Why should we use predictive sales forecasts in sales? This article is aimed at anyone thinking of using AI for more efficient sales planning and sales management.
Every Saturday morning, Mr. Meier visits the magazine store around the corner to buy the weekend edition of his favourite newspaper. This has been going on for half a year now. The saleswoman knows Mr. Meier by now, and because he stops by every Saturday, she always addresses him with the same question as soon as he enters her store: “Good afternoon, Mr. Meier! The weekend edition, as usual?”
For Mr. Meier, buying his newspaper on the weekend has become a ritual. It’s a pattern that repeats itself every week. The saleswoman has recognized this pattern and addresses Mr. Meier about it, almost automatically.
Predictive Sales Helps Sales Uncover Customer Patterns
However, the patterns of some customers do not only exist in magazine stores. They exist in every industry and in every company. Patterns of customers and prospects make it possible to compare different criteria and identify desirable characteristics.
In modern sales, the aim is to analyze as much customer data as possible, to derive patterns from this data and, in turn, to predict possible customer behaviors and actions. To stay with the image of Mr. Meier: When he enters the magazine store, there is a pretty good chance that he will buy the current weekend edition of his favorite newspaper.
Predictive Sales Answers Key Sales Questions
Such forecasts of likely behavior can be made with predictive sales software solutions – only on a much larger playing field with much more customer data. Predictive Sales answers the following questions for sales, among others:
1. Which prices are most likely to be accepted by customers?
2. Which customers promise potential for cross-selling and up-selling activities?
3. How likely are potential new customers to buy?
4. Which customers are most likely to churn (churn prediction)?
5. Which existing customers will add the most value (customer value prediction)?
The goal of predictive sales, much like weather forecasting, is to create the best possible estimate of realistic future scenarios.
Let’s take a closer look at the individual questions and the issues behind them.
Recognizing and Exploiting Pricing Potential per Customer and Product
“All prices are dynamic. Some are just more dynamic than others.” So writes Lucas Pedretti in an article on dynamic pricing in B2B. Dynamic pricing is a pricing strategy in which companies adjust their prices for products or services based in part on historical data about their customers and the market.
Most B2B companies set their prices based on factors such as production costs, list prices, or competitive prices. But they forget something important: The past behavior of their customers.
Past data can be used to identify patterns that can be leveraged for more customized pricing. Predictive sales analytics software gives you a calculated price range per customer and product that is most likely to be accepted by the customer.
Such software is an important tool for B2B companies to get data-based predictions about their customers’ price acceptance.
The emphasis here is on “tool” – because the final decision is still made by the human being! The software’s recommendations merely provide an additional basis for decision-making.
Identifying Cross-selling and Up-selling Potentials
You know this from Internet providers, for example. Once you’ve become a customer, you don’t hear from the provider again for a very long time. It seems as if you are simply a lifeless number in the customer database. Yet it is not only likely, but also realistic, that customers’ needs and thus their (buying) behavior will change over several years. These needs must be recognized on the supplier side. But how?
As a rule, sales staff cannot visit every customer in person and present them with suitable offers. Here, too, AI helps in the form of predictive sales analytics. It derives new requirements from past buying behavior and provides statements about where hidden (buying) potential lies dormant.
Predictive sales analytics enables a comparison to be made between the sales that a company has made with a particular customer and the forecast for future sales with this customer. In this way, opportunities for up-selling and cross-selling can be identified, and it may even happen that the sales department receives indications of products (to be offered) for the customer that it would not have considered itself.
Forecasts on the Purchase Probability of New Customers
What applies to existing customers naturally applies equally to potential new customers. AI or predictive sales analytics, for example, helps to assess the attractiveness of customers a priori and identify the appropriate addresses for them.
With predictive sales analytics, which combine consumer data and historical sales information, companies can forecast how likely it is that potential customers will become one-time or repeat buyers. With predictive sales, companies can anticipate complete patterns of customer behavior over a specified period of time. In this way, a very high degree of accuracy in forecasting is achieved. From this, in turn, recommendations for action can be derived with a high probability of success for sales.
Identification of Potential Customers Willing to Churn
The auditing firm PricewaterhouseCoopers GmbH (PwC) has chosen a creative and at the same time appropriate title for an article on the topic of customer churn risk: “Not every goodbye hurts”. By this, the authors mean that not every customer identified as likely to churn is worth persuading to stay with the help of costly customer retention measures.
This may sound harsh at first, but it is logical in terms of the business application of AI-driven processes such as predictive sales analytics. The sales team must always ask themselves what use of human and financial resources are justified for customer retention measures. And in the case of customers, for whom the sales forecast is comparatively low, such measures tend not to be worthwhile.
Predictive sales software can therefore help to focus on valuable customers who are at risk of turning their backs on the company.
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Forecasting the Value Contribution of Existing Customers
As mentioned above, predictive sales can be compared to weather forecasting. It is about events that are likely to occur or about customer behavior that is likely to occur – for example, churn, cross-selling or up-selling activities, change from lead to purchase status, or the revenue or value contribution that can probably be expected from customers. Customer Value Prediction determines the revenue expectation of each individual customer over a defined period of time.
Sales can be effectively managed by means of a forecast of the expected revenue per customer. This applies to specific sales measures or even the scope of individual investments in strengthening customer relationships. Predictive Sales thus enables companies to deploy their resources in a focused and efficient manner – that is, where business relationships are most fruitful and valuable.