What is Predictive Lead Scoring?

According to a Hubspot survey, obtaining leads and boosting revenue is the main difficulty for 65 percent of businesses. Moreover, prospecting and lead qualifying are the main challenges for salespeople, according to 34% of respondents. This is one reason why many companies have shifted to a predictive lead scoring marketing strategy. Predictive lead scoring is a scoring methodology powered by AI and backed by customers’ historical data and activity to foretell future events. It helps to determine quality leads and drive effective marketing campaigns. Though it is similar to traditional lead scoring, there are some apparent differences.

Traditional Lead Scoring deals with data gathered from prospective customers. Since it is done manually by humans, it utilizes fewer data and manual filters to determine which prospect is more likely to purchase. The problem with this system is the low accuracy since humans cannot analyze large datasets

and make their decisions on a small number of factors which introduces a lot of room for errors. Traditional lead scoring is also time-consuming and can’t be adjusted to suit the rapid-changing market trends.
Thus, marketing teams lose critical statistics to generate quality leads and significant revenue. On the other hand, Predictive Lead Scoring uses an adaptive algorithm programmed to update the system with new stats and quality leads. The advantage is that predictive lead scoring easily adapts to the ever-changing markets and can predict great tips in real-time. This gives a marketing edge to companies and results in significant ROI.

Predictive Analytics involves the use of gathered data to anticipate future events. Some AI methodologies used on data are data mining and predictive modeling to forecast future events. In marketing, predictive lead scoring uses the data derived from predictive analytics to determine which lead is more likely to make a purchase. This is done by AI automatically scoring leads. Lead scores give insight into the prospect’s level of interest, engagement, and purchasing intent.

Marketing teams can use lead scoring insights to drive marketing strategies and anticipate future marketing trends. With these insights, marketers can determine excellent prospects from average ones. Then they can spend their energy and resources converting those leads into purchasing customers. Marketers can also use lead scoring insights to design a marketing strategy that will best suit average leads and help to convert them.

With Lead Scoring, no lead is left unattended in the sales funnel. This is because the scoring system tells the interest and intent of each lead. It also gives the best avenue to engage with a lead (like phone calls, text messages, or emails). This converts more prospects which drives more sales. Available stats indicate organizations that have employed a lead scoring system have experienced over 77% increase in lead generation and ROI.

Data-driven Insights and analytics can help to tailor recommendations to specific prospects. For example, data analytics would recommend more cars to a prospect who has already been searching the internet for vehicles. Such a lead is more likely to purchase a car than a lead who has been checking out baby foods.
Thus, marketers can use data insights to predict a prospect’s interest and recommend lasting solutions. Companies that use data insights and predictions see more success because they can predict exactly what a prospect wants and communicate effectively.
Predictive analysis helps businesses identify what interests an active customer and direct marketing strategies to keep them hooked.

All this is made easy by software which cuts out the tedious work of identifying and assigning values to great leads. This software is armed with algorithms to predict quality leads and recommend effective marketing solutions. Unfortunately, they come with crippling prices that many businesses may not afford. As if that is not enough, one has to have some training in machine learning to use this software.