5 Ways Retailers Can Leverage Next-Gen Data Management Technology

The retail industry is no stranger to data. For years, retailers have made extensive use of data to help drive initiatives like identifying customers, determining how to target ads, and devising pricing strategies.

However, today's breed of data analytics and management solutions — including but not limited to artificial intelligence tools and services — have opened up a host of new opportunities for retailers to leverage in powerful new ways. The caveat is that risks and challenges related to data management in the retail sector are also increasing, which means businesses must invest in strategies for mitigating data risks if they want to take advantage of next-generation data innovations.

To prove the point, here's a look at five novel ways for retailers to integrate data into their operations, along with the challenges they'll need to solve along the way.

1. Retail Data Enrichment and Federation

While retailers have historically been adept at using certain types of data for certain purposes, their data and data-driven processes have tended to be siloed. They didn't typically combine different types of data to address complex use cases.

Modern data integration and management tools, however, make it easier than ever to meld unique types of data together. In turn, retailers gain the ability to enrich and federate data to support new goals.

For instance, a retailer might enrich its customer database with manufacturing data to identify the ideal products to offer customers based on their preferences as well as what the manufacturer offers — a strategy that effectively combines customer analysis with supply chain analysis in a unique way.

2. Demand Forecasting and Supply Chain Optimization

Another way to improve retailers' ability to anticipate what's coming next in product supply lines and adjust their strategies accordingly is using large language models (LLMs) to analyze supply chains and generate summaries of forthcoming trends.

Supply chain analysis isn't new in retail. But historically, the tools available to evaluate supply chains were more basic and they couldn't generate detailed summaries of changes ahead. Next-generation AI technology, like LLMs, opens up new opportunities in this area, leading ultimately to more efficient operations and higher profits.

3. AI-Generated Product Descriptions

Automatically creating product descriptions is another key use case for LLM technology in the retail space. Rather than having to write descriptions by hand for each of the potentially thousands of products that a retailer offers — a time-consuming and (measured in terms of labor costs) expensive process — retailers can outsource that work to AI models.

Taking this strategy a step further, retailers could potentially use LLMs to customize or update product descriptions on a continuous basis. For instance, the description that one customer sees when browsing a product listing in an online store might emphasize product features that are of most interest to that customer, while a different shopper sees a different summary aligned with his or her preferences.

4. Combating Retail Fraud

Fraud has long been a challenge in the retail space. Although many retailers have invested in fraud mitigation solutions, such as tools that evaluate online payments to detect anomalous activity, those solutions haven't always worked as well as they could.

Modern data analytics and management tools open the door to major improvements in this realm. Instead of analyzing just a handful of data points (e.g., the payment history associated with a customer's account) to detect fraud, retailers can now analyze millions or potentially even billions of points of information in real time. Retailers could build systems that evaluate data such as images of customers from in-store video feeds to provide much richer, more accurate fraud detection.

5. Multimodal Customer Personalization

To the extent that retailers have been able to personalize content for customers in the past, it has usually been by modifying text. With modern AI and the data management tools that drive it, however, retailers can now engage customers in a personalized, multimodal way. They can generate custom images and videos for each shopper, delivering an experience that feels truly personal in all respects.

Data Management Challenges in Retail

As I mentioned, taking advantage of modern data management solutions to unlock opportunities like those described above requires retailers to address novel data management challenges.

One major risk is privacy and regulatory concerns surrounding customer data. Increasingly, retailers face mandates to govern customer data in specific ways; for example, the European Union's GDPR law requires that customers have the option to opt out of personal data collection. To avoid running afoul of data privacy laws like this when building systems that analyze customer information, retailers must integrate granular governance controls into their tools.

At the same time, retailers need to manage the inherent risks that arise when outsourcing sophisticated tasks to AI models, such as the risk that models will generate content that doesn't align with company policy or strategy. This has already become a challenge in the real world. For example, a Canadian airline found that an AI-powered chatbot offered customers discounts they shouldn't have received. More generally, retailers must ensure that their AI solutions follow brand guidelines and messaging, which may be challenging when relying on generic AI tools and services not designed with a specific company in mind.

Conclusion: The Data-Driven Future of Retail

With the right data management tools and processes, retailers can conquer these challenges. When they do, they'll open the way to take data-driven retail operations to the next level, opening up rich new experiences for customers while also improving efficiency and profitability.

Daniel Avancini is the chief data officer and co-founder at Indicium, an AI and data consultancy that helps companies gain an analytical edge through data.