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Varij Saurabh

Varij focuses on creating innovative products that use analytics and deep customer inisghts to drive customer engagement, personalization and targeted marketing, with the aim of helping consumer facing businesses and customer insights professionals drive greater ROI for their marketing dollars.
Automated customer segmentation for marketing goals

AI powered prescriptive segmentation is here

Automated customer segmentation for marketing goals

Wouldn’t it be nice if your marketing tool could help define the target segment based on your marketing objectives?

Imagine the current scenario – you and your data scientist spend days, if not weeks, to figure out the characteristics of customers most likely to respond to a given message, and create the list. What if this could be done immediately?

One of the benefits of having a CDP with clean and connected omnichannel data of each customer is that you can start predicting customer behavior with high accuracy. The challenge lies in defining the variables for modeling. This is the value a data scientist brings to the table. She can look at the data profiles, experiment with different variables, and finally, come up with the most relevant variable(s) to feed into the Machine Learning algorithm.

Given the needs of different B2C verticals vary – restaurants, grocery, fashion, specialty, loyalty programs, e-commerce, there can be an infinite combination of measures and filters that must be evaluated.

Sounds tricky? We at Manthan have automated this process.

Our approach dynamically creates multiple combinations of possible variables for each of the verticals, tests for their efficacy and then select the best ones to build the model automatically. Some applications of this model are in look-alike modeling – which can be used for higher cross-sell, upsell and growth marketing. Details on how this is done are here, in a blog by my colleague.

Another ask we often get is around explainable AI. The users aren’t comfortable with black box AI, and we are glad they seek control and want visibility into how the algorithm works. To a marketer, knowing which behaviors are most relevant is key, so other parts of the business can also benefit from the data science.

Let’s take an example, if the algorithm finds that customers who shop across multiple channels are most likely to become stars in the future, they can craft programs that encourage customers to explore new channels. This insight is critical to business growth.

All of this plays out before the campaigns are even executed. Then, there’s the question of returns on campaign spend – how do you know whether the campaigns were effective? Are you able to prove Marketing ROI to your CFO? To aid this, marketers need extensive testing, experimentation, measurement, and attribution capabilities. But, let me reserve that for another piece….

Better late than never – Salesforce acquires Tableau… 3 years after Manthan-Tableau integration

Manthan is always looking for ways to make data consumption easy and natural for those who need it the most, and are pressed for time. From AI-driven intuitive interfaces to self-serve customizable dashboards, we are constantly pushing the bar to provide the B2C segment insights into their data using our analytics software.

In line with this vision, Manthan partnered with Tableau over 3 years ago to bring together Manthan’s deep expertise in pre-built, industry specific analytics with Tableau’s rich visual analytics.

This week, Salesforce came to the same conclusion, when it announced its intent to buy Tableau. Salesforce’s decision to acquire Tableau highlights their shift from CRM into the analytics field.

Is Salesforce just patching up a flop?

“The acquisition will test Salesforce’s focus”, said Zane Chrane, an analyst at Sanford C. Bernstein & Co. According to Chrane, Business Intelligence is “not Salesforce’s core competency and there is much Tableau does that doesn’t pertain to the CRM world, making the fit slightly imperfect,”
According to this Bloomberg article, the acquisition is also an “implicit admission” that Salesforce’s analytics product, Wave, “was a flop.”

Understanding the drive towards self-serve analytics

Manthan customers can directly access data using the Manthan Customer Data Platform through Tableau and publish dashboards. The solution merges the benefits of strong data governance and pre-packaged data sciences (served by Manthan), and business dashboards (served by Tableau), all in one holistic solution.

Manthan had a CDP in 2012, even before the term was formalized in 2013. Earlier this year, Salesforce recognized its need, and they are now building a CDP.

With over 6 years of serving both business and data science users in the retail industry, and now successfully venturing into the QSR segment, Manthan has long understood how critical customer data management is, and the need for democratizing data and analytics.

Before there was a Customer Data Platform, there was…

…Manthan Customer360, a unique customer data and AI platform to serve as the foundation for personalized marketing. It made the life of the marketer, analyst, data scientist, and IT team easier by helping them organize customer data effectively. Out of the box analytical capabilities directly helped in creating better and targeted campaigns much faster.

Manthan has been a pioneer in the CDP space and has been offering the vertical specific CDP with advanced analytics capabilities to retail businesses globally long before the term was even coined.

Salesforce is behind us on both counts. The way I see it, they’re playing catch up with Manthan.


Let’s not forget the AI-driven power-up

Last year, we added AI capabilities to our Customer Data Platform for B2C enterprises; this enabled the system to constantly upgrade unified customer profiles based on every action, generate astute customer insights and action them with personalized product and offer recommendations.

With Manthan Maya, these insights are made available through an easy conversational interface to business users. This is AI on your desk, and in your pocket on your mobile phone – you ask a question in English, and get the answer, without having to sift through lengthy reports. This means the ability to proactively act on opportunities and respond to market.

Predicting what’s needed for a future-proof offering is something that comes easy to Manthan.

According to TechCrunch, post the Tableau acquisition, Salesforce is now looking into the possibilities of expanding their AI-based initiatives.

Any bets on who Salesforce will acquire next to bring Einstein to life?

Build or buy a Customer Data Platform? Here’s the answer

A packaged, industry focused CDP provides ‘people ready data’ and quick time to value

This article is part of our CDP series. To read other articles in this series, click here.

Any marketer looking to invest in a Customer Data Platform (CDP) will have to grapple with the build vs. buy conundrum. The points of view are more entrenched in this space than any other. This is primarily because the end deliverable is a customized data platform, which resembles a services engagement. Let’s compare the build and buy options.

If you know exactly what you need, can describe it in exact requirements and have a service partner or an in-house team who can deliver to the requirements, build might be a path to consider. You will still have to wait for 6 to 12 months to get access to the solution. If you have a stop gap technology that meets your requirements while you build, or time to market is not critical consideration, you can opt for the build route.

The build option

Organizations with substantial IT budgets and large development teams with a complete range of data, design and IT skills might opt for building a CDP in-house. The people investments required to deliver a custom CDP and maintain it are considerably higher than the buy option.

Done right, there could be competitive advantages with a custom solution. However, be cognizant that at every stage there are very real challenges – scope creeps, failure to accurately define specs, cost overruns, overheads of vendor management and staff attrition.

A large-scale IT project is often lost in translation – what the customer explains is different from what the project manager understands. Further translations happen to the engineer, and then to programmer, and alas, the result ends up looking significantly different from what was envisioned.

The buy option

A pre-packaged CDP that is crafted for a specific industry offers the best of both worlds – quick time to value of a plug and play solution and close fit of a bespoke solution. While you would need some technical resources for the set-up and upkeep, the time and costs involved are much lower, and there are no harsh surprises.

This means you can run pilots and POCs quickly before you go all-in, you can examine and are sure that the solution really works for you. A much lower upfront cost and quick onboarding takes away risks, and still provides a solution that is tailormade for your needs.

CDP that delivers people ready data

A well-designed solution must cover scenarios and requirements that haven’t been thought of and encountered before while being cost-effective.

A CDP should serve multiple user groups that have unique needs. It should be scalable to serve data scientists who work with large data sets and require clean, well-organized data. It should be flexible to serve marketers and campaign planners who want to promptly analyze metrics and decide targeting strategies, or perform customer segmentation. Senior executives form another user group, and would typically be interested in trend reports and business dashboards.

Recognizing the distinct personas in the value chain, a differentiated CDP must have distinct interfaces and functionality for each. It’s time you addressed the unique data needs of different people.

Download CDP Handbook Now!

3 Levers to boosting productivity of the next-gen data scientist

3 Levers to Boosting Productivity of The Next-Gen Data Scientist

Data Scientists in every sector are grappling with the implications of Big Data. Data Scientists are facing it difficult to deal with the increasing volume, type and detail of information captured by enterprises. The use of video, emojis, text in social media, and the range of information Internet of Things emit every minute will fuel exponential growth in data for the foreseeable future. In my day to day job, I interact closely with the data scientists. These are some of the brightest people, looking to solve the next set of business challenges. Unfortunately, many of them are a frustrated lot today because of three key challenges they face:  

Poorly defined business use-case

The data scientist is expected to do some magic, identify hidden patterns in data and provide ground-breaking insights. Unfortunately there is no magic in data sciences – that is why it is called a “science”. The basis of a successful project is a well defined business use-case. The selection of data or the statistical techniques are secondary. Anything the customers, business users can do to concretely define the scope of work and expected outcome, the more effective the data scientist can be. So always start with a business use-case and not a statistical technique. Avoid being a hammer looking for a nail.


This is probably the most non-value-add activity a Data Scientists, and something they are not trained in the first place. The foundation of clean connected data is critical for a successful outcome. Data cleansing, connecting keys across data sources, and managing the data set is a significant amount of work that requires special skills. For example, In the customer analytics world the ability to draw signals from customer data from one channel and predict their behavior in another channel is the holy grail. People want to know what is the value of a positive review on their brand page. If the social marketer could quantify that a customer who leaves a positive review is twice as likely to become a high value customer, he will be able to justify the value of social marketing. The data scientist can do this, but they need a single-view-of-the-customer to start their work. Today the data scientist is spending 70 to 80% of their efforts getting the data and not doing their real job. Avoid “garbage in-garbage out” analytics.

Model management

Once the data has been reasonably organized the data scientist can develop model that are suitable for the use case at hand. Now the challenge of moving the model into production becomes a bottleneck. Typically advanced data scientists work with their own preferred tools. Some of these tools are flexible and allow quick development, but are not meant for a large scale deployment. They lack the ability to run at scale and in production environment. With multiple models in play, the data scientist also finds it hard to manage all of them. This is where industry standards like PMML and advanced customer analytics platforms come into action. Once the model is developed it can be converted into PMML format and can run in any modern customer analytics platform. Using such formats and platforms enable the data scientist to quickly move models into production without any bugs creeping in at the last stage. It also helps them in sharing results with the business users and automating the usage of their models in a salable manner. Today, businesses who believe analytics can be a key competitive advantage should focus not just focus on buying the shiniest piece of technology in the market place but should spend effort on addressing these 3 challenges. Fixing them will have a dramatic improvement in the accuracy, development times and adoption of analytics across the organization.