Mapping Objectives to Metrics

Success metrics

Revenue

• Orders

• Average order value (AOV)

Cost

• Orders

• Cost per sale (CPS)

Note: 'Orders' is duplicated above as both key success metrics in this example are dependent on the number of orders generated.

The next step is to work backwards from these success metrics in order to identify the supporting metrics which can affect either the revenue or the cost.

Supporting metrics

Revenue

• Visits

• Bounces

• Visits from new visitors

• Carts

• Checkouts

• Brochure requests (micro-conversion)

• Email sign-ups (micro-conversion)

• Quantity of products sold

• Quantity of related products upsold*

• Quantity orders including related products

*This is important as a related products feature is being introduced to the checkout process to enhance AOV.

Note: Not all metrics above are certain to influence revenue (e.g. micro-conversions) but it is important to monitor as much as possible initially to gain a better understanding of what will affect performance. As mentioned in the previous post, micro-conversions can lead to subsequent macro-conversions (in this case, orders).

Capturing the metrics above will then allow us to calculate the following:

• Bounce rate

• % New visits

• Visit to cart %

• Cart to checkout %

• Checkout to order %

• Cart to order %

• Visit to order (overall conversion %)

• Visit to micro-conversion %

• Micro-conversion to order %

• Average quantity per order

• Featured products per order

• Average item price

Some of the calculated metrics above could be used to form KPIs further into this process as they can all heavily impact revenue performance.

Cost

• Impressions

• Emails sent

• Ad position / quality score

• Clicks

• CPC

• CPM

• Cost per email

• CPA (for individual channels)

Once the list is compiled, it can be mapped to show the relationship between the supporting metrics and the overall success metrics. This will be useful when it comes to designing and implementing a suitable analytics solution and also further down the line when analysing and optimising performance.

For the sake of simplicity, a comprehensive metrics map does not feature a definitive list. There are many other supporting metrics which could be factored in such as the number of 'recommend to a friend' conversions, survey satisfaction scores, etc. The complete list will vary from business to business. As previously mentioned, it is important to set out to capture as many key metrics as possible initially in order to understand which particular factors affect the success metrics. During the dashboard and report creation phase (to follow later in this series), the metrics list can be further refined and distilled into executive formats.

Knowing the core metrics of interest and mapping these to our business objectives allows us to move onto the next stage in the process: audience segmentation. It is important not to treat all visitors as equal as the behaviour of different audience segments will differ. Therefore, their contribution towards our success metrics will also differ and this needs to be measured and understood. Hence, before moving onto the implementation of an analytics solution, it is important to split the target audience into suitable segments. This will be covered in the next post in this series.