So many people struggle with web analytics for a number of reasons:
- Some simply don’t understand the importance of web analytics.
- Some understand the importance of web analytics but struggle with selecting the right program.
- Some have the right program but have great difficulty interpreting the continuous stream of data and providing strategic action plans based on the wealth of information provided.
So we’ll take it right back to basics.
What is the purpose of your web site?
Starting at the beginning, sit down with your executive team including marketing, finance, HR and the ultimate decision maker and decide what the purpose of your web site is. That sounds so obvious, but it is something many businesses do not determine in their haste to get on the internet band-wagon, to improve their site, to make it prettier, faster, more navigable, more accessible… somewhere along the line the primary business purpose of the web site is forgotten.
There are a number of objectives to take into consideration. Do you want your web site to:
- Disseminate information and require no further interaction
- Disseminate information and engage the user into an action (such as a download)
- Generate leads (eg, via query form submission)
- Generate sales (ecommerce)
- Provide customer support
- Support specific offline marketing campaigns
- Support specific emarketing campaigns
- Be a test market for offline marketing campaigns
- Engage internet users for research purposes
- Engage users to generate buzz
…The list is not comprehensive, but it’s a good indication of the types of business goals you might want to consider when having the discussion with the exec team to determine what you want to measure to facilitate the integration of effective analytics.
How do you best determine whether your online business objectives have been met?
We’re heading into the realm of web analytics here. It’s really not that bad. We’re going to use the example of an online web site whose primary objective is to maximize online sales via organic listings. We’ll assume all design and associated development costs in getting the web site online are fixed or sunk costs. We’ll assume that the site has been effectively designed for usability, developed for search engine exposure due to content relevance, and is an attractive navigable site.
So, what do you want to measure?
That’s easy enough. You want to measure your direct and indirect web source income.
The second part of that is a little trickier, so we’ll leave that for now and focus on direct web based income. It gets really fun from this point onwards. This is not a full analytics tutorial, it is basically Web Analytics Possibilities 101, and as such we will mention key points.
Measuring web based income
You can measure your web based income simply from actual sales revenue. That’s a no-brainer as you get that data from your ecommerce package. What you want to assess via your web analytics is what is driving that income.
- What search keywords are driving the revenue
- What is the percentage of keywords that drive the revenue to overall keywords resulting in a visit
- What landing pages are driving the revenue
- Which [conversion] paths are driving the revenue
- Which product or service portal pages are driving the revenue
- Which individual product or service pages are driving the revenue
- What geographic areas are driving the revenue
- What is the proportion of new visitors to the site that are driving the income
- What is the proportion of returning visitors that is driving the income
- What is the average length of time spent on the site by people who convert
Conversely, you want to understand
- What is the percentage of keywords that are not driving revenue
- What landing pages are not converting
- Whether these pages have a high bounce rate
- Are the keywords that do not drive revenue associated with the bounce pages and non-converting pages
- What visitor paths are losing visitors via bleed-out
- Which are the common pages in these paths that have high bounce rates
- What are the commonalities between path bleed-out and bounce rates
- Which product or service portal pages are not driving revenue
- Which individual product or service pages are not driving revenue
- What geographic areas are failing to result in online sales
- What is the proportion of new visitors to the site that are not driving revenue
- What is the proportion of returning visitors that are not driving revenue
- What is the average length of time spent on the site by visitors who don’t convert
In the next post we’ll consider what this data reveals in terms of strategic opportunity, how you can get the data, which analytics programs might suit you best, and analytics integration options.