Einstein Analytics for B2B Marketing was unveiled during Dreamforce – what is Einstein Analytics for B2B Marketing? What does it do? How is it different? So many questions rattling around in my head, I made it my mission to get a sneak peek and learn more about this futurist reporting platform.
Einstein Analytics for B2B Marketing (let’s just use EAB2BMA) is still in the development phase, however, exciting news on the ground is that the beta will kick off in February 2020 (Spring ’20 release), for selected users to get their hands on the product. As well as experiencing the energy in the room first-hand during the Roadmap session, it was fantastic to attend the dedicated session (twice) and meet Danielle Grau (Product Manager) and members of the team that built the product.
Einstein Analytics for B2B Marketing – No BS Overview
EAB2BMA is an analytics platform* for marketing data – but adds a predictive element into the mix. Analytics tells you what happened already (descriptive), whereas Einstein Analytics tells you what will happen (predictive).
In short, EAB2BMA will crunch the numbers and tell you:
- What happened
- Why it happened
- Predictions and improvements
My guess is this will be best illustrated by a use case, which I will come to later.
*with an analytics platform, you can display your data (Salesforce/Pardot/external) in different charts and graphs, with the ability to ’slice and dice’ (filter etc.) data as you wish.
Important: Einstein Analytics for B2B Marketing vs. B2B Marketing Analytics
The most important thing to know is the difference between Einstein Analytics for B2B Marketing (EAB2BMA) and B2B Marketing Analytics (B2BMA); something we are familiar with, to paint a clearer picture of what’s to come. As I just mentioned, there’s a difference between descriptive analytics vs. Predictive analytics – let’s start with this distinction and build on from there.
|Nutshell||Descriptive - what happened||Predictive - what will happen|
|Availability||Generally available - released late 2018/early 2019 (Spring ‘19)||Beta starts February 2020 (Spring ’20 release)|
|Licensing||B2B Marketing Analytics permission set, which comes with Pardot Plus/Pardot Advanced editions.||Einstein Platform license.|
|What do you get?||‘Restricted’ version of Einstein Analytics.|
Gives you access to B2B Marketing Analytics only - which comes with 5 dashboards out-of-the-box, plus ability to create your own using the 14+ Pardot datasets.
|Gives you access to the Einstein Analytics platform which includes 20+ apps (eg. Sales, Service).|
|Predictive Data Modelling||No data modelling.||Templates will give you prediction modelling out of the box (otherwise building your own would be a very steep learning curve!).|
|Perfect Fit||Any Pardot customers. |
(providing they have the budget for Plus/Advanced)
|Enterprise customers - it’s a beefy tool which will come on to the market at a meaty price-point.|
Use Case: Account-based Marketing
The first EAB2BMA template developed (that will enter Beta) is Account-based Marketing (ABM). We’ve heard about ABM – a hot trend that’s continued to have the industry talking for years – because it makes sense for B2B marketing teams to reimagine their database from an account view vs. individuals.
To summarise this analytics template in three words: Improve My Pipeline. Here are three insights the demo covered, that aim to help marketers improve their pipeline.
Predicted Pipeline vs. Actual Pipeline
The predictive data models (that come pre-built with the analytics templates) look into the datasets for underlying patterns/causes, and use these to predict future outcomes; in this way, EAB2BMA will look into past opportunity data, look for underlying patterns/causes, and use these to predict future pipeline.
Placing Actual Pipeline head-to-head with Predicted Pipeline in the same chart clearly shows the missing potential in pipeline. This can be sliced by region, product line, or any other dimension; revealing that one product has the potential to add an exceptional amount of pipeline is an intervention marketers have the power to make with a product-specific initiative!
Number of Employees in Account
Number of Employees is a common measure for an organisation’s size, and therefore, who to target your product to. You may have defined a specific segment, for example, 6,501 to 14,800 employees. Then, you want to zoom in on what positively influences opportunities, and what factors negatively impact pipeline:
- Cases where segment did better: this may have been worsened by “Campaign Type” being blank.
- Cases where segment did worse: this may have been worsened by “Industry” being blank.
Of course, these are demo examples, but you get the idea.
Another example was explaining variation – uncovering factors that are causing whether pipeline is high, or low and the extent of that factor’s influence. For example, factor A explains a 10% variation in the measure (eg. Amount).
A campaign example was shown in the Pardot Roadmap session as a static screenshot. Example insights from this campaign showed that there’s $14.5 predicted revenue, with an extra $2.7k potential pipeline by adding these prospects to the ‘Fall Promotion Campaign’.
As I said, this is the template that will go to beta in February, with the idea that more will join over time.
EAB2BMA is an analytics platform for marketing data – but adds a predictive element into the mix. The Einstein Analytics pre-built templates and data models are well-suited for the enterprise B2B Marketer that wants to hit the ground running with predictive analytics.
Meeting the team that developed the product was one of my Dreamforce ‘19 highlights – did you know that EAB2BMA was built within the space of a couple of months? Very impressive! I’m looking forward to hearing what’s to come.