“Clever” analytics solutions that cause confusion
Digital Analysts work with powerful tools that offer a lot of variables and options to customise their setup. But taking full advantage of all of these customisations can actually be counter-intuitive and result in organisations getting little-to-no value from these tools.
Slight contradiction there?
Well, it comes back to the purpose of Digital Analytics being to “inform the decisions and actions of an organisation in order to achieve better results.” These decisions and actions are not made by Digital Analysts, instead, they come from everyone else in the organisation. So, for Digital Analytics to have a chance of achieving its purpose, the data needs to be accessible to the marketers, product managers, designers, UX, developers, strategists, sales team and of course the C-suite.
Digital Analytics needs to support these people by feeding them the intelligence they require so the decisions they make and actions they take are as informed as possible and therefore more likely to achieve the best results for the business.
An amazingly “clever” solution that is designed for the needs of the Digital Analyst can actually be a barrier to the rest of the organisation. If people find their work is too difficult to do with the use of data, they simply ignore that data and get on with making decisions and actions using their own knowledge and experience.
That’s bad for business.
How to create a non-analyst friendly setup
1. Don’t split page information across variables
Problem: Every Digital Analytics tool provides variables where you can capture page information. A “clever” set-up records the page type, page category (and subcategories), site section, page language and the actual page name/identifier in separate variables. For an analyst to use multiple variables at once to identify pages and page groupings is simple and powerful. For a non-analyst to open a report and be unable to relate page names to pages on the website is just confusing.
Fix: A useful tool setup includes the page types and page categorisations within the page name itself. This allows the non-analyst to learn how to access data using a single report. Within this, page names are intuitive (the homepage is called the /homepage) and it is easy to apply a filter to view a desired set of pages.
2. Create user-friendly naming conventions
Problem: A common push back from developers when we provide tracking instructions is a request to use an abbreviation or ID instead of a name. If the developer can use an existing variable and just push it to the data layer, it is much less work for them.
As analysts, we can usually live with this. We can quickly memorise all of the key IDs or what abbreviations mean so it is just as fast as working with names. For everyone else, it turns the reports and data into meaningless gibberish that needs a translation table (or analyst translator) to be usable.
Fix: The simple trade-off is that a bit of a time investment for developers now to use a user-friendly naming convention will save days of time for the rest of the business in the future.
3. Build data manipulations and data cleansing into data analytics tools
Problem: A common pain point in any organisation is when numbers don’t match up. If no two people can produce the same numbers, it is typically the tool providing these numbers that is blamed and not trusted. One cause of this is needing data manipulations and cleansing to get to the official “business numbers,” but the knowledge/skill of these requirements is limited to a small number of people.
Whilst the Digital Analyst understands exactly how the business performance data is easily extracted from the tool, this gets lost in translation for the rest of the business. If the numbers don’t match up, trust goes out of the window.
Fix: Build a solution where segments and report combinations are not required, and instead, manipulations and data cleansing are built into the tool itself. This extra work allows anyone in the business to easily find the business numbers that match the official set – leading to far fewer miscommunications and issues.
4. Ensure aggregated data is easily available and accessible
Problem: One of my constant surprises has been to talk to people and companies who are convinced that data should only be accessed in its raw format. Perhaps they know SQL themselves (or an alternative tool) and use it to access all the data they need. Therefore, they believe analytics tools are a waste of time and a limitation on business.
This is great for them until the organisation hires someone without this skill set who is unable to access and manipulate raw data, and then the whole solution falls apart.
Fix: Simple dashboards and aggregated data are critical for a business to function. Quite simply, a solution that only the analyst or a technical user can use is a bad solution. I wrote long ago about the Quadfurcation of Digital Analytics with the 4th level of analysis being the use of raw data. I still believe that this level offers the most potential for insights, but it should only be developed once the organisation has the three prior levels in place.
The importance of design
The key word here is “design”. That is what we are doing when we define the tracking and do all the configuration work for a Digital Analytics tool setup – we are designing that tool. Which means we should be thinking about usability.
I have read exactly one website usability book in my life – Don’t Make Me Think by Steven Krug. And really, all you need to do is memorise and apply that title to everything you do.
Digital Analysts can easily fall into the trap of designing a Digital Analytics solution (tool set-up and/or how data is accessed) for their own needs. I know this from my own errors in the past.
Also, no business can rely on their analyst to provide all the reporting and insights. This is not practical because the analyst becomes a bottleneck and is unable to provide real value.
The truly “clever” Digital Analytics solution is one designed to empower the rest of the business, the decision-makers and action takers, to be able to use the data and insights to make smarter decisions/actions.
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