Data may be the lifeblood of organizations, but it seems like there’s never enough good data to go around. As anyone with a sales or marketing operations background is all too well aware of, it takes a small army and an arsenal of tools to combat the ongoing challenges of data cleanliness, accuracy, and governance.
At Evergage, we recently undertook an extensive evaluation of our data needs, sources, functional usage, and costs across the organization. From our discoveries, we made a number of decisions, including which platforms to consolidate and how to address gaps in our current data tech stack. In this post, I’ll explore some of the key criteria we developed for evaluating new data partners.
To start the process, we inventoried the needs of our sales, marketing, and product teams. Each team uses a different data provider. And while they shared some similar needs, they also each had specific requirements that did not translate across departments. For example, for account firmographic data the product team needed API access, sales valued list-building abilities, and marketing was interested in appending and enriching data. To get a handle on some of the differences, we mapped out the data type and purpose by team:
We also compiled a list of our current data partners and matched them against these needs and their relative costs according to their pricing structure (by seat or number of records) and our current level of usage.
Our primary goals were to:
- Maximize data acquisition and internal productivity
- Minimize data costs and conflicts
- Consolidate tools over time as they improve
Main Evaluation Criteria
When it came to picking our shortlist of new data providers, we had to be realistic about balancing our goals against all the potential trade-offs, especially with dozens of vendors to evaluate. Below are the six main areas we considered:
Our number one criterion and question that we asked ourselves is, “Can we trust in the data provided?” That may seem to to be a very simple question, but our collective experiences have taught us that it’s not a straightforward answer, even for the vendors themselves. The very best we encountered were honest about their data collection and maintenance policies, and even gave an average confidence level for every data field, as well as an overall confidence level for the account or contact record.
Our second concern was the provider’s ability to cover our total addressable market, which relies heavily on understanding location, revenue, industry, and technology stack. We wanted to be able to serve and align our SMB and enterprise teams by sourcing from the same partner.
Having developed our Ideal Customer Profile (ICP) earlier in the year, we knew there were specialized data points about the buying team and company profile that were more predictive of our success. We were looking for a provider that could enrich the data automatically and replace our time-consuming manual data collection process.
As you saw illustrated above, the ways that sales, marketing, and product wanted to access and use the same data sometimes varied widely. While we hoped to find a way to meet both sales and marketing use cases, we also recognized most providers were focused on meeting the needs of one or the other. There were also a few items on our wishlist like predictive analytics, intent data, and tracking champion movement that fell under this umbrella.
Cost is an important factor for any organization, but don’t forget to factor in time saved and internal buy-in. We focused on total ROI and the relative value of being able to meet the criteria listed above, rather than just the total list price. We had done our research and knew the pricing range for a company of our size and data usage, which was to our advantage when negotiating a better deal, but we were also willing to pay a modest premium if it helped us meet our other goals.
Since we had already had a history of working with multiple data vendors, our primary consideration was to avoid adding more vendors to the list while balancing the trade-off between vendors/data sources and their relative costs. Here we factored in the productivity costs of training users, logging into multiple platforms, and time spent managing contracts.
In addition to differing functional needs, we also had challenges when it came to picking a solution for data coverage and enrichment needs.
We noticed that very few vendors had good coverage for both SMB and non-public companies, as well as enterprise companies. Fundamentally, we saw this required a somewhat different approach. Many companies specializing in SMB were using newer approaches like social verification and machine learning to capture public information, but were not as accurate when it came to mapping out the complex legal relationships of well-established organizations. On the other hand, those specializing in enterprise data were meticulous in their parent-child hierarchies (often through human verification) but did not have extensive SMB coverage.
For data enrichment, we had difficulty finding a vendor that offered the specialized data points we needed in addition to meeting the six main requirements.
Though we started by searching for a single silver bullet and “source of truth,” we recognized that the challenges we were trying to tackle were beyond the domain of a single platform or vendor. We ended up selecting several vendors we were comfortable with, and defining rules of engagement for how to prioritize our “truth” if there were data conflicts — especially when it came to alignment for our sales team.
The final lesson we learned was that no amount of demos and sample data matches can beat working in the platform itself. We were most satisfied with the vendors that allowed us to be hands-on in their platform and try different use cases out for ourselves.