AI for CRM Business Series : 2

How do organizations increase Sales and Revenue ? Information about prospects plays a key role in staying updated on the various Accounts and for successfully grabbing every Sales opportunity.

A good Sales strategy aims at increasing Sales revenues and at eyeing higher targets by reaching out to as many interested customers in the shortest possible time. But, the real question that actually troubles the Sales teams is ‘How and When ?’. They are also concerned about accelerating Sales.

A conventional method ‘rates’ or ‘scales’ the interest and buying readiness of customers and their organizations. Identifying right prospects, potential customers or ‘Qualified Leads’ is the objective, and to nurture these Qualified Leads and successfully close deals at the right moment determines the Sales Teams’ strength and the strength of the individual Sales reps.

Qualified and Unqualified Leads can be distinguished on the basis of various Sales defined criteria. This classification helps us focus on the qualified leads to create and reach out to customers through promotional content and catchy marketing channels to stay in minds of their prospective customers. Once this setup is established, it helps the Sales team to prioritize leads. However, this is easier said than done. Lead prioritization is based on the key factors that helps score individual leads, commonly termed as Lead Scoring.

Lead Scoring is a Sales and Marketing methodology, ranking leads in certain order to determine their Sales readiness and their business worth to organizations. Traditionally, lead scoring has been based on lead’s behavior and interest towards products and services.

Lead Scoring in the modern day context can be broadly put into one of the two: Traditional and Predictive Lead Scoring.

Traditional Lead Scoring vs. Predictive Lead Scoring

Traditional Lead Scoring is a technique based on a Sales representative’s personal and organization’s professional criterion, which is commonly termed as ‘Rule based Lead Scoring’. This is a process where Sales teams determine the criterion based lead quality to then assign them a Score. This Score is then used to measure whether or not that lead is ‘qualified’ to make a purchase and their likeliness to actually do so [1].

Predictive Lead Scoring is a technique that uses an algorithmic tool, which uses your history of lead conversion (gathered from organization’s historical data) along with the Sales defined rules to predict which leads are likely to convert based on each of their computed scores. It is an automated lead scoring mechanism based on historical customer data which uses structured data sets obtained from large raw data, followed by identifying activity, interest and communication from leads along with other custom parameters defined by Sales representatives for score allocation (everything happening in background).

This collected data helps in identification of potential customers and leads, increases the probability of lead conversion and an increased revenue. Predictive Lead Scoring helps Sales Representatives to identify easier targets and score on to achieving higher revenues.

Traditional Lead Scoring faces certain drawbacks:

  • Focuses on eliminating bad leads rather than identifying great prospects and leads amongst the average leads.
  • It isn’t naturally adaptive (doesn’t work on feedback), thus needs constant revision and upgrade of defined rules for rapidly changing markets. This leads to missed opportunities and slower growth.

The Dynamic nature of the market filled with competitive products and services has forced the cons of the Traditional Lead Scoring to pave way for Predictive Lead Scoring to take over. The feedback triggered adaptive nature and the potential to identify the trends of prospective great leads hidden amongst the average leads provides with a competitive edge to the Sales representatives using Predictive Lead Scoring technique.

Sales Cloud Einstein

Salesforce being a vast and commonly used CRM tool and platform helps build custom capabilities for the users. The Sales Cloud Einstein is the Data Scientist like application with several features for providing assistance to the Sales professionals based on the Data from Salesforce. It deploys various Machine Learning Models to find the best fit based on opportunities, leads and other critical information from the data in the background. The features offered by the Sales Cloud Einstein are: Lead Scoring, Account Insights, Automated Contacts, Activity Capture, Opportunity Scoring, Inbox and Analytics.

Einstein Lead Scoring is a key capability of Sales Cloud Einstein that helps sales representatives convert more leads, faster. It uses artificial intelligence to automatically analyze customer’s historical sales data and discover the top factors that determine whether a lead is likely to convert to an opportunity. Sales representatives can segment and prioritize leads, and gain insight into the factors that explain why leads are likely to convert or not. The factors are displayed on each lead record, helping sales representatives prepare for calls quickly. It’s like giving each representative a personal data scientist to take connection and conversion rates to the next level [2].

Einstein Lead Scoring : The Optimized Science for Lead Scoring

Einstein Lead Scoring models are built specially to suit each customer and individual organizations ensuring that the models are tailored for individual businesses. These automated Lead Scoring features analyze all standard and custom fields associated with the Lead object, then tries different predictive model based on algorithms such as Logistic Regression, Random Forests, and Naive Bayes to automatically select the best based on sample Datasets. The Statistical Analysis and Mathematical expertise is taken care of by these pre-built Data models in the Sales Cloud Einstein package to drive Lead Conversion [2].

Monthly updates are made to the models to make sure that the most accurate predictions are made for the leads. Leads are scored every hour using the latest model. If something changes on one of your leads, it will be rescored within the next hour [2].

Key Benefits: Einstein Lead Scoring

  • Increase connection and conversion rates
  • Accelerate engagement with the best leads
  • Understand lead score factors [2]

Top Features: Einstein Lead Scoring

  • Zero Setup — No implementation of or import/export to separate tools
  • Custom Lead Score-Driven Workflows — Easily assign tasks based on predictive lead scores
  • Smart Lead Lists — Surface the best leads quickly [2]

Technical Specifications of Einstein’s Lead Scoring

The Data Sheet describing the Einstein Lead Scoring technical specifications :

Source: Salesforce Einstein Lead Scoring [2]

UI for Salesforce Sales Cloud Einstein

Source: Salesforce Help and Training documents

  Locate Leads with Maximum Lead Score

Source: Salesforce Help and Training documents

 Indicating lead score and sales representatives which fields have a maximum impact on lead scoring

Measuring Lead Scoring Accuracy for Salesforce Sales Cloud Einstein

  1. Lead Field values more commonly seen in Converted Leads

Source: Salesforce Help and Training documents

  1. Lead Field values more commonly seen in Unconverted Leads

Source: Salesforce Help and Training documents

The numbers on the screenshot indicate the following:

  1. Company-Level Predictive Factors chart shows which lead field values are seen more often in converted leads.
  2. View the percentage of leads that have a specific field value by hovering over a bar on the chart (2).
  3. Dive into the details and see the leads that have that field value by clicking on the bar and then Launch (3).
  4. Company-Level Predictive Factors chart shows which lead field values are seen more often in unconverted leads (you can see this in the following screenshot).
  1. Conversion rates for different slices of lead score. Right side indicates average lead score for each lead source

Source: Salesforce Help and Training documents

References:

  1. https://www.impactbnd.com/blog/traditional-lead-scoring-vs-predictive-lead-scoring
  2. https://www.salesforce.com/content/dam/web/en_us/www/documents/datasheets/sales-cloud-einstein-leadscoring.pdf
  3. https://releasenotes.docs.salesforce.com/en-us/winter18/release-notes/rn_sales_einstein_els.htm#rn_sales_einstein_els
  4. https://releasenotes.docs.salesforce.com/en-us/summer18/release-notes/rn_sales_einstein_els.htm?edition=&impact=
  5. https://releasenotes.docs.salesforce.com/en-us/winter18/release-notes/rn_sales_einstein_els.htm#rn_sales_einstein_els
  6. https://releasenotes.docs.salesforce.com/en-us/summer17/release-notes/rn_einstein.htm?edition=&impact=
  7. https://releasenotes.docs.salesforce.com/en-us/winter18/release-notes/rn_einstein.htm?edition=&impact=
  8. https://releasenotes.docs.salesforce.com/en-us/winter19/release-notes/rn_einstein.htm?edition=&impact=
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