Generative AI in Retail: Transforming Customer Experience and Enhancing Efficiency
By Santhosh Subramaniam
Prioritizing Customer Experience (CX), retailers in recent years have worked with different strategies to improve this aspect in their transformation efforts. As buying behaviour moves online with omni-channel engagement, the CX journey is acquiring deeper meaning with bottom-line implications for business. Expectations of brick-and-mortar stores have also changed post-COVID; people do not go to a retail store just to shop but expect an end-to-end experience from the retail outlet. In addition to the end consumers, the CX journey addresses internal consumers too, namely employees.
Slips in plotting the CX journey can lead to a negative consumer experience with major consequences for the brand. Therefore, how the retailer plots the customer journey is integral to success. This means thinking about hyper-personalization, stitching together points of connection and interaction, looking for intelligent insights, and relying on automation.
Generative AI can change the retail game
Although still in exploratory stages, generative AI has become a mainstream discussion topic with game-changing potential for all kinds of businesses. Retailers prioritising CX, can explore its possibilities beyond higher efficiencies. They can reimagine business functions, offer personalized and targeted marketing efforts, streamlined services and more, helped by advanced analytics of consumer behaviour.
Broadly speaking, GenAI’s applicability is three-fold: creative content generation, content summarization, and support for content search. Today, content can range from text to image, video, or even conversational formats. GenAI’s applicability must be considered for all these areas, which have a major influence on enhancing the end-consumer experience across the value stream of retail and is not limited to the engagement or differentiation layer.
The applicability of GenAI is being tested in various sub-verticals of retail, and some popular use cases across retail touchpoints and processes are listed here:
New product development: Retailers can be bifurcated as those who source products from different companies and others who market their private brands. Here, GenAI can be used to develop new products by helping their team of designers look at customer feedback, market trends, and other factors. It can help bring all these aspects together to channel creativity that leads to a set of new designs which can be enhanced by designers before production. Innovative content generation such as unique product descriptions with legacy brand style can follow messaging for broadcasting on different channels that are geared towards customer engagement and results.
Product display options: For instance, a high-end jewellery design, can be handy for tier 2 and 3 suppliers who typically have limited stock or rely on superlative quality of images from the original supplier or manufacturer. In such cases, AI can be used for 3D-image generation to enhance the CX.
Product recommendations: Top-notch retailers are expected to provide product recommendations in a highly personalized manner. Providing store assistants and retail sales associates involved in clienteling with an AI-based handheld devices to offer their customized recommendations is finding traction among retailers.
Virtual stylists: An interesting use case, is a kiosk at the store where the consumer can ask for suggestions based on certain parameters. They are provided with complete style and attire recommendations based on pointed questions from the virtual styling assistant. Offering virtual try-ons using avatars and special features can make the CX even more captivating and personalized.
Conversational commerce: Here, the consumer uses a mobile app and states the kind of product they need, say a mobile phone with specifications like, “a high-performance chip and good camera without unnecessary apps.” Based on the app’s suggestions and ratings, the consumer asks to go ahead with the purchase of a shortlisted product and automatic check-out just by conversing. Although this was available with standard chatbots, the explosion and rapid advances in language models for GenAI will contribute to further CX enhancements, offering differentiation to the retailer.
Product catalogue management: Product information must be pushed to different channels of sales, and the challenge for the retailer is to find ways to enrich the content. As an example, a mobile phone that is creating buzz on social media for being “the coolest phone ever.” The retailer can ensure their algorithms can catch that particular phrase and add it to the description so that when somebody searches for that phrase, search results are more efficient. In the bargain, an end user can search using a phrase that is trending, and sales are enhanced thanks to search engine optimization.
Marketing and campaign automation: Using GenAI’s ability to analyze vast amounts of data, personalized marketing messages and campaigns can be generated based on customer preferences and demographics – this enhances conversion rates. Many retail brands use past marketing performance data to predict trends, customer behaviour and campaign outcomes to help optimize strategies. The market research needed to identify trends, customer preferences, and competitive intelligence can also be accomplished with GenAI. Overall, using GenAI in marketing automation empowers marketers to create more personalized, data driven campaigns that lead to better results.
Fraud prevention: Typically, when a transaction is marked fraudulent, a host of checks are necessary along with legal implications if it turns out to be a false marking and in turn, adversely affects the CX. GenAI can help improve the processes to reduce different anomalies and false positives.
Some of the use cases discussed here talk of GenAI models serving as enhancement technology on top of the output provided by the traditional/conventional AI models. Likewise, there are more GenAI use cases that help enhance the employee experience as well.
In the CX space, GenAI can be offered in combination with AR, and VR elements. Its’ enormous applicability and significance in augmenting the retail consumer journey is undeniable, which makes this space even more alluring.
Considering its newness, there are various concerns on responsible data management and regulatory compliance aspects. Enterprises are also reviewing the value derived from GenAI initiatives to decide how to prioritize their investments in this area. The way forward for enterprises would be to establish an overall GenAI-first strategy that emphasizes robust and responsible data management, being responsible by design. This would include a bimodal approach wherein the first step is to experiment with newer evolving technologies followed by a factory approach to productionize the learnings.
(The author is Santhosh Subramaniam, Associate Vice President & Head of Domain Consulting Group for Consumer, Retail and Logistics, Infosys, and the views expressed in this article are his own)
