Amazon retailers that sell a broad portfolio of SKUs with multiple variations know that seasonal buying can feel like sinking in quicksand. It’s easy to get in over your head, fast.
All inventory goes through a lifecycle, with some cycles lasting much longer than others. Inventory that is ordered in January may not go on sale until September or October, and then may not be even fully sold until the following January.
With complex, seasonal inventory, you need to take a data-driven approach to ensure proper sell-through. We’re going to cover inventory lifecycles into three phases: pre-buying, selling, and liquidating.
Let’s go over some decision making strategies for each phase.
Whether you are a brand or a reseller, figuring out how much to buy can be tricky. Tools like Teikametrics provide analytics that allow you to combine your intuition with factual data in order to make more accurate predictions.
Let’s walk through a pre-buy forecasting process to pre-buy inventory for the fall season. We’ll start with our pre-buys in January, move on to selling in the fall, then handle liquidation the following January. For this process, we’ll show you how to:
- Develop a Context
- Forecast Carryover SKU Demand
- Forecast New SKU Demand
Pre-buying: Develop a Context
Buying involves a blend of both science and art. Before forecasting demand for specific SKUs, it’s important to calibrate your gut—your merchant instinct. We’re going to do this in three ways:
- Understand Company Growth
- Understand Brand Growth
- Identify Exceptional Events
Understand Company Growth
Understanding how your company sales as a whole have been growing or shrinking helps you understand the risk tolerance and investment potential of your company. If you’re strapped for cash, you may invest less in your pre-buys in order to mitigate risk. Likewise, if you’ve had healthy growth and a strong cash position, making smart investments can generate a higher profit.
With Teikametrics, we use the Historical Performance Analysis function in Amaze Analytics to help you understand your company’s overall trends. The Historical Performance Analysis uses time series charts to show you the performance of products, brands and more.
When you first open the Historical Performance Analysis, the performance of your entire company is shown in a Multiple Year Comparison view. This view shows each year as a separate time series. The years are stacked upon one another to easily compare your performance year-over-year.
Tool tips show you the performance for a chosen metric across all years. An info panel in the upper left shows more detailed data for the specific date you are hovering over.
On the left, a chart options panel allows us to select among a series of different views aimed at solving specific types of problems, to change which metric to show on the chart, and to set the period at which to aggregate the data. To understand company growth, you’ll want to switch your period to ‘Monthly’ to remove the daily fluctuations and focus on the overall trend.
Let’s look at how we performance last fall compared to the fall of 2014. Hovering over these months we can see we sold about 3x more in 2015 than in 2014. Focusing on sales so far this year, we can see that we’ve grown another 50% over 2015. Let’s keep this growth in mind as we do our forecasts.
Understand Brand Growth
Company growth doesn’t tell the whole story. Brand performance can differ from company performance drastically. While company performance helps us understand our appetite for risk and investment, brand performance tells us whether and how we should be investing in a brand.
Let’s use Steel Rain, a supplier of winter clothing, as an example. To view the data for a specific supplier, use the search box above the chart. Amaze allows you to search for data related to a specific ASIN, a parent ASIN, a supplier or an arbitrary labeled group of products.
Following the same process we did to analyze the company as a whole, we start comparing the performance of last year with 2014 during the period we’re interested in reordering for to see how sales increased or decreased. Steel Rain has performed well. While sales started later last year, they quickly grew to more than double or quadruple the sales from 2014. This has continued into January, where we have almost double the sales from last year when we’re only halfway through the month.
The slow sales in September last year versus 2014 may indicate that we ordered too late, so we’ll want to keep that in consideration as we decide when to receive our shipments.
To check on how our margins have been doing, we can switch the chart’s metric to Adjusted Gross Margin.
Furthermore, you are able to view Profit/Unit to see the data in more concrete terms. Here, you can see Profit/Unit has grown from $32 a unit in 2014 to $37 in 2015 and $49 a unit so far in 2016.
Overall, this looks like an excellent brand to not only continue to invest in, but to increase our investment.
Identify Exceptional Events
Before forecasting individual SKUs, there’s one more thing we want to do: check for exceptional events that might be skewing our data. To do this, you want to switch back to daily Units Sold.
In the screenshot above, you’ll notice a spike in September. It turns out this brand has done a big promotion on September 29th for the past two years. That promotion tripled our sales for that day. However, this supplier has cancelled that promotion for this year.
As we move into forecasting individual products, we’ll want to subtract any extra sales on that date so we don’t over-forecast our demand.
Forecast Carryover SKU Demand
Let’s start by forecasting our demand for women’s snow pants. First we’ll look at the parent ASIN to see how all styles and sizes have performed.
Overall, it looks like we had much stronger sales this past season than in 2014. Let’s switch our view to Performance Over Time. This view shows us all our sales over time in a single chart and allows us to zoom into that performance.
After zooming into an area on the chart, we can move our mouse off the chart to see the aggregate data for the period we zoomed into. Below we have zoomed into the period from mid-September 2014 through mid-January 2015.
The info panel on the upper left shows us we sold 226 units at an average of $45 per unit for a total profit of $3,466.
Now, let’s zoom back out and select September 2015 – January 2016 in order to check our data from the same period from one year to the next.
We can see we sold 923 units at an average of $37 per unit for a total profit of $9,284. That’s almost a fourfold increase in unit sales and three times the profit. That’s even more aggressive than our overall company and brand growth.
Let’s see how that plays out at the individual SKU level. Below, we’ll dive into the ‘small’, ‘white’ variation and switch back to a monthly Multiple Year Comparison view.
Here we can see that 2014 and 2015 had extremely similar sales curves, despite the brand growth. This means we should order about the same for this year.
Notice that 2014 sales started in September, while 2015 sales didn’t start until October. This is probably because we ordered too late. For the timing of this pre-buy, we’ll make sure to get delivery by September this year.
Below, we’ll look at the data for our medium, black pants. You can see that from 2015 to 2016, sales more than doubled. Based on what we know about this brand, we can expect similar growth next year.
Below, we’ve switched back to the Performance Over Time and zoomed into just this season in order to determine our forecast.
So far we’ve sold 119 units for a profit of $1,142. Based on our growth forecast of 50%, we should order around 210 units and stock them earlier in the season to take advantage of the early demand we saw on our other snow pants.
Then we can repeat the same forecasting process for our other carryover SKUs.
Forecast New SKU Demand
This year our supplier is introducing a new color: ‘indigo blue.’ How can we forecast demand for this new product?
The key is to triangulate from our existing data. While we haven’t ever sold indigo blue snow pants before, we have sold other products from this supplier that are indigo blue. And our sales for fuchsia snow pants last year were strong, indicating a demand for alternative colors.
Let’s see how indigo blue compares to our existing colors of white and black. To do this analysis, I’ve added labels in Teikametrics to all of this supplier’s products based on their color.
Using our Historical Comparison Analysis, we can compare the relative performance of each of these colors. This view shows us the performance over time of suppliers, products, or groups of products, and allows us to compare how they perform against each other.
In this case, each chart is a separate label within Teikametrics. Looking at our comparison chart, we can see that indigo blue products performed better than our white products and almost as strong performance as our black products.
Below, you can see our Benchmark Baseline view, which allows us to compare the performance of each group of products against a baseline group.
This allows us to see more specifically how white and black performed relative to indigo blue. In this view, the graph is colored green if the sales for a product exceeded the sales for indigo blue for that period, or red if the sales were below the sales for indigo blue.
From the amount of red on each graph, we can immediately see that indigo blue beat out white significantly and even beat black on some days.
This indicates we can use the performance of our black snow pants to predict the performance of the new indigo blue SKUs.
Let’s wrap up pre-buying by summarizing what we’ve learned. To be effective at pre-buying, we need to first calibrate our gut by developing a context based on our data. Then we can forecast by looking at prior periods, accounting for exceptional events and applying a multiplier based on how much we expect sales to grow or shrink. For new SKUs, we can look at sales of similar styles or colors to predict sales for the new SKU.
Now, let’s imagine it’s mid-November and we’re selling our products. How do we maximize our profit and ensure we don’t miss any opportunities to sell more? Teikametrics provides two tools to help you maximize your selling efficiency:
- Repricing Engine
Our repricing engine enables you to reprice your products in real-time to remain competitive with others on the listing while protecting your margins.
- FBA Opportunities
Our FBA Opportunities is a forecasting tool to help you replenish your inventory through fill-in buys, allowing you to maximize your profit on fast-selling products. Let’s go over how you can use it to streamline your reordering.
FBA Opportunities shows you what you need to reorder to maintain a specific number of days of inventory for each product from a given supplier.
The top of the page contains a chart showing the revenue, profit, or units sold from this supplier over the past 90 days. To the right of that is a list of the recent purchase orders we’ve submitted to re-order more products.
At the bottom, you can see a list of all the products requiring replenishment to maintain a specific number of days of coverage. For Steel Rain, we order every two weeks and it takes two weeks for the shipments to reach Amazon’s warehouse, so we’ve set our days of coverage to 28.
As you can see, we have dozens of products to reorder. Many of them are even completely out of stock, as indicated by the “On Hand” column. In order to prioritize what to reorder first, we’ll sort by Estimated Profit. This shows the amount of profit we can expect to make in 28 days of sales if we re-order.
Next, let’s select the top items and add them to a purchase order.The same process applies for any other products we want to reorder. If it’s getting near the end of the season, it may not make sense to reorder based on the sales over the last 30 days.
To adjust for this, we can modify the days of coverage we set. For instance, if we anticipate sales are going to slow by 50%, then we’d adjust our days of coverage from 28 days to 14 days. Conversely, if it’s early in the season and we anticipate sales picking up, we can increase our days of coverage to simulate increased demand.
If a product has been discontinued and we can’t reorder it again, we can simply hide the product to remove it from FBA Opportunities.
Maximizing your profit when selling can be tricky. With FBA Opportunities, you can monitor your inventory levels in relation to your actual sales rate in order to avoid stockouts or over-ordering, which leads to stale inventory.
Imagine it’s January of next year. It’s nearing the end of the season and sales are dropping off. How do you ensure you sell through your remaining inventory while maintaining your margins?
In this next section we’ll talk about:
- Timing Your Exit
- Tracking Stale Inventory
- Avoiding Long-Term Storage Fees
Timing Your Exit
Figuring out when to start dropping prices to drive sales or liquidating your inventory requires combining last year’s performance with real-time trends.
The Historical Performance Analysis in Amaze Analytics allows us to review the shape of last year’s sales to help us in forecasting this year.
Looking at the Multiple Year Comparison view above, we can see that sales have traditionally started dropping off in mid-December, with random spikes through January and February. Those spikes could be due to regular demand, or it might be demand generated by lowering our prices to liquidate the inventory at that point.
We can do a spot check by hovering over Dec 3, 2013. Below, we can see our Adjusted GM was 25%.
Then, by hovering over the spike the following February, we can see we have an Adjusted GM of 20%.
This indicates that sales were driven by price reductions rather than increased customer demand.
It seems like demand falls off around mid-December, yet this year looks different. We’ve continued to see strong demand through the second half of December and into January.
To detect when demand starts to weaken in real-time, we need to switch to the Rolling Average view. The Rolling Average shows us our sales over time with a rolling average trend line overlaid on top of those sales. The Rolling Average is the gray line, and our sales are the orange area chart.
Zooming into recent sales, we can see the trend is still holding steady, with only a slight downturn in the past couple of days.
The Rolling Average view allows us to smooth out the random daily fluctuations and focus on the overall trends for our sales. In addition to sales, we can change the graph to show Adjusted Gross Margin, allowing us to see when our margins are starting to weaken, indicating it might be time to exit a given product or brand.
Below, we have the Rolling Average Difference view. This view highlights the differences between the current sales and the current trend to see if sales are being driven up or down.
Once again, let’s zoom into some of our more recent sales:
The days that have red are when the sales are below the trend, pulling the trend downward, while the days with green are when sales are above the trend, pulling it upward. This allows us to quickly evaluate which direction our trends are going.
Tracking Stale Inventory
No matter how good you get at timing when to exit, you still may end up with stale inventory. The Inventory Analysis view in Amaze helps you track and reduce your stale inventory.
In this heat map, each box represents a product. The size of the box indicates how much of each product we have in inventory in terms of dollars. Larger boxes show where we have more capital tied up in inventory.
The color of each box indicates how many days of inventory remain, based on the current sales rate and inventory level. Orange products are selling, but have more than 30 days of inventory remaining, while red products aren’t selling at all.
This heat map shows that we have more than 30 days worth of inventory for most of our products, indicating most of our inventory is stale. Products from Outdoor City have stopped selling entirely and should be liquidated.
To help us sell some of this stale inventory, we can move to the Lower Floor Opportunities view. This view shows us all the products that haven’t sold in the past 30 days which have target prices below our current floor price.
By setting a minimum gross margin percentage using the color slider, we can color green all the products that remain profitable even if we lowered our floor to the target price. We can set this directly from the info panel.
The floor price can be altered directly from the panel on the left side of the screen:
Finally, by switching to the Target – Floor view, we can see how our products are priced compared to the current target price.
Products in green have a target price above the floor price, indicating products with few competitors. Products in black are priced at the target price, indicating they are in a Buy Box rotation with other competitors.
Finally, products in red have a target price below our floor price, indicating competitors are undercutting us on price. If these are MAP products, this may indicate places where MAP is being broken by competitors.
Here is a dropdown of the various views the Inventory Analysis tool allows you to pick from to examine your inventory data:
Avoid Long-Term Storage Fees
Protecting your margins is critical to running an effective Amazon store. The pressure to eliminate stale inventory becomes more acute as February 15th and August 15th approach. These are the dates when Amazon charges a long-term storage fee for inventory that has been in their warehouse for more than 6 months. Those fees can be 10-20 times more than the monthly storage fee, eating significantly into your margins.
To give you insight into your inventory aging, Teikametrics provides the Storage Cost Analysis.
This heat map shows your entire inventory based on storage costs and inventory age. Again, each box represents a product. The size of the box represents the total monthly storage cost for that product, while the color indicates how old that inventory is.
In this view, all colored boxes are candidates for a long-term storage fee. Yellow and dark red boxes are potential candidates for the 6 month and 12 month fees, respectively, while orange and red boxes will definitely incur the fee. Since Amazon doesn’t provide exact ages, there’s no way to tell which yellow boxes will get charged.
To minimize your long-term storage fees, focus on the big colored boxes. Taking a little less margin to drive a sale in January might give you more profit in the end than risking not selling the inventory and being charged the long-term storage fee.
In this section we talked about how you can use year-over-year comparison and rolling averages to understand when demand might weaken so you can reduce your inventory early; how you can track your stale inventory, identify potential repricing opportunities, and monitor competitor prices to sell off slow moving inventory; and how you need to watch for long-term storage fees that could eat into your profits.
Additional Tips for Smarter Seasonal Buying
Use third-party data sources
Use third-party data sources and models to predict demand. Two excellent tools are Google Trends, for analyzing demand based on searches, and the National Weather Service’s climate forecast, which provide outlooks up to a year in advance.
Schedule multiple shipments
When doing a pre-buy, it may cost you a bit more to do multiple shipments, but it can give you additional flexibility in adjusting your pre-buys based on actual sales. If you’re buying for the summer, for instance, rather than receiving a single shipment in May, schedule three shipments: one for May, one for June and one for July. For suppliers that allow you to adjust pre-buys, this gives you more agility to react to market conditions.
Use Amaze Analytics to negotiate with your supplier
Suppliers want to know you can drive volume. Being an FBA seller allows you to showcase access to more than 54 million customers.
On top of that, you can use the charts from Amaze to show the advanced capabilities you have for tracking and driving sales to negotiate preferred or exclusive status, discounted shipping, and other benefits that help your margins.
Hopefully you learned how to improve the three phases of your inventory lifecycle: pre-buying, selling, and liquidating, in order to achieve Smarter Seasonal Buying.
While the examples we provided used Amaze, the Teikametrics analytics module, much of this can be done in Excel too.