Ops Designed Articles

When to Automate and When to Iterate

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The Problem

When determining what to automate, among other things, we weigh the time-to-build against the impact and prioritize accordingly. In a recent implementation, when a deal reached a specific Pipedrive stage, the system would automatically create the following activity:

Screenshot of a Pipedrive automation, set up to prepare and send a quote.

The Process

A few weeks after onboarding the sales team we discovered that in many cases (close to 50%) the users were forgetting to:

  1. Update the deal value to the quote amount
  2. Move the deal to the "Quote Sent" stage

These two forgotten steps meant that:

  1. The company could not rely on the Pipedrive financial data
  2. Subsequent automations would never trigger and deals would get "stuck" in the Preparing Quote stage

We did a couple of rounds of additional training, but users were still forgetting these steps. What we found, however, was that when users were marking the "Prepare & Send Quote" activity as done, the quote had truly been sent, it was just these new Pipedrive related steps that were being forgotten.

The Solution

So what did we do? We added two additional automation flows that would act as a "catch" if either of these steps were missed. Here's what was built:

  1. If the activity "Prepare & Send Quote" was marked as done, the system would assume that the quote had been sent. Therefore, if the deal was still in the "Preparing Quote" stage, the system would automatically update the deal stage to "Quote Sent"
  2. Then, if a deal was in the "Quote Sent" stage and the deal value was $0, the system would create an additional activity for the user to update the Deal Value

Check out the video below to see both workflows in action.

If all of the instructions in the original activity were followed, great, nothing new would happen. However, for the cases where users were forgetting to complete some of the steps, these automation layers supported the Pipedrive user, ensured the deal would continue to progress through the pipeline, and helped to maintain Pipedrive data accuracy.

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