Personalization Glossary
A/B testing? Recommendation models? The personalization engine space uses several acronyms that may seem daunting, so we're here to help. You can find definitions to the most common e-commerce customer experience optimization terms here!
Conversion Rate Optimization
A conversion is defined as the completion of a specific predetermined goal on the site by a visitor. Often, there may be several micro-goals leading up to a macro-goal on a website.
Conversion rate optimization is the process by which increased conversions are achieved. It typically involves picking out strategies to improve site/app elements and validating them through A/B or multivariate testing.
Click-Through Rate And Search Ads
Usually used as a metric to evaluating search ads in Google Adwords and other ad platforms. As search ads are most often pay-per-click (PPC) the average CTR helps to determine how a given ad will perform in driving traffic, and how much the ads will cost.
CTR of a search ad impacts the quality score in addition to gauging the performance of ad campaigns. The higher the click-through rate of an ad, relative to its ad position, the higher its quality score will be, leading to lower CPCs and lower cost ads.
Similarly, CTR can also be important for SEO as the click-through rate of a search result is believed by many to be one of the ranking factors in Google. The same is true of display ads and ads across other platforms such as Facebook ads.
Unique Click-Through Rate
Unique click-through-rate (UCTR) is an estimate of people who clicked a link divided by impressions, that is, the number of people who saw the ad. But where it differs from click-through-rate (CTR) is that an individual click and user counted only once towards that number. The click-through-rate is a count of all the clicks a user performs whereas unique click-through-rate only counts one click per user. For example, if a user clicks on a link while checking emails on the laptop, and then clicks on the same link once again through their phone, the total number of clicks counted towards UCTR would still be 1, while the contribution towards CTR would be 2.
But it is not dependent on just the device. For example, say the user opens a mail in the morning and clicks on a link that opens to a new page. But for some reason, they don’t read it. If they return later in the day, and click on the link in the email, using the same device, and read the page then it would still contribute only 1 click towards UTCR, since it’s the same user.
By default, UCTR counts people and not actions, i.e., it calculates the number of unique link clicks divided by impressions.
UCTR is a useful metric to keep track of, as it can help cut down noise from the CTR to give clearer insights. But you will need both numbers to get a clearer picture of how well your ad/email links are performing. CTR will tell you how much traffic your link is generating via shares, clicks and more. UCTR will give you a detailed picture of the number of individuals visiting each link.
This is a much more impactful number to understand the effectiveness of your ad and email in converting your target audience.
Click-Through Rate & A/B Testing
Click-through rate can be defined as the ratio of users who click on a specific call-to-action (CTA) button or element on a page, to the total number of users who view the page. This extends to other channels such as emails, or advertisements as well. It is a metric that is commonly used to measure the success of an ad/email campaign or the user experience on a webpage.
When running an A/B test, most people aim to optimize the click-through rate (CTR) of a CTA button or some other element on the page being used for the test. CTR can also be tracked as a secondary metric to gain useful insights, along with the primary conversion metric for the A/B test. CTR can help highlight a wholesome picture of user behavior in A/B testing.
Particularly, tracking CTR is useful in conversion optimization. It can be used to identify user behavior, user interest, and can also be used as a micro-conversion to build insights during A/B testing. In general, CTR-based optimization is considered the better approach for faster results in A/B tests, as opposed to optimizing for revenue or other big-picture metrics. When opting for click-based or CTR-based optimization of an A/B test, it is important to pick the right goals for optimization efforts.
There are several ways to decide what the variants of the A/B test should be when the goal is to optimize for the best CTR. Moving the main CTA button to a better position on a page could improve its click-through rate. Perhaps changing the color of the CTA button can increase its click-through rate. Without the right hypotheses, it is possible to make wrong assumptions about what users like and what they are likely to click, These decisions could easily derail the strategy of the A/B test.