Data Unlocked

Today, marketing teams need to stop collecting all data by default and instead think about "What's the right data?".

What data is required to drive:

• Effective customer engagement
• Personalization
• Proper customer marketing outcomes

According to Steven Aldrich of Ragnarok, many teams have built a model where they're collecting everything but then found after an audit that nobody was using it.

Check out the podcast to learn more.

🔑 What's the key to making your customers come back? 

Use past behavior to personalize your communications with customers. Learn more as we have an insightful talk with Abby Feuer, DonorsChoose's Executive Vice President of Marketing and Growth. 

🎧 Don't miss out on this insight and more. Listen to our podcast now!

“Our main tactic for driving retention is personalization. We lean a lot into that first gift and the behavior that you've shown us about how you like to give on our site. We have a recommendations email that we send once a month – imagine your Netflix recommendations email – this is our version for classroom projects on DonorsChoose and we find that the strongest indicators to drive return based on your past behavior are acquisition source and geography.

Another way that we drive retention is creating big moments that create this feeling that there's a sense of urgency to give to a certain type of project or classroom. There's the major moments in the year, like back-to-school and Teacher Appreciation Week. But we have also found that we can create moments, like something around our anniversary with our most engaged donors, where we resurface the first project they ever gave to, to inspire them to give again. We rely on a lot of automated emails and messages based on historical preferences and giving behavior and then these big events throughout the year to drive reengagement.”

🎙️ On The Latest Episode of Data Unlocked 👉 Building A Cross-Functional Data Dream Team

❓ How do you build a team, culture and process that allows you to leverage your data to drive great marketing outcomes?

🌟 Mayur Gupta shares with Jason Davis his approach to building a world class team, culture and process at Gannett | USA Today Network.

"I'm a firm believer that the customer experiences that any business needs to drive at scale or a startup gold stage doesn't matter. It happens at the intersection of product, data, engineering, marketing, storytelling, finance. It can no longer happen in any of those areas in isolation. Now, many organizations create many different organizational models to drive it. Some companies have created growth teams, started by Facebook back in '07, '08. Some companies just hold core teams accountable and create pods.

At Gannett, we have created a concept of cross-functional pods that are focused on a very tangible and quantitative outcome, which is measured through tangible KPIs and key results. We use OKR, which is very common now across growth companies. Those pods are staffed, obviously, based on the outcome and the KPIs itself, but they are staffed cross-functionally, led by one pod lead. A pod could have 2 full stack engineers or front-end person, a data scientist and analysts; a researcher, probably staffed part time; a storyteller; a marketeer; a media buyer; a performance; or growth marketer."

“I think back to this example when I was at HotelTonight where we built this beautiful statistical model to forecast hotel prices because we were trying to figure out what to price hotels out on different days. It was the heaviest data science I’ve done in my career from technical complexity, and it performed terribly. And we went and looked at it. We were just like. “Why is this doing so badly?”

And we found out that one of the main drivers of hotel room prices is when conferences are in town. I know that’s obvious. When Dreamforce happens, it affects San Francisco hotel prices. If that’s not in your forecast dataset, it’s pointless to build a nice model.

So what we actually needed to do was build a process to understand the levers that weren’t in our data set and make data so that we could use a more naive forecasting. So we ended up with a logistic regression, but we ended up with a new business process around flagging events. 

That is data-driven to me. It’s not smarter. It’s applying the concepts better” 

4 Things Vivino Did To Engage Customers and Build a Wine Community

• Technology & Timing: “With technologies, it’s a lot about timing.  The smartphone was launched in 2007. The App Store launched in ‘08. By ‘10, it was a pretty good product. At the same time, all the phones had a camera. They were all online. All those things came together around the time when we launched Vivino.”

• Creating A Vision For The Future: “I said, ‘When I build Vivino, it’s going to be this amazing scanner where you can look up every bottle of wine in the world. Over time, we’re going to put a marketplace on top of that so when you’re sitting at restaurants or somewhere else, you just do 2 clicks and buy this wine.’ That was always the vision.”

• Defining Your Target Customer: “What we wanted to do is to really target the casual wine drinkers, not the experts. We wanted to find casual wine drinkers that love wine but did not necessarily want to make it a hobby. We really defined that very clearly.”

• Utilizing Data & Personalization: “Not only can it give you the generic information about the wine, the rating, all those things, now we put it in perspective from you. Will you like this wine? We’ll tell you how likely it is that you will like this wine. That just hasn’t been seen in the industry before because nobody’s really had the data that we have to be able to do that.”

What Is “The Messy Middle” of Data?

“The idea about the messy middle came from a conversation I had with Michael Ramsey with ServiceNow. It was related to customer service. He came up with the idea of the messy middle. We realized that the messy middle is not something which is exclusive to the customer service and there’s messy middles all over organizations.

One piece of research claims that only somewhere between 15% and 17% of all organizations are making good on their data strategy. They’re not hitting their objectives.

This messy middle problem isn’t exclusive to data. It’s all over the organizations. We tend to look at things in the exterior but don’t necessarily think deeply about how we do things. It’s when we look deeper that we actually find the real nutty problems.”

I remember talking to a friend of mine, James, who is a process and data guy. His job in larger organizations is to go around and sort of improve processes. He said to me, “You know what? 95% of problems don’t happen within teams. 95% of problems happen in the gaps between teams.”

The problem is that’s 100% of where our focus is not. Our focus is generally in our teams and in the domains that we control, not in the things that connect what we do. The gaps in between all the teams, processes and systems: That’s where we don’t focus but that’s where most of the problems show up.”