Today we are talking to Christian Monberg, CTO of Zeta Global, and the founder of Boomtrain. Chris joined Zeta a few months ago when the company acquired Boomtrain. He is our resident AI/ML expert and is sharing his thoughts on how AI can be used effectively to propel your marketing efforts forward.

Here’s the interview:

Q: What made you (Boomtrain) want to become a part of the Zeta Global organization?

Marketers spend a tremendous amount of time guessing what tactics will engage users en masse. No solution treated customers as individuals, rather than loosely defined segments until we set out to build a platform to solve the problem at Boomtrain.

Our platform listened to customers and understood their latent desires. But, it required an entirely new way of thinking about everything from data structures to campaign flows. We made decisions that were hard to architect, scale, and design.

At Boomtrain, we believed in a brand-customer economy where experience was the currency — a vision that Zeta clearly shares. We can capture a disproportionate amount of the value if we can help customers fall in love with brand experiences they’re having. Salesforce and Adobe aren’t going to rebuild their stack from the ground up to enable this type of empathetic, real-time consumer comprehension. Zeta is. It’s happening and it’s exciting!

Marketers are still looking for a better way to nurture 1:1 relationships at scale. They tend to have troves of data or have identified the need for a machine learning strategy to realize their vision. Unfortunately, most organizations still lack the resources or prioritization to enable the right content, on the right channel, at the right time for each person.

Q: What is the most significant pain point marketers are facing today?

Nothing has really changed here — it’s getting all the data to the right place, in real-time, to create a single view of the customer.

The rise (and anticipated value) of big data created a mad dash for marketers to collect as much information as possible and keep it in a data lake. The problem is, this tactic lacks clarity about how the data can be used in real time to propel business outcomes. The collection, processing, and insight-creation process has been slow, disconnected and laborious.

Q: You talk about AI being a strategy, not a tool. What exactly does that mean? How can marketers use it most effectively?

Rather than looking for a tool that provides a feature, we encourage business leaders to architect a strategy that understands their users individually and caters great experiences to them.

Develop your data strategy orchestrated around the user. Focus on the incremental knowledge you gain on a specific user, rather than focusing solely on the collective engagement of a campaign. That said, you can’t dig into every user record to find out what’s happening with them, but machine learning can help you with scale.

Next, consider how you abstract that data to understand a user’s intent. Environmental factors like weather, location, time of day, and device all contain signals. Behavior matters. Are your consumers just browsing for gold lamé pants, or have they added them to their cart?

It’s not about the data you have, it’s what you make of it. A customer’s latent interests can be abstracted through natural language processing and help you understand their nuanced needs or interests.

Last, examine the journey. It’s easy to re-target someone with a message — but retargeting may come at an opportunity cost. It’s akin to building a better mousetrap or helping the mouse find better accommodations. Observing people over time, you find patterns, like higher proclivity to buy after seeing social proof. This is a big deal when you think about your spend in various channels on a per-person basis. To make the most of your engaged customers, you need to build systems that can create purposeful experiences that drive towards brand engagement.

Q: Can you give us an example of how AI and machine learning has helped a client become more customer connected?

We were working with an online health care company that wanted to improve engagement for pre-defined segments based on a drop-down opt-in for specific ailments. As our algorithms chewed through their data, it came across micro-segments. As it turned out, diabetes treatments fell into two categories: treatment through medicine and treatment through exercise/diet. From an automation standpoint, AI correctly engaged the users. But the real learnings occurred when this specific insight informed their content marketing strategy to be more focused and steer towards optimal SEO strategies. Reach out if you want to hear about the cohort of high LTV users that love Zombie Apocalypse…

Q: What one thing should marketers be considering in 2018 to build their brand and increase ROI?

Brands need to listen to their customers at scale. This means they have the data, the machine learning, and the automation. While many marketers are laser focused on click through rates, we’re seeing a re-emergence of consumers that are willing to postpone a purchase decision to find a brand that resonates.

Responding to your user’s journey with your brand, rather than putting them through a pre-canned user journey, will lead to a path of higher revenues, louder brand advocacy, and more meaningful life-time value.

 


Hope you enjoyed reading the first post from our interview series. Stay tuned for the next one!

%d bloggers like this: