Here’s a brief glimpse of our Marginal ROI Analysis. Rather than pitching our services off capabilities, I thought it would be more sensible to offer an honest assessment of the financial lift our services are likely to provide for each prospective client. This sets realistic expectations from the start, and helps us qualify clients more effectively. On occasion, we encounter prospects who may not be large enough to see the absolute lift in profit needed to make the relationship work. But for most prospects, we are able to provide a very reasonable and accurate projection for the lift in revenue and profit our services will drive.
We start this analysis with an evaluation of a recent sample period. Since our PLA strategy focuses heavily on query optimization, we pull a sample of query data from each prospect account. We then classify all queries within the account and measure their performance in aggregate. Typically, this reveals variances in performance across query types, exposing opportunities for improved performance within the account.
In the table above, you can see this prospect has a pretty decent program when viewed in aggregate. A total of $324k in sales on $31k ad spend. Given their margins, this is pretty good, as they generated $50k in profit at a 16% rate of profit during this period. Most retailers with similar margins would be pretty happy with this level of performance. Note how there is no ROAS column here – it’s a dated metric. If you are still measuring performance primarily via ROAS or A/S you are absolutely leaving money on the table.
However, when you segment performance by query type, you can see there are clearly pockets of inefficiency and overspend, as well as highly efficient segments where we could increase spend. In this example, this account generated losses from generic queries while low-funnel product-specific queries are highly efficient and offer an opportunity for increased spend.
From here, we forecast the relationship between an increase or decrease in bid on each of these query segments, and the bid point that will yield maximum profit. This allows us to produce a forecast that, in my experience, runs conservative as we are only forecasting opportunities from query segmentation alone. Opportunities from catalog segmentation, device optimization or other advanced features such as dayparting or RLSA will only add to the value we can actually deliver.
If you are currently advertising on Google Shopping and you either 1) do not know the performance of your program by query type or 2) you are optimizing against a ROAS or A/S KPI, I encourage you to request our Marginal ROI Analysis. It is absolutely free and at a minimum, will help you gain a better understanding of how consumers search for your products.