How do companies currently measure, attribute, and manage their data in martech? And how can they do it more effectively?
In our recent research report with Fospha, we found that the current state of data management in the martech industry is (to few people’s surprise) rather lackluster.
Well for starters, there’s a lot of data out there.
An estimated 2.5 quintillion bytes of data are created every single day.
To be clear, a quintillion is 1 followed by 18 zeros (think billion, trillion, quadrillion, quintillion). I.e. a big number.
These bytes of data contain everything from Instagram posts to Spotify songs, from LinkedIn profiles to Amazon sales. All of us who do anything online participate in creating this massive amount of data every single day.
And as is the case with many seemingly impressive things, that huge potential only becomes meaningful if we can make it useful.
Unfortunately, many marketers — and many marketing technology platforms — just still haven’t mastered the art of getting the most use out of those 2.5 quintillion data bytes.
Brace yourselves — it gets a little wild.
In theory, the fact that we have so much data available to us creates a dizzying world where everything becomes measurable.
We can see shocking insights with pinpoint accuracy. How much does the weather in a city impact marketing performance? How do customers rank based on their individual ROI and likelihood to convert? What’s the effectiveness of display advertising impressions?
All of these questions can be answered with data — ultimately feeding into better, more profitable business decisions and customer engagements.
What stands in the way? In our survey, 33% of brands cited “data complexity” as their biggest challenge today.
Many companies are already aware of the vast potential within their data, but just not sure how to best take advantage of it.
With each new technology marketers add to their stack, they often add a whole new set of data points. And that data often gets trapped or hidden within its silo.
The good news? For a long time, the cost to run data-driven, multi-touch attribution models made it not worth the ROI for companies. Now, however, the realities of what can be done with AI are finally catching up to expectations.
Needless to say, this is an ongoing process. But these four tips can get you started:
It would take years to sift through all the potential insights we could find. And obviously none of us doing any regular work has that kind of time.
First, ask what question you want to answer. If you could know any one new thing about your audience, your product, your content, your sales, etc — what would it be? Pick one question you want to answer, or one problem you want to solve. And start there.
Thinking about all the potential insights to be had gets overwhelming. You don’t have to solve everything, you just need to start. Pick a success metric to start with, and knock yourself out.
So you’ve clearly identified and articulated your particular challenges that need solving. The next step is figuring out which platforms will help you find those answers.
Whatever data companies you work with — whether attribution providers, DMP’s, CRM vendors, or otherwise — make sure they’re helping you find the answers you set out to look for. Otherwise, you’ll just be adding to the noise with more disparate facts, rather than gaining actionable insights.
Remember that 44% of companies in our survey said they plan to invest in at least one new technology over the next year, and they already use an average of seven platforms. Are all of these helping you find the information you’re looking for?
Again, any data solution that you’re considering should be adding value, not giving you more work.
We’re looking for partners who simplify complexity, consolidate fragmented parts, and integrate with what you already have. Anything that’s not accessible and practical is just adding to the noise.
Beyond that, look for partners who can educate you in what they’re actually doing.
Remember that just 9% of marketers believe their organization has an “excellent” understanding of this discipline.
Data science, multi-touch attribution — these aren’t things you learn by reading one article. And you don’t have to be an expert in them yourself — that’s what partners are for — but there’s still a competitive advantage to be had in understanding the gist.
Marketing measurement should not be a restraint on your work. Rather, it should provide a healthy perspective on the success of marketing strategy over time.
For example, one of the best approaches in general is to work toward customer-focused metrics, such as customer lifetime value (CLV).
Note here that just 32.5% of businesses in our survey had a clear view of CLV within their data set.
Again, what is your data for if not to get you a few steps closer to success? If part of your business’s success can be linked to CLV, then that’s a measure you should name and keep tabs on.
Uniting your team’s efforts around success measures like CLV keeps everyone focused on delivering for the long-term health of the business — rather than just their channel performance.
To learn more about the state of marketing measurement, attribution, and data management, download our recent research report here.
The post Data in martech: How to better measure, attribute, and manage it appeared first on ClickZ.Reblogged 2 years ago from www.clickz.com