3 ways AI dashboards are transforming retail and e-commerce

Artificial intelligence (AI) is revolutionizing retail by transforming how retailers and e-commerce companies engage with customers and run their businesses. AI enhances retailers’ understanding and prediction of customer behavior, driving revenue, loyalty, and conversion. The technology allows retailers to optimize inventory management and streamline their supply chain processes to improve efficiency and order fulfillment.

If you’re looking to deliver deeper, more actionable insights powered by AI to your retail end-users, now is the time. A recent study of over 400 retail professionals by Nvidia found that 53% of respondents want to leverage AI to improve store analytics and insights.

The key to success is to make the complex, simple, and to make AI-powered decision-making part and parcel of your end-users’ everyday experience. Provide easy-to-use retail AI dashboards that deliver data science-powered insights about customers and operations, to enable retail end-users to run their businesses smarter and make better decisions.

Why AI dashboards are essential for retail success

If you’re providing software, services, or reporting and analytics to retail or e-commerce businesses, a critical goal is providing your end-users with an AI-powered dashboard. Not only is this high-value for your end-users (read on to learn why), but the technology to provide it quickly is now more accessible than ever before—it’s scalable, incorporates the latest in AI, and can be highly integrated into applications retail end-users use every day.

There are fewer barriers to delivery than just a few years and even months ago, with platforms now providing advanced built-in analytics that can be easily embedded into the user experience by delivery teams. This new ease of implementation enables organizations to leap into AI-powered dashboards more confidently.

AI in retail: Game-changing benefits of AI dashboards

An AI-powered retail dashboard offers significant advantages over a non-AI dashboard. For example, what if you could help your retail end-users reduce lost sales or out-of-stock situations by more than 60%? McKinsey found just that. By applying AI-driven forecasting to supply chain management, retailers can reduce errors by between 20 and 50 percent, which they noted translates into a reduction in lost sales and product unavailability of up to 65 percent. This competitive edge is not to be underestimated.

AI-infused retail analytics can provide predictive insights, allowing retailers to better plan for and anticipate customer trends and demand. Retailers who carry inventory can precisely align stock with demand and get the best use out of working capital with more efficient and proactive inventory management.

An AI dashboard can harness machine learning algorithms to scrutinize customer behavior and preferences, equipping retailers with insights to offer more personalized product recommendations and targeted marketing strategies. This depth of understanding can result in heightened customer satisfaction and increased sales.

Predictive insights and machine learning algorithms represent a sea change from old-school pre-AI retail dashboards, which were limited to offering historical sales data and basic performance metrics. Pre-AI dashboards lack the predictive capabilities and personalized data that AI offers. As a result, retailers relying on these dashboards will struggle to adapt quickly to changing customer behaviors and market trends, missing opportunities for growth and business optimization.

So to recap:

• AI-infused retail analytics can provide predictive insights to anticipate demand better.
• Retailers can use AI and machine learning to manage inventory more effectively.
• An AI dashboard can enable retailers with ways to understand customer behavior for more personalization and better ROI on marketing.

Next, we’ll drill into the three big opportunities in retail made possible by AI-infused dashboards.

1. AI dashboards for greater sales forecasting and demand planning

An AI-powered dashboard can significantly improve sales forecasting and demand planning for retailers compared to traditional analytics. For example, you could provide an AI dashboard to your end-users to help them analyze historical sales data to identify patterns, trends, and seasonality in customer demand.

Sisense’s Compose SDK revolutionizes e-commerce by seamlessly integrating insights into digital storefronts.

That dashboard could analyze average sales by day, week, month, and year. It could identify sales peaks, troughs, and seasonal trends. It could leverage forecasting techniques like time series analysis, regression analysis, exponential smoothing, or advanced capabilities like decision trees or neural networks. The key is to use analytics platforms and composable development to present advanced analytics intuitively for your end-users―making data science-derived insights simple for your users to understand and act on.

Machine learning algorithms for predictive analytics

By using machine learning algorithms, you can provide predictive analytics for future sales and demand. These predictions can be made at different levels of granularity or across various business dimensions. For example, you can analyze data for diverse products, categories, and locations. This allows you to predict the impact of marketing campaigns or new product launches. Consequently, you can plan your inventory accordingly.

A Shopsense AI marketing dashboard that allows a user to pull up data on average reach, spend, impressions and more, and use that data to make smart decisions about inventory.

It gets even better when you incorporate third-party data. For example, say you integrate external sources into your data set, such as economic indicators, weather patterns, social trends, and industry benchmarks. Here, you are leveraging third-party data to enable your retail end-users to understand how these factors might impact future sales and demand. With that understanding, they can adjust their forecasts and plan accordingly.

2. Enhancing customer analytics with AI dashboards

Retail and e-commerce success hinges on one key principle: having a crystal-clear understanding of your customers and closely aligning your products with their needs.But for retailers that sell several different products or sell online to a vast array of customers, it’s a difficult principle to execute. Add in the fact that there are more opportunities for product, pricing, and personalization than ever before, and it’s even more of a challenge.

AI for retail customer segmentation

AI is a powerful way to achieve strong customer segmentation. And if you surface it in an easy-to-consume way, it enables e-commerce and merchandising managers to elevate performance.

AI algorithms can auto-segment customers based on purchasing behavior, preferences, and demographics. This segmentation can provide insights into which customer groups are the most profitable, which products they prefer, and how their purchasing behavior changes over time. By understanding customer segments, e-commerce and merchandising managers can effectively tailor their sales, marketing, and product strategies to target specific customer groups.

AI for competitive pricing

Retail users can also leverage AI to achieve more competitive and dynamic pricing. For example, AI algorithms can provide prescriptive pricing analytics by analyzing pricing and sales data to generate pricing recommendations based on demand, competitor pricing, and customer behavior. An AI dashboard could surface pricing insights to an e-commerce marketing manager with metrics on demand sensitivity to pricing changes, and the impact of promotional pricing. Based on this data, the manager can make pricing and promotion decisions to maximize sales and profitability.

3. AI dashboards: optimizing inventory management in retail

Strong inventory management is the key to operational excellence and driving the bottom line. In the study we shared above, McKinsey also noted that with more predictive inventory management, warehousing costs can fall by 5 to 10 percent and administration costs by 25 to 40 percent. It’s a powerful way to delight your end-users.

AI for retail inventory optimization

It starts with the basics, providing core metrics such as inventory turnover rate, days of inventory on hand, and stockout rates. Then, you can leverage AI for inventory optimization by analyzing sales data, lead times, stockouts, and carrying costs. Applying predictive models can help you forecast how those metrics will vary in the future, based on current and seasonal trends. With this information, retailers can ensure they have the appropriate volume of stock to meet demand while minimizing overstock and stockouts.

AI can also help optimize inventory allocation across store locations or distribution centers by considering factors such as demand patterns, product popularity, and geographical locations. Based on that day, it can then provide recommendations on how to distribute stock effectively to balance inventory levels, minimize stock transfer costs, and improve overall customer satisfaction.

Weaving AI into retail and e-commerce operations

Composable development is a new way to embed analytics components and data directly into user experiences and your users’ everyday tasks. With composable development, you can tightly weave AI into your end-user’s day-to-day work. For example, you can monitor inventory levels, lead times, and demand patterns to determine the optimal reorder points for each product. You can then use AI to evaluate historical sales data, current stock levels, and expected demand. Additionally, AI dashboards let you set automatic alerts for managers to reorder, or alerts to generate purchase orders when inventory levels reach a predefined threshold. It’s the fusion of AI-driven analytics with operations.

Future of data analytics for retail: the e-commerce GenAI opportunity

The advent of Generative AI opens up new possibilities for organizations that want to provide strong retail or e-commerce analytics by enabling teams to ask more questions and then easily understand their business data. There’s a wide range of potential beneficiaries, like merchandising managers, e-commerce analysts, heads of procurement, retail buyers, and more.

Conversational analytics in retail

There’s a fast way to infuse GenAI into your dashboard: using conversational analytics. Think of it as providing an analytics copilot to your users who understand your retail data model, metrics, and fields, from product profitability to SKUs. Conversational analytics is a powerful emerging feature that allows people to interact with the dashboard using natural language queries, such as asking questions or requesting specific insights. The AI algorithms then process the request and deliver the results visually or in a conversational format. It can even help interpret retail data and provide narrative insights.

Conversational analytics is particularly useful for retail businesses because it lets decision-makers access information quickly and easily, without requiring technical skills or long training processes.

Take the steps toward your first retail AI dashboard

There’s never been a more exciting time to focus on retail analytics, and infuse your insights with AI. AI-powered dashboards present an opportunity to transform retail performance, revenue, profitability, working capital, inventory management, customer loyalty and retention, and more.

At Sisense, we’ve designed our platform so you can quickly deliver dashboards directly within your application or service for the best user experience. This can be infused with the latest AI, from predictive and prescriptive machine learning to conversational GenAI delivered by our composable platform.

To learn more about analytics solutions that will keep you competitive, schedule a demo.