Differentiating your brand from the competition is a major challenge in today’s crowded digital market. No matter the industry, customers today have more ways to research their options than ever before.
Finding products and services online has become the norm, which means you’re competing with the best.
Customer experiences are the ultimate deciding factor that separates successful companies from the rest. And in many cases, that experience is more important than price or quality. That’s why so many organizations now use artificial intelligence (AI) to create an exceptional retail experience.
New trends in online shopping have drastically changed the way consumers approach the buying process. A simple ecommerce store isn’t enough today, you must consider how buyers progress through the buyer’s journey, how they engage with your products and services, and create optimized catalogs that cater to their exact needs.
One way many ecommerce brands are accomplishing this is through the use of product catalog optimization. But what does this transformative technology actually look like in practice? And how are companies using AI classification to revolutionize the customer experience?
The Goal: Unifying In-Store and Online Browsing
Despite popular claims, brick-and-mortar stores are still relevant. Even online shopping giants like Amazon are opening up Amazon Go, a series of physical stores. Artificial intelligence thankfully goes beyond eCommerce catalog management and helps businesses provide a seamless experience across the board, whether it’s online or in-store.
Buyers today do product research online, browse in-store, and make purchases wherever it’s convenient for them. Retailers must aim to make switching among these sales channels easy. That’s why many businesses are incorporating both online and in-store activities as part of their customer profiles.
Catalog Price Optimization
So, how can you decide on the perfect price for that new product? There are many factors to consider, including:
- Supply and demand
- National and state taxes
- Local preferences and buying habits
- Omni-channel delivery (both online and in-store)
These complexities make for a difficult workflow for marketing teams. AI advancements in retail catalog optimization are here to help by eliminating human error and improving the usability of customer data.
But what does this look like in practice?
One way AI improves the customer experience is by optimizing price based on a variety of complex factors, such as the product’s initial, best, discount, and promotional prices, how a product’s pricing influences other products, competitor prices, and more.
Human-led pricing optimization could never assess all of these factors across thousands of products. That’s where AI comes into the picture.
The End Goals of AI-Based Product Catalog Management
The objective of integrating artificial intelligence with retail management is to:
- Optimize product catalogs using demand analysis per item listed, on an ongoing basis
- Automate more workflows like product listing, attribute enhancement, product identifications, etc.
- Speed up time-to-market
- Achieve seamless multi-channel distribution using GTINmatching, ongoing price competitivity checks, etc.
These goals ultimately allow your company to provide better customer experiences, generate more sales with improved margins, and raise customer loyalty.
Predictive Inventory Management
Nothing is more frustrating than attempting to buy something, only to realize that it’s out of stock. Carrying too much stock, at the same time, results in lower profit margins.
How can companies strike a balance between the two? The answer is AI.
AI can help businesses create an optimized product catalog that ensures new products are ordered and available at the right time. AI automatically predicts future demand using data like historical sales, holidays, weather patterns, promotions, and others.
Let’s say you see two products in a specific category trending upwards—similar to the COVID-19 consumables boom at the start of the pandemic. Predictive inventory management uses AI to analyze product trends in specific categories to forecast inventory levels and ensure a store will have enough inventory to keep up with demand.
One of the most well-known ways AI helps marketing teams is through targeted promotions and recommendations. Customers like it when businesses can provide relevant offers.
AI algorithms now exist to collect and parse customer data, matching individuals with preferences and price points. The result is a more personalized shopping experience that’s proven to generate more conversions.
Personalization is something that’s taken over all aspects of digital marketing. The largest ecommerce marketplaces use behavioral analytics to serve personalized content. The majority of paid ads on large platforms like Google and Facebook are served to users based on their past search history, cookies, and other tracking data.
Personalization can make all the difference in ecommerce by making relevant suggestions that increase the visibility of specific products. The end result: more conversions. After all, making it easy for customers to find the products they want is the definition of a great customer experience.
Natural Language Automation
Retailers are always trying to make searching their product catalogs easier, especially when it comes to new releases. Natural language processing is becoming more advanced to the point where it can generate product descriptions automatically while still sounding genuine.
Another application of natural language processing is integration with voice assistants like Apple’s Siri and Google’s Assistant, where more and more buyers are doing their research. AI categorizes products using both keywords and natural voice commands.
AI-Driven Shopping Assistants
AI-driven chatbots and shopping assistants are another example of a low-barrier solutions that is redefining the ecommerce industry.
These AI-powered assistants work in-store too. Macy’s and Lowe’s have “On Call” and “LoweBot” respectively. These programs mimic human interaction, allowing customers to ask questions and receive personalized responses.
Other companies use these chatbots to reach their customers on third-party social media platforms like Facebook’s Messenger. These AI-driven systems ensure that companies can reach their customers on any channel, increasing product visibility and brand awareness.
Cluster: All the Data Needed for Better AI Catalog Management
It’s no secret that every company wants access to the latest insights and data. AI has the power to transform these insights into actionable recommendations.
But what good is AI without the right data in place? Cluster is the ultimate market intelligence engine that marketplaces, retailers and brands can use to better understand their market, how market share is divided among product categories, and more.