Salesforce Einstein Lead Scoring


Every marketing company wishes that the sales team gets the most qualified prospects and prays for them to close at a higher rate. However, it is not possible every day for the sales team to rely on regular ways to decide which potential customer to focus on. And if they do not, the team would have to settle with less of a conversion rate as salespersons begin to contact leads who are still in the research phase, which may put off the lead. This, in succession, can impact company profits and even sales predictions.


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 

How Einstein Lead Scoring Works Using Salesforce’s Marketing Cloud?

Einstein Lead Scoring in Salesforce Marketing Cloud

According to researchers, marketing teams send about 60% of the leads directly to sales teams, without any lead qualification strategies in place. And about 70% of them get ignored by the sales team, which leads to, in terms, a downfall in lead scoring for the company. The key to reduce such instances, it is very crucial to have a proper bridge between sales and marketing teams. This can be achieved by Salesforce’s Marketing Cloud Connect, which is used to sync data between sales and marketing teams.  

Companies are more focused on ads targeting, social listening and monitoring. With these in mind, Salesforce’s Marketing Cloud is the perfect choice which has features such as Social Studio, ads or SMS targeting along with high scalability.  

There are various ways the lead scoring can be integrated with marketing cloud. Following are few examples –  

decor blue
decor blue

– Reduction in manual labour.
– Sending personalized messages to customers with dynamic content.
– Intelligent algorithm based lead pattern predictions.
– Contacting prospects when they are hot.
– Branded landing pages and out of the box templates.
– Superior email marketing in place for lead’s actions.
– Campaign management and paid searches.
– Attraction with the customer through different webinars.
– Analyse and compare campaign success across different channels.
– Data driven analytics and insights for continuous improvement.

Journey Builder

– Harnessing of customer’s data from any source browsing behaviours, shopping history.
– Personalized messaging.
– Real time event tracking – downloads, purchases, service cases.
– Visual customer journey.
– Connect sales, commerce, marketing and service departments.
– Anticipation of lead’s behaviour.
– Smart analytics engagement for predictions.

Social Studio

– Collate feedback from service, sales and marketing teams.
– Gain post level tracking of company mentions.
– Monitoring of audience discussion based on machine learning.
– Get a picturised view of customer’s action across social and other channels.
– Service delivery via social channels.
– Built-in social advertising.
– Digital insights – Track interactions from leads, their feedback, etc.
– Individual response to customers.

Google Analytics 360

– Engagement of the leads on company’s contents.
– Easy to use interface.
– With deep Google’s advertising and publisher product, drive marketing results.
– Tag Manager 360 - Modify tags on the fly without touching the code, secure and reliable.
– Optimize 360 – Take personalized actions based on analytics on the lead.
– Build with ease, test with ease and ad with ease.
– Quick authentication with marketing cloud and analytics 360 integration.
– Differentiate between different channels like emails or SMS.

Email Studio

– Helps in reaching an ideal audience with great branded contents. This provides drag and drop tools to help with branded mailing.
– Automating the process – Helps in automatically filtering, importing and segmenting data from any source to better the campaigning process.

– It uses intelligent algorithms to discover the ideal audiences, engaging right subscribers more often or engaging less with the inactive ones.

– Share segments by activating first party mailing.

– Reaching audiences with any device with responsive designs.

– Customizable templates to make the job even easier.

– Delivering dynamic content via different channels to engage and inspire customers.

– Personalized and data driven content uses Einstein and AI to tailor emails based on individual customer experience. It helps build a one-to-one relationship with customers.

Interaction Studio

– This helps in coordinating engagements wherever the customers are interacting.
– Beyond digital connects – engage with customers through online, offline (such as call centers), in-store POS, and even ATMs.

– Interact with customers in the channels they prefer.

– Anticipating next interaction and conversation. Inspire customers to take action.

– Listen and monitor customer’s activities across owned channels.

– Prioritize critical and urgent offers, messages and interactions.

– Trigger events for sales, marketing and services teams in real time for engagement.

Data Studio

– Helps in discovering quality audiences at big scale.
– Finding new audiences through deep insights.
– Activating data partnership with most premium data providers can get exclusive unique audiences.
– Huge set of about 200 channels can help reach new prospects with ease.
– Taking control of a company's data and securing them is a major plus point of using Data Studio. This is done by real data rights management which gives complete control.
– Transparent reporting can help you get the complete picture of real data and current positions.
– It also helps being ready for new regulations and policies.
– Increasing demand of company’s information is another considerable aspect.

Advertising Studio

– Discover endless benefits of one-to-one advertising.
– Trusted Salesforce architecture and security ensure company’s data is safe.
– Synchronizing customer’s data from any digital channel makes the information up to date, whether the campaign is for dozens of customers or for millions.
– Targeting customer’s preferred channel.
– Integrate the campaign with sales, service and marketing teams to put the perfect ad for the right person.
– Discover new lookalike audiences, integrating with Facebook, Google, etc.
– Build more efficient campaigns by optimizing them. Dynamically remove and increase campaigns.
– Integrate email and ad campaigns to reach more of the audiences.
– Putting together email nurture campaigns with sales teams help build better lead scoring. This can be achieved when Pardot and Sales Cloud are connected.
– Creating Facebook campaigns for a greater reach.
– Google search optimization will surely increase the rate of interest.

how does salesforce einstein lead scorring work

How Einstein Lead Scoring Works?

There is no universal formula for lead scoring. Each company has different approaches with different products and customers. So creating a custom model is necessary to achieve a well formed lead scoring system. Proper setup of lead scoring takes effort and sales and marketing teams benefit from that. For any action the lead earns points. This happens in the sales funnel. Each of the actions have defined point value. When the lead arrives at a certain point sum, they fall into the list of valuable prospects. Below is the pictorial demonstration on what happens. 


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. 

Einstein Lead Scoring

With key predictions, intelligent algorithmic recommendations, and well-timed automation. Salesforce’s Sales Cloud Einstein is a company’s very own data science division that learns from different team’s sales activities and CRM data and helps the company identify the best leads, convert opportunities more efficiently, and retain customers with ease. Sales Cloud Einstein also includes the Sales Analytics app and Inbox.

To define simply, Einstein Lead Scoring brings intelligence to a company’s daily workflow, forecasting, and decision-making. It uses data science and machine learning to analyze the company’s business patterns of lead scoring. Einstein scoring basically helps reps prioritize leads and opportunities, so they can focus on what’s most likely to convert and close.

Smarter sales pipelines can be built using Einstein which will eliminate traditional guessing the forecast. Einstein Forecasting uses artificial intelligence technology to bring more certainty and clarity to a company’s forecasts.


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. 

Salesforce Sales Cloud Einstein Insights

Salesforce Einstein Lead Scoring

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. 
Need Help with Salesforce Einstein Implementation?
Send Message
Follow us

Austin, TX 78759, USA