Current Order Value [Adobe SiteCatalyst]
I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel. For example, what percent of visitors have between $25 and $50 in their cart when they reach the “Billing” step of the conversion funnel? Does this percentage remain constant throughout the funnel or are there significant drop-offs? Unfortunately, this is not something that can be easily derived in SiteCatalyst, but with a bit of creativity, I will show you how you can add this data to your implementation.
Calculating Current Order Value
The first step in this process is to work with your developers to create a new Counter eVar that will hold the current order value. As soon as a visitor adds an item to the cart, pass the dollar amount associated with that cart addition to the Counter eVar (in addition to passing it to a currency event as prescribed in my “Money Left On Table” blog post). This value will be bound to the Cart Addition success event and future cart events unless it is modified. If the visitor adds more products to the cart, pass in those amounts and if the visitor removes an item from the cart, subtract it from the Counter eVar value (remember you pass values to Counter eVars using the “+” or “-” sign). I would expire the Counter eVar at the Purchase or Visit (if your site doesn’t have a persistent cart).
By having these values in the Counter eVar, you will end up with many different dollar amounts when you open the eVar report with one of your cart events. Here is an example of what the eVar report might look like:
Obviously, this report is not that readable, so the next step is to classify it into meaningful groupings, such as Under $20, $21-$35, $36-$50, etc… This will allow you to analyze the data in buckets and look for insights. Which groupings you choose are up to you and you can use SAINT to have multiple groups, such as every five dollars, every ten dollars, etc… Here is what it might look like after the SAINT Classification:
This general concept is similar to one that I described in my Revenue Bands post, but in that scenario, we were just passing the final order amount to a regular text eVar. The difference here is that we are using the Counter eVar to adjust the order value up or down as it progresses through the cart process.
Viewing Distribution
Once we have the current order values tied to each stage of the cart funnel and have grouped them accordingly using SAINT, our next challenge is to compare the distributions. There are a few different comparisons you can make with this data, so I will touch upon each of them. The first one you might want to see is whether the various percent distributions are steady or going up/down over time. In this case, you may not care about the actual raw numbers that are associated with each order value range, but rather, are most likely more interested in the percent of the total. For example, it may not be that interesting that 2,500 checkouts fell into the range of $15-$25, but it may be interesting to know that this dollar range represented 15% of all visits to the checkout step of the funnel. If you could see this percentage, then you could trend it over time and see if that $15-$25 bucket is increasing, decreasing or steady over time.
To see these percentages, you have two options, the first is to download data to Excel and create formulas to calculate the percent and trend it over time. If you want to use the SiteCatalyst interface, the best way to do this is to employ the “Total Metrics” feature. This feature allows you to create a calculated metric that divides the row value by the total at the bottom of the report. For example, if you wanted to calculate the percent of each dollar band while at the Checkout step, you would divide Checkouts by Total Checkouts using a formula like the one shown here:
This formula moves the percent shown in the regular eVar report front and center so it is the actual metric of the report. To visualize this better, let’s look at the previously shown report with this new metric column added:
As you can see, the percentages that were previously on the right side of the column (more as an FYI), are now present by themselves as a real metric in SiteCatalyst. Now you can use this percentage as a true metric, meaning that you can trend it over time and see its historical performance:
This allows you to see how each dollar amount band does and do some hard-core web analysis!
Another analysis you may want to do with this data is to see the drop-off between the dollars amount percentages added to cart, the percentages making it to checkout, etc… This is a bit more complex because you are looking at one dollar amount grouping, but seeing how it changes as visitors get further in the cart process. Unfortunately, there is no great SiteCatalyst report for comparing different percentages over time, so this analysis will have to be done in Excel.
To begin, you will want to create additional “Total” metrics like the one shown above for the other cart steps that you care about. In SiteCatalyst, this is what a report might look like, though it is limited in its use. In this case, the client has a customization step in the funnel, a billing page step and then a checkout step. Using the “Total” metrics, you can compare the changes in dollar amounts at the various steps of the funnel:
In this case, we are looking to see how consistent the percentages are across each row and seeing if we can identify any problem areas. However, to do analysis on this, Excel might be a better tool since it is easier to compare the percentages between different columns. Also keep in mind that you can break this report down by Product or Product Category to see how these percentages change by Product.
Final Thoughts
If your website has discrete steps in its funnel and if you are curious to see how much money visitors have at each step of the cart, the preceding is one way to do this. In addition to what I have shown here, having this information can be useful in other ways. For example, if you want to build a segment of all cases in which a visitor had more than $100 at the checkout step, but did not purchase, the eVar described here can be used as part of your segment criteria. I am sure there are many other ways to use this data as well, but hopefully this gives you some food for thought.
