Machine Learning in Sales

Artificial Intelligence, particularly machine learning, revolutionize many sectors and aspects of doing business, sales as well. Today, we are going to take a closer look at this subject. As it turns out, machine learning can significantly speed up the pace of sales reps work and allow them to focus primarily on the most promising prospects. Sounds interesting, doesn’t it?

Naturally, the sales level is a key factor in every company’s growth. The company that doesn’t sell cannot survive in the long run. We are pretty confident that this rule applies to your company as well! Thankfully, now, you have an entirely new ace in the hole, and it’s called machine learning. In this article, we are going to examine how is machine learning revolutionizing sales and what are the machine learning sales examples. Finally, we will think about the future of ML in sales.

However, before we do that, it is vital to see how machine learning is influencing sales today.

• According to Gartner[1], up to 30% of B2B companies will employ some kind of AI technology to augment at least one of their prime sales processes by the end of 2020. This tells us that companies begin to realize that sales machine learning is a not-to-be-missed opportunity.
• Companies that have pioneered the use of AI in sales noticed an increase in leads and appointments of more than 50%[2], cost reductions of 40%–60%, and call time reductions of up to 70%.
• Forrester forecasts one million U.S. B2B salespeople will lose their jobs to self-service e-commerce by the end of 2020[3].

Although it’s still a new trend, machine learning, and Artificial Intelligence have more and more to say about the way companies promote and sell their products and services. In what way exactly?

Interested in machine learning? Read our article: Machine Learning. What it is and why it is essential to business?

Machine Learning in Sales: Revolutionizing the Industry

There are several aspects of this AI revolution in sales, but, in general, we can state that the prime goal is to automate repetitive tasks and to allow sales reps to focus on the most promising prospects. The ultimate goal is to sell quicker and more, without investing a lot of new resources (mainly financial) into the sales departments.

Currently, we can see a paradigm shift in sales: from being reactive to proactive, from instinct-driven to data-driven. In the not too distant past, almost every promotional activity was based on this sixth sense (or instinct if you will) and previous experience of a sales representative. Although it could produce some results, it was far from being called a repetitive and efficient strategy in the long run. In many instances, it was more like running around in the fog. Things have changed when AI and ML knocked on the door of the sales departments.

Now, it’s AI that guides the sales journey from identification to customer retention. Without a shadow of a doubt, it’s a positive development. It means that sales departments can work more effectively and, above all, more predictably. Which facets of sales can be revolutionized with the assistance of machine learning? Let’s investigate some of the examples of how machine learning is transforming sales.

You may also find it interesting – Machine Learning in Applications.

Examples of using machine learning in sales

In this article, we indicate five of what we think are the critical aspects of machine learning in sales:

Hiring people with right skills

One thing will probably never change. There will always be a human factor in sales. People are innately social creatures–we buy from people and companies we trust, we want to engage in a conversation before the purchase. That’s why currently, companies and online stores provide many different ways of communication–to offer quick and direct contact. That’s why salespeople will be necessary, even twenty years from now. What can ML do here?

Actually, a lot. Let’s say, there is a company with a quite large sales team. They hire 30 sales reps, but the company noticed that only 6 of them bring the highest income. Of course, that’s related to the Pareto principle, but our point today is different. A company decides to take resumes of these 6 employees and transfer them to the ML algorithm. The algorithm processes these resumes and, based on this knowledge, creates the profile of the desired sales rep.

Next time when the company has an opening in the sales department, they use the same algorithm to scan received resumes and match them with the previously created ideal profile. As a result, the company obtains just five resumes, which are closest to the perfect sales rep profile. After interviews, they hire two people, which are most likely to bring about a significant contribution to the company’s sales results.

Machine learning sales prediction

It’s another crucial facet of sales. Every sales department tries to estimate future sales levels, particularly for the coming month. Naturally, mostly with target levels in mind, but the fact is, sales forecasting helps in maintaining the company’s growth pace. Now, how can you predict the future? Some people go to astrologers and some use AI. We are huge fans of the latter option!

You see, machine learning sales prediction is a real thing, and it’s based on past results. Combining historical selling, pricing, and buying data with other indexes (i.a. the number of customers, traffic on the website) in a single machine learning application gives you a very accurate sales prediction. And it goes further! For instance, you can integrate your AI and machine learning algorithms with your CRM app and sales management applications and make these predictions even more accurate!

Selecting and prioritizing prospects (lead scoring)

Concentrating your attention on prospects that are unlike to convert into customers is a massive waste of time and a nagging problem of most sales departments. Thankfully, machine learning consulting comes to the rescue!

Today, companies have a lot of data about their prospects and customers. Even a free Google Analytics tool can provide you with a mind-boggling amount of data about your customers, present and potential alike. The same way you used to select the best sales reps, can also be used with ease to select the most promising prospects. Nowadays, the AI-based CRM applications are providing the ability to define a series of attributes, characteristics, and their specific values that pinpoint the highest potential prospects. Sometimes, it’s called lead scoring. All you have to do is to compare them with your current prospects and choose the ones which are most likely to convert. A snap! 🙂

What happens, though, in the absence of categorized data? The ML algorithm identifies patterns and correlations on its own (that’s why we call it machine learning). As you can see, an AI application can be a crucial part of the lead scoring and nurturing strategy, which strengthens sales pipelines and make it accessible for your sales representatives to move prospects through the pipeline more efficiently.

Improving productivity and efficiency

Although it’s strictly related to the previous element, it’s a much wider question. It’s all about assigning meaningful tasks to the sales reps and cede all the mundane, monotonous tasks to AI. These tasks can comprise i.a.:

• Sending automatic, personalized messages
• CRM profile updates
• Assigning a call or a meeting

All of them are time-consuming and relatively simple. A properly trained ML algorithm should be able to handle such repetitive, simple chores. Moreover, the AI-based CRM application can define each salesperson’s schedule and optimize their time, so that they can go from one customer meeting to the next one, without unnecessary delays.

Data analysis and pricing

Another vital issue is data analysis. The machine learning sales software can analyze unimaginable amounts of data in no time in order to provide your company with an insightful analysis, which can be used in the business decision-making process. We can state that companies that use data analysis make better decisions and act more consciously in comparison to firms that don’t use it.

With the assistance of the data analysis applications or professionals, you can i.a.:

• Predict the customer churn ratio
• Devise effective pricing models
• Adjust your business model and/or your products and services, so they meet the demand coming from the market

What about pricing? Many sales representatives have severe problems with proposing a price to the given customer. They are afraid to propose too high price (and don’t close the deal) or too low (and don’t make enough money on a commission). Thankfully, machine learning can help with that as well!

All the company has to do is to input past and current pricing data, purchasing history, discounts, promotional programs, and other relevant coefficients. With this foundation, AI and machine learning applications can easily calculate the optimal price for each one customer, which, in turn, significantly improves the odds of closing the deal. In the same way, AI can be an invaluable support in your upselling and cross-selling strategy.

You may also find it interesting – Machine Learning in Social Media.

Future predictions

It’s time to talk for a second about the future of machine learning sales. Based on what we currently observe, we can draw some conclusions about the future.

First of all, we can expect that sales automation will develop dynamically. Possibly, even to such an extent that, in the near future, sophisticated AI applications will write quotes, offers, and contracts all on their own. In such a situation, a human employee would only be responsible for checking them and sending directly to a client or a contractor.

Second of all, we can expect that the AI-based prediction apps will be more and more accurate. All machine learning algorithms are designed to learn themselves, similarly to humans. Therefore, the more data they receive, the more advanced and efficient they become. Companies that use ML sales tools may look forward to seeing the noticeably better results.

Third of all, in the near future, every (or almost every) repetitive, administrative work will be done 100% by the AI software. Human sales reps will be able to concentrate solely on their most important task–bringing new customers.

Your company CAN be a part of this bright future of sales! And the good news is, you can start even today. Almost all of the aforementioned applications and solutions can be implemented at hand! Would you like to see them in action? You’re in the right place! Addepto is the best place to start, as we have all the necessary know-how and vast experience. Drop us a line and let’s talk about tweaking your sales!

See our machine learning services to find out more.

References

[1] URL: https://mc.ai/ai-in-sales-how-artificial-intelligence-will-transform-b2b-sales-in-2020/. Accessed Mar 24, 2020.

[2] Thomas Baumgartner, Homayoun Hatami, Maria Valdivieso. Why Salespeople Need to Develop “Machine Intelligence”. June 10, 2016. URL: https://hbr.org/2016/06/why-salespeople-need-to-develop-machine-intelligence. Accessed Mar 24, 2020.

[3] ZoomInfo. 65+ Statistics About Artificial Intelligence. Oct 24, 2019. URL: https://blog.zoominfo.com/statistics-about-artificial-intelligence/. Accessed Mar 24, 2020.