07 Jan 5 Seasonal Merchandise Best Practices (2021)
Seasonal merchandise planning carries a unique challenge for retailers, as product life cycles are measured in weeks or even days.
This is a problem because retailers plan their inventory months in advance — having almost no window to react to out-of-stocks or an unexpected up-lift in demand.
With seasonal product life-cycles becoming ever shorter, retailers need more strategic and granular ways to plan their seasonal assortments so they can maximize revenue while minimizing markdowns.
Here are 5 powerful ways retailers can optimize seasonal merchandise planning in their business:
1. Account for more variables in your seasonal merchandise planning
Planning for seasonal sales has always been a bit of a gamble.
Most retailers lock in their seasonal plans long before the selling season starts. And many seasonal products are new with no sales histories to form the basis of a forecast.
This creates uncertainty in merchandise plans, because there are a huge number of factors that will impact your merchandising strategy — many of which are dynamic, and difficult to predict in advance. Some of these factors include:
- Consumer preferences
To illustrate how just one of these variables can throw your plan into disarray, let’s look at a very common business practice — vendor bulk discounts.
While potentially profitable, opportunity buys are double-edged swords. Retailers can’t know in advance when one of their vendors is going to offer a massive discount. This means retailers have to make a quick decision on whether or not to take advantage of the opportunity — without having the benefit of having planned for it.
If the product is in high demand, this can turn out to be a very profitable decision. But if the seasonal merchandise doesn’t sell, it will tie up cash flow and force steep end-of-season discounts.
And that’s just one factor out of dozens.
That’s why simply knowing that it’s the “back-to-school season” again is not enough.
The only way to make better merchandising and purchasing decisions is to control and plan for as many of these variables as possible. If you could do this effectively, your seasonal merchandise plan would:
- Cut inventory costs
- Increase sales
- Reduce needless markdowns
- Improve relationships with vendors
- Improve customer service levels
- Optimize inventory KPIs
But this is easier said than done.
Although it’s possible to account for some of these variables using spreadsheets and typical ERP features — it becomes nearly impossible when you factor in the complexity of multiple stores, channels, and thousands of SKUs.
Instead of trying to accomplish this manually, consider using a retail-specific demand forecast that will automatically factor in all of these variables to build your seasonal merchandise plan.
2. Calculate the seasonality for every SKU, at every store
Retailers often forget that seasonality does not apply to every SKU at every location in the exact same way.
The truth is, each unique SKU / Location combination will have its own seasonality curve.
This means that if you have 1,000 SKUs at 10 stores, you have to plan for 10,000 different seasonality curves.
If you have 10,000 SKUs at 1,000 stores, you have to plan for 10,000,000 different seasonality curves.
Extending that granularity to a SKU-by-SKU, store-by-store basis takes demand forecasting to a level of complexity that retailers have never been able to manage with spreadsheets and statistical models.
Even a mid-sized retailer can easily have millions of product / location combinations.
So, because this is too complicated, retailers avoid the problem altogether.
Instead, they focus on just a few key SKUs while lumping everything else into category-level forecasts. But consolidating SKU forecasts in this way can lead to stock-outs and many lost sales.
Consider a department store planning for Valentine’s Day.
Forecasts on the stores’ core inventories may not focus on books because the holiday is not a big time for book sales. But buried in that data are books like Love Poems by Pablo Neruda that see sales spikes during the Valentine’s season. The standard approach to seasonal merchandise would ignore this demand spike, and miss out on lots of sales.
So if retailers can’t account for this complexity manually, how can they find these hidden opportunities?
One easy solution is to start using merchandising software that leverages AI to quickly calculate the demand of each store / SKU combination, and automatically optimize your seasonal plans with these predictions. These types of granular demand forecasts can identify what truly drives demand for each product at each location, making once-hidden opportunities visible and addressable.
3. Optimize inventory levels to ensure successful promotions of seasonal merchandise
Promotions are key to increasing seasonal traffic and sales, as well as winning customers over from competitors.
But when promotions are launched without considering other business units and plans, retailers set themselves up for poor performance. This is most obvious when a promotion doesn’t take inventory into account. And not just the inventory of the items on sale.
With every promotion, some products in your assortment will sell more, while others will be cannibalized. So even if you plan to have the perfect amount of inventory for the products being promoted, the secondary effects of your promotion can undermine ROI in ways that may only appear in depressed turn rates and other KPI metrics.
For example, in promoting one product, you may create an unexpected demand for a related product that you hadn’t considered — stocking out completely and losing sales as a result. Or, conversely, running a promotion may actually cannibalize sales from regular-priced SKUs, slowing turnover down to a halt and necessitating end-of-season markdowns.
So, how can retailers account for inventory in their seasonal planning?
While there are statistical models that can be used to estimate cannibalization and affinity — they are nearly impossible to apply on a level of granularity that medium and large retailers face. As mentioned in the previous sections, these effects will be different for every SKU, and every store.
Alternatively, retailers can employ an analytics-driven promotion software that plugs into their inventory and pricing data. This would all retailers to automatically factor all of these variables into their seasonal promotions, and get data-driven recommendations for ideal promotion types, media channels, and timings.
By integrating the promotion planning and inventory managing functions, advanced analytics automatically adjusts forecasts, purchasing, allocation, and replenishment quantities. These updates will apply not only to the promoted product but to any products the promotion impacts.
4. Rebalance seasonal merchandise across stores to minimize costs and maximize sales
Retail seasonal cycles are inherently variable.
Spikes and troughs in demand, along with store-by-store variances, distort inventories and leave some stores starving for product while others are tied up with unsellable inventory. And once a seasonal event ends, stores are left with gaps in their assortment.
These situations frustrate customers which has long-term consequences, beyond the immediate lost sales.
Analytics reduce the frequency and severity of seasonal variability by making retailers more efficient at planning for the right products, in the right quantities, at the right locations.
When less-predictable factors like weather cause last-minute issues, retail analytics make proactive stock balancing easier to execute.
An AI based forecast identifies slowing demand much quicker than spreadsheet-scanning analysts can. Factoring in the cost of transfers, an inventory balancing system can recommend inter-store transfers before problems occur, thus avoiding lost sales and overstock issues.
As summer comes to an end, for example, a good inventory transfer system will reallocate warm-weather clothes away from stores in cooler regions to stores where the summer heat lingers.
With a more proactive approach to rebalancing their assortments, retailers free up physical space for new products, sell more of their seasonal merchandise at full price, and minimize excessive markdowns.
5. Protect ROI by optimizing markdowns of your seasonal merchandise
Post-season sales have always been a “necessary evil” of retail.
If retailers forecast inaccurately, end-of-life markdowns are their last resort for freeing up cash flow and salvaging their inventory investments.
Worse yet, retailers often have no way of knowing how much to mark down the product. As a result, their markdowns are often too steep, and cut deeper into their profit margins than the sales uplift warrants.
As such, retailers have two potential avenues for maximizing their ROI in these situations.
Firstly, retailers can minimize the need for markdowns in the first place by bringing the correct inventory in. The first 4 tips address multiple ways that retailers can do this.
But since retailers can’t predict everything, and some markdowns will be inevitable — the second approach involves maximizing profits while getting rid of inventory.
This requires finding the absolute minimum discount required to increase demand just enough to get rid of remaining end-of-season stock. As we mentioned above — if you discount too much, stock will definitely sell quicker, but you’ll miss out on profit. But if you discount too little, stock won’t move quickly enough, and you might be forced to markdown even more drastically later on.
Finding the optimal balance is difficult enough for one product — but nearly impossible to do manually across hundreds of stores and thousands of SKUs. That’s why more sophisticated retailers are leveraging advanced analytics to optimize their end-of-season markdowns.
By creating accurate, granular forecasts of true demand, these types of advanced analytics systems help retailers avoid the financial impact of last-minute markdowns. Final inventory levels will be more manageable, requiring fewer markdowns. Further, the best of these systems will automatically suggest markdowns with optimal discounts to maximize GMROI.
Get the most out of your seasonal merchandise
Seasonal products are a powerful element of a retailer’s business strategy. But by their nature, they compress many of the forecasting and inventory risks into such a brief period that they can undermine margins and distort inventory levels across the business.
Applying a more intelligent and automated approach with Retail Analytics lets retailers maximize the opportunities of seasonal events while minimizing inherent seasonality risks.
If you’d like to learn about how advanced analytics has solved similar issues for leading retailers like GameStop, Simon’s, and The Paper Store, view our case studies here.
If you want to explore whether advanced retail AI is right for your business, schedule a free demo now.