What is Salesforce Einstein Lead Scoring?

For each action performed by a lead, a predefined point is assigned against the lead by the lead predicting system. There is a variety of actions that can be performed by the leads. For example – page views, site searches, downloads, email actions, landing pages, link clicks, videos, webinars, etc. Each one has different points and predefined triggers against the actions performed. Below are some examples.

• Page Views – The page with pricing values will have higher value points and a career page will have lower points.
• Site Searches – Lead’s searches show their interest and priorities.
• Email Actions – The generated page views can guide what the lead is interested in.

All of these actions are done at Sales Funnel, where the lead is given points based on their actions. The points keep on increasing unless it reaches a specific predefined point total, from that minute the lead is considered a hot prospect.

Understanding when a lead should be contacted is a very crucial part of the sales process. Analyzing where the lead is in the sales funnel, salespersons can prioritize their time, effort, and resources in order to focus more on the desirable lead profile.

Customers usually feel more convenient when they also show interest in the product. Being reached out by a salesperson on the very first visit to the product page is generally not embraced. Rather a visitor who has downloaded a price sheet, or the one who has made a request for a free quote, or has checked product reviews is a more suitable candidate for the sales team to contact. And if contacted at the right time, can be closed at a higher rate.

Salesforce’s Lead Scoring is a system which uses AI and brilliant algorithms to predict the lead scoring by analyzing the patterns of leads. It helps and guides company’s sales and marketing departments to determine which of the prospects are most valuable for the company and which of them can be closed at higher rates. Apart from that, it saves a lot of time, effort and resources. 

When the lead scoring is properly set-up, it can promise below. 

• Improved sales productivity 
• Generating more sales for the company. 
• Useful sales predictions. 
• Higher lead quality
• Preventing sales funnel dropouts. 
• Huge reduction in manual efforts. 
• Enhanced lead management processes 
• Better lead nurturing 

Depicted below are some of the actions that can be performed by the leads when they are in the sales tunnel, which earns them points

Predictive Salesforce Einstein Lead Scoring using Salesforce Analytics

Predictive Analytics is a technique which uses data mining, machine learning and modelling to predict futuristic events. When a well assembled lead scoring model is in place for your company and once the sales and marketing teams have a fine pace, it is time to concentrate on predictive lead scoring. Predictive lead scoring is based on artificial intelligence (AI), a smart algorithm based approach to lead scoring systems. 

How do companies use predictive lead scoring – 

• The most popular use case would be targeting accounts or contacts within and outside the company’s CRM. 

• Customer profiling to collect and analyse personalized information to take action based on customer’s interests. 

It is good to keep in mind that this is not a one-time activity and needs regular maintenance to keep the algorithm relevant to future scenarios. This may include (and is not limited to) – updating the sales funnel, changing the process of AI based on purchasing and sales funnel action patterns. 

Traditional Einstein Lead Scoring v/s Predictive Lead Scoring 

Traditionally, lead scoring systems allow us to work based on people and their decision making skills, it depends on sales and marketing team employees to decide, based on their skills, experience and data to choose which customers to focus on. On the other hand, a predictive lead scoring system does the work automatically based on data and customer interactions. This requires and uses lots of data with AI to determine which customers are hot prospects. Therefore, the companies which use predictive lead scoring systems have thousands of customers and their informative data to make the smart AI algorithm work as accurately as possible. Hence, it is very ideal for companies to have enough customers and information to try predictive lead scoring instead of traditional ones to help the company close the sales on time. No need to mention it is cost-reducing as well in the long run as a result of a reduction in manual analysis job. 

Below are some key differences in the working nature of predictive lead scoring versus the traditional ones. 

• Predictive lead scoring uses lots of information. It also stores new information automatically to keep analyzing. 
• It also consumes historical trends to see if that helps make the decision easier. 
• It works with big data to guide the lead quality in comparison to other businesses in the industry, which can help companies one step ahead of the industry competitors.  
• The AI makes a comparison of the current and previous customers to make the profile of a qualified lead. 
• Leads are automatically scored, rather than keeping a tap manually to every customer. This score is compared to the qualified lead to determine when a lead becomes a hot prospect. 

How Salesforce Einstein Lead Scoring Works

It determines which of the current leads to be prioritized. Machine learning makes it better, simpler, quicker, and less error-prone compared to traditional lead scoring approaches. Einstein Lead Scoring analyses all the current and past leads in the system including all data available for leads. It does the analysis based on comparing the data of already converted leads and that of current leads to determine which data is similar. Basically, the most prioritized leads would have lots in common with the already converted ones. Consumption of all data can be suppressed for some data fields for which the admin (with support of the sales and marketing team) tells Einstein not to take into consideration. This usually happens for the set of data the reps know must not be useful for the analytics to determine hot prospects, or might be personally identifiable information about the customer in a few cases. 

Einstein, in many cases, will create new internal categories to collect different sets of information which will help determine a lead’s current position. For example, if one lead is a CEO and another is a COO, it will categorize them into the same group as they both are having a C-level job position. This helps Einstein collect and calculate patterns easily. Based on the analytics, Einstein will create a predictive model for the company, and it will reanalyze every 10th day and recalculate the scores so that it doesn’t miss any new changes or trends that have emerged in between.  

In the leads, Einstein adds a field named ‘Lead Score’. This helps the reps determine which leads to prioritize first. In Salesforce, the lead score appears in the lead’s details page on the right-side frame. It also displays the field values of the major factors which helped Einstein prioritize that lead. Additionally, Einstein also comes with a dashboard with reports that display key lead score metrics, which include – Average lead score, Conversion rate, Lead scoring converted, and lost opportunities. 

Below are a few of the considerations and constraints for setting up Einstein Lead Scoring – 

• Einstein Lead Scoring is available on Sales Cloud Einstein, which can be made available to Enterprise, Performance, and Unlimited Editions orgs at an extra cost. 
• The org must have at least 1000 leads created in the past 180 days. 
• Out of the above 1000 leads recorded, at least 120 must have been converted and have an account and a contact entry in the system. Optionally, out of those 1000 leads, for 120 converted to account and contact, the opportunity is created at conversion time. 

Einstein Opportunity Insights

 To get relevant updates about the company’s opportunities, so more deals can be won. Opportunity Insights include predictions about which deals are likely to be won, reminders for follow-ups, and notifications when key moments in a deal occurs. Each insight shows details about why it is displayed, tying it to relevant metrics. Below are the insight types. 

• Deal predictions – To find out the predictions about recent activity and existing opportunities. For example, if the deal is less likely to close in time. 
• Follow-up reminders – The team will get reminded to connect with the lead if it has not contacted in a while. 
• Key moments – Team to get notified when key moments related to a deal take place. This will also include any activity by an inactive opportunity. 

Salesforce Einstein Account Insights

This lets artificial intelligence help the company maintain its relationships with customers. With Einstein Account Insights, the company and its team stay informed about business developments and other major moments that affect your relationships with leads. 

• Key business developments – These are shown by news-related insights. These may be scenarios when the company is expanding, or changing any leadership positions, or is involved in merger or acquisition talks, etc. These news articles come from reputed English news sources. The insight is supported by up to 3 news articles and is displayed in the Einstein Insights component, which is on the account page or the home page. 

Few other benefits apart from winning deals

• Einstein frees up salespeople’s valuable time and increases their productivity with less effort.  

• Einstein Automated Contacts discovers new contacts and opportunity contact roles and adds them to Salesforce.  
• Once salespersons connect their email and calendar to Salesforce, Einstein Activity Capture adds email and events to related Salesforce records (Accounts, Contacts, Leads, Opportunities, etc.). 

All this busy work is done automatically, so reps don’t have to. 

Einstein Scoring Best Practice

• Negative Scoring – There are many people who visit the site but all of them are not leads, understanding this is the very first key to achieve a proper lead scoring method. For example, there are many job seekers or content writers who may be visiting the company’s site very frequently, but they are not the leads. 

• Using different lead scoring models – It is very important to implement different lead scoring methods with respect to different products or services. 

• Establishing a designated maximum score – Leads must be considered as hot prospects once they reach a certain score threshold value. Then, automatically sending the details to sales teams can help them close it. 

• The specific assignment of points – For each action, a specific point should be given to the lead and this has to be strategized well. 

• Using the right action – Taking the right action at the right time. Specified actions shall be well planned and put in place so that the action can be taken quickly. For example, if the marketing team hears some concerns from a potential prospect, the sales team must be aware to be ready with suitable responses before contacting the lead.