Improve Your Sales Outcomes by Using CRM Predictive Analytics

Organizations typically utilize multiple sales tech software to bring in more customers. However, having more of these tools doesn’t necessarily equate to more products sold. Sometimes, the only thing that your sales and marketing team needs is the right CRM system and its predictive analytics to secure more deals.

So how can CRM predictive analytics boost the company’s sales outcome? By discovering qualified leads, organizing prospect contact, tracking call outcomes, proving better product prices, and retaining more customers, the secured sales of a company may increase steadily thanks to CRM predictive analytics.

What CRM Predictive Analytics is and How it Can Improve Your Business

Customer relationship management (CRM) software serves as the anchor for the sales team in an organization. In 2019, around 74% of businesses in the country utilize CRM for better access to their customer data. However, most of these businesses are not aware of the full capabilities that the right CRM software can bring them. Most CRM software feature AI-informed predictive analytics which can help the organization boost sales outcomes.

Predictive analytics is simply the utilization of historical data on customer behavior to create a more accurate prediction of future results. Thanks to machine learning and other technology like CRM software, predictive analytics can parse more data and produce more accurate predictions without requiring long manual processing hours.

There are several predictive analytic theories out there, but only three of them can help you create smarter decisions about improving the sales in your company.

3 Types of Predictive Analytics

1. Sequencing

This type of business analytics is about analyzing the probability of a potential customer buying the product if they perform other actions. For example, if a person downloads a whitepaper and clicks a pricing page, what’s the possibility that they will buy the product too?

The concept of sequencing came from the works of A.A. Markov, a Russian mathematician. His works about probability theory state that an action is equally likely to happen if the two previous actions occurred first. All you have to do is observe the historical patterns of actions A and B to predict the likelihood of action C happening.

2. Cross-Selling

Cross-selling utilizes the analytic data to predict what companion products a buyer may purchase. The best example of this predictive analytics is the “frequently bought with” suggestions – an effective marketing strategy found in online selling sites like Amazon.

This strategy has a higher success rate for existing customers than potential ones. Using the key customer data collected by the CRM, it’s easier to analyze their buying habits and suggest companion products whenever they visit a pricing page.

3. Lack of Action

While the first two types are about interpreting the existing data about a customer, lack of action focuses on the behavior of the customers that have stopped buying from the company. One of the possible reasons why a customer’s communication with a brand wanes is due to an unsatisfactory product or service. By identifying the cause of their dissatisfaction, it’s possible to reach out and save the relationship.

CRM is helpful in this case because the software can analyze trends or patterns that you can use to create a “fall-off” model. By combining the pattern collected in the “fall-off” model and the sequencing data, the team can identify which buyers are likely to buy the product.

5 Ways to Boost Your Sales with CRM Predictive Analytics

Predictive analytics has become widely used by different companies and organizations because it is an effective sales strategy to boost sales outcomes. Here are five ways that CRM predictive analytics can help your business grow:

1. Discover Qualified Leads

Lead scoring is a practice that’s much older than machine learning or predictive analytics itself. It’s the method of determining which among the potential buyers are likely to purchase the product using luck, guesswork, and spreadsheet data. But with the recent advancement in technology, lead scoring has become more accurate and less time-consuming.

CRM predictive analytics allows the sales team to determine which among the interested parties can become customers, as well as how long they can continue buying from the brand. By identifying which potential buyers to focus on, the team can dedicate most of their time and efforts to fruitful transactions.

The predictive analytics today can allow the team to access important information about the buyer, such as how similar the product is to the customer’s needs and how far a potential customer is from buying the product. It can even predict which member among the sales team is likely to secure the purchase.

2. Organize Prospect Contact

Analytical solutions like CRM predictive analytics allow team members to have more productive transactions. With the right information, agents will have to spend less time on the wrong transaction.

The right information can advise the team which customer to call and when it’s best to call them. CRM predictive analytics analyzes the information about the potential buyer, including the number of calls needed before a conversation is reached. It also includes data such as email open rate and length of sales talk time from past interactions.

These kinds of information help the sales team form a strategy and the marketing team plan a campaign while reducing administrative tasks and increasing contact time with the prospect. – Read More

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