Deep Tags: The Key to Accurate Demand Forecasting and Inventory Planning 

Learn how to use AI-generated product tags to detect patterns and trends that can help retailers more accurately plan their upcoming inventory.

Accurate demand forecasting and strong inventory planning are a must in order to thrive in today’s retail landscape. However, changing consumer expectations and behaviors – caused in part by the pandemic and high inflation rates across the world – are challenging brands and retailers to keep up. Paired with a volatile retail environment that is suffering from ongoing supply chain issues, it can be extremely difficult to predict demand with certainty, and the negative impacts of poor planning are significant, ranging from stagnant inventory to lost revenue. 

That’s why AI-generated deep tagging has become an indispensable tool for online brands and retailers trying to combat the unpredictability of the market. In this article, we’ll discuss how automated product tagging can help ensure a steady inventory flow, make business decisions backed by data, streamline operations, and guarantee customer satisfaction.

How eCommerce is Changing

Since the onset of COVID nearly three years ago, there’s been steady growth in eCommerce retail sales. This makes sense, as consumers of all ages have had to rely on and become more comfortable with online shopping.

elderly couple using a mobile phone to make an online purchase

In the United States, for example, online sales rose to $815.4 billion in 2020, a 43% increase from the year before. Meanwhile, the share of global internet users who made purchases online increased from 53% in 2019 to 60% following the onset of the pandemic. The rise in eCommerce revenue was sustained in 2021 with global sales amounting to $5.2 trillion, compared with $3.35 trillion in 2019, and that number is predicted to grow by 56% over the next few years, eventually reaching $8.1 trillion in 2026.

The eCommerce disruption over the last few years is not all positive, however. Inflation rates are climbing all over the world, with the United States hitting a 40-year high in January 2022, and a global recession is now in full swing. Ninety-four percent of Fortune 1000 companies are experiencing supply chain issues, which have been attributed to the pandemic, cyberattacks, commodity pricing fluctuations, and diverging regulations. As a result, US and European companies have reported $4 trillion in lost revenue and 55% of businesses plan to downgrade their growth outlooks (or have already done so). 

Yet, even with an uptick in prices due to inflation, consumer demand is still high. As it turns out, inflation is actually a major driver of eCommerce growth. For example, Adobe reported that online shoppers could spend $27 billion more in 2022. 

However, stockouts remain a challenge, with the prevalence of out-of-stock messages rising 325% in October 2021, compared to pre-pandemic levels in October 2019. Companies that are not prepared to deal with inventory and supply issues will lose business, as 37% of consumers will shop with another brand if they encounter stockouts. Other retail challenges include costlier operations, talent gaps, and abrupt price increases. 

Demand forecasting can address these issues, but traditional methods may be inflexible in factoring in recent global events. Newer eCommerce technologies based on artificial intelligence, however, show promise. They can fill in the gaps for stronger, more reliable demand forecasting and inventory planning. 

What is Demand Forecasting?

Demand forecasting, which also goes by various names such as demand planning and inventory forecasting, is the process businesses undertake to predict future customer demand for their products and services. To do this, prediction models take into account historical data and current trends. 

coworkers looking at data in order to make an informed demand forecasting decision

At the most basic level, it can be done using a simple spreadsheet to track revenue over a certain period of time (e.g. a year). Recently, sophisticated methods that use AI and machine learning have enabled brands and retailers to create more robust projections that consider previous data but also identify emerging patterns and trends. Now, forecasts can be long-term or short-term projections, and macro-level or merely internal. Your business needs dictate the technique to use with AI-based demand forecasting.

The Significance of Demand Forecasting in eCommerce

Demand forecasting provides brands and retailers with insights to make data-driven decisions. By analyzing historical data, market trends, patterns, and other relevant considerations, you can meet consumer demand and ensure company growth. Specifically, you can boost profit margins, allocate resources properly, maximize finances, and improve staffing.

Those who apply AI-driven forecasting are more likely to beat supply chain issues. McKinsey & Co. found that errors are reduced by 20% to 50% and lost revenue and product unavailability are lessened by up to 65%. AI-powered management also improved logistics costs by 15%, inventory levels by 35%, and service levels by 65%.

AI-driven demand forecasting can reduce lost sales and product unavailability by up to 65%

On the other hand, inaccurate demand forecasting can lead to poor business decisions that result in lost revenue and increased operating costs. Not addressing supply chain issues can also impact customer satisfaction. Oracle reports that 61% of consumers felt frustration, 46% became impatient, 45% grew anxious, and 34% were angry over supply chain shortages since the pandemic hit.

How Demand Forecasting and Inventory Management Work Together

While demand forecasting impacts other business operations, it’s mostly tied to inventory management. With accurate predictions through historical data and current trend analysis, retailers can maintain a profitable, steady amount of stock. In particular, you can prevent understocking and excess inventory. This is important because: 

  • Understocking often results in delays or shortages that customers are unhappy about. According to Oracle, 84% would cancel their orders and 80% would stop buying from a brand entirely  when faced with product delays and shortages. Shoppers might initially give their preferred stores a chance, but 58% say that they would cease to support a retailer after three instances of disruptions. Suffice it to say that ensuring your customers can get products when they want or need them is one of the most critical steps you can take to guarantee a good customer experience and brand loyalty.
  • Overstocking and stagnant inventories can keep operating and warehousing costs up. You get lower profit margins from unwanted stock and spend more money storing them. Furthermore, the return on investment is lessened when you opt to clear supplies through means such as deep discounts and donations.
two people in a warehouse looking at inventory so they can make an informed demand forecasting decision

Accurate predictions that consider real-time data can inform the number of new supplies and restocks to be ordered. Instead of solely relying on previous sales performance or even guesswork, brands and retailers can use AI-based forecasting to plan based on the projected demand, which will help to maintain a reasonable production schedule as well as formulate competitive pricing. 

What is Product Tagging and Why Is It Important?

Product tagging is a process that categorizes and organizes your products according to specific attributes, such as color, size, style, material, and brand. It’s helpful to both you and your customers because: 

  • Accurate product tagging is integral to streamlining inventory management.
  • Detailed product tags allow your search engines to show customers only the most relevant search results and recommendations. 

Merchandising teams are typically in charge of product tagging, collecting data, and assigning labels to SKUs for inventory management, which can be done either manually or automatically. 

manual product tagging by woman salesperson

The problem with manual tagging is that it’s time-consuming and subject to human error. Product tags should always be up-to-date and consistent in order to maintain an organized catalogue, and that’s not always possible for brands doing manual tagging with a large number or variety of products – regardless of the size of their staff. 

In addition to failing to connect customers to relevant items on time and missing out on revenue, poor product tagging can also negatively impact inventory insights and planning.

These problems can be eliminated with automated deep tagging. Powered by visual AI, automated deep tagging can extract attributes quickly and accurately through advanced image recognition algorithms. The technology is intuitive, automatically updating tags when patterns and trends are analyzed via deep learning and neural networks.

Bottom line: When you have automated product tagging that’s not subject to human error, you minimize costly mistakes and make products available to customers faster via online merchandising, site search, and navigation. 

Using AI to Overcome Challenges in Merchandise Planning and Inventory Management

We’ve stressed the importance of accuracy in demand forecasting, but it’s easier said than done. There can be unexpected variations that quickly affect product demand. As inventory management is linked to demand projections, retailers can benefit from AI tagging technology that solves the following challenges:

  • Understanding your customers. AI-based product tags provide automated insights that come from the customers themselves – what they’re searching for, how they explore different products (right down to the minute details), and which items they’re likely to buy based on past purchases and real-time behavior. Tracking tags can also quickly detect any emerging patterns.
  • Dealing with a dynamic and volatile retail landscape. Demand can change in a snap due to various factors – the pandemic is just one example. Other considerations include competing brands, economic climate, and seasonality. Automated product tags can capture how these variables impact consumers’ shopping habits as they interact with products on your site.
  • Achieving channel-based forecasting. Today’s customers have an average of six touchpoints before purchase, emphasizing more than ever the importance of an omnichannel experience. However, the traditional approach to demand forecasting doesn’t necessarily take into account sales channels that are isolated from others. Getting this information manually can be time and resource-intensive. With automated tags, you can generate accurate demand predictions at a larger scale across all of your sales channels.

5 Additional eCommerce Benefits of Deep Product Tags

Disruptions that make or break your business, tough competition in retail, and high but volatile consumer demand all stress the importance of accurate forecasting and robust inventory management. Here are five ways that automated product tags can help brands to accomplish these two critical goals:

  1. Use AI Tags to Implement Smart Merchandising Rules

The products you want to be most visible on your eCommerce site or app can depend on the season, your inventory, or your business goals. Automated tags can be used alongside merchandising tools to rank SKUs across your store, be it through on-site search or recommendation carousels. For instance, if you have old products you want to clear out, you can prioritize these items to display at the top of product listing pages according to various attributes, such as style, price or availability. 

woman shopping online on her laptop from the couch at home

What’s more, you can also use AI-generated product tags to promote specific products that are more likely to appeal to a particular customer’s preferences. Capturing shoppers’ interests by surfacing highly relevant suggestions is crucial. After all, 86% of shoppers only click on the first five items suggested in recommendation carousels. If you’re showing irrelevant options in those five suggestions, you’re more than likely to lose out on potential business. In addition, 76% say that an unsuccessful search will push them not to purchase and 46% will consider purchasing the item from another website. On the other hand, 69% of shoppers will purchase additional items after a good search experience – underscoring just how critical smart merchandising and accurate product tags really are.

  1. Improve Visual Search Results With AI-Generated Product Tags 

Camera search, which is also powered by visual AI, helps customers find products they want but can’t describe. Shoppers who aren’t familiar with industry jargon can connect with items they’d like to purchase by uploading a photo of the item or something similar to the on-site search function and get immediate results. Product tags will surface the items in your inventory that most closely match the attributes detected in the search image.

example of camera search
  1. Maximize Revenue Based on Product Performance

Smart product tags do more than organize your inventory, they can also capture the popularity of SKUs according to attributes. The technology can identify shopper behavior and preferences based on the characteristics they favor while browsing eCommerce websites. With the knowledge of high-selling and most-searched products, retailers can then strategize supplies and prices to maximize revenue and maintain a healthy inventory. 

  1. Free Up Resources With AI-Based Automation

The automation of product tagging through AI makes the entire process more efficient. Brands no longer have to go through thousands or millions of products to update their labels, as all relevant characteristics are automatically assigned. This means that every part of the business benefits, from production to fulfillment, by making it possible to allocate your staff to other efforts that require human intervention. 

Note: The adoption of AI is only expected to increase in the future to save companies time and effort, with 80% of retail executives expecting their companies to adopt AI-powered intelligent automation by 2027. So make sure your brand gets on board and doesn’t miss the boat!

  1. Improve Conversion Rates With More Accurate Search Results

When customers have an easy time locating products they want or need, they’re more likely to become loyal customers. Shoppers who use visual search tools that display only visually similar products convert 840.6% more than non-users and spend 6.7 times more every session. The average revenue per user is also 451.2% higher. 

Almost every shopper agrees that excellent on-site search positively impacts customer experience, with 97% saying that their favorite retail websites are ones where they can quickly find what they are looking for. Moreover, you can increase the lifetime value of existing customers when they have a positive experience and want to come back to shop with your brand again. For example, increasing customer retention by just 5% can increase profits by 25% to 95%. Relevant recommendations also drive profit, with 49% of consumers deciding to purchase a product after seeing a personalized suggestion.

The Road to eCommerce Success is Paved With AI Tech

Visual AI-powered product tags generate valuable data for brands and retailers that want to be able to predict demand based on real-time trends while keeping a steady flow of profit and stock. The technology can also mitigate losses from supply chain issues, and the eventual impact those disruptions have on the customer experience. Ultimately, the wealth of information generated by deep tags allows brands to make strategic decisions, ensure business growth, and keep the purchase process friction-free and memorable for shoppers.