Lead scoring is a methodology in marketing and sales in which a value is assigned to leads based on various scoring factors. This value is often numerical in nature and it helps the sales team to prioritize the leads, increase lead conversion, improves the productivity and increases the Return on Investment (ROI).
Earlier, leads were scored manually which was a time consuming and a research intensive activity. This led to automation of lead scoring. We discuss one such tool in this article which uses predictive lead scoring to score leads - Einstein Lead Scoring.
Einstein Lead Scoring is a feature of Sales Cloud Einstein. It uses AI and machine learning to automatically score leads and It uses historic data of previously converted leads to streamline lead scoring.
The higher the score, The better chance of converting leads into potential opportunities. Einstein Lead scoring also displays the factors on which scoring is based. This helps to instill trust in lead scores for the sales team. Also, It helps the sales team to understand the hidden patterns responsible for the conversion or the loss of a lead.
The score also helps to prioritize the leads. Sales reps usually have a large number of leads. So, They have to utilize their limited time efficiently by pursuing leads which are hot and are more likely to convert. The Leads list can be sorted from the higher lead score to the lower lead score and leads with higher score can be pursued first to convert leads faster and efficiently.
Also, Leads can be assigned to different sales reps depending on their scores. For example, The higher scored leads and the lower scored leads can be split into two groups and added to two different sales teams queues. The lower scored leads can then be assigned to sales reps and nurtured till they become more likely to convert. The hot leads can be assigned to the other team’s sales reps to save time and increase productivity as now they have to focus on strategies to convert the hot leads only. Assignment of leads to different sales reps can be done by building a process in process builder.
To understand the impact of lead scoring on an organization’s business, we can use an operational dashboard and an Einstein Analytics dashboard with reports which are included in Einstein Lead Scoring. These dashboards show the conversion rates by lead score and average lead score by lead source. Also, we can see the distribution of lead scores among the converted and lost leads. Every organization's products, targeted demographics and selling channels are different and unique. Hence, One size fits all strategy doesn’t work. Einstein Lead Scoring creates scoring models built specifically for each customer and organization. It analyzes all standard and custom fields attached to the lead object, then tries different predictive models like Logistic Regression, Random Forests and Naive Bayes. It automatically selects the best one based on a sample data set.
Models are updated monthly and leads are scored every hour using the latest model. If there is a change in lead record,then the score is updated accordingly within an hour.
At present, at least 1000 new leads and 120 leads conversion in the past 6 months are required for Einstein lead scoring to build its model. From Winter ’21 release of Salesforce, organizations with smaller data sets can use a global scoring model that uses anonymous aggregated data to allow smaller organizations to score their leads. When enough leads records are created by the organization, Einstein lead scoring will start using a model built from that local data only. The advantage of this change is that the organizations can start using Einstein Lead Scoring earlier and with less data.
For any query on Einstein Lead Scoring, contact firstname.lastname@example.org