Written by Ravi Hulasi
“Tamr allowed us to get to views and insights we otherwise could never have reached, ultimately improving our win rate.” – Iana Dankova, Business analytics manager at HPE, big data
As an established company, HPE Software has an excellent track record of success with existing customers — but wanted to identify opportunities to improve engagement with prospects for new growth. To do this, HPE wanted to build a data-driven customer’s journey that would go beyond the activities of individuals and capture activity at the company level. A major challenge was joining different leads and opportunities for a given account in their systems so they would appear in their customer relationship management (CRM) as they are in real life. This was a challenge because in HPE’s CRM, the same company can appear as many accounts. Further, if a lead comes in with an email that is not associated with the company ID, it will not be associated with the right account.
“We know intuitively that buying enterprise software is not an individual endeavor, but we have not been able to measure and monitor that through our systems,” said Terra Samuels, World Wide Go-to-market lead for Big Data Platform Software at HPE. The challenge is being able to join all the leads and opportunities in an account so they can establish a real understanding of buyer behavior at a company level in terms of interaction with sales and marketing. “The issue is that thousands of people can determine how to type in words — this creates inconsistency in data. I don’t think that goes away as long as you have a self-serve data entry model.” Rather than try and govern the way data was entered across thousands of users and distracting those users from their jobs, the team decided to look for a way to prepare the data downstream.
In evaluating solutions, the team at HPE decided to start with a small, manual, in-house trial: “We spent four days going manually through a very long list of lead data in Excel that had Lead Email and Lead Company name. One by one we tried to match the records back to the parent company,” said Iana Dankova, Business Analytics Manager at HPE. This manual exercise revealed that what had often looked like three or four different companies in their system was actually a single company. For example, a known lead — “Mike Smith, VP of Operations” associated with “Acme Supply” — was not being mapped to a contact record — “M. Smith”, associated with account “Acme” and title “VP of Ops.” “After finishing the manual exercise on a small subset of data, instead of having three timelines that were all missing critical parts of how and when we had interacted with prospects within an account, we now had a single timeline with every interaction going back to the first touch. We knew the manual approach was only a snapshot, what we really wanted was a continuous solution that could scale to all of HPE software.”
After evaluating internal and external options, the HPE team chose Tamr to develop the proof of concept because of its unique ability to scale. Where most solutions struggle as more data is added, Tamr’s machine learning actually turns that on its head and actually improves as more data is added. “We have always had the data. It was a matter of being able to prepare it fast enough at scale for analysis. We had a need to do a pilot really fast, and at a large scale across all of HPE software to show the insights from our manual exercise could scale.” added Dankova.
Tamr’s machine-driven, expert-guided approach scaled customer journey analytics across the entire marketing base in days, then regularly keeps the data clean and up-to-date. The output has helped the software marketing team make informed marketing investments and optimize marketing activities. “It’s really exciting, the visibility that the analytics are providing us in terms of buyer behavior. We can also use this to develop predictive analytics that enable us to iterate on our marketing mix and the campaigns in that marketing mix,” said Samuels.
As Dankova summarized, “Tamr allowed us to get to views and insights we otherwise could never have reached ultimately improving our win rate,” HPE is now applying this capability to integrating other rich and valuable data sets. HPE is looking to expand their use of Tamr and possibly incorporate service call data into their analysis in order to identify possible risks of customer churn. The end result of fueling customer teams with more signals and data: more effective decisions and agile strategies.
To learn more about how Tamr can help you unify data to build a more accurate customer’s journey schedule a demo to talk with our experts.