Predictive analytics is one of the most recent technological advancements that can help the business to improve decision making. The advanced decision-making technique can help businesses to make accurate predictions related to foreign events.
A report by Dresner Advisory Firm showed that less than a quarter of businesses are using predictive analytics for improved decision making. The same research had also found that nearly 90 percent of the business was interested in the potential of the technique.
Predictive analysis can be used in almost all phases of business operations. In this post, you will learn what exactly predictive analytics is and how it can help improve B2B marketing effectiveness.
What is Predictive Analytics?
Predictive Analytics involves the use of different techniques including data modeling, mining, and machine learning to make predictions. The technique uses data gathered from different channels for making predictions.
By recognizing patterns in past events, predictive analysis can help businesses to understand hidden relationships. Businesses can effectively apply the technique for improved foresight.
Implementation of Predictive Analytics in B2B Marketing
One of the most promising applications of predictive analysis is in attracting leads through effective marketing decisions. Businesses can use the technique in different ways to boost the effectiveness of marketing programs.
1. Prioritizing Leads
Predictive analysis can help in prioritizing leads. The machine learning based technique can be used for scoring prospects based on their buying intent. By recognizing patterns, the AI technique can help identify prospects that are most likely to covert.
The action predicted by the AI software can be a likelihood of buying, profitability potential, lifetime value, and responsiveness to the marketing message. Go online to find more info. Especially on how the technique can help prioritize leads here.
2. Generate Qualified Leads
The AI-based technique can help in sorting out through thousands of data and signals to determine the most qualified leads. The algorithm can consider leads such as company revenue, size, position, and other factors to determine leads that are more likely to convert.
3. Personalized Services
Using the machine learning technology can help in creating a buyer persona. The algorithm can predict behavioral patterns. This can prove invaluable in creating an online buyer personally.
The buyer persona will help in grouping prospective buyers with similar traits. The information can be used to personalize the marketing content more effectively.
4. Coordinate Marketing Activities
Predictive AI based technology can help marketers to better coordinate their activities. The technique can help in determining company division relationships, hierarchies, and decision makers. This information can allow the sales and marketing personnel to align their strategies for effective target marketing.
5. Cross and Upselling
AI-based predictive technique is also invaluable in cross-selling and upselling. Marketing staff can use the information derived from predictive models to target customers that are likely to buy additional related products. The technique can help in targeting customers with the highest potential to upsell and cross ell.
Predictive analysis represents the next advancement in B2B marketing. The technique can help turn around the marketing efforts and improve the effectiveness of marketing campaigns.