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Mapping the digital journey with better attribution w/ Emine Dekkar and Jeff Kew [The Flipside]

Welcome to The Flipside, where we follow the adventures of a shiny new marketing team as they navigate the ups and downs of using Uberflip to drive our go-to-market. From triumphs to tribulations, we're sharing the whole story.

The customer journey is growing more complex by the day, often leaving no clear attribution map for sales and marketing teams to follow. One of the greatest roadblocks we face in tracking a customer’s digital journey is being able to identify all the activities they’ve engaged in before reaching a purchase decision. 

"How do customers get from point A to point B to point C?" That’s the question Jeff Kew, who leads Uberflip's marketing operations, was trying to answer for Uberflip's team when Emine Dekkar, Business Intelligence Manager, was receiving similar inquiries from our customers. 

Together, Emine and Jeff set out to fill in the gaps. Their collaboration combined marketing ops best practices with internal analytics and integrations to create a much more accurate understanding of first-touch attribution within Uberflip.

I chatted with them to find out more...

Colin: Hey Emine and Jeff. Let’s get rolling by hearing about your roles at Uberflip?  

Emine: I’m a business intelligence manager at Uberflip, where my goal is to ensure we have a warehouse of data that drives internal operations and delivers analytics to our customers. 

Jeff: My title here is the Director of Marketing Operations, and I manage Uberflip’s internal marketing tech stack. 

C: Can you tell me what you’ve been working on? 

J: Put simply, we wanted to be able to identify where Uberflip visitors were coming from, be it organic or paid efforts. We set out to understand the digital journey before customers visited our site.  

E: We’ve built an algorithm that respects the will of the customer by standardizing the UTM medium and source values. And now, if that fails, we’ve developed an auto-detect strategy that scans many data points and prioritizes the ones where customers have invested money.

C: What was in place prior to this? 

E: Uberflip Analytics didn’t have any of this before. 

It only had UTM medium and source displayed with the caveat that we were trying to guess if the first touch was organic or not. Basically, there was 1/10th of the logic we have today. 

J: Prior to me coming to Uberflip, there was a different attribution strategy in place. 

Oftentimes, different marketing ops teams will have different attribution perspectives or views, whether it's first-touch, last-touch, or multi-touch attribution. We needed to build a process to identify and bucket all our first-touch records into paid and organic leads.

That was our starting point. 

C: Emine, can you give us a big picture of what your goals were in creating this algorithm? 

E: Our goal was to create a multi-step process that actually gave a more coherent picture of what's happening at Uberflip. If we want to label it simply, it’s "attribution... with all the details." That is, knowing what campaign or asset worked best by identifying the first touch in the life cycle. 

This project will allow our customers to leverage the Uberflip platform to reach their goals. It isn't just leveraging the plain vanilla Uberflip. We've tweaked Uberflip to better understand what channels are converting the most customers and the ones bringing in the most business. 

Our end goal is to let our customers know what channel, what campaign, and what marketing asset to use to reproduce a successful marketing campaign that has influenced revenue

C: And how are you both measuring success? 

J: We not only saved money by not buying an external multi-touch attribution tool, but we were also able to provide clearer ROI per content piece without additional headcount or time. 

E: Our measure of success is the immediate clarity of surfacing insight that was buried. Going forward, we’ll measure success by seeing how customers use this data and then taking the next step by asking, what's the best campaign to reproduce? Or, what’s the best channel to invest in?

If they can answer these questions with clarity, then that’s our success. 

C: Jeff, you work with Uberflip as a central part of our tech stack. How is this update to Uberflip helping you achieve your objectives? 

J: When paid activities leverage a solid UTM strategy, each successful click or search can be attributed back to a specific campaign, channel, or source—for paid and organic searches, it could be Google or Bing or social media on Twitter, LinkedIn, Facebook, Instagram, etc. Our new algorithm now allows us to sort this traffic into granular-level attribution categories

Taking this information in combination with the rollout of Uberflip Analytics starts to show customers a lot more attribution information that wasn't previously accessible. 

It’s basically being able to go in with the explorer option in Uberflip Analytics where you can search under UTM values. If a customer is using UTM campaign values accurately in all their paid and organic activities, they can start to bucket leads and identify them under the appropriate UTM values. And that shows success with specific campaigns, spends, and efforts. 

This ultimately helps customers—our marketing team included—better leverage Uberflip Analytics to make decisions.

C: What was the most obvious advantage you feel using Uberflip provides?

E: Our customers no longer have to apply stringent UTM medium and source strategies because Uberflip will auto-detect it for them with an ever-evolving attribution algorithm.

J: There are a number of dedicated attribution platforms available, but businesses not needing that level of complexity can effectively leverage Uberflip’s program management, session data, and a UTM strategy around content and activities. There’s a great value-add here. 

C: Why was it important to start this project?

E: We were getting a lot of feedback that our customers wanted a better understanding of where their business was coming from and where interest was being generated. Internally, Jeff was also already asking these questions. 

Our first conversation about these lingering questions was in March 2022. Together, we identified the missing attribution information with the goal of helping our customers better understand where to focus their next marketing activity. We started implementing the algorithm internally in November 2022. I'm proud of how fast we worked to get this up.

C: What was the most challenging part of the project? 

E: Everyone has a different way of doing things. It's always a challenge to identify the product that will satisfy the most customers. Our biggest challenge was identifying what to put in our algorithm to make it accurate enough for every customer behavior to ensure everybody, or at least most customers, are happy. 

J: There's no consistency across the industry in how businesses do their reporting. In some instances, leaders want very granular data while on the other end of the spectrum, some just want to see what was spent and earned. This project is all about granularity in reporting, which is key for marketers. 

C: And what’s next for your project? What does completing this project make possible for future projects?

J: Once that digital journey data has been revealed and inferred, the question now becomes what can we do internally within Uberflip Analytics to further break down the individual sessions or touchpoints that are occurring. We’re starting to look at giving weight to touchpoints. 

We can already capture session data. Let’s say, for instance, someone comes in across 10 sessions over 10 days and there’s some content engagement. We can identify where their journey originated because they’re in the hub. And we can ask, what did they engage with in each session? Or if they read a PDF, how many pages did they read?

Being able to connect that to an opportunity is sort of the next step. And then being able to show the value or weight of each touchpoint. 

E: The next step is using this accurate attribution method to depict where the customer came from and what they are doing next. If we know that a customer visited five assets before they converted, we can understand at the channel level how people are using and leveraging Uberflip.

We’ll surface this visitor-journey type or funnel-type approach data in the next project. There are a few more steps, but this is definitely a precursor to the next projects for attribution.