Can Machine Learning Create Predictive Lead Scoring?
How do you decide on which leads to work on? Which one has the highest chances of conversion? Do you use a certain criterion or only a gut feeling?
In this article we will answer the question: can predictive lead scoring be created with machine learning; in other words, how can you automatically score leads? To better understand the topic, it would be necessary to have a clear knowledge of the basic concepts: lead, lead scoring, and machine learning.
What is a Lead?
In sales, lead is a term used to describe a business or person that is likely to become a client. For a person or organization to become a sales lead, they must have declared an interest in the goods and services offered by your business.
It means that we can also define a sales lead as a potential sales contact, individual, or organization that has shown interest in your services. Still, they cannot pass for a prospect until they are examined further to ascertain their intent and interest.
Most times, a business can get leads through referrals from existing customers or marketing.
What is Lead Scoring?
Lead scoring is the process of assigning values, which are mostly in the form of numerical points to the leads of your business. This process of ranking the leads involves considering some vital factors, and they include their readiness, level of interest, their location in the buying cycle, the professional information they provided, their level of engagement with your website and other brands across the internet, and how well they would fit into your business.
There are several ways of assigning values to your leads, including A, B, C, D; 1,2,3,4; or comparative terms like ‘hot,’ ‘warm,’ or ‘cold.’ The concept of lead scoring allows companies to determine if prospects would need to be moved quickly to sales or developed with lead nurturing.
This procedure helps sales and marketing teams prioritize leads, respond to them appropriately, and increase the rate at which those leads become customers.
When performing lead scoring, ensure you do not leave any lead behind because the objective scoring is to identify which leads are ready to move to sales and the leads that would require further nurturing. So, avoid picking only the hot and promising leads and leaving behind the other ones.
Machine Learning and Predictive Lead Scoring
Machine learning is an aspect of artificial intelligence (AI) concerned with building applications that would learn from the provided data and enhance their overall accuracy over time without needing to be programmed to do so.
Unlike in data science, where an algorithm is used to describe a sequence of statistical processing procedures, algorithms in machine learning are trained to find patterns and features in massive amounts of data to make decisions and predictions based on new data.
A good algorithm would ensure that the predictions are more accurate, ensuring that it processes more data. The better the algorithm, the more precise the decisions and predictions will become as it processes more data.
The process of learning in machine learning starts with making important observations or data, including examples, direct experience, or instruction, to create patterns in data and make better decisions based on the standards provided to the system. The major goal of machine learning is to help computers learn automatically without human intervention or assistance and adjust actions accordingly.
Bringing it all together, you can leverage the idea of machine learning and artificial intelligence to automate the whole process of following up with sales leads and allowing prospects to move through the sales funnel. Machine learning enables various salespeople to channel their time, energy, and determination on the essential components needed to execute a sale that would require a human touch.
Machine learning makes it possible for most of the lead scoring systems to use demographic and firmographic attributes, such as company size, industry, job title, and behavioral scoring, such as clicks, keywords, and web visits to determine the ranks to assign to their leads.
Furthermore, the predictive lead scoring aspect of the lead scoring system can look at the past data and analyze the won sales and their characteristics (such as activities performed and their timeline, the deal sizes, number of decision-makers involved etc.) and create a robust lead scoring that can help you identify quickly the most promising leads.
Conclusion
Working on the right leads that will buy makes the sales team more efficient by increasing the conversion and won rate. Machine learning can be leveraged to create an automatic system that can use different aspects of the current lead as well as the past data from the CRM system to automatically score all the leads in the pipeline helping salespeople work on the most promising ones and move the least promising to the nurturing process.