Interview with Shekar Raman, CEO and Co-founder, Birdzi

Shekar Raman, CEO and Co-founder at Birdzi talks about the role of data analysis and AI-driven solutions for personalized grocery shopping.

1. Tell us how you came to be the CEO at Birdzi having an engineering background. How much of your typical day is involved in innovating AI tech for your customers?

After going to college for electrical engineering at Villanova, I worked on the Human Genome Project, went to Bell Labs to work in speech recognition and joined a Wall Street firm. Then, in 2010, my daughter had an idea to make an app that helped people find products in the store. Thus in 2014, Birdzi entered the market and has since evolved from product location services to leveraging grocer data to enable personalization.

I spend 25% of my day innovating AI tech for our customers.

2. What are the applications or rather opportunities you seek to have with your service?

Our mission as the world’s leading AI relationship builder is to supply retailers with the data, support and tools needed to inspire meaningful customer engagement. We know loyalty is the new engagement metric, so we use our AI solutions to provide grocers with actionable insights, strategic hyper-personalization and engaging shopping experiences.

3. How did you define the vision of Birdzi?

Our vision is to find new ways to put technology in service to people. Innovation is our middle name and we will never stop developing new and creative solutions to strengthen the bond between grocers and their customers. Birdzi’s customer centric approach allows us to continue to grow and develop in response to shoppers’ needs.

4. What are some of the unique lessons you have learnt from analyzing your customer behavior?

As much as we think that traditional methods of grouping customers into segments are useful to a point, what the data really tells us is that each customer is unique.
Each has their own unique circumstances and preferences that dictates their shopping habits. While it’s good to try and find commonalities between shoppers for understanding trends, it’s better to personalize the experience than to use a mass marketing approach.

5. In your recent bylined ‘Tea Leaves’ you talked about the abundance of data in grocery stores and not enough soothsayers to understand it. Can you elaborate more on this?

Most of the data that grocers collect is used to create reports and endless analysis of what already happened. However, there is minimal emphasis on reading the ‘tea leaves,’ and making data-informed decisions while examining next steps.

At Birdzi, Inc., we focus on precisely this; reading the ‘tea leaves.’ Over the past couple of years, the team has taken an intense interest in helping retailers understand and predict shopper behavior. We have focused on teaching machines how to pour over mountains of data to draw conclusions and provide predictions. The reading from the ‘tea leaves’ then powers up a wide range of services. Our insights serve as an early warning system, as well as an intervention system that identifies trends and seizes the opportunity to act.

6. Birdzi recently launched Visper, a new personalization tool for grocers. Can you elaborate more on the same?

Visper is a personalization capability proven to increase customer sales, visits and retention for regional supermarket retailers. The tool recommends the most effective promotions from a retailer’s product catalog for chosen shopper audiences, based on digital models created by thousands of data attributes for each individual customer.

Designed to support regional grocery retailers’ monthly or quarterly programs, Visper campaigns can be executed via email, direct mail or even print-on-receipt. Not only does this true customer-first approach drive strong ROI in the form of increased sales, shopping visits and retention, but Birdzi’s approach automates a growing portion of the marketing process, helping the retailer reduce costs while accelerating cycle time.

The new capability builds on Birdzi’s industry-leading customer intelligence platform capabilities. Able to ingest a retailer’s historical data in mere hours, Birdzi builds a digital duplicate of each shopper based on thousands of data attributes that are updated in real time with each new purchase. These digital customer doppelgängers are then used to model behavior, providing deep understanding of how each individual customer will respond to promotional offers.

Visper is already being used by Weis Markets, and is rolling out to other grocers, including Coborn’s and Niemann Foods.

7. How do you keep pace with the rapidly changing tech space?

We listen to our customers and work hard to solve the problems they need answered. By keeping this open and honest dialogue we can stay ahead of trends. Meanwhile, our team consists of some of the best in the industry both on the retail and engineering sides, enabling us to lead the charge in the industry from all angles.

According to Diana Barr, Director of Loyalty, Coborn’s, “Birdzi is incredibly nimble and flexible and a great partner. Anything we can ask, they’re ready to jump at the task and figure it out. At the same time, they keep that forward-looking context, creating the foundation for further enhancements and innovative features down the line.”

8. What are some of the common pain points that your customers commonly approach you with?

Grocers come to us seeking to revamp and transform their digital offerings to attract and maintain shoppers. Most retailers are dealing with siloed, fragmented data with no holistic approach to using this data. Birdzi drives actionable insights with shopper data. No matter how a shopper chooses to interact with a retailer, they’re going to enjoy personalized experiences across channels thanks to Birdzi.

For example, in 2016, Coborn’s approached Birdzi without a comprehensive loyalty program to drive stronger customer engagement across its in-store and digital channels. Looking to foster stronger relationships with its shoppers by offering savings on relevant products they want to buy, Birdzi has helped forge Coborn’s path to digital transformation by driving Coborn’s MORE Rewards program in just three years. Offering a platform that Coborn’s could grow into, the journey to create a successful loyalty program started with web and mobile app integrations and has expanded to more thoroughly target shoppers and build lasting loyalty.

9. What advice would you like to give to the upcoming AI-based tech start-ups?

Focus on easy to digest insights that speak to a retailer’s basic goals and suggest action that will help achieve them.

10. Can you give us a sneak-peek into some of the upcoming product upgrades that your customers can look forward to?

Birdzi is working on a shopper quality index (KIC score) much like a FICO score that allows retailers to easily measure shoppers’ loyalty level and track growth over time to offer better visibility into impact marketing campaigns. This will also enable retailers to run more strategically targeted campaigns and deliver true 1-to-1 engagements.

11. Which is the one AI-based technology breakthrough you will be on the lookout for in the upcoming year?

I’m looking forward to advances in VR and AR and the impact it can have on delivering revolutionary shopping experiences

12. What is the one leadership motto you live by?

There are three rooms that are critical for growth in an organization. The room to fail, the room to learn and the room to excel.

For more such updates and perspectives around Digital Innovation, IoT, Data Infrastructure, AI & Cybsercurity, go to

Shekar Raman

CEO and co-founder of Birdzi

Shekar Raman is CEO and co-founder of Birdzi, a grocery retail AI solutions company that was inspired by an idea his 11-year-old daughter had about locating products in the supermarket. He is passionate about building data-driven technologies leveraging AI and machine learning to help retailers and brands elevate the customer experience.
Shekar began his career working on the Human Genome Project at the Dept. of Human Genetics, Univ. of Pennsylvania, developing algorithms for protein modeling.

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