5 Ways AI and Machine Learning Is Revolutionizing Sales
Artificial intelligence and Machine learning revolutionize B2B Sales
Machine learning and artificial intelligence (AI) in sales are not dreams of the future. According to Gartner, 30% of all B2B companies will already use AI next year to expand at least one of their sales processes.
What is machine learning anyway?
Machine learning is one of three areas of artificial intelligence. It means that an artificial system (e.g. a software) learns from already existing data and draws new conclusions from it.
We refer in this article to data mining as an example of machine learning for sales. Data mining uses statistical methods to identify dependencies and patterns in existing data structures. In short, the algorithms used to make valuable sales predictions are not magic. They use mathematics and probability applied to ERP and CRM sales data.
This blog post shows five ways you can use machine learning for your sales organization to increase your sales potential – in our experience – by 25%. Depending on the size of your company, this can be a seven-figure amount.
First, however, we have compiled three exciting facts and figures about AI and Machine Learning:
– According to IPlytics, there were 78,085 new patent applications in the field of artificial intelligence worldwide in 2018. Patent applications have increased exponentially since 2008.
– A study by McKinsey states that machine learning in retail reduces lost sales due to stock shortages by up to 65%.
– A Harvard Business Review article reports that sales teams using machine learning gain 50% more leads.
The following five ways show you how successful sales teams use machine learning to their advantage
1. Machine learning increases sales team productivity
The machine learning definition we offered above explains that sales leaders can gain new knowledge from past data. This process allows AI to identify the most effective actions and behaviours for a sales team. Specifically, data-based alarms and insights save the sales manager and his sales team valuable time.
Specifically, data-based alarms and insights save the sales manager and his sales team valuable time.
AI and machine learning significantly reduce manual analyses and unsuccessful customer visits, and sales campaigns lead to more closed sales.
2. machine learning analyzes previous CRM/ERP data using a learning algorithm for an optimal price optimization strategy
Bye, bye trial and error. In the B2B sector, the development and implementation of pricing strategies are one of the most effective levers for improving results. AI and machine learning provide exceptional support for sales and marketing. They evaluate previous price data and information, such as sales history, discount campaigns and promotion measures. Based on this data, the learning algorithm calculates the price elasticity of the various products and customers so that the price can be optimally determined.
3. AI and machine learning help to increase customer lifetime value
Customer Lifetime Value (CLV) is the number of years a customer remains with a company and the resulting value for the company. However, what does AI have to do with it?
Here, too, the answer is hidden in the previous data. Historical sales data can be analyzed to see what changes there were before a customer left. AI and machine learning identify early warning signals and transfer them to existing customers. In this way, changes in customer purchasing behaviour offer information on the likelihood of customers churning. Predictive Sales Software warn the sales team as soon as a customer has a high probability of churning.
4. AI and machine learning enable the recognition of the highest potential in new prospective customers through pattern recognition.
AI-based software analyzes the most valuable customers of a company to date for specific patterns. These patterns form data profiles consisting of values, attributes and characteristics.
These patterns form data profiles consisting of values, attributes and characteristics.
AI can compare the data profiles of potential new customers with those of existing customers and thus predict the value of the new prospective customer for the company. Such information enables sales teams to allocate their scarce resources efficiently.
5. artificial intelligence to optimize inventory levels
Machine learning and predictive analytics can accurately predict customer purchases. These predictions allow logistics to plan product inventory better and minimize inventory shortages. An AI system determines the demand for specific products on-site and decides which goods should be available in which quantity in the various warehouses. A potential analysis by Hermes says only 20% of companies employ AI in logistics. However, 37% of companies plan to do so in the future to improve their processes.
5 Ways AI and Machine Learning Is Revolutionizing Sales – Summary
Machine learning will be indispensable for sales soon. AI-Based Software offer sales managers a variety of opportunities to work more efficiently, save time and increase sales.
Finally, however, we give you an important tip: AI and machine learning alone are not enough to make sales successful. The sales managers and their sales teams should also make use of the knowledge gained from the intelligent systems. Optimization happens gradually, once sales managers take appropriate measures.
If, for example, intelligent software identifies a high attrition risk for specific customers, the sales team should activate a retention plan. Ideally, a set of micro-strategies for customer retention will be in place, working together with the churn prediction software.
Which action is most useful here? Perhaps a personal visit, a discount or a phone call? Taking timely action – even if unrequired – is better than doing nothing. Hopefully, artificial intelligence will have been wrong, and the customer will stay with the company for a long time to come.
Do you have any further questions on Machine Learning? We are happy to help!