Retail has already dipped its toes into the waters of generative AI for language-based applications, particularly in customer support, but predictive AI holds even greater promise. Vital functions such as promotion spending, offer permutation, and big-data-driven consumer trend forecasting are now within reach thanks to the retail industry’s wealth of numerical data, particularly through UPCs. While generative AI has its merits, predictive AI stands as a game-changer for an industry built on the backbone of barcodes.
However, the next frontier in retail marketing lies in achieving true one-to-one personalization. This is imperative because acknowledging the individuality of each shopper and providing a tailored retail experience that mirrors their unique preferences and needs is crucial for staying competitive, particularly in the face of escalating competition from eCommerce giants like Amazon. Today’s consumers not only desire personalization; they expect it.
Eagle Eye’s recent eBook, “AI and the Current State of Retail Marketing,” cites research showing that 71% of consumers anticipate personalization, with an even higher percentage (76%) expressing frustration when it is lacking. Consequently, it’s no surprise that AI adoption in retail is projected to surpass 80% within the next three years.
For retailers, several critical points must be considered:
1. Data quantity and quality are paramount: While predictive AI holds promise, it is still in its nascent stages. Just as future customer behavior cannot be predicted from a single data point, effective retail AI outputs (such as measuring a shopper’s brand affinity) require ample data. Moreover, AI models trained on poor-quality data will yield subpar results. Therefore, preprocessing data is crucial for optimal performance.
2. Optimal integration of AI outputs: When deploying an AI model’s outputs, there is a delicate balance between full automation and manual review. While some scenarios may call for automated actions triggered by AI outputs, others necessitate human oversight, especially when predictions are uncertain. Finding the right implementation balance often entails adapting existing tools, establishing common-sense guardrails, and enforcing manual review protocols.
3. An AI-driven virtuous circle: The relevance and accuracy of AI outputs hinge on the ability to assess the correctness of predictions. This feedback loop enables continuous improvement, driving performance enhancements over time. Retailers stand to gain a significant competitive edge by embracing this iterative optimization process.
Personalization is undeniably the next frontier in retail marketing, and AI serves as the catalyst for achieving it. By leveraging AI, retailers can maximize data utilization, transitioning from a mere 5% utilization to nearly 100%. This allows for unprecedented levels of personalization, with the potential for millions of variations tailored to individual customers.
One exemplary case of successful personalization and AI implementation is Carrefour, a global grocery giant, which runs personalized challenges in collaboration with its suppliers. Powered by AI and machine learning algorithms, these challenges set custom thresholds and goals for loyalty program members based on their purchase history and predictive analysis. This gamified shopping experience effectively incentivizes engagement with promotions and loyalty programs.
As retailers navigate this evolving landscape, organizational readiness, strategic planning, and ongoing optimization will be critical to realizing AI’s full potential. With each advancement, retailers inch closer to unlocking new dimensions of customer engagement and profitability, paving the way for a future where AI-driven personalization becomes not just an expectation, but a cornerstone of retail excellence. Explore Eagle Eye’s latest eBook for further insights into AI and the current state of retail marketing.
Source: ecommercenews.com
