In retail, merchandising is the link between product, sales, and marketing on the front end and supply chain at the back end. Merchandising tells retailers the all-important 4 Ws:
These four Ws are critical for a product, sales, marketing, and supply chain because retailers must know them to balance a positive consumer experience, available inventory, and the ability to meet consumer’s future needs on time.
Analytics can help retailers make better merchandising decisions. More than half of retail leaders already do it. So if you want to keep up and get ahead, you should use the data wisely. Data is your friend because it can teach you what works and what doesn’t.
All eCommerce managers know how important is it to present the products in a way that appeals to the customer. This is a matter of how you do it, but also of what items you present.
Why? Well, mainly because retailers struggle with an average online shopping cart abandonment rate of nearly 70%.
When product information is not searchable companies lose out on a sales opportunity on giving customers what they want.
Brands face numerous obstacles when executing strong merchandising campaigns. Insufficient reporting and data, lack of responsibility, and overcomplexity in the retail merchandising process, to name a few. These challenges magnify tenfold during busy shopping seasons.
A common problem among retailers is not having enough of the most popular flavor or a version of a product. The problem does not arise from a lack of production, but rather from a lack of inventory control. The number of products in stock can determine the priority of displaying a product.
Pricing along with delivery plays an important role in the customers purchase decision. With the right pricing and convenient delivery options, sales become more convenient.
Did you know that more and more consumers start shopping by searching for a specific product in search bars?
This number is on the rise. A small change by adjusting search results to include top items sold can increase product sales.
Most retailers sell through different platforms. Some have brick-and-mortar stores, physical catalogs, eCommerce platforms, and even sell through different marketplaces.
Challenges in online catalog management are diverse. If you manage several spreadsheets and databases, you probably struggle to provide consistent and quality data while lacking a single source you can trust.
Identify customers who are browsing into buyers by recommending the right product to win the sale.
Segment your customers to sell the right product to the right consumer.
Certain customers come to your site ready to buy.
The question is: Are they going to buy from you or go to your competitor?
Where did your visitor come from?
Are they female or male?
Are they a first-time visitor or returning shopper?
Are they on a mobile device, tablet, or traditional computer?
Where did they go on your site and what did they do there?
Where in the world are they?
Visuals are extremely important in the digital era. To compensate for lacking the possibility to touch an item, online retailers and marketplaces have started using spectacular images to catch the visitor’s attention.
Scaling up your eCommerce is challenging, especially when your online catalog is not properly structured or consistent. Adding more SKUs while avoiding inaccuracies or mistakes in your product data is certainly not a piece of cake. And these difficulties might discourage you from expanding the content on your product pages.
Several problems may arise in online catalog management when trying to keep your data consistent. From typos to inconsistent category names and missing information. Having different human annotators introducing product information into different platforms can lead to contradictory or erroneous data.
Optimizing your product pages requires a great amount of effort from your team dedicated to online catalog management. But we know that, for them to rank well in search engine result pages, retailers need to pay attention to relevant keywords. Infact, 43% of all eCommerce traffic comes from organic search.
The Catalog is the core of the commerce system. It helps merchandisers to manage the Category, Product, Price, Inventory, and Promotions. A Modern merchandising system also has advanced Publishing Workflow, Preview Engine, Multi-site, Multi-locale, Feed Integration capabilities.
Merchandising plays a significant role in the modern trade. There has been a tremendous shift in the way merchandisers manage product, campaigns, inventory, and pricing. The shift to a more experience-driven approach is quite prominent. The business tooling also needs to support these transformations. The modern merchandiser needs access to near real-time data to make quick decisions. So data-driven merchandising initiatives with live dashboards and visualization tools come into play to monitor the product lifecycle in real-time. Retailers are also exploring new sales channels, especially ones which are contactless. Support for delivery channels like curbside pickup, Buy Online Pick Up in Store (BOPIS) are becoming more commonplace. It is time for merchandising tools to become more intelligent, experiential, and data-driven.
ARC by RETISIO is a next-gen reactive commerce, cloud-native platform for modern retailers developed using a microservices based architecture. Designed using domain-driven design principles, ARC is fully extensible and provides business agility along with savings in terms of development efficiency and infrastructure reduction.
ARC is a platform for the retailers whose applications require the highest forms of resilience, speed, and scalability for offline/online scenarios. It is built using headless, API-first, and cloud-native architecture which takes advantage of the underlying platform’s ability to optimize resource consumption, scale dynamically, and recover quickly if any portion of the system goes down.
ARC delivers personalized experiences across the customer journey using merchandising, search, and content management tools, with intelligent search capabilities and proven AI/ML based recommendations engine.
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