Lots of companies struggle with digital analytics strategy, from data collection to analysis and presentation. We defined the eight most important steps to help you super-charge your digital analytics strategy in a professional way:
1. Data Collection
"Out of the entire lifecycle of analytics measurement, there is only one place guaranteed to pollute everywhere else and that’s the data collection layer. (Simo Ahava)"
Data collection is broken for most companies. Research by OptimiseOrDie shows that 95% of companies had high priority issues. Only 0,6% had no tracking issues and less than 1% had a solid digital analytics strategy. So a lot of companies are struggling to collect data.
Over 50% of companies use the default settings when setting up Google Analytics. It's not because you use the free version of Google Analytics that you need to use the default settings. It takes work and insight to properly collect data in order to have a trustworthy analysis. If you put crap data in, you will get crap reporting and dashboarding.
Investing directly into supporting data collection as part of the project outcome is key. Data is part of everything!
2. Data Skew
Having skewed data means that the data is correct but needs to be cleaned and untangled before you perform any analysis. Some might think that time spent on cleaning data means less time for analysis, but people will start losing trust in the data if you don’t do this. Not everyone will necessarily know that the data is skewed or which steps need to be taken to clean the data.
4 things to check for good data hygiene:
- Does the data I’m working with make sense to me?
- Does it make sense to someone else?
- How clean is the data collection & processing?
- How much work do I need to fix this?
The issue of data quality grows in importance as we strive to make decisions on strategies, markets, and marketing in near real time. While software and solutions exist to help monitor and improve the quality of structured (formatted) data, the real solution is a significant, organization-wide commitment to treating data as a valuable asset. In practice, this is difficult to achieve and requires extraordinary discipline and leadership support.
3. Data Pollution
Data pollution is a very common problem but, it’s virtually invisible, unless you look carefully. Data pollution means that your data contains extra stuff (more data) that shouldn’t be there. A 3rd party is adding data (bot, spam) or you’ve done something wrong. Not everyone will necessarily know that data is polluted or which steps need to be taken to clean the data. If you don’t tackle this, it can have strong negative effects. Pollution impacts trust in data (visible or invisible) and corrupts key site metrics (CR, Bounce rate, etc.). Filter your data until you only have what you explicitly need!
Common reasons for data pollution are:
- Not filtering out 3rd parties (agencies, dev, partners, employees, contractors)
- Bots and automated services
- Site Performance Tools
- Internal Testing (Dev)
- External Monitoring (Uptime)
- Misconfigured tags
- Double firing tags
4. Data Enrichment
Data enrichment is the process of adding more context and meaning to your data by going beyond your standard analytics implementation, importing and blending data from on-premise systems and tracking all available consumer touch points.
What can you do to capture more of the important interactions and events as part of the user journey? Are you capturing all key information needed to analyze the customer journey? Maximize use of your analytics tool to store additional information, you will be glad that you did in a year time. Think about what you need to capture in order to have every short- and long-term customer goal reached. Lastly investigate in how to integrate or blend additional tool data (e.g. VOC, Session replay) with your analytics tool.
5. Data Automation
"Automation will significantly change many people's lives in ways that may be painful and enduring. (Moshe Vardi)"
If a task takes longer than 15 minutes per week, then automate it! To save, you have to spend. One hour of automation is days saved. Remove manual reporting tasks, save multiple repeat efforts, making it 1 click, auto e-mail or dynamic. This won’t save time but will save productivity at the end.
Creating automated reports and dashboards will free up the resources for more in-depth analysis. And with prescriptive analytics you can automate any number of marketing actions to create a personalized experience for your clients.
6. Data Presentation
“Visualization gives you answers to questions you didn’t know you had. (Ben Schneiderman)"
There is no point in spending time and effort in analyzing your data if it ends up looking like crap. Make use of data visualization tools like Google Data Studio to visualize your analytics data.
Don’t assume that everyone needs to know everything. Don't overwhelm your stakeholders or marketing manager with a huge amount of graphs or meaningless data. A good dashboard should only answer one question: "Should I act?".
7. Data Consistency
Data consistency refers to the usability of data. This is often taken for granted. It is important because there should be a single truth to every piece of data. How else do we know which data is right? As data becomes bigger and bigger and more frequently used it becomes even more important. For example, in large software companies, financial institutions, healthcare, government, etc.
Data consistency problems may arise in any circumstance, for example during recovery situations when backup copies of the production data are used instead of the original data. This way, data inconsistency can result in misinformation and incorrect decisions.
Watch this video by Simo Ahava on Agile Integration & Meaningful Data.
8. Training & Investment
Train your team so they can grow in their job through data, know how everything works in analytics, and know how to get data out with less work. Achieve self-servicing for data needs, as mentioned before, automate repetitive tasks and get more proficient at finding value.
One important aspect is to train your employees on how to make data-based decisions. Anyone who has ever worked on a project with large amounts of data can confirm that it can be overwhelming sometimes.
Another important aspect is to invest in technology. Although big data is great, there will still be many advances in technology in the future. Companies must have the ability to think many years in advance when making decisions. Analytical and logical tools are used to determine and accurately learn data analysis. These skills need to be learned and mastered over time in order to land yourself a good position in this field.
Analytics Strategy needs training strategy!
If you're looking for some hands-on training in Google Tag Manager, Tealium IQ, Google Analytics, Adobe Analytics, etc. MultiMinds offers these trainings. Take a look here: https://www.multiminds.eu/training-academy