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Data Activation Done – What’s Next?

What teams need to do to turn data into predictable and measurable growth.

Created :  
August 24, 2023
Created :  
Updated :  
April 30, 2024
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This is part 3 of a 3-part series titled Data Activation Is Not The Goal.


The premise of this series is that teams need to figure out how to turn data into predictable (and measurable) growth for the businesses – “activating” data is just one of the activities of the process. 

In the previous part, I offered a simple definition of Data Activation, along with a summary of steps that lead to the activation of data. In this final part of the series, I’m going to suggest some actions that growth and data teams must collaborate on to answer three key questions about their activation efforts:

  1. Did the resulting personalization lead to the desired outcomes? 
  2. What insights and ideas (for future experiments) have been derived?  
  3. Have the efforts led to measurable growth for the business?


To discuss, list, and track the desired outcomes of activation workflows is extremely useful, and teams can do this much before the necessary tools have been procured or pipelines have been set up

Activation workflows, in essence, are experiments, and running an experiment without a clearly defined goal is not something motivated individuals like to engage in. 

It’s also worth keeping in mind that “building personalized experiences” – a phrase that’s thrown around a lot – cannot be the desired outcome of activation efforts. Personalization is the result of running data-powered campaigns and experiments, and personalization is of little value if it doesn’t lead to desired outcomes.

Therefore, data and growth teams must be on the same page regarding the expected outcomes of the activation efforts, and collaboratively figure out the best way to report on the results.

B2B List

Here are some desired outcomes that personalization should lead to for B2B businesses: 

Category-agnostic outcomes

  • Complete the onboarding survey
  • Invite a team member
  • Set up an integration
  • Increase usage
  • Join the community
  • Upgrade account or talk to sales

Data integration tool outcomes

  • Add a source
  • Add a destination
  • Create a workflow/sync 
  • Turn on the workflow/sync

Productivity tool outcomes

  • Create a project/space
  • Create a list/doc
  • Create a task
  • Close a task (mark as done)

Email tool outcomes

  • Import subscribers
  • Set up a form or a data source
  • Create a broadcast or a campaign/sequence
  • Send a broadcast or turn on a campaign/sequence 

Hiring marketplace outcomes

  • Complete profile
  • Post a project
  • Invite talent
  • Hire talent
  • Browse projects
  • Apply to projects
  • Accept a project
  • Complete a project

B2C List

In no particular order, here are some desired outcomes that personalization should lead to for B2C businesses: 

  • Create a playlist
  • Increase watched minutes
  • Start a subscription
  • Renew a subscription
  • Add another subscription
  • Complete a purchase
  • Create an account
  • Provide feedback
  • Rate a product
  • Write a review
  • Upvote
  • View recommendations
  • Book a service
  • Complete a service
  • Rate a service
  • Add more connections
  • Follow more people
  • Subscribe to more newsletters
  • Read a post
  • Like a post
  • Share a post
  • Leave a comment
  • Write a post
  • Start a course
  • Complete a module
  • Complete a course
  • Request a quote
  • View health dashboard
  • Add money
  • Make a transfer
  • Refer a friend
  • Install an app
  • Send a message

And so on.


Needless to say, not all activation efforts will lead to desired outcomes but will lead to insights that will further lead to ideas for future experiments. 

The insights, however, don’t appear magically, right? And by insights, I don’t mean vanity metrics like email open rates or ad click-through rates. 

To derive true insights, one needs to measure the impact of campaigns and experiments on the user journey by asking a lot of questions and defining a lot of metrics. 

Here are some questions that teams must try to answer to derive further insights:

  • In a specified timeframe, what percentage of users have performed the desired action after opening an email from a particular campaign? What percentage did not?
  • What’s the ratio of users who performed the desired action without clicking a CTA (only viewing the email) to the users who first clicked a CTA and only then performed the action?
  • What percentage of users performed the desired action in the specified timeframe without opening any emails from that campaign?
  • Did an in-app message prompting the user to try a feature lead to the adoption of that feature? Did the adoption further increase the usage over the next 30 days?
  • Did an in-app notification about a paid feature lead to more trials or demo requests than the email announcement?
  • What percentage of users who completed the in-app onboarding walkthrough also reached the activation milestone within the first week?

The insights delivered from the above can help teams iterate on the campaigns, test new messaging, and understand why users behave the way they do via proactive outreach and surveys.

Moreover, in the absence of these insights, there’s no way to optimize existing campaigns, run A/B tests, and figure out the factors that lead to conversions.


So far, we’ve listed down the desired outcomes, set up activation workflows to drive users to those outcomes, derived some insights from our campaigns, and listed down ideas for future experiments. 

Those are all important activities but did any of this lead to measurable and predictable growth for the business? 

And how do we measure this thing called “growth”? 

More customers and more revenue are leading indicators but once the growth engine truly takes off, one can find signs of growth in so many areas – here’s a list of items I could think of:

  1. More Usage
  2. Higher Engagement
  3. More Customers
  4. Higher Revenue
  5. More Efficiency
  6. Higher Retention
  7. More Referrals
  8. More Partners
  9. More Evangelists
  10. Thriving Community
  11. Increased Loyalty
  12. Higher Brand Value
  13. Higher Customer Satisfaction

Lastly, to attribute growth to their activation efforts, teams need to answer even more questions – here are some examples:


  • What percentage of users who performed the desired action after engaging with an email or in-app campaign also started a subscription in the first 30 days?
  • Did the activation efforts help identify prospective partners or product evangelists?
  • Has the customer satisfaction (CSAT) score increased as a result of a new experiment?
  • Are users more engaged in the product and adopting new features faster?
  • Has there been an increase in referrals or expansion from existing customers?
  • Are evangelists championing the product at their new organization?
  • Is everyone in the industry talking about our brand, alluding to our growth?


  • Has the active user count increased as a result of certain experiments? Are users spending more time in the app?
  • Are notifications getting users to log in more frequently?
  • Have the retargeting ads led to an increase in the average revenue per user (ARPU)?
  • Are celebration offers leading to higher conversions for a low-converting cohort? And are the offers leading to lower revenue from the top spenders?
  • Are personalized recommendations or abandonment emails resulting in higher conversion rates? 
  • Are reminder notifications increasing the frequency of repeat orders? Or are they annoying loyal customers?

Finally, finding accurate answers to these seemingly simple questions is not easy unless growth and data teams have the same incentive – to drive predictable growth for the business. 


What comes after data activation is a lot of work, but most of the planning and scoping can take place even before activation begins. 

Activation workflows, once set in motion, must lead to the desired outcomes, insights and ideas for future experiments, and of course, growth


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As the founder and operator of databeats, Arpit has made it his mission to beat the gap between data people and non-data people for good.

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