The most stubborn, expensive issues retailers face are due to poor demand forecasting.
Retailers who rely on approximated demand disappoint customers, bleed profits, and lose market share to more tuned-in competitors. Tackling the challenges in demand forecasting, however, is not easy.
Spreadsheets and gut instinct can’t handle the sheer scale of the task. That is why leading retailers use AI-powered technologies to get consistently more accurate results.
So, how can you meet these demand forecasting challenges and improve your retail sales? Let’s find out.
Why is demand forecasting important?
The forecast of future sales demand informs almost all of the decisions a retailer makes throughout the product journey:
An accurate forecast enables you to order the most in-demand assortment mix, down to size, color, and version of each product.
What’s more, you’re able to buy enough inventory to meet demand without being left with overstock.
Demand forecasting at store/SKU levels leads to better allocation of inventory across the entire business from the start.
It greatly decreases broken assortment issues like out-of-stocks, replenishment costs, and disappointed customers.
Price elasticity of demand, meaning the effect that a set price will have on demand, is an important consideration when setting prices, running promotions, or markdowns.
Having an accurate future demand forecast means setting prices that will lead to the highest ROI.
When Inventory is proactively allocated among locations to meet demand, customers are not repeatedly met with empty shelves pushing them to other retailers.
Order fulfillment becomes more efficient; meeting consumer expectations by enabling lower shipping costs, faster fulfillment, and a greater variety of fulfillment options.
What are the major demand forecasting challenges?
Both the nature of the retail business and the methods used to forecast sales contribute to challenges in demand forecasting. Let’s take a look at what those are.
1. The dynamic nature of retail
The only thing that is consistent about retail, is its inconsistency.
There are a host of factors that influence demand, and these factors are in constant flux. Basing a sales forecast on what happened in the year prior yields inaccurate results. Consider just a few things that change from year to year:
- Changes in product mix
- Channels and locations open, close, or move.
- Changes made by competitors
- Changes in geodemographics
- Changes in promotional calendars and strategies
- Changes in seasonality
- Changes in vendor pricing
A shift in any of these, or other relevant factors has a domino-like effect on the business, from supply chain to SKU level demand.
Planning inventory based on sales history depends on a logical approximation. Unfortunately, approximations often lead to lost sales, drastic markdowns, and unfulfilled potential.
2. Identifying and accounting for relevant factors
The fact that there are so many factors affecting demand and supply is bad enough, what’s worse is that none of them exist in isolation.
A successful forecast must consider every relevant factor for each SKU at each unique location to identify how their interplay will affect demand.
This is why retailers who have adopted demand forecasting instead of a sales forecasting have had so much success.
Of all the challenges faced in demand forecasting, this one is very difficult to confront for two key reasons.
A. It is very difficult to identify all of the variables influencing demand.
Here’s a short list of some variables which directly impact sales demand:
B. Each additional variable makes computing demand exponentially more complicated.
In fact, an omni-channel retailer, with 100s of thousands of SKUs across multiple channels is facing millions of data points.
Traditional spreadsheet-based forecasting relies on analysts to manually sift, organize, and compute all that data. Time-consuming, expensive and prone to errors, this approach is difficult to scale without the aid of retail technology like advanced analytics.
3. Fragmented approach to forecasting demand
The digital transformation has created omnichannel retailers who juggle brick and mortar stores, e-commerce. 3rd party distribution, and many other channels.
Managing business at this scale often leads to a ‘divide and conquer’ mentality, which leaves channels and departments siloed. Working within a fragmented structure creates inefficiency hurdles:
- Every operation is time-consuming
- Miscommunication and lack of visibility
- Human error and missed opportunities
- Constant manual intervention, making it hard to scale
- Category level planning
- Inability to account for all necessary variables
A successful omni-channel retailer needs to be cohesive. A unified approach to business operations across the entire product life-cycle enables optimization and enables retailers to get the most accurate demand forecast.
This is especially important in creating a smooth customer journey and meeting their expectations.
Benefits of AI in tackling challenges in demand forecasting
AI-powered analytics lets you meet and overcome the challenges in demand forecasting. These unified solutions easily handle the factors impacting demand. And they deliver results: McKinsey & Company found that advanced analytics let retailers outperform their competitors by 68%.
Here are some of the benefits your business could get from retail analytics.
1. Improved Visibility
You make better decisions with a clear picture of demand and inventory across your business. Ordering the right quantities for each store’s demand cuts off most inventory problems. If issues do happen, AI-powered analytics notice them faster so you can respond more effectively.
2. Reduced inventory cost
Hedging your forecasts with safety stock is expensive. It ties up inventory dollars, undermines inventory KPIs, and leads to overstocks. With an optimized forecast, logistics costs fall, open to buy grows, and inventory KPIs improve.
3. Reduced markdowns
With more accurate forecasts, you’re not forced into drastic last-minute markdowns to sell off overstock. You’re able to proactively plan price drops and set optimal discount prices to maximize ROI.
4. Increased sales
Advanced analytics ensure your stores have the product your customers want when they are ready to buy. Moreover, prices are optimized to increase demand by accounting for the price elasticity of demand.
5. Increased inventory turnover
Matching demand and supply boost turnover. Stores get just enough product to meet demand without risking empty shelves. Forecasting demand at store/SKU level accounts for variables like geodemographics, which means retailers are not stuck with fringe sizes, styles, or colors.
6. Improved collaboration between teams
A unified analytics platform gives your teams a shared view of each department, channel, and store location. With everyone on the same page, communications improve and your teams work together more effectively.
7. Reduced manual labor and resource costs
Initial setup of an advanced analytics solution requires the retailer to input their business-specific data, parameters, limitations and goals.
Once that’s done, the heavy lifting is done in the background to produce faster and more accurate forecasts of what your customers want to buy. The agile nature of the technology enables retailers to run scenarios and make quick changes, without incurring real-world risk.
8. Easier scalability
These cloud-based platforms process millions of data points in seconds, which allows them to scale with your business on-demand. Add more stores, add new channels, use more variables. Advanced retail analytics seamlessly integrate every aspect of your growing business.
Leverage AI: Solve the challenges faced in demand forecasting
Not meeting the challenges in forecasting demand and supply causes the most intractable retail issues from slow inventory turnover to anemic GMROI. But now you can root these issues out at the source.
Modern, AI-powered retail analytics let you leverage forecasts of true customer demand to streamline your business:
- Squeeze costs and inefficiencies from your planning processes.
- Increase revenue, profits, and market share.
- Unify your sales channels.
- Improve collaboration and communication.
For a more in-depth understanding of retail analytics definitions, types, examples, and tips, discover the Ultimate Guide to Retail Analytics.