Machine Learning in Retail and Wholesale: accurate and affordable Demand Forecasting by catsAi

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This guest post is originally authored by Stephen Kinns, Founder and CEO of catsAi.

Many business decisions can be traced back to a simple question: ‘How much will we sell?’. Firms, both large and small, widely rely on experience and historical trends to make that assessment yet the accuracy of these approaches can be very poor, which in turn translates to missed efficiency savings through the business.

Machine Learning-based predictions can be much more accurate, but historically the cost and complexity of such technology have made this an uneconomical option for many firms. Despite this challenging backdrop, catsAi’s unique approach makes Machine Learning useful, easy to implement, and cheap for retail, wholesale and other businesses. As such, we have created a lightweight, off-the-shelf solution for demand prediction, which drives easy and rapid adoption at firms of all sizes through supply chain intelligence. With the aid of state-of-the-art Machine Learning powered by BigML, catsAi offers reliable predictive sales numbers on a daily basis, for the week ahead, on each and every product. 


Our existing clients and partners make up a wide variety of retail firms from the smallest high-street store such as bakeries, through to large global wholesaler enterprises. The core challenge is that every client, every location, and every product is different. Therefore, being able to adapt to a wide variety of products and clients has proven key to catsAi’s burgeoning success.

From Raw Data to Production and Benchmarking

Machine Learning as a tool excels in exploring historical patterns. For many firms, the factors that influence sales patterns are diverse; no two firms are alike. Location, weather, cultural influences, and of course changing inventories may all affect likely sales. This means that should a firm wish to implement a Machine Learning solution themselves all this data must be acquired, cleaned, assessed, and analyzed. Datasets can come in a huge variety of shapes and sizes ranging from a few thousand rows to 10+ million observations.

To solve for this variety and keep costs-down, catsAi continually builds bespoke datasets, trains models, evaluates, and then deploys them automatically without any human intervention. When done, catsAi’s data pipelines paint a detailed picture of the influencing factors behind changing sales dynamics complete with custom-developed features for the specific to the client. 

The datasets are then securely sent to the BigML system to initiate the model training process, which we manage through the use of available tuning parameters, configuration options, and event handlers. catsAi assesses and evaluates the results of each training run before making a final decision on deployment for ‘live’ predictions in an agile manner.

Over the years, we have evolved from a neural network on a laptop, to a full-fledged cloud-based system thanks to BigML’s support. The BigML suite of tools, both at the REST API and graphical Dashboard level, has considerably accelerated our deployment time-frames. We are now able to scale to match our clients’ expectations while simultaneously maturing our models iteratively.

The resulting effect of our approach replaces the typical complex and time-consuming data-science process by breaking it into small manageable pieces that can be executed automatically. This means customers can autonomously deploy the predictions swiftly and affordably whilst maintaining accuracy and control. 

Although a few successful runs can help secure the client’s trust we often need to prove the ongoing value of the predictive models to a client, so we continually set some simple benchmarks. In the absence of Machine Learning, in a typical retail business, common methods of prediction are either based on the sales of a given product last week/month or a moving average.  At a minimum, we use those as easily relatable, effective benchmarks. As seen below, traditional methods have a tendency to over-shoot and suffer from a forecast accuracy standpoint in comparison with catsAi model predictions.

CatsAi Benchmark

Delivering Real Value

Indeed, our experience has shown that catsAi predictions are commonly between 85% and 94% accurate, often anywhere from 30% to a whopping 70% more accurate than initial state or benchmarks. This translates into up to 80% reduced waste based on category or SKU being analyzed.

Furthermore, our customers love that we can go from initial contact to the first set of predictions in as little as 48 hours, iterating on from there. They also highly value the lightweight process and client journey which can be summarized as sales data in, on-the-mark predictions out. Did I mention, no setup charges and low subscription options? All of this really means, with Machine Learning-as-a-Service platforms like BigML everyone from the smallest high-street companies to global enterprises can easily deploy Machine Learning. This is no longer a wish list item but a day-to-day reality of many early adopter businesses willing to experiment with this foundational technology that will determine whether their businesses can withstand the macro challenges of our world as well as increased competition.

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