TechBytes with David Booth, Co-Founder and Chief Commercial Officer, Cardinal Path

David Booth, Co-Founder and Chief Commercial Officer at Cardinal Path

David Booth
Co-Founder and Chief Commercial Officer at Cardinal Path

Cardinal Path is the largest Google Marketing Platform (Analytics 360) reseller in the world. We spoke to David Booth, Co-Founder and Chief Commercial Officer at Cardinal Path, to understand the core tenets of their technology integration for Google 360 and how it helps marketing teams to work with CRM.

Tell us about your role at Cardinal Path and the team/technology you handle.

I’m the co-founder and Chief Commercial Officer at Cardinal Path.

What brought Merkle and Cardinal Path into a partnership?

We’re both a part of the Dentsu Aegis Network, and we already operate collaboratively as leaders in our respective specialties, bringing each other into engagements wherever it makes sense for our clients. Merkle is truly a market leader with their CRM, performance media, and Google Cloud offerings, and Cardinal Path is the largest Google Marketing Platform (Analytics 360) reseller in the world. It made sense for us to offer a joint solution, and in fact, we’re already doing this for a Fortune 500 technology client. Cardinal Path is delivering Analytics 360 licensing, implementation and ongoing support to augment Merkle’s delivery of a broader global digital transformation initiative. The idea is to let each agency bring what they do best to the table, but work together as a single team for a seamless client experience.

What are the core tenets of your technology integration for Google 360? How does it help marketing teams to work with CRM?

We’ve done literally thousands of implementations on the Google Analytics platform, and we consider it a foundational building block. It’s a critical part of the toolkit, but the whole point is to enable revenue-based outcomes. By understanding the larger perspective across the entire marketing technology stack, we can provide our clients with a holistic view of the customer in an age that crosses channels, devices, and data silos. A great example of this is the recent integration of Salesforce and the Google Marketing Platform. This has made the vision of bringing together sales and marketing data from digital and offline touchpoints for that entire view of the customer journey a possibility within the grasp of enterprise marketers. It’s already happening for some organizations, and it really is a big deal. Salesforce is bringing Cardinal Path to their clients so they can find success across the Google integrations with these investments, while Merkle has been doing the same across an array of Salesforce activation and value-add services.

Do you think CRMs are lagging in performance compared to other marketing technologies? How does your partnership boost the CRM offering in the market?

Like any other technology investment, there’s a lot that goes into a deployment in order to gain value, and organizations that are doing CRM well are taking advantage of technology to serve their customers in truly ground-breaking ways. In the case of the native integration between Salesforce and the Google Marketing Platform, sales people and marketers are getting access to insights they didn’t have easy access to before — data that shows the entire customer experience from the earliest touchpoints through to a sale, whether online or offline. The ability to use first-party customer attributes, as well as the clickstream data, across both platforms allows for analysis with a unified data set that was previously out of reach for many marketers. And leveraging this combined data set to create segments and audiences allows for activation across channels that span the realities of today’s complicated customer journey.

What does your product roadmap look like for 2018-2020? A lot of people talk about digital maturity but what it is specifically is unclear.

We’ve seen for many years that our customers need help in three areas. First is the foundational technical layer. In a marketing technology landscape, our clients need to know what tech they need, they need to plan for and implement that tech, and they need to integrate it into their broader stack. Second, they have to make use of that data. This means moving from static reports to dynamic, multi-sourced data visualizations and dashboards that reflect business-driven KPIs. It means formalizing measurement frameworks that incorporate customer segmentation and consumer journeys, and standing up for formal data governance. Third, they must leverage the power of good data and well-defined success frameworks to apply data science. We see our customers gaining tremendous value from analyses like content attribution, forecasting, purchase intent and predictive modeling, attribution, and more.

How much of your platform is based on AI/ML, analytics and real-time data visualization benchmarks?

Artificial Intelligence is a broad discipline and Machine Learning is one aspect of AI that has proven especially useful to the discipline of digital marketing. While we’re not quite ready to task AI with our creative processes, what it’s really good at is churning through large data sets to uncover useful insights. I’ll give you a couple of examples from the current work we’re excited about. First, Machine Learning is particularly well suited to help us define audience segments. There are so many attributes and behavioral signals we’re collecting from digital that it would be nearly impossible for a human to sort through all that data and organize groups of like-performing people. This is where Machine Learning excels. It can figure out that males between the ages of 35-39, who have done 3-5 searches for rabbits or dogs on your website after visiting pages that showcase pet food, are twice as likely to convert on a particular type of e-commerce purchase. And once the machine has figured this out, with the right technology in place, a marketer is only a few clicks away from creating an audience of that group that can be targeted across search, display, social, and more with targeted campaigns.

Another example would be the work we’re doing in “data-driven creative.” In this case, we can take every visual advertisement a client has ever run and apply Machine Learning to pick apart all the visual elements that are present in those ads. This can get pretty specific, and products such as Google’s Vision API can define thousands of attributes, as well as probabilities around their presence in these images. Coupling all of those attributes with performance data that comes from in-store POS, ad serving, and web analytics data, we can model the likelihood of those attributes against success metrics. With a list of virtually any element a creative team can put in an ad along with its probability of successful outcomes, this can be a great guide towards designing and deploying new ideas and creatives.

How does it help find more traction from CRM that integrates with Google Analytics 360?

Integrating first-party customer data with largely aggregated and anonymized clickstream data opens up huge opportunities for marketers. It’s important to note that this is a bi-directional integration, meaning we can take data from both systems and inject it into the other. For example, if we have the ability to bring CRM data into a web analytics environment, we can maintain that anonymity but still open up entirely new opportunities for analysis. By appending to our web analytics data set information such as customer lifetime value, last purchase date, demographic or psychographic details, customer segmentation profiles, offline funnel step, or any other piece of data we’re tracking in a CRM, we not only open up the ability to enhance our analysis, but we can directly action new and specific audiences. In a Google Marketing Platform environment, this means we can create audiences from these CRM-driven attributes, and then leverage DV360, DS360, Optimize 360, and more to create campaigns to target those audiences with specific ads, messaging, and even on-site personalization. On the flip side, by bringing online behaviors that we’re tracking in a web analytics environment into the CRM system, we can start to build marketing automation journeys, email or SMS contacts, or even touchpoint triggers that are defined by online activity that the CRM was blind to before.

What are your predictions on the state of CRM and Customer Intelligence for 2018-2022?

I think that the merging of these data sets in more autonomous and fluid ways is simply a given, and as platforms continue to evolve and convergence across the MarTech landscape continues, the unified data set will become less of a challenge. It’s important to choose your platforms and peripheral vendors wisely, and even more important to ensure they’re integrated into your larger stack, and this will enable the next step of evolution: What will you do with that integrated, trusted, holistic data? Layer in the expanding capabilities of Machine Learning and I think we’ll see an increasing dependence on this to uncover insights and surface recommendations. We’re seeing this in many platforms already — rather than a team of analysts scouring these increasingly large and complex data sets to find weekly or monthly recommendations, Machine Learning is bringing anomalies and opportunities to the forefront, along with recommended courses of action and predicted results. The role of the marketer is to simply approve those changes or run an automated test against a control to validate that the recommendation has worked.

Another aspect of this data convergence and massive capacity for action will be the holy grail of personalized marketing at scale. We’re already seeing advertising platforms finding success with Machine Learning-driven creative optimization, where marketers provide multiple variations of creative that can be mixed and matched to create thousands of permutations. The machine can quickly and efficiently identify which combinations of images, headlines, messages, and more are most likely to result in positive outcomes, and as more and more signals become available to these models, the capacity for mass personalization will continue to increase.

Thanks for chatting with us, David.

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