power is more affordable, and storage is increasingly inexpensive. With all three of these factors combined, machine learning is changing the way businesses evaluate their own teams and customers because of new insights that have been uncovered.

This blog will look at two positive outcomes in relation to sales and machine learning: analyzing the behavioral patterns of your customers and analyzing the behavioral patterns of your team.

Analyzing Behavioral Patterns of your Customers

Currently, machine learning is predominately used in business to customer transactions. Just think about all the websites that send you weekly emails with “recommendations” based on your previous purchases, or the shows and movies Netflix recommends for you to watch tonight. All of these recommendations are based on data collected from your previous interactions and purchases online. The algorithms take your online history and extrapolate what types of goods or materials you may want to view or purchase in the future.

Due to the collection of large amounts of data, sales representatives and managers have more information than ever before at their disposal about the purchasing and behavioral patterns of customers. The patterns that emerge give sales new insight into customer behavior that was previously hidden. Let’s illustrate this with an example:

  1. A website analyzes its customer’s behavior over time, and a pattern emerges that demonstrates similar customer behavior when it comes to purchase time.
  2. Once the pattern is identified, sales or marketing teams can identify cues on the website that might indicate a customer is ready to make a purchase.
  3. Based on these cues, sales understand where the customer is in the sales cycle and can prepare to close the sale.

Sometimes it might be the case that the customer isn’t ready to purchase, but based on historical patterns, there is a high possibility they will be ready.

Here’s another example: customers have profiles within an organization’s CRM, or where they store their customer data. These profiles change when new information is added by someone from the sales team. If we take these profiles and apply machine learning, a model can be created whereby new prospects are matched to customers with similar profiles and the models are used to predict new buyer behavior. Machine learning not only predicts what products prospects will purchase, but also their level of investment and the order in which they are most likely to purchase for cross-sell and upsell opportunities. Sales can then test and experiment whether or not the right offerings are in place and what type of actions can be taken to lead to revenue growth. The outcome for sales is a more efficient and systematic sales process where the discussion with prospects immediately focuses on areas that are most likely to be relevant to them.

Analyzing Behavioral Patterns of your Team

Analyzing customer patterns is important because businesses can increase revenue and improve the overall customer experience when they know what their customers want. But how can machine learning be applied to the sales team to make them more efficient, effective and agile?

Let’s use the example of the monthly sales forecast. As a manager, you’ve received all your forecasts from your team, you’ve done all the aggregation, and you think things are looking pretty good. However, in the end, the forecast still missed the mark and where management went wrong isn’t clear.

Collecting and analyzing the behavior of sales teams will help prevent last-minute forecast surprises because there is previous data on how individual members act based on their historical performance. If someone on the sales team sandbags month after month, it’s important that management takes this into account and keeps an eye on their forecast submission. Keeping track of your entire sales team can be tricky, but if their historical patterns and performance are available and easy to access, then management has full transparency into the behavior of their team. Next time the forecast happens the manager will know who is going to sandbag and can override the forecast amount to what they deem reasonable.

Machine learning can also be applied to the sales forecast itself. If there is a historical repository of all sales forecasts, a profile can be built up whereby a typical trajectory of progress to close can be examined. This trajectory can then be compared to current performance and management can see first hand whether or not the forecast has begun to go off track. An off track forecast would be a forecast that does not follow the typical forecast trajectory. With this information at hand, management can make strategic decisions and plan accordingly rather than analyzing the forecast after-the-fact (especially if it was a missed forecast). Machine learning enables sales managers to take a much more proactive approach and improve sales effectiveness.

Collecting and analyzing rep behavior is also an incredibly useful tool when it comes to training. With machine learning behaviors can be analyzed more quickly rather than an after-the-fact approach. It’s about focusing on leading indicators rather than lagging indicators. Not only can this make sales managers more effective coaches, but it gives sales representatives the opportunity to understand what they’re doing wrong, and how to fix their behavior to improve their overall sales effectiveness.  In a recent study, 38% of participants credited machine learning for improvements in sales performance metrics. The metrics tracked in this study included new leads, upsells and sales cycle times.

Are you searching for a sales tool that understands the behavior of your customers and tracks the behavior of your sales team? Book your no-obligation demo with Vortini and see how we apply machine learning to improve sales forecasting and analytics.

Jess is a communications professional and Vortini’s lead content/web developer. Her current interests lie in the intersection of sales technology and machine learning. In her free time she reads a book-a-week, practices yoga, and is an avid gardener.

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