Quantive Product Team
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The legend is born: Cross-system metrics

AUTHOR: Quantive Product Team

One of the initial  (maybe the first?) very fundamental ideas behind the Gtmhub platform was to provide easy way to get valuable insights from all of the business systems across the company by sourcing, cleaning and matching the data. While this may sound vague or too general I want to stress on “all business systems”. We know there are plenty of them even in the startups and sometimes even few that serve same purpose – for example two, three or more CRMs in the bigger companies.


Having many teams, doing the same job but with different tools is an actual problem. How do you even get simple numbers like Number of deals in the pipeline or Acquisition cost or … or … You go through all the systems and collect the data manually and enter it somewhere? Or wait every Friday for an Excel report? Well, while this is commonly used strategy there is a better way to spend your time.

Last week we introduced our second CRM connector – Zoho CRM. So I decided to create one of the first cross-system (cross-CRM) insights that takes full advantage of both our ETL system (the magic that collects the raw data) and the Insight Engine (the magic that converts raw data in meaningful metrics). The goal of this insight is simple: Show one number that comes from two systems – Number of Deals . 

So, we need two entities for  the two CRMs. Here are my simple YAML blueprints:

 name: zoho_potentials title: Zoho Potentials description: Your potentials in ZohoCRM fields: -   name: potential_name   title: Potential Name   type: string

And here is the Pipedrive’s blueprint:

 name: pipedrive_deals title: Pipedrive Deals description: Your deals in Pipedrive fields: -     name: deal_name     title: Deal Name     type: string

After creating the entities, you should map the single field for each of the systems. It shouldn’t take more than 15 seconds each.

Give me some drum roll

The best part – merging the power of two softwares/systems/teams/workflows. Here is the algorithm that requires all deals from both entities and then counts the number of deals:

 pipedrive_deals <- entity("pipedrive_deals") zoho_potentials <- entity("zoho_potentials") count <- nrow(zoho_potentials) + nrow(pipedrive_deals) out(count = count)

Simple as that. With four rows of R you can save unbelievable amount of time, emails, mistakes.

Some minimal HTML to display your work would be:

 <div class="title-sm mb-2">Total deals</div> <div class="title-xxlg">{{ data.count }}</div>

This insight shows all deals no matter of their status or stage.  You can add stage  field, status, owner, size, etc…  

But in this example you saw how easy it is to get the data and extract the value – we do the heavy lifting with the data, you take care of the deals.

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