The role of AI in FMCG and fashion is becoming more crucial as consumer demands evolve. Today, shoppers are seeking quicker, more precise personalized experiences than ever before.
Deloitte reveals that 44% of consumers feel AI can improve their fashion sense, and Adobe's findings show that 58% of US consumers believe generative AI has enhanced their shopping experience, with half of those respondents being open to using AI tools even more for making purchase decisions.
While there's a generally positive outlook towards AI, it's important to remember that these sentiments could quickly shift if the AI fails to deliver. In the realm of personalization, accuracy is essential, yet the fashion and FMCG sectors face unique challenges in achieving it. Let’s explore three key challenges and how adopting a balanced approach can effectively address them, ensuring AI personalization in retail remains responsive and relevant in this AI-driven era.
Not All Retail Data Is Useful. Blend AI and Human Insight for Optimal Personalization
In the fast-paced fashion industry, data becomes outdated quickly, presenting a real challenge for retailers using AI for customer personalization. Anna Kuragina from H&M Group pointed out in her Forbes interview that relying solely on historical spending data to predict customer preferences might lead you astray, as fashion trends and consumer tastes shift rapidly. This highlights a key point: not all data is equally useful.
For effective personalization, it's crucial to blend internal data, like customer feedback, with external insights, such as the latest fashion trends and social media sentiments. This enriched data pool can fine-tune AI's understanding of current consumer desires. However, diving headfirst into comprehensive AI personalization can be risky and often, premature.
A smarter approach involves using AI for initial tasks like filtering out less desirable options. This allows AI to handle the heavy lifting of narrowing down choices, while human stylists and consultants take these curated options to add the personal touch where it truly counts. This strategy not only lowers the risks associated with AI-driven personalization but also makes the most of both AI capabilities and human expertise, ensuring a smoother integration and a more delightful customer experience.
Getting Useful Data is Complex. Start Fast But Think Far
Contending with a vast and complex mix of data, from structured data like purchase histories and customer preferences to unstructured data such as social media feedback and conversation logs, can be overwhelming. Handling this variety, along with the immense volume and speed at which it needs to be processed, is a challenge many companies face. Often, they find themselves short on the necessary data volume or lacking the technological expertise and infrastructure needed to extract valuable insights that could transform their planning, buying, and manufacturing processes.
To effectively manage these challenges, it's essential for retail companies to establish robust data practices and partner with AI specialists. This approach isn't just about keeping up; it's about strategically advancing your own innovation and growth based on an aligned business and product strategy. Developing and refining your own data and AI strategies allows you to create tailored solutions that can scale with your business. While leveraging solutions like those offered by Jellibeans, a fashion intelligence and analytics company, can help smaller retailers reduce guesswork and stay competitive, it's also about finding the right balance between utilizing off-the-shelf tools and customizing your own solutions. This balanced approach ensures you maintain agility, adapt quickly to market changes, and retain control over your strategic direction.
Can AI scale existing successes?
In practice, personalization in retail through AI requires a judicious mix of data sources and the right technological tools. For instance, companies like Sephora have leveraged customer data effectively through their Beauty Insider loyalty program, using purchase history to fuel email-driven personalized product recommendations for top shoppers. However, even well-implemented programs like these need to continually evolve with advances in AI and changing consumer behavior if they want to be relevant and scalable to a larger audience. This evolution depends heavily on the integration of the right mix of data and AI-driven experimentations, which can further refine the customer's experience.
Partnering for the long term
As retail evolves, the importance of sophisticated data management and AI becomes increasingly clear. Retailers who are incorporating AI into their strategies must also consider partnering with skilled data specialists and AI experts. This collaboration is crucial for unlocking the full potential of AI, ensuring that it works alongside human insights rather than overshadowing them. By choosing the right partners, retailers can gain a deeper understanding of customer needs and maintain relevance in a dynamic market.
About TICTAG
TICTAG is a leading provider of data collection, annotation, and AI enhancement services, dedicated to revolutionizing industries through the power of artificial intelligence. Specializing in maximizing AI-enabled ROI, TICTAG empowers organizations to unlock new levels of efficiency and innovation. With a focus on enabling organizations to harness the full potential of AI, TICTAG offers cutting-edge solutions tailored to meet the unique needs of clients across various sectors. By combining advanced technologies with unparalleled expertise, TICTAG is committed to driving innovation, enhancing efficiency, and unlocking new opportunities for businesses worldwide.
With offices strategically located in Singapore, South Korea, India, Hong Kong, Indonesia, and Malaysia, TICTAG operates globally to cater to the diverse needs of its clients across different regions.
For more information, visit TICTAG's website.
