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Case Study

Google Analytics Integration Increases Sales 45% & Sales Efficiency 50%

Challenges

Google Analytics Salesforce Integration

ABB PG uses Google Analytics was collecting an enormous amount of behavioral data about their website users. This Google Analytics user data includes which pages a user view, what they are clicking on, how recently and frequently have they visited, etc. This information was required by all sales teams to analyze the leads and related product interests with additional information such as:

  • How long has it been since this person last visited the site?
  • Has the visitor viewed promotional content (such as one-pagers or videos)?
  • What search terms have they been entering?
  • Which help articles have they been reading?

This was required for the sales teams to become effective at addressing leads and close more deals, meaning directly within Salesforce Lead, Contact and Opportunity records. However, this type of information was not passed into Sales Cloud with the standard integration that are typically standard integrations between Google Analytics and Salesforce that are intended to benefit marketers and not necessarily sales teams (who manage relationships with individual leads)

What did CCI do?

Established the limitations and required decisions related to the standard integrations namely:

  • Required access to the raw hit-level data that Google Analytics captures to do this (You cannot access the behavior of an individual user with the Google Analytics reporting APIs alone).
  • Decide build or buy an application that can automate the data transfer process to push this data into Salesforce.
 

The only way to get around these two challenges is to use the Collect Tracking Code, which is JavaScript that you can add to a website to fire behavioral data directly to Salesforce. This is a non-starter for most companies because it means re-tagging your website or application to copy the same information it sends to Google Analytics and fire it into Salesforce.

This is inefficient and introduces a whole host of other new challenges. If you are already capturing the data you need in Google Analytics, it is better to overcome these two challenges than attempt to skirt around them.

Solution
As we work closely with Trailheads technical bench on innovative approaches, we chose one of the three options, listing each:
  • Salesforce Data Pipelines
    Data Pipelines new approach with the explicit purpose of making it easier to import data directly into your Salesforce objects. If you purchase this product, you do not need to buy a 3rd-party tool or build a custom application that interacts with the Salesforce APIs (solves challenge #2 above).
  • Google Analytics 4
    Google Analytics 4 was released recently, GA4 does a lot of new things, but most importantly it allows you to gain access to your raw (event-level) data through the integration with Big Query, which solves challenge #1 above.
  • Tableau CRM [formerly known as Einstein Analytics]
    Tableau CRM through not new but we had analyzed this option as well because it’s an excellent tool for solving this problem. This is because Tableau CRM provides similar functionality to Salesforce Data Pipelines described above, but it also allows you to create data visualizations and embed them in your records. Tableau CRM is not required to pull GA data into Sales Cloud, but it simplifies and supercharges the integration.

There are three methods for importing your data into Sales Cloud that we used at Discovery stage, and the method that was right for ABB depending on the Salesforce applications available to them.

Preparing the data: Google Analytics Data

First, the “garbage in, garbage out” rule applies here. If you do not trust the data you are capturing in Google Analytics, you should address that issue before embarking on this process. So, we established a parallel initiative and worked closely with the business process owners to refine and cleanse the data.

Second, we established the official integration between GA and Salesforce as a prerequisite. This is how we passed the user identifiers that are used by Google Analytics into Salesforce lead records, and this will be the join key that we used to merge the two sources.

While we used the Cloud Platform approach for ABB PG, we are listing three suggested methods each based on the viability:

Method 1: The Cloud Platform Approach

[Use this method if you have not purchased Salesforce Data Pipelines or Tableau CRM]

In Method 1, we need a way to push your Google Analytics data into a Salesforce object. There are several tools that you can use to accomplish this, but since your Google Analytics data is already sitting in a BigQuery table in the Google Cloud Platform (see “Preparing Your Google Analytics Data” above), then the simplest method is to use the tools available in Google Cloud.

To do this, you will follow these three steps (#1 and #2 are the same for all methods):

  • Create a query that pulls the data that you would like to import to Salesforce into a flat table, and schedule this table to be updated daily after the prior day’s data is added to a BigQuery table.
  • (optional) At this point, you may also choose to conduct analysis on this data before it is imported to Salesforce. For example, this is a great time to run a recommendations engine, propensity model, or clustering algorithm.
  • Then, you will trigger a function that transfers the data to an object you’ve prepared in Salesforce using the Salesforce data management APIs.
  • Once the data is in Salesforce, you can use Salesforce reporting to generate visuals and add them to your lead and opportunity records.

Method 1 is the most difficult to configure, because it requires a data engineer who is familiar with the tools available in the Google Cloud Platform.

Method 2: The Data Pipelines Approach

[Use this method if you have purchased Salesforce Data Pipelines but not Tableau CRM]

In Method 2, we will use Data Pipelines to transfer the Google Analytics data into a Salesforce object. This is a huge advantage over Method 1 because you don’t need a data engineer to build and maintain the function that transfers data between systems.

To do this, you will follow these three steps (#1 and #2 are the same for all methods):

  • Create a query that pulls the data that you would like to import to Salesforce into a flat table and schedule this table to be updated daily after the prior day’s data is added to a BigQuery table.
  • (optional) At this point, you may also choose to conduct analysis on this data before it is imported to Salesforce. For example, this is a great time to run a recommendations engine, propensity model, or clustering algorithm.
  • Then, you will use Data Pipelines to transfer the Google Analytics data sitting in BigQuery to an object you’ve prepared in Salesforce.
  • Once the data is in Salesforce, you can use Salesforce reporting to generate visuals and add them to your lead and opportunity records.

Method 3: The Tableau CRM Approach

[Use this method if you have purchased Tableau CRM]

Method 3 is by far the most advanced because Tableau CRM can manage the data transfer process (just like Data Pipelines),_and it can also be used to generate advanced reporting.

To do this, you will follow these three steps (#1 and #2 are the same for all methods):

  • Create a query that pulls the data that you would like to import to Salesforce into a flat table and schedule this table to be updated daily after the prior day’s data is added to a BigQuery table.
  • (optional) At this point, you may also choose to conduct analysis on this data before it is imported to Salesforce. For example, this is a great time to run a recommendations engine, propensity model, or clustering algorithm.
  • Then, you will use Tableau CRM to transfer the Google Analytics data sitting in Big Query to a Tableau CRM Dataset. From here you can set up a data flow to merge the Analytics data with your lead records.
  • Once the data is in Tableau CRM, you can build reports and generate visuals that will be embedded back to your lead and opportunity records.

Results

  • Net Sales increased by 45% within first 2 quarters in FY’21. Sales efficiency increased by 50% QoQ.

Client's Feedback

CCI team helped us reduce the customer response time and improved the overall experience. Their team was very professional and 

Mary Jane

Project Lead,  CRCS Courier

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