Author
January 31, 2023
Retail information systems must find a way to work as a conduit between the digital world and the physical world, a real challenge when both are constantly changing in different ways. On one end, the customer expects a quick, easy, personalized omnichannel experience from the comfort of their couch, on the other end your retail IT solutions have to not only provide that, but then also provide a fast and seamless delivery of the physical items they bought, even if it’s 200 pounds of Italian marble for their kitchen counter.
Every physical product requires raw materials, manufacturing, and then shipping to (at a minimum) the end user. As there is no way around incurring these costs, ending up with excess inventory can quickly become a major expense. But since this manufacturing process also takes more time than a customer would be willing to wait, in most cases it has to be completed prior to a client’s order, meaning a retailer again ends up in a challenging position where they risk having too much or too little on-hand. In this way, accurate retail demand forecasting is essential for preserving your bottom line.
Retail Forecasting & The Data of Yore
Data analytics is a hot topic for technology in retail, and while the way it has expanded in recent years is astounding, its core concept is exactly what the term implies – using the data you have collected and analyzing it to make better business decisions. In its simplest form, it has been employed manually by retail businesses for hundreds if not thousands of years to help alleviate the problem of estimating how much new inventory to purchase.
Until recently, analyzing your company data for retail demand forecasting was a fairly manual process involving someone adept at Excel attempting to find the patterns in who bought what, and when (or before that, manually looking over the ledger!). Today, data analytics solutions for the retail industry involve not only statistical math, but also the use of computer programming languages such as Python, R, and SQL, data visualization software, and even machine learning algorithms and AI.
With these tools, integrated data analytics can synthesize information from larger and more complex data sets than could be handled previously, and drastically shorten the feedback loop so that businesses can quickly see the impacts of their merchandising decisions and adjust accordingly.
Sold on Data
Customers are seeking a personalized omni-channel experience, and companies that align with their core beliefs such as environmental sustainability according to Gartner’s Top Trends in Retail Digital Transformation and Innovation for 2022. Data analytics integration can deliver on both these customer expectations while simultaneously helping inventory prediction through its increased ability to understand the customer.
“Traditionally ‘untouchable’ domains such as merchandising are realizing that they must go through a once-in-a-lifetime tectonic pivot toward building a strong foundation on customer behavior hierarchy models rather than on product hierarchies.”
Gartner Top Trends in Retail Digital Transformation and Innovation for 2022
Retail demand forecasting is the process of using data to predict future customer demand for a product or service. By analyzing large amounts of data, retailers can gain valuable insights into their customers’ purchasing habits that future product orders can be based upon.
Machine learning algorithms are especially adept at the challenge of demand forecasting, as the model can be trained on large sets of historical data to pick out micro trends or correlations between disparate data sources. They have even been used to find innovative ways to reduce waste by applying dynamic pricing at grocery stores based on nearing expiry dates.
Big Data, Little Access?
The quality and quantity of data available to analyze are the main limiting factors to the business insights data analytics can deliver. The standard company maintains a complicated array of retail technology solutions including e-commerce, POS, and inventory management, generating and storing customer and product data. With so many retail IT solutions siloes can easily form that create barriers to accessing the full breadth of information. Where this happens, the accuracy of analytics results will suffer, as if only one aspect of customer purchasing behavior is captured, important correlations may be missed–or misinterpreted–leading to a skewed picture of what drove a customer’s decisions.
Enterprise integration is a crucial component in improving data analytics by ensuring that data from all the retail information systems within an organization are accessible, consistent, and reliable.
This broadens the range of sources from which data analytics can be gathered, providing a more accurate and comprehensive view of customer behaviors and purchasing trends. An improved speed to market of new products and services with EI further assists data analysis as external sources such as new social media channels or market research can be rapidly integrated to incorporate new insights.
Digibee Supports your Retail Technology Solutions
To get the most out of your retail data analytics integration, partner with Digibee. With real-world experience helping retailers like Payless integrate the retail information systems of 200+ stores in 15 countries, we understand what you need to support your customers, whoever and wherever they are.
Because we know a retailer can’t just close their shop for updates, we have flexible integration solutions for the retail industry that allow you to run your legacy systems in tandem with the new platform. This empowers you with always-on business continuity and a seamless customer experience.
Learn more about how fully integrated e-commerce with Digibee can take the guesswork out of demand forecasting, or book a demo to see our solution in action.
