Coming full circle with Marketing Cloud & Einstein Analytics


Marketing departments are no different than sales or finance, they too want insight. With all the data marketing is generating it becomes that much more interesting to start having a tool that can give you good insight. Salesforce have two marketing automation tools – Pardot and Marketing Cloud. Pardot already has an app that leveraging Einstein Analytics, but Marketing Cloud doesn’t, however, we do have a default connector we can use. Therefore, I decided to explore what we could do with Marketing Cloud and Einstein Analytics, which ended up being 5 blogs.

December 2019 update: with the new authentication in Marketing Cloud there is a new type of installed package as well as connector to use, which is covered in the blog A new OAuth 2.0 Marketing Cloud connector in Einstein Analytics.

I hope this blog series has inspired you how to get Marketing Cloud and Einstein Analytics connected.

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9 thoughts on “Coming full circle with Marketing Cloud & Einstein Analytics”

  • 1
    Tom H on December 3, 2019 Reply

    Hey Rikke, wouldn’t Datorama be a better tool then EA for marketers when they are bringing in more then just MC data? EA doesn’t seem scalable for all of the different marketing datasources..

    • 2
      Rikke on December 6, 2019 Reply

      It always comes down to use-case and tools available.

  • 3
    ANDERS BLAASTRUP KNUDSEN on June 9, 2020 Reply

    Hi Rikke,

    What are the use cases of using both EA&D and Datorama?
    Where do they compliment each other?


    • 4
      Rikke on June 9, 2020 Reply

      I don’t know enough about Datorama to give a qualified answer. But to my knowledge Datorama really strong on connecting different marketing tools and providing a out of the box rapporting to those. EA is fully customizable but you don’t have all the connectors to marketing tools. Also I guess EA is better embedded in CRM.

  • 5
    ANDERS BLAASTRUP KNUDSEN on June 9, 2020 Reply

    HI Rikke,
    Thanks for the immediate answer.
    I have reached out to Gert and Oskar from SFDC 🙂

  • 6
    ANDERS BLAASTRUP KNUDSEN on June 12, 2020 Reply

    Hi Rikke,
    A little mind teaser on a Friday.
    We are on a new project and have stumbled into an exciting issue.
    We aim to add a sum or total of a column into the dataflow leaving us with a new column all containing the same value (the total of the other column). Is this possible – haven’t found anyone mentioning they have achieved this.
    The idea is to use flow, not recipe.
    kind reg.

    • 7
      Rikke on June 12, 2020 Reply

      Check out compute relative. But if it’s the sum of a whole column isn’t it just enough to aggregate on the dashboard?

      • 8
        ANDERS BLAASTRUP KNUDSEN on June 12, 2020 Reply

        HI – thing is we tried the compute relative. But I can’t figure out how to get the total of the column, even if I flag each column and set it as a partition.
        The total is needed for further calculations in the dataflow, due to the issue with making a rolling total on a column based on a formula in the compare table.
        hope it makes sense 🙂

        • 9
          Andrew T. Carroll on September 17, 2020 Reply

          This may be a bit late, and but hopefully helpful nevertheless.
          I understand you want a groupby aggregation that then gets broadcast to all rows with the same groupby key in the original dataset. In Pandas, we would use .groupby(key)[‘field’].transform(sum) or something like that, but that’s not available in a dataflow. (There is a recipe way to do this as well, but I think you’re looking for a dataflow.)
          The fundamental idea I’m using here is the following: Suppose that table A has columns X, Y, Z, where X is a column of identical entries “dummy”, Y is some ID that you’re interested in, and Z is a numeric column that you want to sum.
          A augment A
          Left id = X
          Relationship = s
          Right id = X
          Right fields = Z
          Method = “Lookup Multivalue”
          what actually happens is that the augment acts as an aggregator. You’ll get columns X, Y, Z, s.Z, where s.Z will be the sum of all of the entries in Z.

          If you don’t have a column like X in your data, then create one with a computeExpression node: name = X, formula = “dummy”

          Incidentally, if you wanted to know the total of Z across the ids in Y, the same idea would work, replacing the id fields with Y in lieu of X.

          In the end, a groupby aggregation-type node would be a good future-state, but look at recipes in Winter ’21 to get really stoked about that.

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