SpyFu is a website that helps users understand how much various businesses spend on online advertising.
Having insider information about accuracy of the data at several multichannel retailers, I can tell you that SpyFu is at best, directionally accurate.
That being said, one can summarize and classify the information. By doing so, many of the numerical inaccuracies are mitigated.
The attached image classifies apparel and shoe multichannel retailers into nine cells. Among these thirty-seven businesses, I use SpyFu data to determine if the retailer spends a lot, or very little on online advertising. Next, I use SpyFu data to determine if the average Cost per Click is inexpensive, average, or expensive.
The best place to reside in this image is in the upper right cell. In this cell, spend is huge, while Cost per Click is low. If these clickers convert at an acceptable rate, there is significant efficiency in the online marketing efforts of retailers in this cell. Interestingly, only Zappos meets this criteria.
Of course, SpyFu data does not have access to online or retail conversions. In other words, a customer might search for denim. The customer clicks on J. Crew in the paid search section of Google, views an item on the website, drives to the store, and purchases the item. Whether the item is purchased online or in a J. Crew store, SpyFu cannot see the conversion.
Many of the businesses in this table sell far more in their retail channel than in their direct channel. Some of the more sophisticated multichannel retailers already link paid search to estimated retail conversions. While it is important to look at cost per click, it is much more important to measure variable operating profit across all channels. This concept certainly isn't new, and has been documented many times in Database Marketing literature (this is frequently called a "matchback" analysis).
The table at the end of this post looks at multichannel profit, obtained by spending $100,000 on a paid search program. Notice how important it is to at least be able to estimate the retail conversions driven by online advertising.
In this example, the multichannel retailer generates $33 of profit for every conversion. Also notice that the multichannel retailer generates $1.49 profit per click, in this example. If these metrics were negative, the multichannel retailer would have to conduct a lifetime value analysis, to see if future sales offset short term losses.
|Amount Spent, Paid Search||100,000|
|Cost per Click||0.75|
|Number of Clicks||133,333|
|Online Conversion Rate||2.5%|
|Estimated Retail Conversion Rate||2.0%|
|True Conversion Rate||4.5%|
|Online Average Order Size||225.00|
|Retail Average Order Size||175.00|
|Retail Net Sales||466,667|
|Online Profit to Demand Ratio||23.0%|
|Retail Profit to Net Sales Ratio||27.0%|
|Less Online Advertising Expense||100,000|
|Return on Investment (Profit/Expense)||1.99|
|Profit per Conversion||33.08|
|Profit per Click||1.49|
I went to SpyFu and tried to find the type of data you've included here. Couldn't come up w/ anything. How did you find the OL ad spending data?ReplyDelete
When you get to the website, type in the URL of a retailer (i.e. http://bestbuy.com).ReplyDelete
Hit enter. A screen will appear, showing average daily spend, average daily clicks, average daily cost per click.
The data is presented in ranges (i.e. online spend of $10,000 to $30,000 each day). I simply took the average of that metric, and the average cost per click.
thanks. silly me, I was typing in "online advertising spending". what was I thinking? :)ReplyDelete