Like in the case of most industries, retail too, is being driven forth and ahead with data. Keeping up with the increasing demands and expectations of customers is no easy feat. However, utilizing traces of data that consumers leave behind, retailers have actually succeeded in delivering exceptional customer service and experience.
A study by Harvard Business Review and Snowflake Computing points out that retailers who choose to make data-driven decisions have the best shot at survival, as reported by CEO Analytics.
Undoubtedly, retail is increasingly becoming extremely data-oriented. As per the same study mentioned before, 89% of retailers consider gaining improved insights into customer expectations a major goal. They also place tremendous importance on the speed of operations and cost reduction.
Giants like Amazon have creamed to the top leveraging a data-driven mindset and deep learning in retail. On the other hand, most retailers are still struggling to effectively use volumes of data available to them.
To make the most out of data that’s being created at the speed of light, 24/7, every single day, companies need to leverage AI and deep learning in the retail business. This is something that the majority of the players are still aren’t doing.
Only 28% of retailers had deployed AI by 2018. That said, the pace of AI adoption has been superb post-2016, when it was only 4%! Most retailers are yet to make the move towards AI, but we see a tremendous transition in retail forecasting already.
Here’s a simple and basic idea of retail forecasting—It is the technique of utilizing existing data of a business and the market to predict future trends, behaviors, and actions, in the context of customers. Leveraging the purchase history of a customer, and tracking insights about his purchase intents can help retailers forecast the demand of products better in that particular consumer segment. This data can further help businesses predict stock requirements during a season and manage their inventory better.
The existing data and market research vary for different types of products that a retailer may sell. But the basic forecasting in retail follows similar patterns even across different product lines.
Accurate retail forecasting is critical for optimizing inventory costs as well as for meeting customer requirements speedily. That said, it is easier said than done when companies aren’t fully utilizing data. There are challenges that need to be addressed for fixing forecasting errors. Let’s take up some of these, one by one, in the following section.
Most businesses in the retail industry witness short product life-cycle. Certain sectors such as retail electronics, fashion, books, and gardening, etc. rely heavily on frequent new product introductions in the market. If seasonal assortment isn’t refreshed frequently, the inventory goes untrendy and the sales drop. Not getting the forecasting of new product launches right can mean a huge loss for such businesses.
However, forecasting demand for a new product is also a huge challenge because of the lack of historical data. What makes it more difficult is the changing market trends. This is a huge blow on the bottom-line because successful new product launches account for at least 27% of the sales made across various retail industries. If only, data could predict the demand for your product, you’d be able to plan your launch with minimum risks.
A huge percentage of sales is often promotion driven. So, if you’re not accurately forecasting your promotions, or are not including it within your forecasting process, you will be missing out on a considerable amount of sales.
While it is extremely feasible to calculate promotion demand forecasts, the challenge in doing so is again the availability of historically collated data such as that collected from display details.
Sales forecasting of items at the SKU level is more challenging than forecasting its sale at the product category level. One reason for this is that most retailers still rely on outdated sales forecasting models. In a consumer-driven market with evolving trends, older models are no longer effective. A high volume of SKUs makes it all the more challenging to work with traditional models to accurately predict future sales.
Companies no longer use only structured data such as sales numbers or percentages. They are digging deeper to work with unstructured data that’s not available in numbers or percentages, and also trying to make sense out of it. Utilizing data such as product descriptions, metadata, promotional copy, etc. in their forecasting models, retail merchants can improve forecasting. The challenge is in picking the right forecasting models that give accurate decisions based on these inputs.
Automated machine learning in retail to a great extent has helped merchants overcome various challenges related to inventory management, demand and supply forecasting, and understanding changing customer demands.
However, traditional machine learning models are incapable of meeting the modern requirements out of retail forecasting. They don’t work so well with unstructured data sets. Also, they are incapable of efficiently processing missing or incomplete data sets - something that’s common in retail analytics. This is where deep learning in retail sees a major opportunity.
The models that Deep learning in retail uses are advanced and sophisticated enough to handle the challenges that traditional machine learning models fail at. Deep learning in retail makes use of Graphics Processing Units for accurately utilizing and processing retail data sets.
To add to this deep learning in retail supports millions of SKUs at the same time. This makes the models capable of learning from the similarities and differences in data to discover correlations.
For example, deep learning in retail uses models intelligent enough to understand that winter gloves usually sell well when sales for cardigans are high. Or, that the release of smartphones with larger screens can eat up the sales of tablets.
In the case of missing data, deep learning in retail can learn from patterns whether an item is out of stock or isn’t selling.
To address the challenge of new product launches, in the case of which historical data is almost non-existent, models that deep learning in retail uses, can fetch and leverage other similar attributes. For example, it can find similar SKUs and utilize/process that data for new product demand forecasting. Consider that you are launching a range of ‘velvet skirts’. Deep learning in retail will, in this case, utilize data from ‘velvet clothes’, ‘velvet jackets’, etc.
There are umpteen number of AI and machine learning tools available to retailers today. However, to successfully implement a model you need the expert eye of a data scientist. To add to this, leveraging deep learning in retail to run your business operations can be challenging without guidance or know-how.
Getting started with retail forecasting may take a significant amount of time as it requires data scientists to experiment with various possible combinations that enable deep learning models to give accurate outputs. Furthermore, setting up the infrastructure for it requires industry expertise. That’s where experts like BluePi offer a helping hand.
BluePi works with retailers across different industries to provide optimization solutions that are tailor-made to their supply chain nuances. With a robust retail analytics platform that is powered by artificial intelligence and machine learning, it enables concrete retail forecasting to add to your bottom-line and top-line; at the same time, improving customer satisfaction.<<<<<<< HEAD
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