With big data, marketers now have a goldmine of information: Atul Soni, Co-founder and CMO, Cuberoot Technologies

A Gurugram-based company, Cuberoot Technologies empowers digital marketers to fast-track their business growth by providing innovative solutions. The company helps marketers to navigate through an excessive volume of data by compiling and analysing data with advanced algorithms.

 

In conversation with exchange4media, Atul Soni, Co- founder and CMO of Cuberoot Technologies, discussed how their unique solutions enable marketers to make smarter decisions. Excerpts:

 

How is big data changing the game for marketers?

 

 Ever since the introduction of big data analytics as a discipline in marketing, the art of deriving meaningful actionable insights has become more imminent than ever. Instead of smaller data sets, marketers now have a goldmine of information at their disposal been offered by data streams running into terabytes and petabytes. Today’s marketers can take better than before informed decisions which can potentially impact their overall businesses.

The ability to segment and micro segment data, analyse it from every potential angle, running simulation based models and predicting future trends is a possibility no marketer would like to omit from his/her daily chore. 

I think that this ability to read between the lines or rather numbers is something which is transforming marketing as a function in a bigger manner than we can possibly even imagine and shall continue to do so as data crunching and mining algorithms keep improvising.

 

When it comes to the use of big data, how is India different from its global counterparts?

 

We think that India is at the cusp of a big data led revolution wherein a lot of base awareness is already there and gradually we are seeing a whole-hearted adoption of this in certain sectors like technology, media, banking and finance etc. 

One example that comes to my mind is from the highly rudimentary and fragmented logistics sector wherein a new age trucking company is using big data led insights to reduce their delivery timelines and fuel consumption related expenses. At the same time, these insights also help them reduce driver fatigue and improve his overall performance and health in the longer run leading to better efficiencies in the overall system. I think that’s commendable and aptly demonstrate what understanding and implementing a big data driven core strategy can achieve for you. 

In our industry specifically, the DMPs have been at the forefront of helping make digital advertising and content delivery more efficient and intelligent. Our product Cuberoot just builds up on that from an algorithm and features purview.

However, having said that, a lot of big data adoption is yet to be undertaken in Indian companies and I am sure that shall gradually happen in due course of time.

 

What are the marquee solutions that Cuberoot offers to its clients?

 

Cuberoot offers a unique opportunity of a custom ‘private-label’ data management platform deployment at the client's premises. Here, we are focusing at the large clients who are looking to build world-class in house DMP where they are concerned about their in-house data security.

This along with other core features like a ‘single customer view’ enhanced with 3rd party intelligence, omni-channel platform mapping and the ability to integrate existing marketing channels like Facebook, Google, email marketing etc for insights visualisation, attribution and channel performance comparison make it stand apart.

Other features include look-alike modelling, custom channel and audience segment creations, ideal targeting parameters recommendation, ad and content personalisation etc. to name a few.

In a nutshell, depending on the use-cases we empower the client to extract more out of their marketing efforts through data-driven marketing harnessing proprietary algorithms and big data led intelligence and try to graduate them to the marketing automation space in the process.

 

How is artificial intelligence and machine learning redefining the use of big data and adding more value to it? 

 

 AI and machine learning are gradually replacing the need of a human brain to analyse huge data streams and draw meaningful patterns out of them. There is nothing new happening other than that software and microchips have taken place of a human brain. The brighter side is that since these software’s and machines are scalable and flexible hence they can handle large complexities easily once trained to do so and in much faster time. The ability to predict and recommend future trends is something that today’s machines are better equipped to do in certain contexts that humans. Hence, we can clearly see the value these technologies bring onto the table for any business or individual alike. 

In our case we clearly see that the DMPs in future will power IoT, marketing automation and future real time technology products across industries and applications.

 

 Do we see greater collaboration of data becoming a reality?

 

Yes, absolutely and that is happening as we speak right now. In the programmatic digital advertising domain, almost all the DMPs are talking to each other via mutually crafted data alliances agreements in order to enrich each other’s audience persona profiles. From a bird’s eye perspective, all the Demand Side Platform (DSPs), Supply Side Platforms (SSP’s), Real Time Exchange (RTB’s) exchanges Data Management Platforms (DMPs) and continually exchanging data sets to ensure that the right ad goes to the right prospect.

Financial stock markets are another industry domain where we know similar data sharing is happening within specified boundaries. In fact, the financial industry has been at the forefront of data collaboration and is an inspiration for other industries.

 

What are some of the new trends emerging in the use of big data?

 

We think there will be more relevant work done as far as Internet of Things (IoT) is concerned, which is pegged to drive future growth of big data use cases. 

From data management infrastructure point of view, the current dependence on Hadoop for big data processing shall be reduced with more efficient programming models coming into play.

Visual analytics shall become more intuitive and simplistic for easier comprehension and lastly, technology will lean towards becoming more descriptive in addition to being predictive in outcomes depiction and handling.