AI in unified retail: personalization and prediction

The use of Artificial Intelligence is transforming unified retail through real-time personalization and behavior prediction. Brands are optimizing conversion and loyalty with intelligent engines and predictive models. With Tweakwise and Openbravo Reporting, they can finally measure the tangible ROI of AI in stores and omnichannel.

In a constantly evolving commercial landscape, AI is emerging as a transformative tool for retailers seeking to optimize their customer experience and anticipate market trends. Integrating generative AI models into unified commerce offers unprecedented opportunities to personalize interactions, predict buying behaviors, and streamline operations.

This article explores how AI is transforming unified commerce, focusing on personalization and prediction.

AI personalization for unified retail

Artificial Intelligence (AI) is revolutionizing personalization within unified commerce, allowing retail companies to deliver customized experiences to every customer. But how does this solution actually work? What exactly is AI personalization?

AI personalization acts like an exceptionally skilled personal assistant for each customer. It uses sophisticated algorithms to analyze vast amounts of data on behaviors, preferences, and individual needs. Imagine a system that remembers your past purchases, the products you browsed online, your social media interactions, and even your reactions to certain ads. With this extensive knowledge, AI can anticipate your needs and offer perfectly tailored suggestions.

Necessary data: behaviors, history, preferences

AI acts like a detective, analyzing the fine details of your shopping behavior. It examines your purchase history, web navigation, social media interactions, and more. Cross-referencing all this information creates a detailed profile of each customer, revealing their preferences, habits, and specific needs. For instance, AI can identify if you are a coffee enthusiast who prefers dark roasts and regularly orders coffee beans online on Sunday mornings.

In stores, these insights feed clienteling, allowing sales associates to offer tailored service and enhance loyalty.

Product recommendations & AI search engine

AI enables offering products that are perfectly aligned with your current desires. Imagine browsing a clothing website. Based on your previous purchases and recently viewed items, AI could suggest a new dress in your favorite style or an accessory that perfectly complements an outfit you already bought. Using these personalized recommendations increases the likelihood of finding products you truly like, thus completing your purchase.

Product recommendations become more relevant and customized. By analyzing purchase history, preferences, and browsing behavior, companies can offer tailored suggestions, thereby increasing conversion chances.

Optimizing communication channels for a seamless experience

AI doesn’t just recommend the right products; it optimizes how they are presented to you. It selects the communication channel that best fits your habits—whether email, SMS, app notifications, etc. Moreover, it customizes the message content based on your preferences. For instance, if you are sensitive to promotional offers, AI might email you a discount code for your favorite products. On the other hand, if exclusive information appeals to you, it might notify you in advance about new collections.

Personalization AI delivers a richer, more relevant, and engaging customer experience. It enables companies to gain a deeper understanding of their customers, tailor offers, and communicate effectively. However, it is imperative to use this technology responsibly by respecting customer privacy and ensuring transparency in data collection and usage.

Predictive AI: purchase scoring, churn, trends & targeted campaigns

Artificial Intelligence (AI) doesn’t just analyze the past; it also predicts our buying behaviors. With predictive AI, retail companies can anticipate needs and desires, optimizing their marketing strategies to offer irresistible deals.

How does predictive AI improve marketing strategies?

Predictive AI uses sophisticated algorithms to analyze vast data and identify recurring patterns in our shopping behaviors. By understanding these patterns, we can predict our future actions with remarkable accuracy. Imagine a system that anticipates your next purchases, identifies products likely to interest you, and proposes them at the right time with a personalized offer. This is the power of predictive AI.

Improving conversion with predictive algorithms

Predictive algorithms enable identifying customers most likely to make a purchase. By analyzing factors such as purchase history, web navigation, and social media interactions, predictive AI can forecast the probability of conversion for each customer. This is where purchase scoring comes in.

AI assigns a probability score to each customer based on their behavior and past interactions. For instance, a customer who has repeatedly viewed a product without buying it will receive a high score, allowing for targeted offers. These valuable insights enable retail companies to focus their marketing efforts on the most promising customers and tailor their offers accordingly.

Identifying emerging trends

Predictive AI doesn’t just analyze individual behaviors; it can also identify large-scale emerging trends. By examining data from millions of customers, it can detect popular products, new consumption trends, and shifts in consumer preferences. These insights allow companies to anticipate market demands, adjust their offerings accordingly, and gain a competitive edge.

Predictive AI is also invaluable for anticipating churn, the likelihood that a customer will stop buying or turn away from the brand. By spotting weak signals, reduced purchase frequency, decreased engagement, and negative interactions, companies can proactively intervene, for instance, with retention campaigns or exclusive benefits to minimize attrition.

Developing targeted marketing campaigns

With insights provided by predictive AI, retail companies can create ultra-targeted marketing campaigns. Imagine an ad campaign directed solely at customers who have shown interest in a specific type of product or live in a particular geographic region. AI enables segmenting audiences with laser precision and delivering personalized messages that resonate with each customer group’s needs and aspirations. This optimizes the effectiveness of marketing campaigns and maximizes return on investment.

Case study – Protest x Tweakwise

Sportswear brand Protest aimed to enhance the relevance of its digital merchandising. Its goal was to streamline product searches, optimize navigation, and better align recommendations with customer expectations.

Protest integrated the Tweakwise suite, which combines predictive AI with intelligent search engines. The solution dynamically adjusted sorting, filtering, and product recommendations based on user behavior, offering flexible settings for complete marketing control.

Result: measurable improvement in conversion rate, reduction in search-related abandons, and increased average basket size through more suitable recommendations.

Integrating AI into a retail strategy

Artificial Intelligence (AI) is a reality transforming the world of e-commerce. But how can retailers practically integrate AI into their strategies to fully benefit from it? It’s not just about adopting technological tools but developing a genuine integration strategy.

Investing in AI tools for customer acquisition

Acquiring new customers is a constant challenge for e-commerce companies. AI offers a range of tools to optimize this process and perfectly respond to demand. For example, AI-powered chatbots can interact with website visitors, answer their questions, and guide them through their purchase journey. AI can also be used to personalize online ads, targeting users most likely to be interested in the company’s products. By investing in powerful AI tools, retailers can automate and optimize customer acquisition while offering a personalized experience to prospects.

Evaluating interaction quality through machine learning

Machine learning, a branch of AI, enables analyzing interactions between consumers and the company. For instance, analyzing chatbot conversations can reveal friction points in the customer journey or frequently asked questions that require better communication. AI can also analyze customer sentiments expressed in online reviews or on social media. These valuable insights allow retailers to improve the quality of their interactions with customers, enhance their customer service, resolve issues, and optimize the overall customer experience.

Measuring the ROI of AI initiatives in retail

Integrating AI into an e-commerce strategy represents an investment. It’s crucial to measure the return on investment (ROI) of AI initiatives. This can be done by tracking key performance indicators (KPIs) such as increased sales, reduced customer acquisition costs, improved consumer satisfaction, or higher conversion rates. By analyzing this data, companies can identify the most successful AI initiatives and adjust their strategy accordingly.

Indeed, the success of AI integration is measured by its ability to achieve the company’s business objectives. It’s not just about adopting innovative technologies but using them to generate value (process optimization, experience improvement). This can translate into increased sales, improved profitability, reduced costs (through better inventory management, for example), better customer satisfaction, or enhanced brand loyalty. By measuring the ROI of AI initiatives and analyzing the data, retail companies can ensure that AI truly contributes to their success.

Integrating AI into an e-commerce strategy thus requires a thoughtful and structured approach. It’s essential to invest in the right tools, analyze the richness of unified retail data to optimize interactions, and measure the ROI of AI initiatives.

Retail sector KPIs to monitor

• Average order value (AOV): measuring the impact of cross-sell recommendations or bundles generated by AI.
  
  
• Customer acquisition cost (CAC): Calculate CAC reduction through better segmentation and more relevant advertising targeting.
  
  
• Repurchase/loyalty rate: tracking how many customers return after interacting with AI devices (chatbots, personalized recommendations, predictive campaigns).
  
  

Omnichannel engagement: email/SMS open rate, mobile app click rate, or responsiveness to push notifications, orchestrated by AI.

Tool examples: Tweakwise & Openbravo Reporting

Born from a growing need in the e-commerce world to control positioning and sort products, Tweakwise (search, filters, sort, reco, personalization) has become a comprehensive suite for personalized search and discovery. The solution helps online store managers to present the most effective and attractive product range to each consumer and even personalize stores for each visitor using advanced technologies.

The software suite integrates tools for managing search, filtering, sorting, recommendations, and site personalization. It even allows online stores to set up decision aids for users to find the ideal product, increasing conversion rate and the average order value in online stores.

Openbravo Reporting (measurement and steering) complements to ensure tracking and steering. The solution offers a comprehensive data model dedicated to retail. With its automated reports and integrated dashboards, teams have a clear view of performance, whether at the store, digital, or central back-office level.

See a demo of Tweakwise + Openbravo Reporting solutions applied to your catalog!

In summary, integrating generative AI models into unified commerce is a necessity for retailers aspiring to sustainable growth. From precise personalization to refined prediction of buying behaviors, AI offers powerful levers to optimize the consumer experience and anticipate market demand. However, success relies on a measured approach where investing in suitable tools, rigorous analysis of interactions, and ROI measurement become the pillars of a successful transformation.

With Tweakwise products and data analysis solutions like Openbravo Reporting, it is possible to personalize exchanges, forecast trends, and optimize operations.

Feel free to contact us for a personalized demonstration of our solutions to see how they can transform your business.

Glossary

• NLP (Natural Language Processing): a branch of AI that allows machines to understand and process human language, used in intelligent search or chatbots.
  
  
• Predictive AI: technology analyzing past data to anticipate future behaviors, like purchase probability or customer churn.
  
  
• LLM (Large Language Model): An AI model trained on vast text corpora, capable of generating text, summarizing, translating, or responding contextually.
  
  
• Reco (Recommendations): personalized product or content suggestions generated in real-time according to a customer’s profile and behavior.
  
  
• Mobile POS system (mPOS): mobile checkout terminal on a smartphone or tablet, allowing sales recording, inventory checking, and service personalization in-store mobility.