Delivering Value Across Hyperpersonalization In Retail
Vamsee is an accomplished technology leader with proven experience and track record in delivering strategic transformational engagements through global teams. He played consultative delivery leader role for Platinum engagements. He has strong expertise and track record in Program Management, Technology led Transformation, large-scale agile transformation, Solution Architect, and Turn around for fortune 100 clients.
Customer segmentation has evolved, thanks to the ever-increasing choice and niche tastes of customers who engage with online platforms and their recommendation engines. To better engage with customer segments, retail businesses lean on hyper-personalized sales and marketing mechanisms to attract and retain their customer base. Quality Engineering (QE), driven by Machine learning (ML) and Artificial Intelligence (AI), plays a critical role in making sure that retail business applications and operations achieve optimal efficiency while fulfilling the personalized requirements of their end customers.
Data Drives Personalization
Customer data comes with an added responsibility as retailers must comply with regional data and privacy regulations. As more customers start rejecting cookie based tracking, there is a reduced volume of user data. With this reduced data pool, retail players must employ focussed machine learning (ML) to arrive at personalization strategies. ML-based applications can analyse past purchase data, shopping cart history and chosen modes of payment and predict user behaviours and preferences for different customer segments. This, in turn, can help them ascertain the most relevant products, create loyalty programs and offer right payment channels.
Artificial Intelligence(AI) takes ML to the Next Level
While ML is used to arrive at data-driven insights, AI can be used to make decisions on behalf of the enterprise or the user. For instance, based on user consent and preferences, on behalf of an environmentally conscious customer, an AI application can decide to bundle multiple orders into one slightly delayed delivery option this reduces an individual’s carbon footprint. From the enterprise viewpoint, it helps retail companies and their partners inch closer to their environment, safety and governance (ESG) goals.
Real time User Experience
User experience is a critical lever for user retention. When customers run into an issue, they expect to find instant help. ML and AI-driven chatbots can address user concerns with relevant responses or redirect users to support staff who can extend their help. Such investments add to the brand equity of retail players and fuel loyalty. Other factors that drive user experience include performance, security, and accessibility levels of the applications.
Phygital(Physical + Digital)Experience
Buy online, collect at store, or visit store and browse the online catalogue. Customers seek flexibility in their retail experience. Such hybrid experiences involve seamless application and data flows between multiple applications. Phygital also increases the overall ‘surface’ of the user experience and calls for a special attention to consistency across media.
Retail Business Operations
A large percentage of enterprise data travels across applications, APIs, and the cloud. Enterprises must arrive at an optimal strategy for cloud vs on premises data in some cases enterprises may choose for a largescale cloud migration for greater efficiency. Retailers also achieve operational efficiency by employing robotic process automation(RPA) for high-volume routine activities such as acknowledgement emails or invoice/receipt generation.
Quality Engineering & Retail Hyper Personalization
Quality engineering (QE) helps in the validation of applications across the retail value chain through automation, accessibility, performance engineering and cyber security.
QE processes that help retail customers adhere to data and privacy regulations and certify the effectiveness of the AI and ML personalization applications. Our teams employ validation mechanisms that increase the reliability and relevance of chatbots/virtual assistants and the last mile delivery of ‘phygital’ work flows.
A Retail Enterprise ecosystem that is certified by an experienced quality engineering team reinforces the trust that businesses have in their digital ecosystem it also improves organizational efficiency. In a fluid marketplace and tech ecosystem, retail players can truly achieve their hyper-personalization and user experience goals when they have a capable, reliable, quality engineering partner.
Customer segmentation has evolved, thanks to the ever-increasing choice and niche tastes of customers who engage with online platforms and their recommendation engines. To better engage with customer segments, retail businesses lean on hyper-personalized sales and marketing mechanisms to attract and retain their customer base. Quality Engineering (QE), driven by Machine learning (ML) and Artificial Intelligence (AI), plays a critical role in making sure that retail business applications and operations achieve optimal efficiency while fulfilling the personalized requirements of their end customers.
The battery management system is the most important part when switching from a car with a petrol or diesel engine to one with an electric motor
Data Drives Personalization
Customer data comes with an added responsibility as retailers must comply with regional data and privacy regulations. As more customers start rejecting cookie based tracking, there is a reduced volume of user data. With this reduced data pool, retail players must employ focussed machine learning (ML) to arrive at personalization strategies. ML-based applications can analyse past purchase data, shopping cart history and chosen modes of payment and predict user behaviours and preferences for different customer segments. This, in turn, can help them ascertain the most relevant products, create loyalty programs and offer right payment channels.
Artificial Intelligence(AI) takes ML to the Next Level
While ML is used to arrive at data-driven insights, AI can be used to make decisions on behalf of the enterprise or the user. For instance, based on user consent and preferences, on behalf of an environmentally conscious customer, an AI application can decide to bundle multiple orders into one slightly delayed delivery option this reduces an individual’s carbon footprint. From the enterprise viewpoint, it helps retail companies and their partners inch closer to their environment, safety and governance (ESG) goals.
Real time User Experience
User experience is a critical lever for user retention. When customers run into an issue, they expect to find instant help. ML and AI-driven chatbots can address user concerns with relevant responses or redirect users to support staff who can extend their help. Such investments add to the brand equity of retail players and fuel loyalty. Other factors that drive user experience include performance, security, and accessibility levels of the applications.
Phygital(Physical + Digital)Experience
Buy online, collect at store, or visit store and browse the online catalogue. Customers seek flexibility in their retail experience. Such hybrid experiences involve seamless application and data flows between multiple applications. Phygital also increases the overall ‘surface’ of the user experience and calls for a special attention to consistency across media.
Retail Business Operations
A large percentage of enterprise data travels across applications, APIs, and the cloud. Enterprises must arrive at an optimal strategy for cloud vs on premises data in some cases enterprises may choose for a largescale cloud migration for greater efficiency. Retailers also achieve operational efficiency by employing robotic process automation(RPA) for high-volume routine activities such as acknowledgement emails or invoice/receipt generation.
Quality Engineering & Retail Hyper Personalization
Quality engineering (QE) helps in the validation of applications across the retail value chain through automation, accessibility, performance engineering and cyber security.
QE processes that help retail customers adhere to data and privacy regulations and certify the effectiveness of the AI and ML personalization applications. Our teams employ validation mechanisms that increase the reliability and relevance of chatbots/virtual assistants and the last mile delivery of ‘phygital’ work flows.
A Retail Enterprise ecosystem that is certified by an experienced quality engineering team reinforces the trust that businesses have in their digital ecosystem it also improves organizational efficiency. In a fluid marketplace and tech ecosystem, retail players can truly achieve their hyper-personalization and user experience goals when they have a capable, reliable, quality engineering partner.
