Trust me; I’ve been there.
A guilt-like feeling sinking into your stomach as you walk over to the engineering team with a data request for the upcoming board meeting.
Even worse, many times I knew that a little more information would bring greater insight, but decided not to walk over at all for fear of overburdening their already loaded plates.
At the time, I didn’t know how important that data was for my position, partly because I wasn’t asked for anything more than delivering a steady stream of MQLs. But, almost a year ago today, that all changed.
The Day I Fell in Love with Data
I was shown a customer journey funnel during an interview.
At first glance, it didn’t look like anything special. I saw people coming into the funnel from their first website visit, they looked at some content, browsed around the website and signed up for a trial of the product.
“Cool, but nothing game-changing,” I thought.
“Ok,” the interviewer said, “let’s see what else these people did.”
He began adding steps to the funnel to show what people were doing inside of the product. I could see which features they engaged with, how often they logged in, the in-app messages they clicked on and what steps within the product led users to become customers.
Now, he had my attention. Ideas instantly began running through my head.
If these actions within the product lead more users to convert, let’s do everything we can to make it as easy as possible for people to take these steps, right?
We could create targeted marketing campaigns with video instructions, in-app guidance or tutorials.
But, he wasn’t done.
He added steps in the funnel to show which of those users had chatted with us on live chat, read one of our blog posts, clicked on a retargeting ad and at what point in the funnel users submitted a support ticket.
We started segmenting the funnel by company size, campaign origin, location, industry and employee headcount!
I was enamored. Not only did I want to work for this company, but I also wanted to dig into the data more, ask questions, learn more about these users, their motivations, behaviors, and influences.
I realized that I was only scratching the surface in my understanding of how marketers can leverage data across the organization to design smarter, more impactful, marketing strategies.
And, I was ready to dive in deeper.
The Fallacy of Traditional Marketing Metrics
In a recent article, the CEO of an analytics company described his organization’s shift away from targeting marketers, toward a focus on product teams.
He says, “when marketers look at analytics, they’re basically looking at where visitors are coming from and then how those visitors convert into paying customers.”
He claims “the product team, on the other hand, is trying to answer more difficult questions, like how different features affect long-term retention.”
I find this generalization insulting to modern marketers. How can we know who to target, appropriate messaging, features to highlight or campaigns to develop without an understanding of the product actions that drive long-term retention?
New research by The Economist Intelligence Unit (EIU) conducted on behalf of Marketo revealed, “86% of marketers say they will own the end-to-end customer experience by 2020, meaning that they will become responsible for the entire customer journey.”
Can we own the end-to-end experience, yet only measure where new users come from and how they convert?
Marketers today have the ability to measure success well-beyond the point of conversion, so vanity metrics like first and last click no longer suffice.
Incorporating product engagement data into your marketing strategy can help to answer strategic questions like:
- Which marketing campaigns are driving the most users to signup and engage with a new product feature?
- Which product features are used most and by whom?
- What does healthy product engagement look like by micro-persona and how can we effectively target those micro-personas?
- What combinations of events, both inside and outside of the product, lead a customer to upgrade? How can we engage those customers at the right time, with the right message?
- Which campaigns are driving the most long-term revenue for my organization? Bringing in the right types of customers that stay with us for years to come?
In answering these, you’ll uncover the right combination of digital, mobile and physical channels, content and product experiences that drive long-term growth for your company.
Integrating Product Data and Marketing Analytics
If you’re just getting started, a great first step is to identify the product events that matter most to your business. This way, instead of asking your product team for access to every product event, you have a list of 5-10 that you can start with to begin asking questions.
At Woopra (full disclosure, I work for this company), I was spoiled in that we already had data from every customer touchpoint consolidated within our platform when I joined.
But, I was able to identify new product events that we were not tracking at the time to include in my analysis.
For example, I wanted to know how many users watched one of our tutorial videos on Customer Journey Funnels and then went into the product and created their first funnel. By adding this product action as an event in my tracking, I can now measure the impact our videos have on feature engagement.
I can see how many people watch the video, create their first funnel and convert into paying customers. I can measure which of those customers stay with us, for how long, how often do they continue to create new funnels and more!
By tracking one product event, I have a whole new level of insight that I can use to better understand and engage with our users.
Since incorporating product engagement data into my marketing strategy, I’ve been able to:
- Advance personalization by fueling email and chat with product data – For example, I can trigger a chat message to users who’ve recently signed up for our product trial but did not complete the onboarding process. I can address them by name and let them know the next steps to take.
- Build a lead scoring model that includes product-engagement data – Often called the Product-Qualified Lead, these leads tend to convert at greater than 35%. We look at demographic, technographic, behavioral and product engagement data to score leads higher based on in-app engagement while they’re on our free package.
- Laser-focus advertising campaigns to do more with less – The more you understand who your most successful customers are, the greater your chances of finding more of them. We create custom audiences of successful customers that dynamically feed into our Google AdWords and Facebook campaigns, allowing us to find more of the right users while reducing ad spend.
In today’s competitive world, innovation in marketing requires understanding every touchpoint in the customer experience, weaving your efforts into the moments where you can have the greatest impact.
This happens at the top of the funnel, at the bottom of the funnel and throughout the dozens of interactions between.
Companies are not their product, their marketing, their sales or support teams. They are the experiences users have with your brand. I believe that if we can shift our thinking away from the data silos of channels or teams and toward end-to-end experiences, we can build better products and perhaps even, a better world for everyone.
For more tips on incorporating product data into your marketing strategy, check out my pieces on How to Master Full Funnel Attribution with Customer Analytics and How to Build a Sales Accelerating Product Qualified Lead Engine. Or, feel free to hit me up on LinkedIn with any questions!
Elle Morgan is the Marketer and Evangelist at Woopra, the leader in real-time customer analytics. Her core mission is to help businesses cultivate data-driven cultures by transforming real-time insights into actions. Her work and research focus on the humanization of data - bringing faces to the faceless numbers - in order to deliver an unparalleled customer experience.
She combines her nine years of marketing experience with a deep understanding of best practices.