Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- Retailers are exploring generative AI (GenAI) to enhance customer experience and gain competitive advantage.
- GenAI can transform
every touchpoint of the customer journey –
from awareness to loyalty. By prioritizing
key use cases, retailers can strategically implement GenAI for
maximum impact.
- We shed light on the potential risks retailers must be aware of and outline some strategies to mitigate these challenges, ensuring a successful transformation.
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GenAI will transform CX
GenAI is not just a passing trend; it’s a transformative force with the potential to reshape the future of retail.
It will profoundly alter how retailers engage with their customers. This path-breaking technology is set to redefine customer experience (CX) in retail by enabling hyper-personalization, enhancing customer support, crafting memorable interactions, and producing engaging content at every step of the customer journey. As GenAI evolves, it will unlock new opportunities for innovation and growth, becoming an essential tool for retailers to attract and retain customers.
The size of the global GenAI in retail market was US$ 541.15 million in 2023 and is expected to reach around US$ 17,268.07 million by 2034. The market is expected to expand at a solid CAGR of 37% over the forecast period 2024 to 2034.
Although the use of GenAI in retail is still in the early stages, many retailers are starting to recognize its immense potential. The adoption of GenAI will accelerate as advancements in cloud computing and AI as a service (AIaaS) make the technology more accessible and cost-effective. With each iteration, the technology becomes more refined, allowing retailers to seamlessly integrate AI across various business functions in their value chain. Retailers must leverage GenAI to fuel growth, boost profitability, and gain a significant competitive advantage. By harnessing AI insights for data-driven decision-making, they can deliver personalized shopping experiences, improve customer engagement, and enhance operational efficiency.
Reconstructing user journeys
GenAI is poised to fundamentally change how customers interact with brands.
The customer journey spans various stages: awareness, consideration, decision, purchase, post-purchase, loyalty, and advocacy. While these stages are not always linear, GenAI will seamlessly guide users through each one, adapting to their needs in real time. Imagine a journey orchestrated by GenAI: personalized and responsive at every touchpoint. Figure 1 offers a glimpse into how that transformation might unfold.
Figure 1
Revolutionizing the customer journey with generative AI
Figure 1
Prioritizing use cases to accelerate outcomes
Enhancing customer journeys with GenAI involves prioritizing use cases and creating personalized, memorable experiences at every touchpoint.
| Use case | Description | Value |
| Personalized marketing campaigns | Create customized marketing campaigns and content based on individual customer’s profile, behavior, and past interactions with the brand. |
|
| Personalized product recommendations | Recommend products suited to individual customer’s preferences based on browsing patterns and profile |
|
| Inspiring product copy creation | Generate inspiring and engaging product copy and metadata to improve the customer’s online shopping experience by telling product stories that draw in the customer. |
|
| Virtual shopping assistant | Offer personalized, interactive, and real-time assistance to shoppers during their consideration and decision phase |
|
| Virtual try-on and AR experiences | Allow shoppers to virtually try on clothes, makeup, or accessories. This will give them a realistic feel of how the products will look and help them make purchase decisions. |
|
| Personalized pricing models | Determine optimal price points for each customer based on real-time market dynamics and the customer’s profile and pricing products based on each customer’s willingness to pay. |
|
| AI-powered customer service | Offer 24/7 customer support, handle customer inquiries, and help through GenAI-powered chatbots that require negligible human intervention to reduce wait times, improve customer service efficiency, and free up human agents for more complex tasks. |
|
| AI-powered sentiment and feedback analysis | Analyze feedback from different sources, offering insights into customer sentiment and opportunities for improvement. |
|
| Personalized loyalty programs and rewards | Tailor loyalty programs and rewards beyond traditional tier-based programs, offering customized rewards, personalized offers, and targeted incentives that align with each customer’s needs and preferences. |
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Table 1: GenAI use cases for retail CX
When adopting GenAI, retailers must prioritize use cases through a structured approach and critical evaluation.
This involves assessing each use case against criteria such as business value, feasibility, time-to-value, risk, and the retailer’s current state. A strategic approach will ensure investments in GenAI will deliver the greatest impact and align with business goals.
Figure 2 offers a roadmap for use case prioritization based on feasibility and strategic value.
Figure2
Mapping GenAI use cases by value and feasibility
Figure2
A GenAI adoption roadmap for retailers looking to elevate CX
For retailers embarking on new initiatives, defining a clear roadmap is crucial.
Such a roadmap will ensure that every initiative is strategically aligned, efficiently executed, and delivers maximum impact and value. We see this journey unfolding in four key phases:
- Phase 1: Quick wins – Retailers must implement high-value, high-feasibility use cases to realize an immediate return on investment. These can include customer service, feedback analysis, and product copy data creation powered by GenAI.
- Phase 2: Transformation – They must develop and deploy strategic use cases that require moderate investments and offer substantial value. Use cases such as personalized marketing campaigns, personalized product recommendations, and virtual shopping assistants can be explored.
- Phase 3: Big bets – Retailers must commit to high-impact use cases that require significant investments but can provide a competitive edge. Instances of these can include virtual try-ons and augmented reality (AR) experiences, personalized pricing models, and personalized loyalty and rewards.
- Phase 4: Continuous improvement – Retailers must continually refine GenAI models and augment capabilities. They must update algos based on feedback and new data and explore emerging use cases.
Navigating the challenges
GenAI offers powerful new capabilities to enhance customer experience in retail.
But it also presents a range of challenges and risks that retailers must carefully navigate. Key considerations include:
- Data privacy and security: GenAI models rely on large volumes of data, including sensitive customer information. This creates risks of data breaches and misuse of personal data, which can erode customer trust, tarnish the brand image, and lead to regulatory penalties. To mitigate these risks, retailers must implement robust security mechanisms, ensure compliance with data privacy regulations, and use anonymized data wherever possible.
- Ethical concerns: AI models can inadvertently perpetuate stereotypes or reinforce biases present in training data. Retailers must prioritize ethical and responsible AI practices, implementing mechanisms for bias detection and mitigation to ensure AI systems operate fairly.
- Ensuring data quality and availability: The effectiveness of AI models depends on the completeness and quality of the data they are trained on. Retailers must invest in building comprehensive datasets by implementing data cleansing, integration, and processes to capture data at the right level of detail across systems.
- Realizing returns on investment (ROI): Measuring and demonstrating ROI in the early stages can be challenging. The benefits of AI models – such as enhanced personalization and improved engagement – are often qualitative and difficult to quantify accurately. These initiatives typically require significant investments in technology and talent and integrating them into existing systems can be resource intensive. Retailers must establish clear metrics and key performance indicators (KPIs) to assess the impact of AI initiatives and ensure they align with organizational goals. Adopting a clearly defined roadmap and starting with small-scale projects will allow for testing, building confidence, and gaining early benefits before committing to full-scale deployments.
- Technical complexity: Developing and deploying GenAI models will require specialized technical skills and expertise. This is necessary for integrating AI with existing enterprise systems, managing large and growing large data sets, and ensuring optimal model performance.Partnering with a strategically aligned vendor is crucial. Retailers must conduct thorough due diligence and test various vendors to assess the robustness of their AI platforms and their models, ensuring they meet the retailer's specific needs.
The way forward
As GenAI continues to evolve, its transformative impact on retail will become more pronounced.
How successfully retailers adopt GenAI will soon distinguish the leaders from the laggards in elevating customer experience and setting new standards for how customers engage with brands. Retailers who embrace GenAI by prioritizing key use cases, developing a clear roadmap, selecting the right technology partners, and adopting a continuous improvement mindset sooner will gain a competitive advantage and redefine the future of retail.
