Last week we provided an introduction to some of the basics of Google Analytics. This week we’re going to dig a little deeper, and we’ll start with key performance indicators, or KPIs. KPIs are core actions or metrics that you can establish that have a profound impact on your business. They are broken into two types: hard KPIs and soft KPIs.
Hard KPIs are the meat and potatoes of your business. In the B2B word, hard KPIs are those hot leads that come in, either by phone or through the contact form on your website. They could also be a demo sign-up for a SAAS product. For B2C or e-commerce, hard KPIs are often as simple as a online sales transaction, but can vary greatly depending on the nature of the business.
Soft KPIs are similar for both B2B and B2C. Social media shares, likes, or follows are typically considered soft KPIs. Newsletter or email list signups are also common examples. Some industry-specific soft KPIs are ticket giveaway sign-ups for entertainment venues or birthday and anniversary clubs for restaurants. Soft KPIs are important because they get people into your marketing ecosystem.
Identifying the KPIs that will drive the results you want are important, but sometimes we can develop tunnel vision. It’s important to know what factors affect your KPIs, and those are referred to as leading indicators. A simple example would be pageviews. If your primary KPI is total conversions, and If you have zero pageviews, it stands to reason that you will have zero conversions. If you increase your pageviews, you will likely increase your conversions, but we have to look at these like we would positive and negative correlations.
A correlation is a relationship between two variables. This relationship can be positive, in that as one variable increases, so does the other. A positive correlation between pageviews and email signups means that as pageviews increases, so does email signups. Correlations can be negative too, such as the negative correlation between bounce rate and pages/session. As bounce rate increases we observe a decrease in pages/session.
When we analyze our leading indicators, we have to be careful not to make attribution errors about the relationship between the variables. In other words, just because variables are connected or behave in a similar fashion does not mean that one causes the other. This is a common mistake that is often repeated.
A classic example of this is that when ice cream sales increase, so do deaths from drowning. These variables share a positive correlation – as one increases, so does the other. Ice cream does not cause people to drown. The causal force is another variable altogether: high temperature. When it is hot outside, more people crave ice cream, causing higher sales. Higher temperatures also create the desire to cool off in the pool, and the higher numbers of swimmers naturally creates more opportunities for drowning deaths.
The moral of the story is to be vigilant when analyzing your KPIs and leading indicators. Become very familiar with their relationship and don’t make the mistake of confusing correlation and causation – they are not the same thing.
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Once you identify the objective of your marketing plan you can identify the KPIs that will provide you with the highest possible return on investment (ROI). The best way to optimize your KPIs is to manage your key indicators.
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