The Difference Between Data Analytics and Data Activation

Right now in the marketing field, there is a lot of focus on data. One could say it’s been this way for some time — who doesn’t love their click rate reports? But there’s a nuance to the focus on data as of late, and that the topic of “data activation”. There are lots of platforms working to gain market share in the activation space, aiming to help companies leverage what they know about their customers to improve their profitability. What I’ll try to do here is describe the difference between this concept of “activation” and the more familiar “analytics” that has been at the forefront of the conversation for many years. Understanding that these are two distinct uses of data will make it more clear why there is currently no one system available to do both.

An evolution of data in marketing

Analytics has been around for a long time. The basis of the technology is embedded javascript in a site’s pages that reports on user activity to a collection point. It can be used to understand when your site is not working properly, or what parts of your site are more popular than others. This alone is very powerful, but there’s been a long-standing potential that analytics platforms have not been able to deliver, and that’s understanding down to the person-specific level what’s going on.

That’s because it’s very hard to figure out which web sessions are actually the same person form device to device, from moment to moment. So web analytics platform providers have by and large side-stepped this deliverable. They deliver insights “in aggregate”, meaning the useful things you can learn by looking at user groups instead of individuals.

While web analytics companies have been busy leveraging their capabilities in the aggregate space to provide value to their customers, other companies have been hard at work to develop reliable means of “stitching” disparate sessions from the same person together. And many have come to market with these capabilities in a segment commonly referred to as customer data platforms (CDPs). These CDPs vary in their nature and their capabilities, but they all offer some manner of stitching together an identity graph.

Identity graph technologies are the necessary basis for keeping all engagement channels aware of the state of a customer conversation. Because once you can connect all the different ways a person is interacting with your brand, you can coordinate your message to reflect that. If a person buys a particular product, it would be good to stop emailing them or serving them ads to promote that same product purchase.

Activation refers to this person-level, real-time use of data to drive automated actions across a company’s marketing and sales stack. It is a use of data which is enabled by technology that didn’t exist when analytics became popular. Platforms which perform data activation first developed in totally separate spaces from companies involved in the analytics industry. And it’s only now becoming the focus for businesses interested in improving their relationships with customers.

For this reason, I draw lines of distinction between the two data use cases. Does that mean they are functionally mutually exclusive? No. It’s helpful for right now in 2019, when people are trying to understand how to get the benefits of analytics and activation, and while these two uses of data are represented in separate products on the market. But the day may come soon where a service provider successfully merges these together while maintaining the integrity of both. But for now, my best-of-breed perspective is that there is no one product which can do both. Let’s explore this juxtaposition a bit further.

Motives

These two uses each have unique motives behind them. While analytics is about exploring, activation is about taking action. Here are some ways to consider each:

Data Analytics:
| – let’s track everything
| – let’s collect data forever
| – let’s make reports and predictive models of behavior
| – let’s make broad business decisions over time

Data Activation:
| – let’s track only what matters
| – let’s collect only while we need it
| – let’s take user-specific action
| – let’s act in real time

Strengths

Both have important perspectives. And they have essential business results. Here are some tings they’re good for:

Analytics:
| – ‘heartbeat’ statistics (visits, conversions)
| – revenue forecasting
| – channel attribution
| – telling what happened in aggregate

Activation:
| – user segment-based actions
| – applying and acting on predictive models of behavior
| – moving actionable data from one system to another
| – directing the digital conversation with each user

Shortfalls

While these strengths are similar, there are functional constrains in platforms which keep them from working well in both capacities. For instance:

Analytics does not:
| – provide a means of acting on data to affect user experience
| – easily move from one system to another (transport)

Activation does not:
| – replace Analytics
| – provide interactive views into data (visualization)

TL;DR

No singular platform exists which is capable of delivering amazing analytics and activation, despite what any platform sales engineer might like you to believe. As of the writing of this article, you still need to buy one of each to get the promised results of managing customer data effectively. But they will likely merge together over time (long run).

The impact significance of analytics platforms is diluted by the introduction of activation platforms to the market, and investment should shift to implementation of customer data (activation) platforms in lieu of enhancing existing web analytics platforms. And that is what we are observing across many customer-facing industries. Rather than doubling down on event tracking and reporting, budgets are being used to automate actions and personalize experiences.

More to consider

What about data science? Well, there is data science in everything. It is used to enhance both analytics and activation spaces. It’s the set of tools which are driving advancement. There won’t be a data science comparison, because it’s active within both analytics and activation.