TechBytes with Vinayak Nair, VP Research Ops and Custom Analytics at Verto Analytics

TechBytes with Vinayak Nair, VP Research Ops and Custom Analytics at Verto Analytics
TechBytes with Vinayak Nair, VP Research Ops and Custom Analytics at Vetro

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

I am our Head of Custom Research, responsible for delivering high-quality data and insights to our clients, some of which like Google are among the biggest technology companies in the world. I am responsible for using our data assets, methodology, panels, to create insights and research to answer our customers’ business questions.

Why are B2B marketing teams steadily moving toward Applied Data Science for Sales and Marketing initiatives?

Traditionally this used to be very F2F-driven, all about relationships and real people talking to each other. However, in today’s digital world an increasing share of B2B decisions and investments are done online which leads to the marketing and sales focus to also pivot to micro-targeting using online clickstream data to understand who is a possible consumer thereby making marketing spend more efficient and targeted. In order to accomplish this, Data Science and Engineering are key to synthesize that information from large volumes of data. For example what actions/pathing indicate that someone is in-market for a B2B product?

What is the current definition of Mobile Customer Experience? How is it different from desktop CX?

We define mobile customer experience to be the result of the user’s activities and engagement, interaction and its smoothness, with mobile devices and digital services through these devices, as opposed to the use of similar assets but on the big screen computer-type devices. Increasingly there is a shift of online purchasing and decision making to Mobile devices as User Experiences on these devices are becoming more efficient and seamless.

Tell us more about the changing nature of Digital Experiences and how it impacts experience delivered through the Mobile Marketing campaigns?

Today’s digital campaigns, and definitely the capabilities brought on by mobile, are enormous. The biggest differences are in the fact that one can reach the individual consumer, on a one-by-one basis (mobile devices are not shared), all the time and everywhere, at the point of context and user attention – using for example locations and other such contextual variables based on online clickstream data are integrated parts of the campaign delivery and targeting.

How have the customer’s expectations evolved in the last 2-3 years when it comes to interacting with brands over Mobile devices? Do you have some specific data related to this evolution of customer preference?

The expectations of customers have changed significantly. Back in the day, it was thought that mobiles are yet another screen or device to use in targeting people. Today’s brands expect more native and sophisticated use of the unique characteristics of mobile devices (see above), and increasingly want to make sure the role of mobile advertising in the overall mix is well thought and optimized.

We have data to quantify this as well given the longitudinal nature of the panel and can compare if panelists are more/less active on their Mobile devices compared to a year or more ago.

Tell us about the three major takeaways from your recent report on Mobile data. How can marketing team better leverage your report findings to understand consumer behavior?

The report’s major takeways are less actionable than they are predictive of several key possible changes in mobile.

Consumer Attention at Unlock Will Become “a Thing”:  The report begins to peel back and inspect the first moments of consumer attention on mobile.  The findings certainly support that those first moments done right positively impact companies, influencing engagement, content consumption and revenues.  So we think that this topic will get greater attention from brands, publishers, ad tech, carriers, smartphone OEMs and more.

Research Must Continue to Evolve: The findings create as many questions as they answer.  Given the potential impact on so many mobile stakeholders, we certainly expect this to be the first, but not the last, set of insights on this topic.

Smartphones Will Change: What is certainly clear, is that our findings support that our “smart”phones could, would and ultimately will be smarter when they do a better job of more frictionlessly delivering the content and apps a consumer may want to use.  There are many ways this might be done.  Many will be tried, and Verto looks forward to trying to understand how well they work.

How do you see Big Data and Customer Intelligence coming together to solve identity-related issues?

We believe the fact that we have opt-in based datasets and services, and aggregators and intermediaries like Liveramp which make it possible to do data deals and overlaps, with anonymous matching, respecting all the local laws.

Tell us about the various steps of the AI-powered Mobile Marketing journeys. How do consumers from different global locations interact with advertisements and marketing campaigns?

There are lots of differences how different cultures react to contextual and more intelligent advertising. For example, Europeans tend to be more conservative with privacy, while Asians and Americans are more receptive to intelligent and contextual targeting.

What are the different scenarios AI could be applied to in Marketing, Sales and Customer Service?

There are many. We see that continuous optimization and on-the-fly adjustment of campaign creatives and targeting is one area where more intelligent use of return-path data and feedback loops could pose a big opportunity for AI (no static campaigns), the big opportunity is to provide real-time support via bots, which we have seen increasing in use lately.

How do you build analytics around Cloud and Enterprise Mobility platforms?

We are very customer-driven, so we have work flows and teams who understand and talk with customers about their needs, and we have our HQ-driven development tracks in which they implement these features into our cloud-based platform – productizing the solutions so that they are available to a high number of customers.

Tell us about your customer experience products. How can MarTech customers benefit from your AI/ML products at Verto Analytics?

Our single source approach (telemetry data on both PC and Mobile for the same user and ability to survey those users) enables us to get in-depth information into the consumer mindset and actions.

We start with business questions, and using our methodology my team and I would propose customized solutions using both telemetry and attitudinal data to address our clients’ business problems.

What is the current state of machine learning and AI for digital transformation?

There appears to be a lot of hype but it’s unclear how effective the results are. Technologies and algorithms are there, but we still need to make sure the data that goes as an input is of high quality, and there is transparency and better ROI measurement especially on the ad targeting/segmentation front.

How much of this state is influenced by the maturity of Data Science and Machine Learning algorithms?

The algorithms and tools themselves are mature however as mentioned above the output/quality is dependent on what goes in. For example, a lot of players claim to be able to do advanced targeting using look alike modelling but is the input of the best quality? Is the output being effectively measured? Theses are questions business leaders need to consider beyond the maturity of Data Science Tools and algorithms.

What are the major pain points for Data Analysts and Engineers in building/ scaling Analytics for relevant customer experience?

It is always the extent of customization. Companies like us are interested in solving problems, but for us to do our work well and maximize the value-added, we need to be able to map similar problems across many customers so that we can scale our solutions more effectively.

How do you work with AI/ Machine Learning at Verto Analytics?

We have people with strong skillsets in this area, and apply machine learning in projects and use cases whenever it makes sense – not because it is a “cool thing to do”. For example, segmentation and clustering similar users is a common use case.

Vinayak Nair is the VP of Custom Analytics at Verto Analytics and currently works with Media, Tech, and Market Research companies to help synthesize digital consumer behavior utilizing a proprietary single-source, multi-platform panel. Prior to joining Verto Analytics, he was the Director of Analytics at comScore where he helped build and launch Audience Measurement products.

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Verto Analytics is a media measurement company that offers a holistic view of the consumer—their behavior along with demographics, lifestyles, attitudes, and interests.

Verto owns and operates single-source, passively metered consumer panels in select markets that gives it the power to measure behavioral changes over time across all media—second by second. Brands, publishers, and researchers can benchmark against competitors and the market, fill in the gaps in the consumer journey, and identify ways to increase engagement and loyalty, with Verto’s services.

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