Madalasa Venkataraman Of TEG Talks About Big Data And AI In Sales And Marketing Analytics

As companies learn to process the flood of data from all sides, traditional models of marketing are slowly giving way to smarter, niche strategies. Firms are now using big data analytics to uncover highly profitable segments, changing their channel management strategy and sales plays. While Big data helps in identifying these micro-segmentation layers, AI is used to personalize their campaign efforts to reach out to this targeted audience, Madalasa Venkataram, Chief Data Scientist at TEG Analytics spoke on these lines at Cypher 2017, India’s most exciting analytics summit.

Big Data: the challenges around

There is no doubt that the amount of data that we have in the present time is beyond anyone’s imagination. However, with growing data, grows the problems around it. As Venkataraman lists, there are three major problem areas:

  1. Too much data: The challenges of grappling with the data and not knowing how to process it is a common problem organisations face and complain about.
  2. Too less information: While there is data in abundance, organisations fail to gather any meaningful information out of it.
  3. Inconsistent information: Varying information derived from data often leaves companies at wit’s end.

Citing examples, Venkataraman pointed how since time immemorial, the capability to process data has always been more than the data available at hand. What she intends to put across is the fact that big data means big processing power.

How can data influence sales, marketing analytics?

During her talk, Venkataraman speaks about the three major challenges that sales and marketing verticals of companies face.

Lack of personalisation towards customers: Companies are trying to make ends meet in order to bridge this gap using artificial intelligence and machine learning. Marketers need to have a single view of their customers because the platform for purchasing has changed.

The declining attention span: In the last 14 years, as Venkataraman cites, the attention span of customers has dropped to a meagre 8 seconds pushing marketers to deliver products that are relevant and has resonance with its users.

“Personalisation is what consumers crave and demand,” she added. Customers are increasingly moving towards personalisation and digital retail transformation is what the changing times demands.

Moreover, optimising channels at a very personal level is how big data helps sales and marketing verticals. The statistical techniques involved are several. It helps to target and delivering personalised services.

What is changing is the processing power and the time frame it needs for implementation of the product offered. Shortened turn-around time or rather to provide solutions real-time is almost what all organisations are working on.

Venkataraman concludes on the note that companies must learn how to use the data and the available tools to benefit both consumers as well and providers in order to answer the right questions with the right answers at the right time.

More Great AIM Stories

Priya Singh
Priya Singh leads the editorial team at AIM and comes with over six years of working experience as a journalist across broadcast and digital platforms. She loves technology and an avid follower of business and startup news. She is also a self-proclaimed baker and a crazy animal lover.

More Stories


8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Yugesh Verma
A guide to explainable named entity recognition

Named entity recognition (NER) is difficult to understand how the process of NER worked in the background or how the process is behaving with the data, it needs more explainability. we can make it more explainable.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM