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Pay-Per-Click Averages Hide Incremental Realities
You’re hitting your pay-per-click (PPC) efficiency targets and everyone is pleased with the results. Congratulations, but are you wasting money in your PPC program? Averages often hide waste, and in this economy it may be worth taking a look at how you’re getting to that average.
Suppose you budget $200 to buy a DVD player. When you arrive at the store you find the model you want is actually on sale for $100! You arrive home, your 5 year-old hears the good news, grabs the leftover $100 and feeds it into the paper shredder. You are: a) indifferent because you planned to spend $200 anyway; or b) irate because your son just wasted $100?
Is this happening in your PPC program?
Let’s take a hypothetical retailer with 50% gross margin on all products and 10% variable costs tied to things like credit cards, pick and pack, cardboard, and commissions. They’re willing to spend the rest (40% of sales) on marketing to capture as many orders as possible at break even and then make money on lifetime value. Maybe that’s the right number for them to pick, maybe not, as we will see the point isn’t how the targets are set, it’s what goes into reaching them.
Let’s say their PPC search program (excluding their brand terms) costs $160K per month and generates $434K in sales for a 37% cost-to-sales ratio. On the surface, it appears that this program hits on all cylinders and achieves the desired metrics.
However, it’s important to look at both the averages and the incremental efficiencies to really determine if the program is doing what makes the most sense for the company writ large.
WARNING: THE FOLLOWING CONTAINS GRAPHS…
Non-brand costs and sales
The mechanics are different, but the math is very similar to catalog circulation. If we look at the paid search spend in incremental chunks, like mailing segments, you’d obviously buy the most efficient advertising first. The law of diminishing marginal returns would then show that each successive advertising chunk would be somewhat less efficient than the last. So, for our hypothetical retailer, the curve might look like this:
Graphically, by plotting the $10K chunks of ad costs on the horizontal and the resulting sales and net margin on the vertical, you see a classic representation of diminishing returns.
Let’s look at these same numbers a few different ways: first, let’s see what happens when we plot just the “incremental sales” rather than the total sales. In other words, for each $10K increment in spend, how much did we generate in sales?
The first $10K generated $100K in sales, but that last $10K in spend (bringing the total from $150K to $160K only generated $3K in incremental sales).
Average vs. incremental efficiency
Plotting this as a function of efficiency and measuring the cost-to-sales (A/S) ratio for each increment yields the following:
Here, we can see that while the average efficiency increases from 10% to 37% as the spend increases, all of the spend after $80K has come at worse than 50% A/S with the last $40K coming at more than 100% cost to sales ratio. That last slug is tantamount to buying your own merchandise with marketing budget to push the top line!
Aggregate vs. incremental marketing income
Perhaps the best way to look at this is as a function of marketing income (net margin – ad cost).
The top line shows total marketing income, which is maximized when the advertising spend is $60K. If PPC advertising is to be a cash generator, this is the point where it makes sense to stop. However, there are many other goals to be addressed, and as with catalog circulation, one must be careful to avoid the death-spiral of collapsing marketing budgets.
Whether to view the program in aggregate or by increments is an important consideration, and the right answer depends not only on your firm’s tolerances, but on the shape of this curve. How smoothly does the efficiency degrade? For some of our clients we’ve found the efficiency curve to increase steadily to a point, then shoot upwards. The shape of the curve depends on your vertical, the competitive landscape, and other factors.
Determining the shape of the curve is not trivial. Experimenting with different efficiency targets to assess the ROI of the last increment and the next increment is the best approach. If you’re currently aiming at 30%, try 25%, and 35%. Recognize that the lag between clicks and orders can make any pull back in bidding look profitable, and any increase look inefficient; you’ll need to study the effect of the change on “same session” sales, or let the test periods run long enough to wash out the latency. Remember also that the tracked value is not the whole picture. If search is responsible for a big chunk of your company’s web sales, keep an eye on overall ratio of marketing expense to sales to make sure any pull back isn’t costing you more top line than you think.
As we study our businesses to try to wring out the last inefficiencies, don’t forget to look for waste in places that appear efficient on the surface. You can hit your target in aggregate and still be spending like a drunken sailor on the margins.
George Michie is Principal, Search Marketing for the Rimm-Kaufman Group, a direct marketing services and consulting firm founded in 2003. He regularly writes for the Paid Search column here on Search Engine Land.
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.