By: Yan Krupnik, Business Development Manager at Retalon Predictive Analytics
Multi-channel retailers are often finding themselves stuck in a vicious cycle of failed promotions, inventory distortion, and endless markdowns which result in decreasing gross margins, and lost sales. Yet all these challenges have only a handful of drivers that when properly tweaked can unlock hidden opportunities and revenue.
In this article, we will explore the five common mistakes that multi-channel retailers make and how predictive analytics technology helps you avoid them.
It has become all too common for many retailers to lose money on promotions. Why are some of the most carefully planned promotions falling flat? The outdated systems used by retail analysts make it impossible to accurately calculate promotional forecasts. Moreover, these methods don’t properly account for the inter-dependencies between important functions of the retail supply chain, like the need for additional inventory and the replenishment process during the promotion.
A predictive analytics system assists retailers with creating a successful and profitable promotion strategy by accounting for the following factors:
•Assortment Depth vs Diversity
•Color Distribution with Styles
•Daily Weights of the week
•Geo – Demographic Diversity
•Promotional Activity (Media Types)
•Price Elasticity of Demand
•Size & Pattern Distribution
• Store Attributes
This strategy allows retailers to run successful promotions by bringing the right merchandise to the right location proactively in order to maintain a high customer service level. This results in maximized return on investment on promotions, and decrease of lost sales, and out of stocks.
Inventory distortion across stores is a retailer’s nightmare, resulting in lost sales, increased carrying costs of unwanted product, and lower customer service levels. It is absolutely critical to have the optimal merchandise assortment mix across your stores. In order to succeed in the area of the business, the following needs to be considered:
Retail is a dynamic industry where nothing is constant. Simply replying on the performance of products & stores alone isn’t a reliable method to forecast demand for the following reasons:
Traditional & Statistical forecasting methods with manual consolidations simply can’t account for the intricacies involved to produce accurate & actionable results.
Each store has its own attributes such as geo-demographic diversity, physical capacity, carrying, shipping, and labor costs. These attributes need to be accounted for when allocating and replenishment inventory as well as when fulfilling online orders from local stores. (Discussed further in the article) A good predictive analytics solution is able to optimize assortment down to a much more granular level such as store/style providing you with an accurate distribution of styles & sizes for each location.
In order to stay competitive, retailers must operate across multiple channels and provide their customers a seamless experience in terms of inventory visibility, promotions, loyalty, and fulfillment options. It is a tricky balance, but it is made significantly easier when retailers create a strong foundation for their Omni-channel business with an end-to-end predictive analytics platform.
The reality is that when brick-and-mortar stores along with e-commerce are viewed as mutually exclusive, the end result is an incomplete and inaccurate demand forecast, poor visibility across the supply chain, and higher inventory & fulfillment costs. Predictive analytics software accounts for retailers’ entire omni-channel business, enabling 360 inventory visibility between all stakeholders like HQ, Warehouses & DCs, Stores, E-commerce, Vendors and Suppliers. More importantly decisions are made for the entire business as a whole and then broken down into the various business functions of a supply chain. As a result, retailers are able to connect important elements such as marketing & merchandising, and optimize operational processes such as warehouse picking logic, logistics, or fulfillment.
When a retailers are in a situation where they are losing sales in one store due to out-of-stocks of a particular item, they will often find that same product sitting and collecting dust on the shelves of another store where the demand is slowing down. At this point the retailer has a choice to make, they can:
Fortunately there is a very good alternative. A smart predictive analytics engine will have a tool called Inter-Store Inventory Balancing which analyzes every single influencing factor of a retail supply chain, and recommends the optimal inter-store transfer schedule to move merchandise from stores where it is collecting dust on the shelf to stores where the same products are out-of-stock and in high demand. proactively. This means:
This can be done multiple billions of Store/SKU combinations if necessary in just hours on a regular PC. It’s easier than you think if you harness the power of end-to-end predictive analytics.
When it comes to solving your retail supply chain challenges, the large, traditional business intelligence software vendors fall short. These platforms are often built on technology that was not intended on quickly calculating hundreds of algorithms on the kind of Big Data that modern day Omni-Channel retailer’s product on a daily basis.
Re-engineering, re-building, and implementing a new approach to analytics is extremely expensive considering these giants are already imbedded into hundreds of retail processes. Instead, these companies acquire new start-ups and go to great lengths to cobble together a variety of customizations and patches in an attempt to meet their customers’ needs. This practice results in greater cost to the retailer in the form of expensive and lengthy integration’s and implementation initiatives. Doomed to failure, this method will never be able to keep up with the nuances of the dynamic retail industry.
Fortunately, modern retailers have a more intuitive option available to them in the form of a predictive analytics platform that is designed for omni-channel retailers from inception with:
An investment in predictive analytics software gives retailers an edge over their competition by providing them with the necessary strategies to successfully avoid the five common mistakes that multi-channel retailers often make.
Yan Krupnik graduated from Ryerson University in Toronto, Canada, and is currently the Business Development Manager at Retalon, the world’s leading provider of predictive analytics for retail. Since 2002 Retalon has optimized pricing, inventory management, merchandising, planning, and marketing operations for retail organizations in a variety of industries. Retalon products range from task-oriented solutions to a common analytic platform, resulting in tangible optimization of the supply chain and significant measurable benefits for the entire organization.