What Comes After Data Activation?
Part 3 of 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:
Did the resulting personalization lead to the desired outcomes?
What insights and ideas (for future experiments) have been derived?
Have the efforts led to measurable growth for the business?
Outcomes
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.
Insights
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.