Why Retailers Need To Use External Data For Demand Forecasting

Updated: Jul 28, 2021

The retail industry has faced challenges in the last year, primarily due to the coronavirus pandemic. Previously, retailers used to be highly reliant on historical data to predict future performance. COVID-19 has uprooted all that and has forced retail businesses to reevaluate their merchandise planning ways. Now, more and more retailers are turning to data to guide their decision-making.

Recent research suggests that the retail analytics market is expected to reach $23.8 billion by 2027. This isn’t surprising, considering how disruptive technologies like artificial intelligence (AI) and machine learning (ML) are becoming more widespread.

Retailers are eager to turn things around after the pandemic by leveraging the pent-up demand. Thus, the data analytics race is well underway. So, what’s of utmost interest to forward-thinking brand managers and merchandisers? The ability to forecast sales and demand with the highest accuracy.

Nobody wants to produce too much of a certain item and have it wither away on the shelf. Similarly, manufacturing too little of an in-demand model will leave customers unhappy and revenue on the table. Hence, demand forecasting methods that provide the highest levels of accuracy are what most retail businesses are looking for. Luckily, emerging technologies are here to help with that.

What Is AI-Powered Retail Demand Forecasting?

Under stable conditions, demand forecasting doesn’t have to be too complicated. Historical data is taken, merchandising experience is leveraged, and predictions are made for the coming seasons. However, as we all know, retail is very dynamic. Especially after 2020 which left little reliable historical data to go on with.

So, to get accurate demand outlooks, brands need to consider many variables:

  • Seasonal variations

  • Weekday-related variations

  • Promotions that can influence sales

  • Changes to in-store displays that can affect demand

  • External factors like weather, local events, and consumer preference changes

That’s a lot of things to take into consideration when trying to predict demand for the upcoming season. As you can imagine, it’s close to impossible and rather time-consuming for any merchandiser.

So, what if we told you that there’s a system that can process these enormous amounts of data, analyze it, and provide you with a highly accurate demand forecast? We bet you’d be interested. Well, that’s what AI and ML do.

Machine learning technologies can look for patterns in data, identify those that lead to the best results, and reveal them for better decision-making. Thus, highly boosting levels of forecast accuracy and helping retailers determine how much stock to send to which location.

Why Combine Historical and External Data For Retail Sales Predictions?

Now that you understand the capabilities of AI-powered demand predictions, it’s time to talk about the importance of leveraging external data.

We’ve briefly touched on what external data is, but let’s make sure we are on the same page. External data can be any of the following:

  • Weather

  • Local events

  • Competitor price changes

  • Changes in customer behavior

  • Social media trends

All of these factors can have a major impact on your sales performance. When it’s rainy, umbrellas and waterproof jackets will have a spike in demand. When an A-list celebrity wears a crop top, chances are, a certain demographic in a specific location will flock to the store to purchase it too.

It gets even more complicated when we consider the uniqueness of each brand. You see, every retailer will be affected differently by a certain external trend. Its key customer demographic may react in a way that’s dissimilar to that of a competitor. As you can imagine, this adds even more complexity to the forecasting process.

With so many external variables affecting demand, it’s no longer prudent to solely rely on historical data. The truth is, past performance can only tell you so much. It will only give you insights into what occurred at that specific point in time and often you won’t even be able to tell if a certain event triggered a spike or downfall of demand.

Thus, retailers who want to always have the right product, at the right place, at the right time, can’t overlook external data. Luckily, they don’t have to. Read on to discover our approach to demand forecasting and find out why you should leverage external data.

How Can You Leverage External Data For Demand Forecasting?

Typically, buyers and merchandisers plan the assortment, distribute the purchasing budget, and rate the performance of new designs based on their own experience and years of accumulated know-how.

Some also research consumer behavior by reading articles, monitoring new research, and keeping track of what goes on in social media. However, even this type of deep-dive leaves a lot to the personal opinion of the merchandisers and their interpretation of the findings.

If lilac is trending, does it mean that the lilac shirt of a particular brand will perform well in a specific country? If yes, how good will the sales be? Does it then mean that sales on red shirts will fall?

It’s very difficult, even for a seasoned professional, to answer these questions with utmost certainty. Unfortunately, in the volatile times of the pandemic, accuracy is particularly valuable. Overproduction leads to monetary and material waste and underproduction is essentially money lost.

We are proposing a new approach with our forecasting solution. So, we would like to share with you the methods we take to ensure the highest degree of accuracy that is reached by combining historical and external data.

Focus on customer interests

Our platform doesn’t take external data from business articles and retail magazines. Instead, it prioritizes the monitoring of customer interests. By digitalizing customer behavior on social media, we ensure the consideration of only the relevant data.

Emphasis on relevant accounts and geographies

Behaviors change depending on demographics and location. So, we only focus on the accounts and geographies that are relevant to your specific brand.

ML-powered forecasting

The relationship between external and internal data is identified by machine learning algorithms, not a merchandiser or buyer. We bypass that step and hence avoid the mistakes that can come from the human factor.

Providing a prediction, not an advice

We provide a concrete sales prognosis for up to 12 months ahead. Our solution doesn’t give recommendations or expert advice. It makes an educated, accurate calculation and shares the results with you so that your stocks are always at the right levels.

Ready To Make The Most Of Your Retail Data?

There’s more and more data created every day and thanks to AI, ML, neural networks, and other emerging technologies - businesses can now leverage it. As the world slowly opens up, customers will rush to enjoy themselves, spend time outside, and visit retail stores. As a brand, you’ve got to be ready.

If you want to begin using external data in your demand forecasts and get accurate sales predictions - don’t hesitate to reach out and request a demo. At GFAIVE, we are excited about helping retailers boost revenues and margins while significantly reducing overstocks. Let’s rely on data and make smart merchandising decisions together.

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