Complete Overview of Pardot Einstein Features [Updated 2020]

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Pardot Einstein is a set of AI-powered features for Pardot marketing automation, namely Einstein Behaviour Scoring, Einstein Lead Scoring, Einstein Campaign Insights, and Einstein Attribution. The overriding aim of these technologies is to find patterns buried deep in your engagement data, recognise themes, and take action, such as making recommendations to users.

Pardot Einstein is taking your Pardot marketing data and pulling out the stops – it really is mind-blowing. A couple of the features (Einstein Behavior Scoring, Einstein Lead Scoring) are enhancing existing functionality, which you could say is the natural progression from rules-based to AI-based functionality – whereas the other two features (Einstein Campaign Insights, Einstein Attribution) are introducing new concepts entirely. Either way, is important to bear all 4 features in mind for future planning.

“Conga"

I had a conversation with Alon Shvo, Product Manager at Salesforce Pardot, who is working closely with the Einstein Attribution and Einstein Campaign Insights products in particular. He shed light on the team’s biggest aims for Pardot Einstein, and explained how the AI-enabled features work below the hood, in laymans. I will share his insights in this post, plus an overview of each of the 4 products.

Pardot Einstein Overview Table

Pardot Einstein is made up of 4 features:

  • Einstein Behaviour Scoring
  • Einstein Lead Scoring
  • Einstein Campaign Insights
  • Einstein Attribution

 

 One-line SummaryCurrent Rules-based method*Available?Licenses Required?Feature PrerequisitesData Requirements?
Einstein Behavior Scoring“Uncover the most influential behavior signals
across past and current prospect engagement”
Padot ScoreYesPardot Advanced Edition

Salesforce Enterprise Edition (and above)
Salesforce Lightning ExperienceNone - but 6+ months of prospect engagement data and 20+ prospects linked to opportunities is recommended.
Einstein Lead Scoring“Score your leads by how well they fit your company’s successful conversion patterns. Let your sales team prioritize their leads by lead score”Pardot GradeYesPardot Advanced Edition

Salesforce Enterprise Edition (and above)

** Also included in Sales Cloud Einstein
Salesforce Lightning Experience1,000+ leads created in the last 6 months. At least 120 should be converted to an account, contact & opportunity (created at the time of conversion).
Einstein Campaign Insights“Trends in prospect demographics and marketing asset engagement”Standard Salesforce Reports

Engagement History Metrics?**
Yes
(Summer ‘19 release)
Pardot Advanced Edition

Salesforce Enterprise Edition (and above)
Pardot Connected Campaigns

Salesforce Lightning Experience
50 connected campaigns (with Engagement History data)
Einstein Attribution“Leaves rules-based influence models in the rearview mirror”Campaign InfluenceSummer ‘20 releasePardot Advanced Edition

Salesforce Enterprise Edition (and above)
Pardot Connected Campaigns50 opportunity contact roles to get started!

*in other words, which existing feature is being transformed with intelligence.

 **Campaign Insights is looking to surface meaningful insights that are hidden in the Engagement Data vs. providing the overview – so, it’s like the “cherry on top”!

Why Use Pardot Einstein? Product Development Priorities

As Pardot Einstein remains available to Advanced edition customers only, the key question becomes ‘should I buy Pardot Einstein?’ How justifies the cost of Pardot Einstein in their organisation is to start with the potential ways you can use Pardot Einstein.

In my conversation with Alon Shvo, Product Manager at Salesforce Pardot, he shed light on how the AI-enabled features work below the hood, in laymans. Already, this was helpful for me to see how I would explain/re-explain the practical application of Pardot Einstein in different business contexts.
The team’s biggest aims for Pardot Einstein were to:

  • Overcome limitations with rule-based features
  • Crunches large datasets, more than you can wrap your head around!
  • Provide ‘rationale’ (explanations)
  • Takes time into account as a factor (ie. scoring) and weights recency more heavily.

Priorities the Pardot Einstein Development teams had front-of-mind when developing:

  • Turn-key: always aim for the product to be turn-key (aka. ‘plug-in-and-play’).
  • ‘Human in the loop’: a human data analyst monitors models to ensure they are running as expected.

Pardot Einstein Setup & Administration

Find these within Salesforce Setup, by searching for ‘Pardot’:

Most up to date implementation guide is found here.

 

Einstein Behaviour Scoring

“Uncover the most influential behavior signals across past and current prospect engagement”

Decays over time automatically – one pain point of classic rules-based Pardot Score.

What it does in a nutshell:

  1. Looks at each Prospect and their Pardot Activities (Engagement History),
  2. Identifies which activities are positive signals, and which are negative.
  3. Trains a machine learning model that scores every prospect based on the likelihood they will convert,
  4. Assigns each Prospect a score from 0-100 in relation to how they compare to all other Prospects in your database.

Read more here.

 

Einstein Lead Scoring

“Score your leads by how well they fit your company’s successful conversion patterns. Let your sales team prioritize their leads by lead score”
What it does in a nutshell:

  1. Looks at each lead and their field data (standard and custom fields),
  2. Identifies field data (characteristics) that are positive signals, and which are negative – eg. Lead source is ‘G2 Crowd’
  3. Trains a machine learning model that scores every lead based on the likelihood they will convert (in Salesforce to an account, contact, and/or opportunity),
  4. Assigns each lead a score from 0-100 in relation to how they compare to all other leads in your database.

Source: Salesforce

 

Einstein Campaign Insights

“Trends in prospect demographics and marketing asset engagement”

  • Analyzes Prospects engagement activity with the different marketing assets, as well as key Prospects’ attributes such as job title, location and company industry.
  • Looks for anomalies, both positive and negative, in the engagement data.
  • Surfaces the most meaningful ones in the shape of Campaign Insights.

In other words, Einstein Campaign Insights is asking two questions:

  1. What is exceptional about the prospect activity in this specific campaign vs. all other campaigns?
  2. Who are the most interesting segments within a specific campaign in terms of engagement patterns?

 

It looks at your entire marketing asset dataset eg. it compares the engagement of emails in a specific campaign, to all emails stored in your org. In the background, it will slice your prospects that are part of the campaign into segments eg. by job title, account industry, country – and point out differences between them.

Tracked marketing assets:

  • List emails (not including Engagement Studio emails, autoresponders),
  • Marketing forms,
  • Landing pages.

Possible explanations (aka rationales):

Email open rate, email open rate with subject, and email open rate with audience segment.

Other things to note:

  • You can create custom reports to further ‘slice and dice’ the campaign insights for specific audiences and content. Find a full list of fields available here.

 

Einstein Attribution

“Leaves rules-based influence models in the rearview mirror”

When I first heard about Einstein Attribution a few months ago, my mind was blown. This product is Alon’s baby – I could tell how proud he was to show me, after all, the product is achieving something pretty remarkable.

‘Rules-based influence models’ are the first-touch/last-touch/even-touch Campaign Influence models you will be familiar with. These models rely on data relationships (opportunity contact roles) and timing (Campaign History at the time of lead conversion etc.), which makes this way of measurement less accurate overall.

What does Einstein Attribution aim to do? Essentially, Einstein Attribution will take you one step closer to the holy grail of accurate marketing attribution, by picking up gaps in attribution using a AI data-driven model that “attributes revenue share based on your actual customers, their engagement, and your successes”.

It’s an additional Campaign Influence model called ‘Data-Driven Model’ (and will appear in the Campaign Influence models list in Salesforce setup, alongside your other standard ones).

Virtual Opportunity Contact Roles

The greatest takeaway about Pardot Einstein Attribution is that there’s no dependency on opportunity contact roles (which are a pain to keep consistent).

‘Virtual’ opportunity contact role records are created. These are not actual contact roles, but are created only to be leveraged in the back end.

The data model crunching all the relevant data (leads, contacts, opportunities, activities etc.) that can help determine which individuals have a role in an opportunity. As a result, this creates an attribution graph using data extraction (eg. domain matching algorithms). The result is ‘Virtual’ opportunity contact roles that plug the gaps where real opportunity contact roles haven’t been created (but should be there). The team say that virtual opportunity contact roles have up to a 10x greater attribution coverage – think about how much opportunity credit that is discovering! The image below shows how this works.

  • Besides Contacts, Einstein Attribution is able to leverage leads to accounts matching algorithms to associate prospects and leads directly with Opportunities that they are involved in.,
  • Uses Campaign Influence architecture, so the model’s output is actually campaign influence records.
  • You can still configure the influence timeframe (up to a max of 2 years),
  • If an opportunity was created before the marketing touchpoint, then this marketing touchpoint won’t be included in the model – so there’s risk of cross-over in time periods,
  • Minimal data prerequisites: at least 50 opportunity contact roles are required to ensure optimal model performance.
  • ‘Data-driven model’ is a new influence model that will appear in the list in Setup beside first-touch, last-touch etc.

I recommend you watch this webinar recording to learn more.

Multi-Touch Attribution Dashboard

The star of the show is exploring the Data-Driven Model in the B2B Marketing Analytics Multi-Touch Attribution dashboard:

As you may know, the Multi-Touch Attribution dashboard is one of the dashboards that comes out-of-the-box with B2B Marketing Analytics. You don’t have any additional setup effort here! Just as you would use the pre-made filter to switch between a first-touch and last-touch view, you do the same to show the Data-Driven Model:

I think it will be fascinating for marketers to be able to compare the dashboard for different models, being able to visually see how much additional opportunity credit Einstein Attribution has been able to pick up, that otherwise would have been lost!

Summary

This guide has, hopefully, given you a clearer idea of Pardot Einstein’s 4 features – what they do, the equivalent rules-based feature, availability, licensing, and any prerequisites in terms of data and other features to enable (refer back to the summary table for all of these points).

The overriding aim of these technologies is to find patterns buried deep in your engagement data, recognise themes, and take action, such as making recommendations to users. There’s no doubt that Pardot Einstein will continue to build from strength to strength as more marketers understand the product evolution and recognise how Pardot Einstein can be used in their own organisations  – especially when solving persistent problems, such as the gaps preventing accurate marketing attribution!

My sincere thanks goes to Alon Shvo, Product Manager at Salesforce Pardot, who contributed to this article, and was patient answering my questions as I wrapped my head around how the features worked!

Stay tuned for more content on Pardot Einstein, especially around Einstein Attribution (coming Summer ‘20)!

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