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

As the Chief Technology Officer at Rokt, I’m responsible for setting the technical direction for our rapidly growing engineering team, influencing product development and am accountable for our success in becoming a leading technology brand. I have technical oversight of over 75 engineers that work on C# .Net and Clojure back-end services, Angular and React Javascript front-ends, and machine-learning and data science teams using Python-based analysis tools and model training algorithms.

What is Rokt and how does it impact customer journeys?

Rokt is a global technology company whose core mission is to make the e-commerce transaction smarter by optimizing the checkout for each individual across products, UX and campaigns. Rokt’s algorithmic engine powers three solutions that include:

(i) Commerce, to help brands get smarter in the transaction,

(ii) Acquire to help marketers acquire customers in the Transaction Moment, and

(iii) CalReply to help brands drive timely action through their calendar.

Why are B2B marketing teams steadily moving toward Applied Data Science and Machine Learning to drive their mobile sales and marketing initiatives?

Traditional marketing platforms that rely on either static, simple optimization or auction mechanisms to determine offer and campaign placements, tend to optimize for the brands’ existing best customers. That is, the majority buying pattern is catered for and as a result, users that don’t fit that pattern, habit or trait get further marginalized with a sub-optimal experience.

Applied Data Science is able to make sense of these varied and clustered user behaviors by surfacing subtle patterns in a wealth of data far beyond human abilities. This allows machine learning models to be trained, and in the best-of-breed cases like Rokt, reinforced learning allows the models to become smarter as they observe the success of the decisions they’re making.

How do you utilize AI/ Machine Learning at Rokt?

Rokt is unique in that we have a platform that can perform real-time decision making and optimization. Using a large set of disparate data that spans the end-user’s transaction, the end-user themselves and the offer being presented, the best campaign and transaction experience can be determined.

Machine learning is the key to providing this optimization in a low-latency, globally-scalable manner.

Tell us about your customer experience products. How can MarTech teams benefit from your offerings?

Our products give e-commerce brands the ability to optimize value across the transaction. By using Rokt AI, we can predict what each end-user is most likely to engage with. Our platform never sees two people as the same as we optimize the right campaigns (upsells, cross-sells, etc) and User Experience down to the individual level. This technology is the driving force behind the relevancy that creates a better end-user experience, and delivers significantly greater profitability for e-commerce sites.

How do you prepare for the advent of data-driven commerce? How can businesses leverage Rokt to fully-optimize the power of commercial transactions?

We’re customer obsessed and work closely with our existing customers and partners to make sure we understand how to be the best possible MarTech platform for their needs. Our investment in our own measurement technology, Rokt Incrementality, allows us to collaborate with customers and make the most of marketing spend with returns that make our offering a market leader.

We’re committed to in-transaction marketing and are driving further value for our customers by creating a product suite for optimizing and monetizing this experience for e-commerce sites and performance marketers alike.

What is the current state of Machine Learning and AI for digital transformation?

Like so many uses of Machine Learning and AI, the uses are both nascent and incredibly powerful. Our ability to make real-time decisions using machine learning has transformed e-commerce marketing for our partners, but we’ve only just begun to scratch the surface. There are myriad algorithms, frameworks and schools of thought emerging from both enterprise and academic institutions that show great promise in making sense of large sets of data to allow for the optimization of end-user experiences in the digital marketplace.

What are the major pain points for Product Management and Innovation teams in building/scaling Analytics for customer experience?

The fast paced evolution of digital marketing in e-commerce brings differing perspectives from customers in terms of their analytical needs. Some need analytics more closely aligned to the traditional forms of marketing that they’re familiar with – while some want to move completely beyond that realm with entirely new ways to instrument and examine their marketing activities in the e-commerce environment.

How do you solve these at Rokt?

Our challenge is to provide a compelling offering that is able to suit both customers while also building a forward momentum toward our goal to drive the best possible relevancy in a strong marketplace. We solve this with our innovative Rokt platform that provides a single, consolidated online platform through which our customers can view and manage all aspects of their marketing activities.

Your advice/ message to the DevOps, AIOps and Coding community.

Keep in mind the need to strike a balance between providing what your customers want today and where you can drive innovation in this dynamic market place. Being too avantgarde can quickly alienate your customers, causing a retreat to the solutions that look and feel more familiar. However, relying on an overly familiar solution and you’ll find it very difficult to innovate your customers out of their comfort zone and toward a more engaging and rewarding future. The sweet spot is providing a suite of products that customers can immediately understand how they will use them, with a forward thinking value proposition that is aligned with how the customer wants to grow their business.