A conversion is a desired visitor outcome such as adding an item to a basket, purchasing a product, downloading a white paper or signing up to a newsletter. There are two types of conversions.
1. Micro Conversions:
These are small interactions which users make on a website and may take them closer to a macro conversion. This might include interactions such as scrolling, clicking a call-to-action button or adding to basket. They may be important steps in the journey, but we should avoid setting targets for them as this is likely to result in the Cobra effect. Micro conversions are of limited value unless they result in the final (macro) conversion.
However, micro conversions are useful to enable you to build funnels to identify where users drop out of a journey. This is often difficult to achieve if micro conversions are not set up as goals in Google Analytics. Or if you have a complex funnel with multiple options at a single step in the journey. Read our post on how to automate a complex funnel visualisation in Data Studio to create funnels in such circumstances.
2. Macro Conversions:
These are user interactions which relate to the primary objective of your website or app. This might include a sign-up, a ecommerce purchase, a first-time deposit or a download being made. They should align with your business and determine the success of your business. Your North Star metric should match your macro conversion. Ensure all activity is focussed on improving your website.
3. The Conversion Rate:
This is calculated by taking the number of macro conversions and dividing it by the total number of unique visitors (or users in Google Analytics) who landed on the website. This will provide you with a ratio which you can turn into a percentage by multiplying it by 100.
The conversion rate will naturally fluctuate, often on a daily basis, in response to many factors. These include traffic source, advertising activity, weather, day of the week and time of day. Other factors that can significantly influence performance include pay day date (often the last Friday of the month), holiday season (e.g. Christmas) and also date of birth.
Finally, using your web analytics you can begin to understand what factors influence conversions and benchmark your performance to monitor how it changes over time. Due to the volatility of the conversions the most reliable way of understanding how to improve your conversion rate is by conducting A/B tests where traffic is split randomly between the control page and a new variant. The use of a control page allows you to measure with confidence the impact of the change in the design of the new variant page.
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