Tech Expert Mythili Banka Explains How Recommendation Engines are Revolutionizing Marketing Strategies

Tech Expert Mythili Banka Explains How Recommendation Engines are Revolutionizing Marketing Strategies
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Recommendation engines are algorithms designed to predict and suggest items or content that users might find interesting or relevant based on their past behaviors and preferences. Businesses leverage these technological advancements to improve customer experience. 

In 2020, the worldwide market for recommendation engines was valued at 1.77 billion USD and is projected to grow at a Compound Annual Growth Rate (CAGR) of 33.0% from 2021 through 2028. due to the increasing demand for personalization among products and services.

Director of Data Management Mythili Banka explains that these recommendation tools are helpful not just to innovate more niche products but also to reshape the way businesses deliver personalized and relevant experiences to their customers across multiple channels

Delivers high-level customer personalization

Omni-channel marketing refers to a strategic approach that provides a seamless and integrated customer experience across multiple channels and touchpoints, such as online platforms, mobile devices, physical stores, and social media. According to Banka, it shares the same goal with recommendation engines in delivering a personalized, cohesive, and consistent brand experience, allowing customers to interact with a brand through their preferred channels and switch between them seamlessly. Recommendation engines play a crucial role in enabling omni-channel marketing strategies by providing tailored products or content recommendations to customers based on their preferences and behaviors. 

Recommendation engines analyze user data, such as browsing history, purchase behavior, and demographic information to create individual profiles. These profiles are then used to deliver highly relevant recommendations to customers, ensuring they are presented with products or content that align with their interests and preferences. 

She adds, “Gone are the days of generic, one-size-fits-all marketing strategies. This personalized content increases chances of capturing and retaining customer attention, drives interaction, and helps businesses reach their target audience more effectively.”

Channel-Specific Recommendations

While recommendation engines focus on personalization, they can also take into account the specific characteristics and constraints of different channels. For instance, recommendations for a mobile app may consider the smaller screen size and mobile context, while recommendations for a physical store may consider in-store inventory and location-based preferences. 

Banka explains that by adapting recommendations to each channel, recommendation engines help ensure that the recommendations are relevant and effective in the context of each channel, giving users a more effective and pleasant scrolling experience. 

Real-time adaptability

Customer preference changes over time, with people’s interests shifting along with the rise and fall of trends. This is when recommendation engines become even more valuable. Banka explains that recommendation engines can adapt and respond in real-time by continuously analyzing and updating changes in customer preferences, market trends, and even external factors. These updated insights provide the most relevant recommendations and ensure market strategies remain effective and aligned with evolving customer needs.

Mythili Banka helps in advancing agile recommendation engines

Banka’s profound 13 years worth of expertise in data analytics and artificial intelligence (AI), founded from her Bachelor’s Degree in Computer Science from Sastra University and her Master’s in Management Information Systems and Services from the University of Delaware, have been instrumental in shaping her ability to design and implement recommendation engine solutions that optimize customer interactions. 

Her specialization lies in driving digital transformations across industries, including telecom, finance, and retail. She has developed omnichannel customer service recommendation engines using Business Process Management (BPM) technologies, AI, and the cloud-based centralized decisioning platform Pega CDH, which helped businesses face customer service issues related to personalization, product relevance, and compliance. Through Banka’s expertise, they were able to provide more accurate and tailored recommendations that boosted their leads and clicks.

As a Pega Decisioning Architect, Banka is accustomed to creating recommendation engines that leverage Pega decisioning tools, customer data, business rules, and predictive analytics to drive real-time personalized customer interactions. Her commitment to staying up-to-date with the latest trends and technologies gives her an advantage in designing relevant and user-friendly tools to enhance how businesses reach customers. 

Banka’s expertise in the spotlight 

Beyond the corporate landscape, Banka’s valuable contributions to the industry were recognized by different respected institutions. Her stellar profile was acknowledged by The India Book of Records. She won Gold Globee for Information Technology World Awards and is nominated for The Globee Women in Business. 

Recently, Banka was also among the awardees of the Titan Women in Business Awards 2023, recognizing her as a Gold Winner for Female Executive of the Year and Gold Winner for Management Team of the Year for her excellence in leading the implementation of the high-level recommendation engine using AI and Pega CDH.

Banka shares, “Data analytics and AI are tools we can use to enhance human interactions and experiences. It ultimately leads to measurable business outcomes, whether brand recall, customer loyalty, or increased sales.”

As personalized experiences continue to be in high demand, recommendation engines designed by experts such as Banka will also continue to be a game-changing tool for how businesses engage with their customers and how they stay relevant and ahead of their competitors. 


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