Generating leads regularly is one of the common challenges faced by sales personnel. However, converting the leads into loyal customers is even more difficult. Spending time on leads that wouldn’t convert would result in the wastage of precious time and resources. Hence, qualifying leads through the proper scoring process can minimize these expenditures.
The process of lead qualification cannot work on intuition, as you can’t merely guess which lead would end up getting converted into a customer. For this, you need reliable sales technology, and predictive analytics might give us that capability.
Predictive analytics is used to predict future events using patterns from past data. It deploys Artificial Intelligence algorithms based on Machine Learning and combines them with data mining techniques to scan massive chunks of data to identify these patterns. Organizations can use the insights from these data-driven forecasts to determine the purchase behavior of their target audience. By establishing the likes and dislikes of their potential customers, the companies would know how to maximize their profit.
The data mining process starts with exploring the data about prospects and customers collected by the CRM, which is usually stored in a dedicated database. The information is then matched with the data available on the web, which can be used to discover more insights about each contact. Once the particulars are available, the collected data is segregated into various categories. Next, the AI algorithms are applied to detect patterns by mapping the information from each category. These patterns from the existing data are subsequently used to identify similar sequences in the future.
As Predictive analytics can forecast the behavior of consumers, it can also be utilized to know if the leads can be converted to customers. Essentially, predictive lead scoring can be used to predict which lead attributes matter most. Based on it, the sales members can prioritize the leads that match the criteria, which can increase the conversion ratio. Moreover, the technology could even detect if the existing customers would purchase an add-on service. Using it as a cue, the sales team can approach them, which can double the average revenue expected from these accounts.
Using predictive analytics, the sales team can also accurately foretell the future actions of the prospects, their reactions to outreach, and their intentions to make the purchase. By enabling the sales personnel to prioritize the leads in an objective way, predictive scoring can eliminate the manual and mundane work associated with sales. Automating the process will no longer matter whether the qualifying lead characteristics have been weighted accurately and adequately. In short, predictive analytics can minimize the doubts about the lead qualification process as well as the time and resources spent on chasing non-convertible leads.