The Future of Customer Data is Bionic Image

The Future of Customer Data is Bionic

If you haven’t had a chance to read about the advanced technology of our Winter release, I strongly urge you to go through this blog post by Karl, our VP of Products. But if you have, you are likely curious to learn more about the new ‘Bionic Rules’ feature. Regardless of the size of your dataset or your company, everyone can benefit from the functionality Bionic Rules has to offer. In this article, I’ll share why we decided to put more power into Rules, how we are doing it, and what it means for Customer Success at your organization.

Why the Engine Matters

We are a Customer Success Solution. Why spend time finding a way to apply cutting-edge technology to data transformation and management?

Well, I think most of you would be familiar with the phrase “garbage in, garbage out.” Our customers are often limited by the quality of the customer data they integrate into Gainsight to drive meaningful action. This issue can often be compounded by the availability of tools and resources an organization has to work with existing data.

This sparked the genesis of the Rules Engine. In its original form, we thought about Gainsight’s Rules Engine in terms of the business actions it would drive (create Calls-to-Actions, set Healthscores etc.), but quickly came to realize its value is mostly derived from the data and our ability to process it.

Over the course of its evolution, we introduced more and more functionality to directly improve the underlying data that is brought into Gainsight. A big component of this objective was to introduce a way to effectively work across Salesforce data and non-Salesforce data stored in Gainsight. Additionally, time-series calculations, aggregations, and data inserts and upserts all helped our customers enhance their data in Gainsight to ultimately drive better customer outcomes. Customer Success teams also now have the ownership and the tools to make these data improvements without waiting in an IT queue.

The connection is clear: Better data tools lead to enhanced customer data which leads to improved customer outcomes.

More Power under the Hood

To deliver this next-level of technology, we began experimenting with various combinations of big data technologies.

Our goals of this exploration were:

  • Truly “big data” processing—we’re talking billions of records at a time
  • Compatibility with a wide variety of data stores—primarily how to combine the best of Salesforce data with the best of data external to Salesforce
  • Create a data pipeline where we can do multi-step processing—consume the results of the previous step as the input for the next step

After several months of soul-searching, we identified Apache Drill as a technology we could use in our backend. The combination of this technology achieved all three of our goals:

  • It’s capable of processing billions of records at a time. This will give us the long-term infrastructure and capacity we need to build the next several generations of customer management technology.
  • It supports a wide variety of NoSQL, SQL, and other source file stores to enable Gainsight to continue to embrace polyglot datastores (the ability to work with a wide variety of data sources), a core tenant of our product development. Best of all, it offers new ways to work across Salesforce data and external data integrated into Gainsight. This can solve a big and persistent problem for our customers.
  • Most importantly, it enables multi-step rules through a lightweight workflow manager, which means you can combine a process that would previously have taken multiple steps (and create multiple dependencies) down to one step. It’s like replacing a manufacturing assembly line with a 3D printer.

Bionic Rules for your Business

First and foremost, your Customer Success organization will have more power to make your customer data useful for you. That means you can take customer data in whatever format you find it, ingest it into Gainsight, and manipulate it to find new insight and drive differentiated action. Also, because your team is in control of your own data tool, you can continue to create transformations and calculations on your customer data, allowing your data to develop with the sophistication of your Customer Success delivery.

Bionic Rules can perform a powerful set of in-memory processes: merge, pivot, inner/outer joins and aggregations, to name a few. To illustrate, Bionic Rules can perform the following scenarios in on step:

Scenario 1: Pivot Raw Product Usage Data

Product Usage tracking tools typically gather data as events performed by an individual user in a format similar to the below:

User Event Date
Sally McField Login 2/1/2017
Sally McField Homepage View 2/1/2017
Katherine Bennett Homepage View 2/1/2017
John George Login 2/1/2017

But to make it useful, the data needs to be converted to the following:

User Date Logins Pageview
Sally McField 2/1/2017 1 2
Katherine Bennett 2/1/2017 1 1
John George 2/1/2017 1 0

Typically, an external ETL or other tool would pivot this data and then it would be integrated into a Customer Success system and subsequent rules will be built. With Bionic Rules, we could conduct this pivot, calculate a Daily Average User metric, and set a Call-to-Action to alert the CSM that John George has zero page views, all in one rule.

Scenario 2: Find the Most Recent or Oldest Record

This example sounds deceptively simple but is very technically challenging. A good use case is: Alert a CSM to the most recently closed Opportunity on an Account to notify them of new business sold by Sales.

With Bionic Rules, you can find a ‘Closed-won’ Opportunity with the most recent close date. Then you can create an alert to notify the CSM and even link the Opportunity record to the Call-to-Action itself so the CSM has easy access to information and for data-entry.

Scenario 3: Find Shared Commonality with an Inner Join

It often falls to the Customer Management team to find and suggest the right reference for an open Sales Opportunity. With Bionic Rules, you can conduct an inner-join to match customer contacts with prospect contacts to pinpoint the people who have the highest ratio of shared attributes between them, and therefore who are more likely to be good references.

Whether you are a Gainsight admin, IT nerd, or Customer Manager, you can see how a powerful Rules Engine can move the needle for your customers and ultimately your business. The future of customer data transformation is looking bright and bionic.