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No two businesses have identical goals and targets, and as such, choosing what we want to measure should be done with care. If we incorrectly set our business goals and track the wrong metrics, we are essentially playing a high-stakes game with the wrong rules.
With automated bidding platforms, selecting a metric is even more paramount. An automated platform will happily optimize toward the metric chosen for it, regardless of how appropriate it is to the company’s business goals.
To ensure we choose the right metrics going forward, we will go through some of the common pitfalls, discuss how to determine the goals for an online advertising campaign, and consider a hypothetical case study that applies these new rules.
Some of the most common mistakes when defining metrics include choosing vanity metrics, measuring what is convenient, and measuring the wrong part of the sales funnel. We’ll go into each.
Vanity metrics, as you could probably can tell from the name, are the kind of metrics that are easy to measure and increase, but don’t tell you anything about the underlying process for the business goals you are trying to measure and improve. These kinds of metrics have been discussed quite extensively elsewhere -- entrepreneur Tim Ferris describes them as “the easiest to measure and they tend to make you feel good about yourself.” Another entrepreneur, Neil Patel, defines vanity metrics as “all those data points that make us feel good if they go up but don’t help us make decisions.” In short, vanity metrics are easily gamed, but changes to them will likely not result in noticeable changes to the underlying business goal.
Here’s an example -- imagine a soccer team that only measured success by the number of passes they completed in a game. Sure, there is probably some relationship between the amount of time they had the ball and how many goals that team scored, but in reality, optimizing toward passes would likely lead to a lot of wasted time with the ball, racking up a large number of passes with no intention of putting the ball in the back of the net -- which is ultimately the underlying team goal.
Similarly, an example of a commonly used vanity metric when optimizing online advertising campaigns is click-through rate. For example, if a company’s main business goal is to sell as much product as possible, and the percentage of clicked ads increases, then we should see more sales, right? Not so fast. Click-through rate can be changed by many factors - a decrease in Ad Views would result in an increase in click-through rate, but for a reason that does not necessarily affect the number of people on the site who end up buying a product.
Despite these drawbacks, metrics like these still have their uses -- often as a supplementary measure to help explain changes in the main metrics you are optimizing.
Measuring What is Convenient
As with vanity metrics mentioned above, there is also often a bias to choosing goals based on metrics that are easily available to us. Often the conversion data currently being recorded in an analytics platform is sufficient to measure our business goals, but occasionally additional integration will be required to pull data from another source.
For example, you may include leads and conversion metrics in your analytics software, but revenue data may be ‘stuck’ in your CRM or accounting software. If you were attempting to maximize profit, the ‘convenience metric’ would be conversions.
Measuring the Wrong Part of the Sales Funnel
One area where metrics are often poorly used is when targeting the wrong section of the sales funnel. Making this mistake typically comes from an error in understanding the relationship between each section of the funnel, and the characteristics of visitors to your website or online store. There are often underlying characteristics of the populations in each section of the sales funnel that we can’t see or that aren’t immediately obvious -- and optimizing toward higher sections in the funnel do not necessarily increase the number of leads in lower sections of the funnel, which is typically where revenue is recorded.
To help understand the process of determining the metrics that require optimization, we will go through a simple hypothetical situation, and explore how we would choose the appropriate metrics to track.
Example: Online Florist
Imagine an online florist that offers two types of plants - a cheap bouquet of flowers, and an expensive potted plant. The owner cares about one thing, maximizing their total revenue. The store currently records Ad Views, Ad Clicks, Site Views, Orders, and Cost for each bidding keyword in their analytics program. The store’s accounting software records Revenue data for each sale, but the team has put off integrating that data with the analytics program.
How would we go about selecting a metric to optimize toward the owner’s business goal?
First, let’s identify some potential metrics that would come under the common pitfalls discussed earlier. Ad Views, Clicks, and Site Views -- while they are all a good idea, increasing these will not directly help us with our business goal of maximizing Revenue.
It seems as though Revenue is the best metric to optimize toward, but it is not available to us without some integration work. However, the Orders metric is easily available to us, and is similar to Revenue - is there any harm in using it instead?
Actually, there is. With this simple example, Orders conversions come from one of two populations - either the low-revenue bouquet customers, and the high-revenue potted plant customers. If we only try and maximize the number of Orders that occur in the online store, we lose sight of our true goal of maximizing Revenue, as each Order has a different Revenue value attached to it, and our analytics platform has no way of knowing that value.
With an automated bidding platform, this distinction between Orders and Revenue is even more important. If we were to let an automated platform optimize toward increasing the number of Sales, a likely result is that bids for keywords such as “cheap bouquet” will increase, and bids for keywords such as “fancy potted plant” would decrease. This is because the algorithm does not know the ‘value’ of a given Order, as the Revenue data is hidden from the algorithm’s view. As such, this would likely result in an increase in total Orders, but possibly a decrease in total Revenue, as potential customers looking for cheap flowers are less expensive to target compared to those wanting to spend a lot more money on a fancier plant.
As such, the recommendation here would be to integrate this Revenue data into the store’s analytics program, and optimize toward the Revenue metric instead.
The florist case study was a very simple (and somewhat contrived) example, but the process in understanding what metrics to choose does hold when applied to real-world business goals.
Each business is different, but being conscious of the pitfalls that might occur when optimizing toward vanity metrics, convenient metrics, and metrics that are targeted at the wrong level of the sales funnel will go a long way toward improving your online campaigns. Doing so ensures that you are measuring what matters, and that you can be confident that hitting targets with your chosen metric will actually result in improved business performance.