Shankar Venkataraman, SVP of Product & Engineering at Jivox shares a few data management best practices in this Q&A:
Welcome to this MarTech Series chat Shankar, tell us about your journey in the B2B Tech market through these years and more about your role at Jivox?
I began my career in the B2B Tech market nearly three decades ago. I’ve had the opportunity to work at both large-scale public companies to high-growth private companies during my time in the industry. Most of my career has focused on the design and management of data platforms, applications and analytics, which ultimately let me to Jivox. As the Senior VP of Product & Engineering at Jivox, I focus on harnessing the growing capabilities of our AI-based programmatic creative platform, alongside collaborating with agencies and brands on how to expand their reach to their target audience.
As someone who’s been part of core development teams, what are some top capabilities in today’s tools (martech/salestech/similar) that you feel product teams need to focus on enhancing more?
Marketers need to take charge of all components of the stack and ensure they select the right infrastructure that is also future proof. Tools that use server side technologies, micro services, security as a core differentiator and a self-service focused UX are essential for scale.
- Marketers need to look at data as their core competency and securing that as an essential part of their tool posture. Data needs to be partitioned and reportable at every aspect of the user interaction, and usable by data scientists.
- Consented first-party data
- Consented first-party data allows marketers to deliver relevant messaging while respecting consumer privacy. Through this consented data, marketers can merge “signals” beyond just demographics –– including features like preferences, purchase or engagement history, and frequency of visits –– that allow them to approach the human behind the consumer with the use of personalization.
- Omnichannel delivery
- Users do not stick to a single channel to engage with a brand and neither should the brand. Different channels should not be treated as silos but instead as multiple junctions along the buyer’s journey. The tools should enable marketers to arrive at the next best action driven by the data in each person’s journey.
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Tell us about your product development process and how much of it is fueled by marketing feedback (based on real-time customer insights)? What best practices would you share with teams when it comes to aligning use of customer insights to product roadmap and development?
We use the agile methodology as the process underpinning the Spotify squad model in terms of organization of the development teams. Each squad is responsible for end to end delivery of the feature set or service they are responsible for. The product itself has built-in analytics to feedback usage as well as challenges. In addition, our product and UX managers solicit feedback from our customers at QBRs and when they are involved in support situations.
With marketers and advertisers getting ready for a cookieless future, how do you feel data clean rooms as an alternative can be the next best thing?
Brands own all the data across all the channels of engagement with the customer. The challenge until now, has been the ability to bring all the data across these channels together in a form that is combinable in a useful form. Data Clean Rooms is a good model that helps them “warehouse” such data. This would allow them to define a clean data interchange model across all participants that they work with, and ensure that the data they bring into these clean rooms are immediately useful for data science and analytics.
In what ways can marketers optimize their data management to prepare better for cookie-less times?
With the expiry of third party cookies, the types of data available are going to be direct, consented, contextual data from the brand or publishers or 1st party data aggregators. The conventional model of keeping paid and owned media would not be sufficient. Brands can derive and port their strategies from email and site campaigns since those have traditionally been based on 1st party, consented data. Personalization, as a result, is a lot more precise. Social channels should be considered “owned” media since there is a lot more direct targeting than generic prospecting.
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We’d love to hear a few predictions you have in mind for the future of martech and B2B marketing!
A block chain based consent management as well as user audiences is probably going to become mainstream in the next 18 months. I expect this to happen in the telco sector first, probably in Asia or Africa, where regulations are not as tight, but evolve to a more mainstream model. ABM will finally come of age with aspects like techniques like intent based audiences becoming useful in terms of lift delivered.
Some last thoughts, takeaways, before we wrap up!
Exciting times ahead for platforms like Jivox, brands and the agencies who have modernized themselves in the last 24 months!
Jivox is transforming the way the world experiences digital marketing by connecting brands with their audiences in the most personalized way. Jivox’s Dynamic Canvas technology drives engagement and digital commerce across paid and owned media, delivering ROI by reducing production costs and increasing media performance using big data, AI/machine learning, Dynamic Content Optimization (DCO), and identity technologies. Jivox Purchase Prediction Engine drives sales by matching the right products with consumers that have the highest purchase intent. Jivox is SOC 2® Type 2 certified and trusted by hundreds of leading companies including Electronic Arts, Marriott International, Mazda, Nestle, T-Mobile, Unilever and more.
Shankar Venkataraman is senior vice president of product & engineering at Jivox, the leader in personalized digital marketing, and heads its AI-based programmatic creative platform, collaborating with agencies and brands to efficiently expand their target audience reach. He has three decades professional experience in the design and management of data platforms, applications and analytics. Venkataraman holds a masters degree from University of California at Berkeley and a bachelor’s degree from the National Institute of Technology at Tiruchirappalli, India.