Out of Stock Products

For retail/e-commerce websites that sell physical products, one of the worst things that can happen is having your products be out of stock. Imagine that you have done a lot of marketing and campaigns to get people to come to your site, led them to the perfect product, only to find that for some people, you don’t have enough inventory to sell them what they want. Nothing is more frustrating than having customers who want to give you their money but can’t! Often times, inventory is beyond the control of merchandisers, but I have found that monitoring the occurrences of products being out of stock can be beneficial, if for no other reason than, to make sure others at your organization know about it and to apply pressure to avoid inventory shortfalls when possible. In this post, I am going to show you how to monitor instances of products being out of stock and how to quantify the potential financial impact of out of stock products.

Tracking Out of Stock Products

The first step in quantifying the impact of out of stock products is to understand how often each product is out of stock. Doing this is relatively straightforward. When visitors reach a product page on your site, you should already be setting a Product View success event and passing the Product Name or ID to the Products variable. If a visitor reaches a product page for a product that is out of stock, you should set an additional “Out of Stock” success event at the same time as the Product View event. This will be a normal counter success event and should be associated with the product that is out of stock. Once this is done, you can open your Products report and add both Product Views and this new Out of Stock success event and sort by the Out of Stock event to see which products are out of stock the most:

In this example, you can see that the products above are not always out of stock and how often each is out of stock. If you wanted, you could even create an Out of Stock % calculated metric to see the out of stock percent by product using this formula:

This would produce a report that looks like this:

If you have SAINT Classifications that allow you to see products by category or other attributes, you could also see this Out of Stock percent by any of those attributes as well.

Of course, since you have created a new calculated metric, you can also see it by itself (agnostic of product) to see the overall Out of Stock % for the entire website:

In this case, it looks like there are several products that are frequently out of stock, but overall, the total out of stock percent is under two percent.

Tracking Out of Stock Product Amounts

Once you have learned which products tend to be out of stock, you might want to figure out how much money you could be losing due to out of stock products. Since the price of the product is typically available on the product page, you can capture that amount in a currency success event and associate it with each product. For example, if a visitor reaches a product page and the product normally sells for $50, but is out of stock, you could pass $50 to a new “Out of Stock Amount” currency success event. Doing this would produce a report that looks like this:

This shows you the amount of money, by product, that would have been lost if every visitor viewing that product actually wanted to buy it. You can also see this amount in aggregate by looking at the success event independently:

However, these dollar amounts are a bit fake, because it is not ideal to assume a 100% product view to order conversion for these out of stock products and doing so, greatly inflates this metric. Therefore, what is more realistic is to weight this Out of Stock dollar amount by how often products are normally purchased after viewing the product page. This is still not an exact science, but it is much more realistic than assuming 100% conversion.

Fortunately, creating a weighted version of this Out of Stock Amount metric is pretty easy by using calculated metrics. To do this, you simply take the Out of Stock Amount currency success event and divide it by the Order to Product View ratio. This is done by adding a few containers to a new calculated metric as shown here:

Once this metric is created, you can add it to the previous Products report to see this:

In this report, I have added Orders and this new Weighted Out of Stock Amount calculated metric. If you look at row 4, you can see that the total Out of Stock Amount is $348, but that the Weighted Out of Stock Amount is $34. The $34 is calculated by our new metric by dividing the normal product conversion rate (26/268 = 9.70149%) by the total Out of Stock Amount of $348 (348*.0970149=33.76), which means that the $34 amount is much more likely to be the lost value amount for that product. The cool part, is that since each product has different numbers of Orders and Product Views, the discount percent applied to each product is calculated relatively by our new weighted calculated metric! For example, while the Product View to Order conversion ratio for row 4 was 9.7%, the conversion rate for row 10 is only 2.6% (4/154), meaning that only $22 out of the $843 Out of Stock Amount is moved to the Weighted Out of Stock Amount calculated metric. Pretty cool huh?

One Last Problem

Before we go patting ourselves on the back, however, we have one more problem to solve. If you look at the report above, you might have noticed the problem in rows 1,2,3,5,6,8,9. Even though there is a lot of money in the Out of Stock Amount success event, there is no money being applied to the Weighted Out of Stock Amount calculated metric we created. This is due to the fact that there were no Orders for these products, meaning that the conversion rate is zero, which when multiplied by the Out of Stock Amount also results in zero (which hopefully you recall from elementary school). That is not ideal, because now the Weighted Out of Stock Amount is too low and the raw amount in the success event is too high! Unfortunately, our calculated metric above only works when there are Orders during the time range, so we can calculate the average Product View to Order ratio for each product.

Unfortunately, there is no perfect way to solve this without manually downloading a lot of historical data to look for what the Product View to Order ratio was for each product over the past year or two, but the good news is that if you use a large enough timeframe, the cases of zero orders should be relative small. But just in case you do have cases where zero orders exist, I am going to show you an advanced trick that you can use to get the next best thing in your Weighted Out of Stock Amount calculated metric.

My solution for the zero-order issue is to use the average Product View to Order ratio for all cases in which there are zero orders. The idea here is that if the first metric is counting 100% and zero-Order rows are count 0%, why not use the site average for the zero-Order rows? This will not be perfect, but it is far better than using 100% or 0%! To do this, you need to make a slight tweak to the preceding calculated metric. This tweak involves adding an IF statement to first look to see if an Order exists. If it does, the calculated metric should use the formula shown above. But if no Order exists, you will multiply the Out of Stock Amount success event metric by the average (site wide) Order to Product View ratio. This is easy to do by using the TOTAL metrics for Orders and Product Views. While this all sounds complex, here is what the new calculated metric looks like when it is completed:

Next, you simply add this to the previous report to see this:

As you can see, the rows that worked previously are unchanged (rows 4,7,10), but the other rows now have Weighted Out of Stock Amounts. If you divide the total Orders by total Product Views, you can see that the average Order to Product View ratio is 4.21288% (16215/384,891). If you then apply this ratio to any of the Out of Stock Amounts with zero-Orders, you will get the Weighted Out of Stock Amount. For example, row 1 has a value of $286, which is 4.21288% multiplied by $6,786. In this case, you can remove the old calculated metric and just use the new one and as you use longer date ranges, you will have fewer zero order rows and your data will be more accurate.

Of course, since this is a calculated metric, you can always look at it independent of products to see the weighted Out of Stock Amount trended over time:

While this information is interesting by itself, it can also be applied to many other reports you may already have in Adobe Analytics. Here are just some sample scenarios in which knowing how often products are out of stock and a ballpark amount of potential lost revenue could come in handy:

• How much money are we spending on marketing campaigns to drive visitors to products that are out of stock?
• Which of our known customers (with a Customer ID in an eVar) wanted products that were out of stock and can we retarget them via e-mail or Adobe Target later when stock is replenished?
• Which of our stores/geographies have the most out of stock issues and what is the potential lost revenue by store/region

Summary

If your site sells physical products and has instances where products are not in stock, the preceding is one way that you can conduct web analysis on how often this is happening, for which products and how much money you might be losing out on as a result. When this data is mixed with other data you might have in Adobe Analytics (i.e. campaign data, customer ID data, etc.), it can lead to many more analyses that might help to improve site conversion.