Blog / Your Retail Data is Worth Gold. Here’s How to Mine It: 7 Strategies for the Modern Retailer

It’s a familiar narrative: Retail giants are buckling under pressure, unable to keep up in an increasingly competitive landscape. These are the same organizations that have been pumping millions into R&D, seeking that elusive competitive edge. So, what’s going wrong?

The answer is not in the money they’re spending but in what they’re overlooking: a treasure chest of data at their disposal. A wealth of insights waiting to be unearthed, capable of transforming the way they operate, interact with customers, manage inventory, and much more.

Yet, many retailers are merely scratching the surface while the real power of their data remains untapped. So how can retailers mine this goldmine?

Enter Decision Intelligence platforms. These new-gen solutions add an intelligent layer to your business systems, helping to unify data, make sense of the vast ocean of information, and translate it into actionable insights.

Decision Intelligence platforms might sound like another heavyweight tech jargon tossed your way. But it’s not just another buzzword. Instead, imagine them as a super-intelligent version of your ERP system.

Current ERPs, like SAP, are great at many things. They can manage inventory, process transactions, handle customer relations, and keep tabs on financial data. But there are things they can’t do. For example, while your ERP can tell you how many of a particular SKU you sold last week, it can’t predict how many you’ll sell next week. 

It can provide data on customer buying patterns but can’t recommend how to personalize the shopping experience for individual customers.

So, here’s the big question – how can you transform your existing retail data into a powerhouse of insights and decisions? Here are seven use cases to help you mine the gold in your data.

Strategy 1: Unifying Multiple Data Sources

Data in the retail industry flows from a multitude of sources. You have sales data, customer feedback, online behavior analytics, in-store traffic, and the list goes on. While each source offers a different perspective, together, they form a coherent, comprehensive picture of your retail operations.

But making sense of this multi-source data and using it to make informed decisions is a challenge, especially when this data sits in silos, disconnected, and difficult to align. 

Consider this example. Your customer, May, visits your online store and spends time browsing through the shoes section. She likes a pair, adds it to her cart, but for some reason, doesn’t go ahead with the purchase. The same customer then walks into one of your physical stores the next day and buys a different pair of shoes.

In the data world, this event creates multiple data points across online behavior analytics, sales data, and in-store traffic data. Alone, they don’t tell much. 

But when these data points are brought together, you get a holistic picture of May’s buying journey. You can learn about her preferences, her decision-making process and even identify points where she might have needed some assistance.

That’s precisely what Pluto7’s Data Platform offers. By acting as a bridge between systems like Google Cloud and SAP, it can pull together disparate data sources, offering a unified, cleansed, and enriched data view. This unified view empowers you to understand your customers like May at a much granular level, enabling personalized experiences and informed decision-making.

Read Case Study: Empowered one of the largest business conglomerates in the UAE with data-driven innovations.

Unifying data is only the first step in making your retail data work harder. There are more strategies to explore, and we’ll delve into those next.

Strategy 2: Improving Demand Forecasting with AI

Forecasting demand accurately is crucial for maintaining the right balance of stock. Overestimate the demand, and you’re left with unsold stock eating up your storage costs. Underestimate it, and you’re staring at lost sales and disappointed customers. In both scenarios, your profitability takes a hit.

Traditionally, demand forecasting has relied on historical sales data and some educated guesswork. But with the advancements in AI, the guesswork can be significantly reduced. AI can crunch vast volumes of data in no time and consider a multitude of variables that could influence demand, from seasonal trends and market fluctuations to consumer behavior and competitor activity.

But the real breakthrough comes when AI integrates external demand signals. Read: Demand Sensing for Supply Chain Optimization.

Let’s dive a bit deeper.

Imagine you are a fashion retailer, and one of your products is ‘Cotton Summer Dresses.’ The AI platform not only analyzes the historical sales data of this product, checks for seasonal trends, and studies customer feedback but also taps into Google’s search trends to understand the latest consumer preferences.

For instance, if ‘Cotton Summer Dresses’ are trending on Google, the AI platform takes this as an external demand signal and integrates it into its prediction model.

This way, it predicts a surge in demand for ‘Cotton Summer Dresses’ in your downtown store while projecting regular demand at your suburban outlet.

This information enables you to make informed decisions about stocking the product, preventing stockouts at the downtown store, and avoiding overstocking at the suburban outlet.

Pluto7’s Demand ML solution leverages Generative and Predictive AI to enhance demand forecasting. It offers real-time, accurate demand forecasts at a granular level – by product, by store, and by region.

It allows you to ask questions and get recommendations. Here are a few examples:

• What if I decrease the price of ‘Cotton Summer Dresses’ by 10% in the downtown store?
• Should I increase the stock of ‘Cotton Summer Dresses’ at the suburban outlet if the temperature rises by 5 degrees?
• What would be the effect on sales if I launch a social media campaign for ‘Cotton Summer Dresses’?

The solution can generate recommendations like:

• Decreasing the price by 10% at the downtown store could lead to an increase in sales by 15%
• A rise in temperature by 5 degrees could result in a 10% increase in demand at the suburban outlet
• A social media campaign could boost sales by 20% across all stores

Read Case Study: A Leading American Department Store Retail Chain gained more than 85% of forecast accuracy

Strategy 3: Personalizing the Customer Experience with AI

In an increasingly competitive retail landscape, providing a personalized shopping experience is no longer a nice-to-have; it’s a must-have. Every customer expects to be treated as an individual, not just another sales number. 

But how can you provide personalized experiences to thousands, if not millions, of customers? Here’s where AI does the magic. Read: Unravel Customer Behavior with Digital Twin of Customer.

AI can analyze individual customer behavior – their preferences, past purchases, browsing history, and even responses to promotional campaigns. It can then use this information to personalize their shopping experiences, both online and offline.

In the e-commerce domain, AI’s powerful capabilities can make a significant difference in enhancing the customer experience, boosting sales, and notably, reducing search abandonment, a problem that costs the retail industry over $2 trillion annually globally. 

Leveraging AI, retailers can introduce smart and personalized features that align with customer preferences and behaviors.

Here are four key AI strategies that can be implemented to revolutionize your e-commerce operations:

1. AI-powered search: Enhance your platform’s search functionality using AI, allowing for broader and semantic queries to yield more relevant results.
2. Dynamic personalization: Utilize AI to tailor the shopping experience based on a customer’s browsing history, past purchases, and behavior on the site.
3. Optimal product ordering: Employ machine learning to determine the best arrangement of product display in various categories based on historical data and trends.
4. Recommendation systems: Deploy advanced AI algorithms to generate personalized product recommendations, prompting further purchases.

Read Case Study: California Design Den, one of the biggest D2C bedding brand in USA reduced inventory carry-over costs by 50%.

Let’s consider the case of a fast-paced online grocery store that wants to improve its user experience and reduce search abandonment. Here’s how ‘Planning In A Box’ could help:

Enhanced End-User Experience

Scenario: It’s Saturday morning and Emma, a regular customer, logs in to quickly order her weekly groceries.

• Emma initiates a search for “organic cleaning supplies.” ‘Planning In A Box’s’ AI-powered search leverages Generative AI to understand Emma’s query and presents a selection of top-rated organic cleaning items, effectively reducing search abandonment.
• As she navigates to the fruits and vegetables section, the platform dynamically personalizes her interface, presenting her frequently purchased items at the top and highlighting in-season fruits and vegetables on discount.
• As Emma moves to check out, her cart gets an upgrade with an intelligent recommendation generated by the platform – a popular organic salad dressing that pairs well with her choice of vegetables.

More insights for the Retailer 

• The retailer gets insightful visibility into Emma’s entire journey – from her searches to her final purchases.
• Such granularity enables the retailer to optimize their inventory at an SKU-level, ensuring that they always have the right stock of each specific product. can adjust their inventory levels accordingly.
• The platform’s AI doesn’t just look at past purchasing patterns, it blends this data with external data like market trends and seasonality. 

Leveraging the platform’s generative AI capabilities, the retailer can query their data with questions like “What specific SKUs are frequently bought together with SKU X of organic cleaning supplies?” This allows for smart, SKU-specific product placement and bundling strategies.

By introducing AI into your e-commerce strategy, you can create a more fluid, personalized, and engaging shopping experience for your customers, reducing search abandonment and boosting sales.

Strategy 6: Revolutionizing Retail with Real-time Shelf Monitoring 

Image Source: Google Cloud 

In the physical retail environment, keeping track of on-shelf inventory in real-time is a challenging task. A lack of real-time insight leads to stock-outs, overstocking, and misplaced items, which impact customer satisfaction and store operations. AI, through its advanced vision and machine learning capabilities, can be a game-changer here.

Here’s how AI can aid in real-time shelf monitoring:

• Tackle Stock-outs & Overstocking with AI: Inventory mismanagement like stock-outs and overstocking can cost retailers heavily. AI can help address these issues by providing real-time insights into shelf inventory.
• Planogram Compliance & Misplaced Items: AI can monitor compliance with planograms and quickly identify misplaced items. This enables retailers to maintain an optimal product arrangement and offer an enhanced shopping experience.
• Product Arrangement’s Impact on Sales: By analyzing product placement and arrangement on the shelves, AI can identify trends and patterns that can help boost sales.

Let’s take the case of a large supermarket chain, ‘SuperMart,’ that wishes to optimize its in-store operations and improve customer experience. Here’s how Pluto7’s Planning In A Box could assist, leveraging Google Cloud’s Vision AI, Shelf Checking AI, and Event-driven automation capabilities:

• SuperMart achieves SKU-level visibility across all its outlets using data consolidation. This forms the foundation of their efficient inventory management, reducing overstocks and stock-outs.
• By leveraging Vision AI-integrated cameras, SuperMart can identify and refill empty or low-stock shelves promptly, enhancing store readiness and preventing potential sales loss.
• SuperMart can ask complex questions to their real-time data, such as “Which products are often bought together with organic cereals in the morning hours?” or “What are the most purchased items during holiday season sales?”  thereby enhancing product bundling strategies.
• SuperMart can effectively anticipate demand fluctuations by blending internal and external data, allowing for quick adjustments to inventory levels.

Read Case Study: One of California’s largest supermarkets improved revenue, productivity, and customer experience with dynamic inventory assortment.

Strategy 7: Enhancing Workforce Efficiency with AI

AI can play a significant role in optimizing your workforce’s efficiency. By automating routine tasks, optimizing processes, and providing actionable insights, AI can enhance productivity and streamline operations.