Media CoverageJuly 06, 2021

MarTech Interview With Michael Katz, CEO And Co-Founder Of mParticle

The foundation of a great martech stack begins with establishing a strong customer data infrastructure (CDI) first. Michael Katz, CEO and Co-founder of mParticle dives into more detail in this interview with MarTech Series.

This article was originally posted by MarTech Series here.

Welcome to this martech chat Michael, tell us more about mParticle and what inspired the starting of mParticle?

Thanks for having me! To level set, mParticle is customer data infrastructure that helps teams accelerate their time to data value, for the purpose of driving better customer experience and business outcomes. 

My brother Andrew and I started mParticle back in 2013 after successfully exiting our last company, interclick, which was acquired by Yahoo in late 2011. At interclick, we built a data platform which was designed to unify data from multiple sources and create a unified customer view in order to drive better targeting, and performance for our customers. The core tenants of the platform were speed and flexibility, and it allowed us to grow the business 600% in two years leading up to the acquisition. While at Yahoo, we saw the shift to mobile and the emergence of OTT but the data being created in those environments wasn’t easily accessible.

So, we decided to do something about that…mParticle was a bet on this platform shift, and the emerging need for data capabilities to go beyond legacy web technologies and help brands keep pace with their customers across all screens and devices. We viewed fragmentation as an emergent property from platform shifts as an opportunity to create new solutions and unlock value for customers.

The ability to pattern match and make a big bet on some important technology trends got us really excited to build a second company together.

How have you seen customer data platforms evolve over the years? What are some of the top predictions you have for the future of this space?

It’s been interesting. Companies like mParticle and Segment are widely credited with creating the category but we both existed before there was an acronym, so it’s been interesting to see what this space has become. There are clearly lots of companies that now identify as CDPs but there’s an important segmentation exercise to clarify some of the confusion.

First off, I’d like to make a distinction between Customer Data Platforms (CDP) and Customer Data Infrastructure (CDI). CDI helps teams solve the foundational challenges of customer data such as data quality, governance, and connectivity. CDPs typically solve the challenges limited to marketing execution such as segmentation. They aren’t mutually exclusive but there are many CDPs who extend into the application layer and deliver campaigns, while our focus continues to be on helping teams solve the operational problems with scalable execution and empowering a completely open ecosystem of partner integrations.

Naturally, marketing is an important use case of customer data but it’s not the only one. Our customers also connect data across their analytics stack, to and from their customer support tools, their data warehouse, and many more systems and applications. Many of the CDPs have built slick interfaces over the years but don’t help teams build the right data foundation or solve the structural challenges. 

I’d summarize it as CDPs are for marketers, but CDI is for the whole business. And this will dictate how each part of the ecosystem evolves in the coming years.

Can you talk about the top best practices used by successful marketing teams in tech when it comes to optimizing their customer data platforms and how they extract better customer experiences because of it?

Great question but I’d just reframe this question a little bit –

Data is a team sport. It’s every team’s responsibility to help make sure data is useful and easily accessible for the whole organization. It should be managed along the entire product development lifecycle, so that the value for each team compounds to the upside. The antithesis of that is when teams only think about their needs and data silos are created; the organization experiences an entropic state, which leads to a disjointed and inaccurate view of customer preferences. It’s a classic tragedy of the commons. This can inhibit or completely undermine the opportunity that companies have to drive holistic customer experience.

The tactics and application of customer data will vary depending on the function and their goals and objectives of the organization, while taking into account the privacy and compliance mandates. But best practices begin with creating the strongest foundation, and iterating on top of that.

How can marketing and even sales teams use customer data in a more strategic manner to improve customer conversations? What are some practices (more like mistakes!) they should avoid while doing so?

In today’s world where the digital experience spans several form factors, the best experiences are personalized and consistent across those touch points. They also respect people’s privacy as a fundamental human right. 

There has long been a desire to get the right message to the right person at the right time. In order to execute a multi-channel CX strategy, teams need to connect the right data to the right applications in real-time. This begins with ensuring that you have high quality data, protecting the integrity and reliability of your data, and making sure it’s merged into the right customer profiles. It’s also vital to make sure that your privacy framework is completely integrated into your customer data pipeline.

Given a number of changes in the privacy landscape, from new regulation to the recent changes dictated by Apple and Google, brands have a greater imperative than ever before to own the relationship with their customers. This is about making the necessary investments to improve transparency and control to deliver better experiences, rather than renting or outsourcing results to large social platforms.

What are some of the core marketing technologies that you feel marketing teams should not be doing without, today, at least in the B2B and tech marketplace, besides customer data platforms!

The stack begins with customer data infrastructure (CDI) as the foundation but the stack of best in class tools usually spans a handful of categories. Analytics, advertising, marketing automation, customer support, and data warehousing. There are several sub-categories to unpack in there as well. 

The main advice I’d give any team is to think less about tools first, as that’s somewhat reactive in nature, and to focus on the customer, then data, then the application of data. This will allow teams to make sure that whichever vendors they use, they can get the most value out of.

A few predictions that you’d like to share on the future of martech?

Every company is becoming digitally native, leading to massive data growth and application adoption. At the same time, today’s customers expect experiences that are tailored to their interests. In the next 3-5 years, to successfully deliver these experiences to their customers, it will be imperative that product managers and marketers can amass a single customer view to power multi-channel personalization strategies, in real time. This means consumer brands will have to solve challenges around personalization, consumer privacy, and data integration. The reality is, those brands who don’t deliver on this will soon become obsolete. I believe this will also result in more martech being sold in through engineering and IT as companies get more sophisticated about their data strategy.  

I also think we will see more consolidation within martech because, more and more silos are being created resulting in a disjointed view of the customer that is already impacting the bottom line for businesses. 

Some takeaways for marketing leaders and CMOs /CEOs in 2021?

The best advice I can give for *all* executives is to think about how you can systematize your functions and your business. All businesses are systems, and every team within the organization must not only create predictable outcomes, but they also must rely on other teams to be successful. What enables and connects those teams is the flow of information and data; and what inhibits those teams and their connections are data silos. 

Customer data infrastructure isn’t just about marketing output, it’s about business transformation where the whole (org) is more than just the sum of the parts. And while there is a lot to be excited about for marketing and CX, it’s important to think holistically about proper use of customer data.

This requires a strong customer data infrastructure, not just a CDP!

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