Confused by the link between optimization and ROI? Here are pointers on how to calculate results.
When optimizing wesite content or ad campaigns, we can talk about improving user experience, creating engagement or brand-building, but in the end we have failed if the exercise does not ultimately lead to more money. After all, driving ROI is the point -- whether working off direct response goals or the goals mentioned above.
Direct: the shortest distance between here and more money
Money has a time-related value, which tends to focus the mind on what can be done now with easily measured results. Hence, there is a bias towards optimization of the easily measured and immediate.
In listening to conversations about client interaction optimization, the language of online optimization starts to sound very similar to the language of direct marketing: A/B testing, lift over control, champion-challenger, etc. When first using optimization, typically you want to capture the low-hanging fruit: the boost in conversions you get from presenting the right current offer to the right current visitor.
For example, let's say you are optimizing a home page, have five distinct products to offer, and establish a goal of maximizing the number of conversions driven from this page.
Before optimizing, you collect data, rotating the five offers equally, and as a result you get the following mix of conversions:
After optimizing to maximize the conversions, the results might look more like this:
Your lift from optimization: (1,633 – 1,066)/1,066 = 53 percent.
If we know nothing else but how much we make on an average sale (let's say that is $87), then these results seem like a good thing from the "more money" standpoint. This means we are getting an extra $49,400 a month, right?
If all we know is the average NPV (net present value) per sale that would be correct. However, if we knew the amount we made on each individual product, we could take a smarter approach to understanding what is going on.
Let's say we know the NPVs of all the products:
That means the money is distributed like this:
In other words, we actually lose money (-$21,800/month), because the highest response rate products selected by our conversion-rate maximization had low NPVs while the low-response rate products had high NPVs.
Armed with this comprehensive product value information, we'd be much better off optimizing to maximize the expected value (estimated conversion rate times estimated value of a conversion). Then the optimized case looks something like this:
That gives us a much lower conversion rate lift: (1,120 – 1,066)/1,066 = 5.1 percent.
But before we get too concerned about the lower conversion rate lift, let's look at the money:
Wow -- now we're talking. We have a value lift of: (171,225 – 93,100)/93,100 = 84 percent.
With this approach, we are pulling in an incremental $78,125 per month. This is the kind of thing that we can use to get promoted, get a bigger bonus, and, in general, enjoy the good life.
But what if the problem we need to solve is different? What if we are new to the market, no one is familiar with our products, and we are trying to invest now in order to make sales later? Can we optimize to that end?
While optimization is excellent at driving to direct-response goals, it is also a fantastic tool for pursuing longer term goals like brand-building, engagement and improving customer experience. Though the link between optimization and money becomes more indirect and needs to be approximated in these situations, make no mistake about it -- engagement is still all about the money.