A good approach in web analytics is to focus on: What works and why? vs. what doesn't work and why? These questions can be applied to products, marketing channels, landing pages, videos,
etc. Then optimize your budget and set priorities.
Overview of my favourite analyses:
It's not always the highly complex analyzes that move a company forward. It is much more important to pay attention to good data quality, to analyze it continuously and to implement the results.
The following analyzes should be carried out regularly in a company:
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Onsite search terms: What products/content are visitors interested in? Should we include these products in the portfolio? Should we expand the content to a topic? A textile
company designed clothes based on the search terms. For example, if visitors are increasingly looking for blue and white striped t-shirts, these can be added to the portfolio. Recommendation
is to edit the onsite search terms in Excel if the web analytics tool is case-sensitive. You can also correct general spelling mistakes there. In addition, you should of course track the
number of search results and filter for "0 search terms".
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Priority of onsite search: In various analyzes for companies, I have often found that search has a high priority for website visitors. Accordingly, budget discussions could
be substantiated with these analyses. Build two segments "Session with search" vs "Session without search" and compare different metrics like conversions, average sales etc.
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Products sold: Product XY is often sold (= works well) but is not yet promoted >> Sales increase through marketing campaigns that have worked well in the past.
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Paid search keyword: Which keywords (paid search) does the user use to access the website? Did these keywords contribute to sales? If not, are these keywords irrelevant and
can you exclude them to save budget?
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Marketing campaigns: Products are only bought in Germany, but there is a lot of traffic from Austria >> check whether your marketing campaigns are set up correctly in
terms of targeting.
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Cart View Rate: Why is the cart view rate below average for some products? Check or optimize the following factors: high price, improve product description, improve product
images, negative rating, missing information such as size table
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Purchase view rate: Why is the purchase view rate below average for some products? Check whether there are any special features in the checkout process for this product, e.g.
taxes or high shipping costs?
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Different devices and browsers: Are there problems with different devices or browsers (e.g. in the checkout, in the configurator)?
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Conversion increases after changes on the website: Do the conversion and other metrics (e.g. shopping cart abandonment) change if, for example, the check-out process has been
optimized?
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Page-not-found websites combined with "previous page": How did the user get to the page-not-found page and which links need to be fixed?
Analysis of content pages:
What do I have to consider when analyzing content pages:
- In general, I prefer e-commerce analysis to content analysis because the goal of e-commerce is clear: closing a sale. With content pages, there is always the question of what the purpose of
these pages is and yes, there are companies or product owners who cannot name a SMART goal for the content pages.
- Main metrics when analyzing content can be: Views & Average engagement time & Unique user scroll
- Other metrics: number of comments, ratings, shares
- You can then search for outliers in the defined KPIs and ask why there are outliers: long texts? Specific author? Is the content up-to-date, educational or provocative?
- To simplify the analysis, you can track custom dimensions and content groups: grouping of text lengths (1-500, 501 - 1000 etc), author, topic etc.
- The question is always, is the chosen metric good or bad. Is a long average engagement time good or bad? Is the goal of the website to read long texts or to find information quickly?
- Content pages can also be understood as a marketing channel. The analysis question is therefore: content and then? Did a website visitor convert after
reading a content page (e.g. downloading, signing up for a newsletter)? Key segments for analysts to save are:
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- Sequential segment "User visited content and then converted"
- "Content-Visitors" vs "Non-Content-Visitors" segments