EverString for Target Account Identification
November 14, 2018
EverString for Target Account Identification
Score 7 out of 10
Vetted Review
Verified User
Overall Satisfaction with EverString
It is being used by sales and marketing to identify target accounts by vertical and geography. Marketing is the primary user.
- EverString has a compelling compilation of company data for the U.S. market, with detailed info on company size, revenue, and tech stack.
- EverString makes it easy to slice and dice their data to create groups of companies for further analysis. It is particularly nice that EverString can take some or all of your current customers as the primary input to create the model against which fit and pattern matching is evaluated.
- It is straightforward to save, further analyze, and publish the end target list of accounts.
- EverString could/should expand to international markets, or at least the top 20-25 countries by GDP. That would significantly increase the value for global companies.
- One of the challenges with EverString is that, while the data set is impressive, it's fairly static. I think EverString could/should work to get my dynamic purchase intent and/or research data into the product and enable alerts whenever companies move in/out or up/down in fit score.
- We use EverString in combination with Discover.Org as the 'whole solution', as we need both the target accounts and the contacts in those accounts to enable our go-to-market. Would be great if that combo was all part of the same solution.
EverString is relatively easy to use, so doesn't require much training. There are some nuances and unknown aspects to how the modeling and fit score works that are important to know. It's not specifically a training issue in the use of the product, but rather training on how the modeling works so that users understand how their filtering choices will affect their results. For example, when a user puts in a particular set of tech filters, the system doesn't expand the list or add points to the score when that tech set is present in the EverString data, which would be a valid way to use that data. Rather, EverString keeps their fit score algorithm exactly the same, and simply filters out all companies who don't have the tech set present. I'd personally like the model to be used both ways (if tech X is found, add fit points OR keep only fit companies with tech X).
I would say we are 80% satisfied. We do close reviews and market-testing of the account lists, and then being outbound marketing cadences to the final list of accounts. By and large, most of the accounts seem to be reasonable fits, and getting the list of accounts from EverString is far quicker and more expansive than manual research.
We've tried to use the data enrichment capabilities a bit, but have ended up using DiscoverOrg for that instead. Better and rich info.
Very simply - EverString is either the source or validation of the list of target accounts by vertical.
Honestly, the sales teams use EverString sparingly. The major use was to develop the list of target accounts for each rep. After that, they use DiscoverOrg for prospect contact info and intent, as well as contact info that the Marketing team has data-mined from other sources.
The biggest impact has been expanding the list of qualified target accounts from about 300 to 1000. From there, we've added ~10 contacts per account, which has expanded our reach by ~7000 names. We get about 30% of our meetings and opportunities from our target account list and contacts.
It wasn't quite an apples to apples match, but we looked at or used EverString, Engagio, ZoomInfo, DiscoverOrg, Distribution Engine, and LeanData. We ended up using ZoomInfo, DiscoverOrg, LeanData, and EverString as our core set of tools. EverString for account identification. DiscoverOrg and Zoom for contact and intent info. And LeanData for ABM and lead distribution.