Account-Based Analytics: Merging 3rd Party with 1st Party Data

We don’t want to discount the power of organizing your owned data assets from an account-prospective as there’s so much wealth to illuminate from this dark data for teams to view and use to optimize daily efforts.

However, do not discount the power of 3rd party data providers who can complement your account data. Below are some various vendors (some we use here at Dynamo). Take note also – there’s many industry and vertical specific providers that may have more depth than breadth. But in many cases, from what we find, both have value, it’s just applying the data in the right direction.

If anyone would like our POV– give us a shout anytime, we’ve researched and used many providers below (and more!). All providers have offerings with a combination of  B2B contact databases, social, news, firmographic and technographic data that can be applied to your account-based endeavors. All have data connecters through APIs that can be ingested within your internal or service provider tools to align to your target accounts.

  1. Clearbit (horizontal)
  2. Full Contact (horizontal)
  3. Leadership Directories (Vertical, government)
  4. Dun and Bradstreet (horizontal)
  5. Redbooks (Vertical, Advertising)
  6. Winmo (Vertical, Advertising)
  7. S&P Capital IQ (Vertical, Finance)
  8. Crunchbase (Vertical, Technology)
  9. Zoominfo (Horizontal)
  10. DiscoverOrg (Vertical, IT)

That’s the quick gist–as always, just trying to find right, not act right. Welcome feedback.

Team Dynamo.

Account-Based Analytics: Systems of engagement

It’s true. The BI industry still has a huge adoption rate issue (  So how can we align our workforce from an account-based prospective? Even if we have all the data now, and we can group and provide advanced analytics – who cares if no one is engaging with the insights?

As systems of intelligence advance in the enterprise, as Jerry Chen, of Greylock Partners, notes in this post,, old and new systems of engagement must align to generate value.

At any rate, systems of intelligence need to interact with knowledge workers to drive action. Not all systems of intelligence will interact with your workforce directly (some may be focused on machine to machine interaction), but for the ones that do, there needs to be an almost zero cost to value for your knowledge workers to understand how it works and to derive insights.

At Dynamo, we breakdown systems of engagement in two prongs for knowledge worker interaction to increase adoption.

  1. Analysis automation – there needs to be a near zero cost to value for the vast majority knowledge workers to ascertain the insights. We believe insights that were derived from various data mining and advanced analytic techniques need to be generated in natural language for highest adoption rate.
  2. Accessibility – there needs to be a near zero cost to value for knowledge workers to rapidly leverage insights. All insights need to be available where your work force already works, do not add in another system for your workforce to learn. Unless there’s such an enormous value to beat the cost to value.

That’s it – there’s plenty of systems your workforce uses already. When evaluating new providers, make sure they seamlessly work with your existing workflows for highest success rates and knowledge is given in natural language within those apps.

That’s the quick gist – As always, just trying to find right, not act right. Welcome feedback.

Team Dynamo.

Account-Based Analytics: Relationship engagement depth and breadth

Revenue teams of B2B firms selling complex high ticket items may want to know how they are trending on penetrating various roles and constituents in their named accounts. It could be an account you are prospecting or current customer you are navigating for cross-selling purposes.

Engaging with and coordinating your efforts with a “relationship map” is a way to measure progress to help optimize those efforts alongside your larger account-based strategy. This analysis can be done with most BI tools as long as you can access the data. Below is a “quick general guide” to apply to your business data.

For this insight module, below data is needed:

  1. Account Contacts: Names, titles, phone numbers, emails of you target account.
  2. Account Engagement: You’ll need enterprise communication software, CRM, marketing automation.

Grouping the Data (develop a direct weighted score to measure impact, adjust weights as you see fit):

  1. Breadth
    1. Determine each touch point with an engagement score
      1. Meeting=.5
      2. Marketing=.05
      3. Email=.15
      4. Phone call=.3
  1. Depth
    1. Measure by time lapsed weight
      1. Within five days = .5
      2. Within 6-10 days=.2
      3. Within 10-30 days=.15
      4. Within 30-60 days=.1
      5. 60+ = .05

Factoring the Score

  1. At each channel touch point multiple weighted time to engagement score, this equates to a unique point tally per account.
  2. Normalize for each account. Divide engagement score by total contacts on your list. This gives a time-weighted engagement ratio. (this method unique per account, higher the score the better)
  3. Consolidate data into a tree map (or other visual to quickly see weighted output).

From this high-level vantage, business users can ascertain which accounts need reinforcement and/or deeper dive. Score below 100 should be a red flag to dig in more(in above example, accounts in lower right hand corner, like Metlife).  Also, it’s great to compare growth or stagnation by looking at a snapshot today compared to 30/60/90 days ago.

After determining health, great tools in the market to help fill your relationship gaps. We will give a shout out to, a great for enterprise B2B firms to leverage their existing relationships for referrals into target accounts.

As always, we’re not trying to act right, just find right, would love to hear thoughts on different approaches to determining relationship depth and breadth into target accounts.

Team Dynamo