Salesforce Einstein Opportunity Scoring


Opportunity Scoring is one of the various well liked strategies to prioritize customers and their demands on their sales cycle and future roadmap. It provides the management and the sales reps an instinctive with a real time rating for each opportunity based on historical and current data. This helps the sales team decide which opportunities to prioritize with very minimal effort and time. This, in succession, helps them to close more deals.


What is Salesforce Einstein Opportunity Scoring?

Salesforce Opportunity Scoring is backed by very strong artificial intelligence (AI), which helps companies build a smarter sales process. Salesforce’s Opportunity Scoring is based on Einstein Analytics. To state simply, Einstein Analytics is an application used to provide insights and visualize analyzed and processed information based on different activities done in the Salesforce Org. We will dig deep into how the Einstein Opportunity Scoring works, but to understand it, let’s first concentrate a bit on how opportunities are handled in Salesforce.

Opportunity in Salesforce

Opportunity by Salesforce is provided to manage and track a company’s potential deals. It handles the deals which are in progress. Each record of an opportunity helps to track multiple aspects about a deal, like the opportunity is for which account, who are the contacts (players) involved in the deal, and mainly giving the perspective of the potential of a deal.

An opportunity is created either once a Lead is converted or it can be created manually for any account in the org. Opportunities can be created for a business or for a single person as well.

What is salesforce einstein opportunity scoring
Salesforce Opportunity scoring with person account

Once the deal start progressing, there are several features provided by Salesforce. Below are some of them.

  • One can attach files like data sheet, or can add products.
  • Companies may log the calls made for a deal.
  • Notes section provides efficient way of creating note per account or deal.
  • One can create tasks for crucial activities, and can also set calendar events for client meetings, etc.
  • Send automated or manual mails to contacts or stake holders.
  • Tracking the competitors – Competitors names can be listed for pending opportunities.
Einstein Opportunity Scoring

Einstein Opportunity Scoring provides sales team with a score for prioritizing opportunities and convert more deals. This insight is driven by artificial intelligence at its core.


  • Available for both Salesforce classic and Lightning Experience.
  • Users with or without a Sales Cloud Einstein license may avail it. Earlier, it could be availed with Sales Cloud Einstein, which is available for an extra cost in: Enterprise, Performance, and Unlimited Editions. It has become available without the Sales Cloud Einstein licence after the Spring ’20 release. As per Salesforce, it is available at no extra cost for eligible customers only
  • Available in Enterprise, Performance, and Unlimited Editions
sales cloud einstein scoring


Einstein Opportunity Scoring is part of Sales Cloud Einstein Scoring, which also includes Einstein Lead Scoring. In the hierarchy, Einstein Lead Scoring comes under Salesforce’s Sales Cloud Einstein model.

Opportunity scores tell the sales person the likelihood of an opportunity to be won. Each score is a result of analysis based on smart and algorithmic artificial intelligence program. The result is driven and calculated using several factors that may contribute on deciding the possibility of an opportunity to be won.

Each opportunity is updated with a score from 1 to 99, 1 being the lowest score and 99 being the highest one. Each opportunity record for a company in Salesforce is updated with the score. It is available both in the record and as well in the list view. Opportunity scores are available in the forecasting page for the companies using Collaborative Forecasting. These scores helps the sales persons to decide on the likelihood for winning a deal. We will understand it with a real time business scenario and real time business use case.


Einstein Opportunity Scoring Business Use Case

A prediction of which deals will get closed is one of the best things that a business can expect. These predictions can be thought as a forward-looking estimate based on current and historic data. As we know that the more the opportunity score for an opportunity, it is more likely to be closed and won. It can be a very key advantage for a company as it can affect the most valuable KPIs – company’s revenue, business win rate, and correctness of forecasting.

To properly define the use case, a company need to answer some questions –

  • What queries does the company need to answer? – Based on the KPIs measured currently by the stakeholders.
  • What will be a suitable good future to target? – To create the forward-looking statement, which will help creating the suitable scoring algorithm for the business.
  • What value can be derived? – How and what will the prediction help the business in terms of the value gains?

The answers could be as follows, which in turn, help resolve our use case.

How much should I invest in supplies monthly in my restaurant business.

Now let’s create a forward-looking statement

If I (a company) can predict “the opportunities that are likely to be won” (business need), my customers or leads or users will be able to “email, call or contact” (action) which, in succession, will bring “more revenue” (value) to my business.

After the use case has been identified and a forward-looking statement has been decided, it’s time to gather requirements. Gathering requirements is also an essential step as it helps on building the right solution, in identifying the key stakeholders and in verification of collection of KPIs and relevant matrices.

Identification of key stakeholders. Key stakeholders can be Sales representatives and account managers or it can be sales manager. Let’s assume all of them are our key stakeholders as all of them will be able to do their job better once they are benefited.

Einstein Opportunity Scoring Key Questions
What are company’s major pain points in sales area?
What is the current process that the sales reps follow?
Who is engaged and who will get affected?
How is the prediction information or data being used?
What are near future goals for the company? How does the future look like?

Determining predictions that need to be created – Below are some relevant questions and respective answers which can help in finalizing the predictions.

  • What predictions are relevant and are important for the stakeholders or for the leadership?
    • Scoring of the opportunities to identify the opportunities that can be closed.
  • How will these predictions help business and stakeholder to come with better decisions?
    • Help on focusing on the opportunities which has higher likelihood of being won.
    • Determine and display positive and negative aspects which contribute to wins and losses.
  • What are possible set of data to help with the predictions?
    • All fields on opportunity object in Salesforce.
    • Lead product.
    • Number of open cases and percentage of all the growth or decline monthly, quarterly or yearly.
    • Average of NPS scores for the contacts in opportunity.
    • Number of meetings with executives involved.
    • Account related information.
    • Number of demos provided.

Preparing and Planning Salesforce Einstein Prediction

To process through the relevant data to support the use cases, avocado framework can be used (we will discuss shortly). The framework aligns with the steps setup in the Prediction Builder wizard. One can begin with choosing the entity (salesforce object like opportunity object), then as next step, can decide if the focus should be in the segment and provide example for the algorithm (Einstein) to learn from.


All the data available on the Opportunity object basically work as the base data set. It is not necessary to be dependent on all the data which is available in opportunity object. One may choose to focus only on relevant data and exclude irrelevant opportunity records. This is using a segment of data to proceed with predictions, it is called segmentation. Also, segmentation can be applicable on particular type of opportunities, let’s say if we want to create prediction based on Enterprise Opportunities only.

Records to score

These include the data for which the pattern is not known if it is aligned to stakeholders’ desire or against it, but would like to predict. These are the ones which has not reached closed won or closed lost and stakeholders want to predict the likelihood for them, so that these can be prioritized properly.

Positive aspects

The positive data is the data which shows pattern or behaviour the stakeholders want. Let’s say which opportunities have reached closed won stage.

Negative aspects

The negative data is the data which shows opposite pattern or behaviour the stakeholders want. Let’s say which opportunities have reached closed lost stage.


Every company and its org is unique and have different demands and handles slightly different functionalities, one can decide what set of data to use. Entering different information into different data set that suits the org. Defining data set into different segments for an org is depicted below.

Preparing and Planning Salesforce Einstein Prediction 1

Defining the prediction set – One do not necessarily have to define which scores to be scored and which to avoid since all records in the segment after example filter conditions are applied will automatically become the prediction set.

The records and the data sets are very essential for the prediction, therefore, it is provision in the Einstein Prediction Builder to check and make sure that there are accurate number of records, including positive examples, negative examples and the records for scoring. When in doubt, this can also be done by using reports.

Preparing and Planning Salesforce Einstein Prediction
Final Step

Positive Examples:
Opportunity Stage equals to Closed Won
Negative Examples:
Opportunity Stage equals to Closed Lost
Base Segment (Segment to analyze):
Opportunity Stage does not equal to Qualification
Records for Scoring:
Records that are not included in the example sets


Salesforce Einstein Prediction Builder

Setting up the predictions with filters –

One does not have to explicitly define additional field to setup prediction. This has become handy after the Sprint ’20 release of Einstein Prediction Builder. The desire is to predict the possibility of an opportunity to land to “Closed Won”, but it does not have a checkbox field to represent the outcome. With this in hand, it is to be done by using special filters to specify which outcomes are considered to be positive and which outcomes considered to be negative.

Won”, but it does not have a checkbox field to represent the outcome. With this in hand, it is to be done by using special filters to specify which outcomes are considered to be positive and which outcomes considered to be negative.


This is how it is achieved using Prediction Builder:

01. Start with selecting the object for which the prediction has to be made – In this use case, it will be Opportunity.

02. Define the segment using the available filter under “Want to focus on a particular segment in your dataset?”

03. Now need to select the answer to the question “Will this Opportunity be won?”, so for this use case, “Yes/No” type for prediction need to be selected. Keep in mind that the prediction will return a number which corresponds to the likelihood of winning an opportunity, but this is still considered a Yes/No prediction.

04. In this use case, we do not have an additional custom field created which can store the outcome of opportunity closed won, but we have a picklist in place (such as Stage: Closed Won, Closed Lost, New, Quoted, etc.), so we select the “No Field” option.


05. Next, the positive examples and negative examples has to be defined using the “Yes” example and “No” example respectively.

06. After this, now it’s time to declare relevant fields. It is recommended to include all fields as some unexpected insight can also surface.

07. Now, pick the name of the field where the prediction or the score will be stored. This will be the field which will represent the opportunity score or the possibility of winning an opportunity. It will display a number from 0 to 99.

08. Now, it can be reviewed and the prediction can be built.


What next? – Now, after the prediction has been created, the scorecard has to be reviewed. The scorecard is a place one may go to find prediction performance and to identify any setup or modelling issues. The scorecard also tells that if the performance of the prediction is too high, when it’s too high, it means that most likely it is set as hindsight bias and have some potential leaks. Removing these leakers, will make the prediction work as the stakeholders want it to. Also, if the performance of the prediction is too low, it hints that there is not enough relevant data for the prediction to work appropriately. In this case, more relevant data need to be added.

Asking the business experts may help in identifying the information they need to make these decisions. Artificial Intelligence helps by trying to think like humans, so it is good if a human analysis making can help it make better decisions. To increase the prediction quality, there are various choices which can be determined to have an added value for it to work better. Some examples are – if this is a red account, % change in number of cases, number of severity 1 cases, customer success manager and solution engineer sentiment or assessment score, Account Health Score, Account Tier, average NPS score, lead product, and much more. Cloning previous prediction is an option which makes a copy of the earlier prediction and holds all the setup. With a little adjustments, a better version can be created with ease.

Also, one might go to the details of the scorecard to see the top predictions and have the stakeholders check if those predictions make sense for them from a business perspective. The predictions may seem very obvious sometimes, not to get discouraged as it is only the confirmation that the Prediction Builder is following the right patterns for the business.

When the stakeholders are happy with the quality of the predictions, it can be enabled to get the scores. To find out the predicted values, add the prediction field or opportunity score to the list view and relevant page layouts for the sales reps to see them. After a few weeks or few months, real life data will be there and it will be known which opportunity laded up being closed won and which of them landed in closed lost. Then the comparison between prediction results and the actual results can help in determining how the prediction is working for real life data. If needed, the prediction can be revisited for betterment.

How to determine if the built AI project worked and succeeded? – This is where the original goals and KPIs can be revisited. It can be compared if the goals are achieved, the set revenue targets were met and if this scoring helped in adding the values to the company.


Additional Einstein Scoring Benefits for Business Leadership

Salesforce Einstein is an addition to other functionalities to make them smart. It adds to regular sales workflow to make it better than ever before. Here is some additional benefits for the company’s leaderships and managers.

  • Einstein can also provide a high-level information on how the sales teams are doing and possibly intervene before teams miss their sales quota.
  • Predicted values help the leadership to analyse how their sales team will perform that month.
  • The managers can have the sales figures in advance, so they would know which are to focus on, where to give a push to the team. A breakdown in detail among the sales teams and sales reps can help in analysing where things can be improved.
  • No lost deals due to delays. The sales reps will always know when to reengage with the contacts to proceed further with the deal. This makes the manager’s work lesser as now the manager do not have to push and do frequent follow-ups with the sales reps.
  • The managers can visualize their team’s pipeline and determine the number of contacts to be engaged by the team. They do not have to ask individual sales reps. Now, everyone can focus on their own work in hand, more efficiently.
  • Salesforce reports and dashboards will be an additional help for the managers and leadership to visualize and watch sales performance.
  • Forecasting is easier when the opportunity scores are their which shows the likelihood of deals being won.
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