MarTech Interview with Alex Dean, Co-founder and CEO at Snowplow

Alex Dean, Co-founder and CEO at Snowplow shares a few data and analytics tips and best practices while sharing a few thoughts on the growth and development of the Snowplow platform:

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Welcome to this MarTech Series chat, Alex, tell us more about yourself and what inspired the Snowplow platform? How has it evolved over the years?

A driving force for me professionally has always been my passion for innovation and organizational change along with a fascination for functional programming, cloud-based architectures and big data technologies.

After doing analytics for major brands, my co-founder Yali Sassoon and I became frustrated by the time it took trying to unlock granular customer data from Google Analytics and other packaged solutions. And so, Snowplow was created as an open-source behavioral data platform using a pioneering private SaaS model.

Snowplow lets organizations track, contextualize, validate and model customers’ behavior across the entire digital estate – in their own cloud. With our platform, AI-ready behavioral data is available in real-time, can be delivered to a data warehouse of the organization’s choice and can be used to power a wide range of use cases.

Snowplow was founded in 2012 and has quickly become the world’s third most-used web analytics tracker, with clients including 150+ global customers like Strava, The Economist, Weebly, Hudl, Auto Trader, Omio and Secret Escapes, as well as 10,000s of open-source users.

We’d love to hear more about some of your recent partnerships/integrations and how that helps end users?

We are always expanding our partnerships to further help customers and elevate the entire data space. To this end, we work with world-leading technology partners that both integrate directly with and work alongside Snowplow in the modern data stack. And we work with many solutions partners to take advantage of their domain expertise alongside our enterprise-grade platform.

Recently, we partnered with Snowflake to deliver an unrivalled digital analytics solution, giving customers complete control and ownership over their own AI-ready behavioral data.

This combined solution gives businesses new capabilities to develop a deep, contextual understanding of customers, along with flexible and near-infinitely scalable new tools to save time, eliminate bottlenecks and deliver superior ROI.

As a result of this partnership, users gain new use cases for marketing attribution as well as the ability to create highly accurate propensity models or other customer-centric machine learning apps.

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Given today’s market dynamics and challenges, how do you feel brands / B2B marketers need to be more focused about behavioral data and how they use it to drive further marketing / business ROI?

Rich behavioral data can granularly illustrate how people make choices, making it perfect for use with artificial intelligence (AI) and machine learning (ML) applications. However, to ensure data facilitates useful insights, quality is essential. There is more data, from more sources, than ever before for businesses and marketers to use and collect. To accommodate for this, many organizations today only rely on packaged solutions, like Google Analytics, to manage their behavioral data.

Packaged solutions can be ineffective because they are designed to focus only on a set of specific functions for certain industries. And teams using packaged solutions may lose sight of opportunities because of preset ideas about what behavioral data should describe, and how it should be structured and processed.

Another common problem is using a “track everything approach,” which marketers often employ to capture as much data as quickly as possible in case it may be of use later. This approach is contributing to greater privacy concerns and resentment toward third parties that track users across websites and branded properties.

If an organization wishes to leverage AI-ready behavioral data to improve their customer understanding, they will need to focus more on collecting data that is fit for their specific requirements. This degree of customized data management may require a dedicated platform that provides greater flexibility and control on how data is used and collected.

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What are some of the top challenges you still see data teams/marketers faced with (when it comes to data collection/data management, etc) and what solutions/tips would you share?

The data landscape is constantly evolving, with new opportunities and challenges presenting themselves. Here are just a few issues we see teams deal with regularly:

• Managing huge data volumes– Many businesses need to manage large volumes of data without incurring giant costs. Finding an open, scalable platform to manage behavioral data can help free up time for engineers and make data more accessible for insights.
• Driving value from data – Companies often spend large sums on data management and tools but see little impact on business goals like service improvement, marketing attribution and churn reduction. This may be due to poor data quality or not focusing enough on how to improve customer engagement and relationships.
• Balancing personalization vs. privacy – New laws, changes to tech platforms and public outcry with brands like Meta and Clearview AI are driving a decline in third-party tracking tools and overly invasive data practices. To adjust, it is time to shift data strategies from understanding everything possible about customers to understanding them “well enough” at an appropriate level to provide great service.
• Making quality data intrinsic to operations – For many established companies, it can be challenging to compete with data-native businesses like Amazon and Netflix that seamlessly use predictive AI models fueled by rich behavioral data. To succeed, businesses often need to refashion processes and infrastructure to handle real-time data and gain access to quality data

For marketers looking at revisiting how they centralize data to draw the most relevant insights from (and faster) what best practices do you think they should follow?

Many struggle to manage data for AI and business insights because they have to spend copious amounts of time merging data from a mixture of various sources. To drive meaningful, predictive, and repeatable results from data it helps to have a single source of truth, like a data warehouse, that teams can pull from for AI-modeling and insights.

As uptake in AI and ML increase, there will also be more demand for data that is easy to understand and work with. This is driving an increased need for data catalogues, visualization and analysis tools to help visualize and derive meaning from data. To tackle this, many have formed dedicated, siloed and centralized teams. This often results in bottlenecks or overreliance on a few experts with limited bandwidth – delaying the data journey of insights that decision makers need quickly.

To avoid bottlenecks, many businesses are harnessing one single source of truth but decentralizing their teams, with some responsibilities assigned to more agile groups who can focus on specific departmental needs. Additionally, some are implementing tools to empower multiple teams with self-serve analytics. These strategies enable organizations to improve efficiency and ensure data can be accessed quickly by the people who need it most.

Five thoughts on the future of data in B2B marketing?

A plethora of factors, such as evolving technology, laws and user preferences, are greatly changing the data landscape. This is leading to many new trends, including:

1. More demand for AI-ready data – The use of AI and ML is becoming more advanced and widespread. In addition to becoming more strategic about what data they take in, businesses will need to ensure the data they collect is meaningful enough to power advanced analytics and AI-driven predictions. Doing this may require removing data preparation bottlenecks or fixing an overreliance on third party tools.
2. A rise in first-party data – New privacy concerns and limits to cookies are giving rise to first-party data use, enabling companies to control their own data and focus on building genuine, transparent data relationships with customers.
3. The Chief Data Officer (CDO) role will expand – Rather than just having technical excellence in engineering and analytics, CDOs will need to expand their communication, HR and leadership skills to transform organizational structures and build a data-driven culture in their companies.
4. Increasing maturity in behavioral data use – Marketers are moving from simply collecting more, to focusing on collecting better and deeper behavioral data. This will help companies improve their personalized services and drive competition.
5. The data talent war is erupting and evolving – Companies are battling to secure highly-skilled data workers. Simultaneously, new data roles will become more prevalent due to architectural changes in the modern data stack, with job titles like data steward, machine learning operations manager, data governance manager and more. Certain platforms that enable democratized data use and self-serve analytics will also help businesses thrive with less engineering talent in house.

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Some last thoughts, takeaways, before we wrap up!

Brands used to compete on product, but today, the key differentiators are data and who understands their customers best. This means rich behavioral data coupled with ML and AI applications will only become more vital. Marketers who master using these tools while respecting users’ privacy can gain a wealth of benefits, from better marketing attribution to better personalization and brand trust.