How SMB Retailers and E-Commerce Companies Can Unleash Inventory Insights with Cloud Analytics

The retail industry is currently navigating a complex landscape of challenges: fluctuating customer demands, rapid technological advancements, supply chain disruptions, and the need to offer personalized experiences. Due to high inflation rates and other disruptions, the retail supply chain, consumer behavior, and spending habits are in constant flux. According to NCR, supply chain disruptions have resulted in an on-shelf availability of 89 percent, yet consumer prices are 5.4 percent higher than pre-pandemic levels. Significant impacts on retail supply chains include increased demands for product transportation and a shortage of resources to meet this demand​​. About 30 percent of e-commerce purchases are returned, highlighting the inefficiency and costliness of the reverse supply chain.​

For small and medium-sized businesses (SMBs), these challenges can be particularly daunting due to limited budget and headcount. SMB retailers must compete with larger entities while also fostering a unique customer relationship, often on a more personalized level. Staffing is always an issue, but there are a few possible ways to augment talent to integrate analytics and AI for greater scale. By leveraging data insights, SMBs can predict and manage inventory levels more efficiently and streamline the customer journey despite the size of their business. This article will explore how cloud-based analytics can transform inventory management from a reactive task to a strategic advantage.

Reducing sales forecast challenges with predictive precision

As retailers work to align their inventory management system with the ebb and flow of consumer demand, accurate forecasting has become a cornerstone of retail strategy. The following key metrics are the most commonly used among SMBs:

• Sales velocity
• Stock turnover rates
• Sell-through percentages

Traditionally, retail analytics relied on linear models and time-series analysis, which often led to oversimplified assumptions that couldn’t capture the complexity of modern consumer behavior or market volatility. Those methods lacked the ability to process large datasets quickly and were not equipped to incorporate real-time data, resulting in delayed reactions to trends and lost sales opportunities.

AI algorithms can pinpoint such metrics with remarkable precision, even amidst the frenzy of peak shopping seasons. This data-driven precision allows for the anticipation of demand at a granular level — by product, by location, and by time. With more advanced analytics, retailers are able to dissect their customer data into meaningful segments. This segmentation informs not only stock levels but also the creation of marketing campaigns that resonate deeply with each customer group’s preferences and behaviors.

What does this look like in practice for an SMB?

Imagine you’re an SMB retailer specializing in outdoor gear. With algorithms, you can track and analyze data on hiking boot sales against weather patterns. If the algorithm detects a trend of increased sales when regional hiking trails open, you can prepare by stocking more boots ahead of these peak periods. This targeted strategy ensures marketing funds are invested in promoting hiking gear when customers are most likely to buy, enhancing the return on investment. With a precise forecast of sales trends and inventory needs, you can dynamically adjust orders and marketing.

How better inventory management can lead to better operational efficiency

Accurate inventory tracking and management predictions are vital; they not only prevent the costly implications of unsold stock but also ensure customer satisfaction by having the right products available when and where they’re needed. Precise predictions also support sustainable practices by reducing waste and enhancing the overall efficiency of the supply chain.

Another example specifically for SMBs

In the apparel retailing, predictive analytics are streamlining inventory management with precision. Let’s continue based off the last example. With this example, pretend you’re a sportswear brand, preparing for a significant game or championship. You could use predictive models to assess pre-event sales and online engagement, stocking the most popular team merchandise. If you do not have the in-house support to do so, you can work directly with an AWS expert part of our consulting network.

Bridging the digital-physical divide in retail

The convergence of online and offline retail experiences is a challenge that many businesses face. However, with real-time data analytics, retailers are gaining immediate insights into customer behaviors, enabling them to create tailored in-store experiences. This strategy becomes even more pivotal during busy shopping periods, where the right insight can lead to increased sales and customer satisfaction. AI solutions for SMBs can enhance the retail experience by powering chatbots that not only answer queries but also suggest products based on customer conversations, mimicking a personalized shopping assistant.

Offload common questions to a digital assistant

Imagine a chatbot that can generate a complete outfit recommendation when a customer types, “I’m looking for a warm, yet stylish winter ensemble.” In the realm of search, AI transforms how customers find products. If a customer searches for “camping gear for cold climates,” generative AI can intuitively understand the request and generate search results that combine customer reviews, product specifications, and suitability ratings, creating a bespoke shopping experience that simplifies product discovery and selection

How an AWS customer creates breakthrough customer experiences

Zappos.com is an online apparel retailer that measurably improved its e-commerce customer experience on AWS Cloud. The solution enables Zappos to personalize sizing and search results for individual users while preserving a highly fluid and responsive user experience. With modern technology, Zappos successfully achieved the following:

• Kept search load times under 48 milliseconds for 99 percent of searches
• Personalized searches for better customer experience
• Achieved higher search-to-clickthrough rates
• Gets fewer returns due to improved sizing recommendations

In the words of Ameen Kazerouni, head of machine learning research and platforms at Zappos, “We are always asking ourselves: how do we differentiate further? How do we optimize return rates without negatively affecting the customer experience? These are the problems we set out to solve using machine learning and analytics on AWS.”

Specific AWS services to achieve the outcomes

With Amazon Redshift, a service that enables you to analyze all retail data, businesses can identify consumer trends and make informed, strategic decisions.

Amazon QuickSight offers SMBs the ability to create interactive data visualizations and dashboards quickly, providing real-time business intelligence essential for agile decision-making in the fast-paced holiday retail environment. This is valuable for business leaders frequently overwhelmed with dozens of spreadsheets.

Amazon SageMaker Canvas extends this capability by simplifying inventory forecasting, customer churn prediction, demand prediction, and fraud detection, allowing SMBs to deploy machine learning models that inform critical business strategies without the need for deep technical expertise.

If you don’t have the in-house talent for these abilities, look into the various types of IT support programs AWS offers.

Conclusion

As the holiday season increases competition in the retail landscape, SMBs can achieve significant business advantage by using the easily accessible and scalable managed suite of AWS AI and analytics services. This setup enables businesses to swiftly adapt to market shifts, enhance customer interactions, and refine operational efficiency with minimal complexity. Learn more about what we can do for your business on AWS Smart Business.