Lessons from SIGNAL Conference: How IBM used Twilio Segment to improve product adoption across 200 products

IBM is a quintessential example of an enterprise. It has operations in over 170 countries, an employee count surpassing 300,000, and more than 200 vastly different product offerings. They’re also a stunning example of adaptability.  Founded in 1911, IBM was instrumental in large-scale initiatives like the US Census and establishing Social Security – evolving from punch cards, to digital data storage, artificial intelligence, and beyond. 

In a time where agility and digital transformation have come to define business success and longevity, IBM offers us a glimpse of its blueprint. How exactly has such a massive organization managed to create internal alignment, scale initiatives, and stay ahead of the curve? 

At this year’s SIGNAL conference, we were lucky enough to hear from Alex Velaise, Product Manager of IBM Growth. Alex spoke about how IBM was able to modernize its data infrastructure, engage with users in real-time, and strengthen retention rates with the help of Twilio Segment. 

First step: an integrated tech stack

IBM’s first step toward internal alignment was focused on integration. They have hundreds of software offerings, and wanted to find a consistent way to track customer activity across their diverse products. Before this, teams had been left up to their own devices, which meant departments often developed tunnel vision. 

But, as with any strategy, you have to prioritize. Trying to instrument every product at once would be a gargantuan task for IBM. As a starting point, they focused on the products that had the most growth potential. Using Segment, IBM was able to connect their products to important internal platforms like billing systems, marketing pages, trial sign-up systems, and more, to better visualize customer journeys.

Now, Marketing, Product Marketing, Customer Success, and Sales all had access to this customer data, which could easily be sent to analytics platforms to better understand user intent and behavior. 

Here’s a quick recap of IBM’s integration strategy:

1. Instrument priority products first, and learn about customer traits and actions through visualization. 

2. Connect to internal platforms and systems such as billing systems, marketing pages, provisioning systems, and more. 

3. Deliver relevant data to the right recipients. 

Scaling with the right schema: enter Protocols

After creating a connected data infrastructure, IBM needed to focus on the accuracy of their data collection, or more specifically data cleanliness. This is a common issue for businesses: inconsistent naming conventions leading to inaccurate analysis. 

The importance of having a standardized naming convention

This is where data standardization comes in (i.e. a framework to ensure that data has a consistent format, no matter the source it comes from). 

Developing a schema that every team can use is tricky for large enterprises like IBM. After all, each product has a different function, different target persona, and different customer journeys. To get started, IBM asked its teams to identify the most important user actions that led to increased product adoption, which they referred to as “milestone” events.

IBM knew they had to strike the right balance between being general enough that data tracking could work for every team, and specific enough to differentiate between platforms – no simple task. To pull this off, IBM focused on making their product events high level (e.g., “product_login”, “product_signout”), and then used properties to provide important context (e.g., “account_type”). 

To do all this, IBM used Segment Protocols to create tracking plans and guide relevant data from the right source to the right destinations. With Protocols, any event that didn’t fit within their predetermined schema would be automatically blocked to ensure accuracy. 

The first use case: IBM Cloud 

IBM Cloud offers over 170 products and cloud computing services, its catalog spanning PaaS, IaaS, SaaS, AI, and blockchain capabilities. 

But the IBM Cloud team couldn’t track customer actions as they occurred. This lag made it difficult to engage with users at the right moment, or have an up-to-date understanding of their behavior.

With Twilio Segment, IBM Cloud was able to rectify this by having customer data update in real time. This allowed their team to launch event-based communications, identify potential churn risks, and pinpoint upsell opportunities. 

As a result, they were able to increase billable usage for IBM Cloud by 17%, and saw a 30% increase in product adoption. Following this success, IBM had a template for how to instrument hundreds of other products in their suite. 

Final Thoughts

Integrations at the flick of a switch. A panoramic view of customer behavior. The ability to fine-tune personalization and event-based recommendations. These capabilities were essential for reducing churn, strengthening retention rates, and giving every team at IBM the autonomy to act on first-party data. And it would have been impossible without Twilio Segment’s scalable customer data infrastructure and data standardization. 

For enterprises, Alex has three main pieces of advice. First, keep flexibility in mind. Especially at large organizations, new products will inevitably be added or deprecated. Also, there needs to be clear lines of ownership and communication for these data initiatives, especially as people switch between departments or roles. And of course, data cleanliness is a prerequisite, otherwise you’re working off bad information. 

You can watch IBM’s entire session here. Or feel free to browse through our other on-demand sessions from this year’s SIGNAL conference, which featured leaders from Staples, Univision, Vista, and more.