Next Best Action – TrailheaDX

Check out the Next Best Action session from TrailheaDX and learn how you can integrate insights from AI services like Einstein Discovery, IBM Watson, and Aisera with the new capabilities added in the Summer ’19 release.

Use the Next Best Action REST API to execute a strategy and retrieve recommendation reactions

Quick guide on how to use the NBA REST API.  

Executing an NBA Strategy for a Given Record

Official documentation: https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_nba_recommendations.htm

Example:

POST

/services/data/v45.0/connect/recommendation-strategies/Jasmine_Yogurt_Service/recommendations

Body:

{
"contextRecordId" : "5001U000006XEpjQAG", 
"maxResults" : 4,
"strategyContext":{}
}

Response:

Raw Response (in JSON):

{
   "executionId":"23e8f474-6fec-4b5d-b2b5-667ef7ffb03a",
   "onBehalfOfId":"5001U000006XEpj",
   "recommendations":[
      {
         "acceptanceLabel":"Add Hot Waffle Press",
         "description":"Add a new Stanley Waffle Cone Press",
         "imageUrl":"https://isnba--c.documentforce.com/file-asset-public/WaffleMaker1?oid=00D1U0000012w03&v=1",
         "rejectionLabel":"No Thanks",
         "target":{
            "id":"0pr1U00000057IYQAY",
            "name":"Add a Hot Waffle Press",
            "type":"Recommendation",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IY"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "name":"Dispatch_Field_Technician",
            "parameters":[

            ],
            "type":"Flow"
         }
      },
      {
         "acceptanceLabel":"Upgrade to an X120 Soft Serve",
         "description":"Upgrade to a Twenty Gallon Single Nozzle Soft Serve Machine",
         "imageUrl":"https://isnba--c.documentforce.com/file-asset-public/SoftServeMaker1?oid=00D1U0000012w03&v=1",
         "rejectionLabel":"No Thanks",
         "target":{
            "id":"0pr1U00000057IXQAY",
            "name":"Upgrade to an X120 Soft Serve",
            "type":"Recommendation",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IX"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "name":"Dispatch_Field_Technician",
            "parameters":[

            ],
            "type":"Flow"
         }
      },
      {
         "acceptanceLabel":"Offer refinance",
         "description":"0% Refinancing on existing equipment",
         "imageUrl":"https://isnba--c.documentforce.com/file-asset-public/refinance?oid=00D1U0000012w03&v=1",
         "rejectionLabel":"No Thanks",
         "target":{
            "id":"0pr1U00000057IZQAY",
            "name":"0% Refinancing!",
            "type":"Recommendation",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IZ"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "name":"Dispatch_Field_Technician",
            "parameters":[

            ],
            "type":"Flow"
         }
      }
   ],
   "trace":{
      "messages":[

      ],
      "nodes":[

      ]
   }
}

Retrieving Recommendation Reactions

Official documentation– https://developer.salesforce.com/docs/atlas.en-us.chatterapi.meta/chatterapi/connect_resources_nba_reactions.htm#connect_resources_nba_reactions

Example:

GET

/services/data/v45.0/connect/recommendation-strategies/reactions?targetId=0pr1U00000057IUQAY

This will return back all of the recommendations reactions for the recommendation with ID 0pr1U00000057IUQAY.

Similarly you can also query for the contextRecordId (e.g. Case with ID 5001U000006XEpjQAG, account, etc.)

GET

/services/data/v45.0/connect/recommendation-strategies/reactions/?contextRecordId=5001U000006XEpjQAG

RAW RESPONSE:

{
   "currentPageUrl":"/services/data/v45.0/connect/recommendation-strategies/reactions?page=0&pageSize=100",
   "nextPageUrl":"/services/data/v45.0/connect/recommendation-strategies/reactions?page=1&pageSize=100",
   "reactions":[
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-02-28T19:13:43.000Z",
         "id":"0ps1U000000TNzxQAG",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TNzxQAG"
      },
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-02-28T19:13:43.000Z",
         "id":"0ps1U000000TNzyQAG",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TNzyQAG"
      },
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-02-28T19:13:43.000Z",
         "id":"0ps1U000000TNzzQAG",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TNzzQAG"
      },
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-02-28T19:13:43.000Z",
         "id":"0ps1U000000TO00QAG",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TO00QAG"
      },
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-03-07T05:11:29.000Z",
         "id":"0ps1U000000TQzqQAG",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TQzqQAG"
      },
      {
         "contextRecord":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "createdBy":{
            "id":"0051U000004fPpPQAU",
            "url":"/services/data/v45.0/chatter/users/0051U000004fPpPQAU"
         },
         "createdDate":"2019-03-18T18:21:15.000Z",
         "id":"0ps1U000000TYoYQAW",
         "onBehalfOf":{
            "id":"5001U000006XEpjQAG",
            "url":"/services/data/v45.0/chatter/records/5001U000006XEpjQAG"
         },
         "reactionType":"Accepted",
         "strategy":{
            "id":"0sg1U0000004edzQAA",
            "nameAtSnapshot":"Jasmine_Yogurt_Service"
         },
         "targetAction":{
            "id":"3001U000000oZixQAE",
            "nameAtSnapshot":"Dispatch_Field_Technician"
         },
         "targetRecord":{
            "id":"0pr1U00000057IUQAY",
            "url":"/services/data/v45.0/connect/recommendations/0pr1U00000057IUQAY"
         },
         "url":"/services/data/v45.0/connect/recommendation-strategies/reactions/0ps1U000000TYoYQAW"
      }
   ]
}

Integration Guide for Next Best Action

In this guide, we’ll walk you through all the different ways that you can connect to sources of insight from Next Best Action to generate and enhance recommendations and to pull in relevant properties for decision making. Note – the Generate and Enhance elements are new updates, shipping with the Summer ’19 Release.

There are 3 main integration points, all of which require Apex invocable actions:

  1. Generate
  2. Enhance
  3. External connections

The Generate element enables you to create new in-memory, on-the-fly recommendations, either from an external data source or from other Salesforce objects.

The Enhance element enables you to modify a given set of recommendations to include real-time updates, personalized information, or AI driven prediction scores.

And finally you can create an external connection to fetch properties in JSON format from external services. For example, you can connect to a Heroku endpoint to obtain a credit score for a given customer.

Let’s dive deeper into each one. You can install the strategies and invocable actions used in the examples by installing this package in your org. Note – your org must be using the Summer ’19 (or later) release.

Generate

Instead of defining recommendations manually as records in Salesforce, you can use the Generate element to dynamically create temporary, in-memory recommendations as a part of the current strategy execution. These dynamic recommendations can be sourced from external data sources like Commerce Cloud or a SQL database or from other Salesforce objects like Accounts or Products.

For example, you can build a dashboard of prioritized accounts to connect with.

Or create recommendations sourced from the Products object.

The Generate element has the following characteristics:

@InvocableMethod(
label='Related Wikipedia Pages'
description='Recommend wikipages that are related to the named input wikipage')
  • It can pass any number of inputs to the Apex Action, either as lists or list of lists of primitives, SObjects, and user-defined Apex objects. To provide more than one input, the input parameter MUST be a list or list of lists of a user-defined Apex object (e.g. a custom class called DataContainer).
List<String> relatedTo

OR

global class DataContainer {
@InvocableVariable
public string accountId;
}
____
global static List<List<Recommendation>> invocableMethod(List<DataContainer> inputData)
  • It returns a list of recommendations. InvocableMethods support returning either a list of an SObject type or a list of lists of an SObject type. As the Enhance element operates on a list of recommendations and not a single recommendation, the method must return a List<List<Recommendation>>.
global static List<List<Recommendation>> invocableMethod(List<DataContainer> inputData)

Example: Recommend accounts the current user should follow up on today
Suppose you want to recommend the top accounts your salespeople should meet with today because the last interaction was more than 90 days ago. With the Generate element, you can call an Apex Action which does a SOQL query for Accounts where the Owner is the logged in user (the salesperson) and identify those accounts where the last contact date was more than 90 days ago. The relevant accounts can be returned in the form of recommendations.

The strategy can be as simple as the Generate element with an Output element.

In the configuration dialog of the Generate element, select Accounts to Follow Up Today as the Apex Action and pass in ‘$User.id’ as an input parameter.

The Generate element, in turn, will call the getAccounts invocable method in the Generate_GetAccountsToFollowUp Apex class. Notice how you can fetch the relevant accounts and create a new list of recommendations where the Description includes the name of the account (account.Name) and the number of days since last contact (daysSinceLastContact).

global class Generate_GetAccountsToFollowUp {  
//NOTE: You can get the user id of the current user by also doing UserInfo.getUserId(). 
    @InvocableMethod(label='Accounts to Follow Up Today' 
                     description='Recommend accounts the current user should follow up on today')
    global static List<List<Recommendation>> getAccounts(List<String> inputData){
        List<List<Recommendation>> outputs = new List<List<Recommendation>>();
        Integer daysSinceLastContact;
        Account[] accounts = [SELECT Name, Description, LastContactDate__c, OwnerId FROM Account WHERE OwnerId = :inputData[0]];
        List<Recommendation> recs = new List<Recommendation>(); 
        for (Account account:accounts) {
            if (account.LastContactDate__c != null){
                daysSinceLastContact = account.LastContactDate__c.daysBetween(date.today());
                if (daysSinceLastContact > 90){
                    Recommendation rec = new Recommendation(
                        Name = account.Name,
                        Description = 'Connect with the ' + account.Name + ' account, the last interaction was '+ daysSinceLastContact + ' days ago.',
                        //Pre-req: Create a screen flow with the name simpleFlow                        
                        ActionReference = 'simpleFlow',
                        AcceptanceLabel = 'View'
                    );
                    recs.add(rec);
                }
            }
        }
        outputs.add(recs);
        return outputs; 
    }
}

Example: Get related Wikipedia pages as recommendations
You can also use the Generate node to create recommendations for external data sources. For example, consider a scenario where you want to show related Wikipedia pages, as recommendations, for a given topic. The Generate element enables you to call an Apex Action which in turn makes a GET request to a Wikipedia endpoint. The pages are returned in the form of recommendations.

A strategy with the Generate element can be as simple as the Generate element with an Output element.

In the configuration dialog of the Generate element, select Related Wikipedia Pages as the Apex Action and pass in ‘India’ as an input parameter.

The Generate element will call the getPages invocable method in the Generate_GetWikiPages Apex class. Notice how there is a mapping of Wikipedia articles to recommendation fields.

//Guidance around Apex class definitions: https://developer.salesforce.com/docs/atlas.en-us.apexcode.meta/apexcode/apex_classes_defining.htm
global class Generate_GetWikiPages {
    //InvocableMethod Guidance: https://developer.salesforce.com/docs/atlas.en-us.apexcode.meta/apexcode/apex_classes_annotation_InvocableMethod.htm
    //Input parameter must be a list or list of lists of either primitive type or Object type
    @InvocableMethod(
        label='Related Wikipedia Pages'
        description='Recommend wikipages that are related to the named input wikipage')
    global static List<List<Recommendation>> getPages(List<String> relatedTo){
        
        List<List<Recommendation>> outputs = new List<List<Recommendation>>();
        Http http = new Http();
        HttpRequest request = new HttpRequest();
        String urlName = relatedTo[0].replace(' ','_');
        // Pre-req: Set up a Remote Site Setting for this to work.
        request.setEndpoint('https://en.wikipedia.org/api/rest_v1/page/related/'+urlName);
        request.setMethod('GET');
        HttpResponse response = http.send(request);
        if(response.getStatusCode() == 200) {
            Map<String, Object> result = (Map<String, Object>) 
                JSON.deserializeUntyped(response.getBody());
            List<Recommendation> recs = new List<Recommendation>();
            for(Object item : (List<Object>)result.get('pages')) {
                Map<String, Object> page = (Map<String, Object>) item;
                Recommendation rec = new Recommendation(
                    Name = (String)page.get('displaytitle'),
                    Description = (String)page.get('extract'),
                    //Pre-req: Create a screen flow with the name simpleFlow
                    ActionReference = 'simpleFlow',
                    AcceptanceLabel = 'View'
                );
                recs.add(rec);
            }
            outputs.add(recs);
        }
        return outputs;
    }
}

Enhance

The Enhance element allows you to modify a given set of recommendations on-the-fly, every time the strategy is executed. These recommendations could be static recommendations that live as records in Salesforce or dynamic recommendations sourced from external data sources or other Salesforce objects. For example, you can use the enhance element to calculate a discount % for your customers based on how long the account has been with your company or you can use it as a means for A-B testing two branches of recommendations.

The Enhance element has the following characteristics:

@InvocableMethod(
label='Enhance with Discounts Based on Age' 
description='Returns an enhanced set of recommendations with appropriate discounts')
  • IMPORTANT – It can pass any number of inputs to the Apex Action. The input parameter MUST be a list or list of lists of a user-defined Apex object (e.g. a custom class called DataContainer) and the user-defined Apex object MUST include a List<Recommendation> variable. The List<Recommendation> variable will be automatically set with the recommendations flowing into the Enhance element.
global class DataContainer {
    @InvocableVariable
    public string accountId; 
    
    @InvocableVariable
    public List<Recommendation> recommendations;
}
________
global static List<List<Recommendation>> invocableMethod(List<DataContainer> inputData)
  • It returns a list of recommendations, List<List<Recommendation>>. Note – these recommendation edits are only in-memory and are not persisted after the strategy execution.
global static List<List<Recommendation>> invocableMethod(List<DataContainer> inputData)

Example: Enhance Recommendations with Discounts Based on Customer Age

Suppose you use Next Best Action to provide upsell recommendations. You’d like to reward your loyal customers by adding a 5 % discount to your product recommendations if the customer has been with your company for more than 1 year, 10% for more than 2 years, and 20% for more than 5 years. With the Enhance element, you can call an Apex Action which does a SOQL query to fetch the Account age and append it to the description of all incoming recommendations.

The strategy with the Enhance element can be as simple as Load → Enhance → Output. All recommendations retrieved or loaded by the Load element are implicitly passed as a list of recommendations to the underlying invocable method.

In the configuration dialog of the Enhance element, select Enhance with Discounts Based on Age as the Apex Action and pass in ‘$Record.id’ as the input parameter.

The Enhance element will in turn call the getDiscounts invocable method in the Enhance_GetAccountDiscount class. Notice how Description of each recommendation has a discount value appended to it (r.Description + ‘ with a 5% discount’).

global class Enhance_GetAccountDiscount {
    @InvocableMethod(label='Enhance with Discounts Based on Age' description='Returns an enhanced set of recommendations with appropriate discounts')
    global static List<List<Recommendation>> getDiscounts(List<DataContainer> inputData){
        
        List<Recommendation> recommendations = inputData[0].recommendations; 
        List<List<Recommendation>> outputs = new List<List<Recommendation>>();       
        Account[] accounts = [SELECT Name, Description,CreatedDate, id FROM Account WHERE id = :inputData[0].accountId];
           Double ageAccountMonths = accounts[0].CreatedDate.date().monthsBetween(date.today()); 
        Double ageAccount = ageAccountMonths/12;       
        List<Recommendation> returnedRecommendations = new List<Recommendation>(); 
        for (Recommendation r:recommendations){
            if(ageAccount > 1){
                r.Description = r.Description + ' with a 5% discount';
            }
            else if (ageAccount > 2){
                r.Description = r.Description + ' with a 10% discount';
            }
            else if (ageAccount > 5){
                r.Description = r.Description + ' with a 20% discount';
            }
            returnedRecommendations.add(r);
        }
        outputs.add(returnedRecommendations);                 
        return outputs;
        
    }

}

External Connections

External Connections have the following characteristics:

  • Like Enhance and Generate elements, they work in tandem with an Apex Action created using by adding the Invocable Action annotation to an Apex Class.
  • Unlike Enhance, External Connections do not take a set of recommendations as inputs. They return a single set of properties that can be referenced in expressions but not mapped to recommendations. Unlike Generate, External Connections do not result in the creation of new, temporary recommendations.
  • They run before the strategy is processed, and not during the processing of the strategy.
  • Their values are globally accessible (i.e you can use them in every element of the strategy).
  • They can return a broad range of different properties (Generate and Enhance always return a List of Recommendations).

Example: Calling a Heroku endpoint to fetch a credit score
Let’s suppose that you make certain upsell offers to your customers based on their credit-worthiness. For example, you offer them a 0% refinancing only if their credit score is above 750. Credit scores aren’t typically something you would store in Salesforce because they change dynamically and frequently. Using an external connection, you can call an Apex Action which will call an external endpoint and return the credit score given some information about the user.

global with sharing class GetFranchiseeData {
    @InvocableMethod
    public static List<Results> getCreditScore(List<Requests> requests)
       {
            Http http = new Http();
            HttpRequest request = new HttpRequest();
               //Make sure you add the below endpoint as a remote site setting
            request.setEndpoint('https://repair-service.herokuapp.com/api/creditScore?customerId='+ requests[0].custId);
            request.setMethod('GET');
            HttpResponse response = http.send(request);
             Results curResult = new Results();
            curResult.creditScore = Integer.valueOf(response.getBody());
           
            List<Results> resultsList = new List<results>();
            resultsList.add(curResult);
            return resultsList;
       }
    
    global class Requests {
        @InvocableVariable
      global String custId;
        
    }
    
    global class Results {
        @InvocableVariable
      global Integer creditScore;
    }
}

To call the above Apex Action, create an external connection and pass the Account’s External ID (If you pass values less than 4000, the API will return 550. It will return 810 for values greater than or equal to 4000.)

You can filter the Zero % Refinancing Offer based on the Credit Score by referencing the connection in the Filter node.

What’s New with Next Best Action in the Summer ’19 Release?

We have a bevy of new features in the Summer ’19 Release with 3 new elements – Generate, Enhance, and Map, the Actions and Recommendations Lightning component, and support for packaging strategies. Check out the video to learn more!

Use Einstein Next Best Action with Flow by Marc Baizman

Ever wanted to inject Einstein Next Best Action into your Flows? Check out this post by Marc Baizman.

Accessing NBA via the Tooling API

The Tooling API provides a way to get information about Action Strategies. I’m more familiar with the Metadata API, but in general Salesforce is trending towards the Tooling API as a more flexible, granular tool, and this is likely to increase. For performance reasons, metadata API deploys and retrieves will often trigger a recompilation of all of an org’s Apex. (This is controlled by a setting in Apex Settings called “Perform Synchronous Compile on Deploy” which is on by default in many orgs). The recompilation can make the mdapi deploy/retrieve too slow for quick data calls of the sort you’d want for your apps. So Tooling API is the way to go.

/services/data/v45.0/tooling/sobjects/RecommendationStrategy/describe

You can provide a recordId and get all the information about the record with:

/services/data/v45.0/tooling/sobjects/RecommendationStrategy/0sgB000000003PcIAI/

This returns a json data structure that’s visualized here in workbench:

To retrieve a list of strategies, use Select query language. For example:

/services/data/v45.0/tooling/query/?q=Select+id,MasterLabel,DeveloperName+from+RecommendationStrategy

As a tip, it is considerably easier to play with this api if you install the Salesforce Workbench Tools for Google Chrome. Just go to a page that’s logged in to your org and then click this button in the toolbar.

Building Your Own Next Best Action Front End

You aren’t limited to using the lightning components that Salesforce has provided for displaying recommendations. You can POST your own execution calls to the Action Strategy Engine REST endpoint and get back recommendations as a JSON blob.

Executing a Strategy

To execute a strategy use one of the supported endpoints.

It’s recommended that you provide a contextRecord ID, although if your strategy doesn’t have any $Record references, it will not be used. Here’s an example of a POST body:

{    "strategyContext":
{ "key1": "val1",
"key2": "val2" },
"contextRecordId": "a0lB0000001G2nFIAS",
"maxResults": 5
}

Note the strategyContext section in the above example. This powerful mechanism allows you to pass in additional context that you can then reference in your strategy’s expressions. There’s currently no way to configure these additional values in the standard Next Best Action components but when you’re making your own REST calls, you can provide all you want.

Handling the Response

The JSON response will include an array of recommendations and some general information such as error text.

Each recommendation in the array contains all of the information typically used by the Next Best Action component to display the recommendation.

Each recommendation will include a reference to a screen flow. If you want to run the screen flow you’ll want to incorporate the lightning:flow component into your solution and pass it the name of the flow and any parameters. Alternatively, you can encode action information into custom fields of the recommendation. Example: You have two recommendations: “Buy Fish Food from Amazon” and “Buy Tools from Amazon”. You’re using these recommendations in a custom Amazon ordering application. You add a custom field to your Recommendation object called “Product” and store ‘Fish Food’ on one and ‘Tools’ on the other. When you programmatically call the NBA engine, you get back the Buy Fish Food from Amazon recommendation. Part of that response includes the recommendation ID. You then query for that recommendation and read the value of its Product field. You now have the value that your private Amazon ordering system needs to automatically order.

Transmit NBA Errors to Custom Notification Targets

When we built Next Best Action, we set things up to fire a platform event whenever an error occurs. You can leverage Process Builder and Flow to create custom notification profiles that flexibly get error messages to where you want them. Learn more.

Next Best Action: What’s a Request?

Next Best Action is a paid Salesforce product but also is available for free use for up to 5000 requests each month.

What is a Request and how do they get generated?
A ‘request’ is a call to the NBA engine that causes an NBA ActionStrategy to run and return some recommendations. The primary way these requests are made is via the placement of the Einstein Next Best Action lightning component on a record page in App Builder or via the placement of the Suggested Actions community builder component on a Community page:

Each time a page with an NBA component is loaded into a web browser, a new request will be generated.

The secondary way requests are made is by calling the NBA endpoint from some other piece of software, such as an IOS or Android native app. An app that does this will make requests in response to custom UI, and get back the recommendation information as text that it is then responsible to present.

A request is also made when i) a record detail page includes the Next Best Action component and a record field is updated and ii) when the communities contact support page includes the Next Best Action component as the user types in the subject/description fields.

Where Can I See My Usage?

Look in Setup —> Company Information.

Learn More: Try out Next Best Action via this video tutorial.

The Top 10 Things You Want To Know About New Next Best Action

1. Generally Available

Next Best Action (NBA) is generally available starting today and the great news is that it’s enabled for all editions. But wait it gets better – every org receives 5000 requests per month for free!

2. Here’s the Getting Started Video

Ok, you heard that it’s free and it sounds very cool, how do you get started? Here is a great video to get the ball rolling.

3. Documentation is online

Need more information about Next Best Action? Here is some ever-improving documentation.

4. This post is part of a new, unofficial, community-powered site that hosts Next Best Action content

This will be a place where you can find tips and tricks, explore interesting use cases, share videos, and more. Here’s the home page. You’ll want to bookmark it for future reference.

5. There are 8 new How To Guides for additional Next Best Action features

Check them out here!

6. Lightning is recommended but not required

The Lightning Experience is not required to run NBA but it is recommended. Now, you’ll need Lightning to build NBA action strategies using the Strategy Builder. However, you can display recommendations on both Lightning and Classic experiences. With Lightning, simply use the out-of-the-box Einstein Next Best Action Lightning Component. With Classic, present your recommendations via VisualForce using Lightning Out. Want to learn more? Check this out.

7. You can add additional request capacity to your Org

If you love the product (and we just know you will) and want more requests, just contact Salesforce. What is a request, you might ask. A request is simply page loads and refreshes which result in your strategy being evaluated for recommendations.

8. Testing your strategies is easy

Like all good things, building the right strategy requires some trial and error. You can quickly test your logic and make sure you’re loading the right recommendations by clicking Test in the Strategy Builder. To learn more, check out this doc.

9. Recommendations can be displayed in a variety of places

Once you’ve built your strategy, you can embed recommendations in any app and even in a Community portal.

10. Early customer feedback shows tremendous productivity gains

We’ve already seen some early customer success stories and anticipate more in the coming weeks. One of our pilot customers, a European electronics company, experienced a 90% drop in agent hold times after adding Next Best Action. Before NBA, agents would spend almost 8 minutes and 9 painful clicks sourcing and pulling information before determining whether a customer was eligible for a special discount. With NBA, in less than 1 minute and with only 1 painless click, agents instantly know whether the customer is eligible and can quickly walk through a Flow to offer the discount.