5 min • 08 October, 2020
Good customer data analytics isn’t just collecting data, but also gaining actionable insights and acting upon them.
For example: Should you offer customers a 3%, 5%, or 7% discount on their next purchase or even lower the base price?
Which feedback is the most crucial to improve product and sales among surveys, reviews, or website behaviour?
In this example, I’m using a no-code tool to analyse feedback sentiment in less than 30 seconds.
Made with GYANA (formerly Vayu) Here is how this would look like in Google Sheets.
About 1/3 of the Formular
Sheets is a great tool, but not for everything. Coding has a steep learning curve, and thanks to the growing no-code movement there are many new and exciting alternatives to choose from.
Marketers invest the majority of their time digging through countless pages of data. This doesn’t just create mental fatigue, it’s prone to lead to errors.
Research shows that more than 30% of marketers are simply overwhelmed. They are swarmed with unclear workflows, seas of data, and contradictive instructions on what to do.
What does Apple, Tesla, and Ford have in common? Streamlined manufacturing and simplicity by design. Analyzing customer data is no different.
Let’s say we are tasked with analyzing a dataset with millions of rows. Just the thought of what formulas organizing such a behemoth would take is daunting.
Now let’s look at a comparison of advanced logic in code versus a drag&drop tool using the publicly available Unsplash dataset.
Made with VAYU | Code by Author
By simply uploading my csv I can instantly tell which the top cameras and models are, where they come from and how clean the data set is.
I can also easily aggregate the data to create stunning visualizations like this racing bar chart — perfect to illustrate for example sales over time.
Made with Flourish Studio
Want to create a choropleth graph showing your customers spending organized by age with just a couple of mouse clicks?
Made with VAYU
To analyze data, we need to understand it. That’s where easy to use tools and visualizations become crucial.
After all, companies of all sizes have meetings, reports, and different teams ranging from developers to marketers.
Even better: if anyone in a company can use them and have everything organized in an interconnected infrastructure effortlessly. That’s partially the reason why highly underestimated tools like Google Sheets dominate the market. If the largest tech giants optimize for simplicity — that’s a clear sign.
I can instantly create a cross-platform interactive document or sheet by simply typing doc.new or sheet.new into my browser. Clicking another button I can connect it to a live feed of data (for example my CRM tool or my domain feed) and it can all be edited cross-channel with everyone that has a copy of the link.
This would have been a tremendous feat just a few years ago, yet it has now become the norm.
Whether you hired tech staff or supplemented them no/low-code tools if you aren’t generating the right data or even worse misinterpret it — all of your efforts are in vain.
One simple example is looking at the most basic metrics imaginable.
Do I have paying clients & how can I get more?
Good marketers can “sell ice to the Inuit”. Imagine what they can do if they have an actually desirable product.
There are many powerful low- and no-code tools like Power BI, Tableau, Flourish, and Vayu. Similarity to Excel is a big plus since this is still the golden standard established since the dawn of the computer.
According to this McKinsey survey, companies streamlining customer analytics strategies report over 100% higher ROIs with similarly high increase in profits.
If you were the general of an army, would you simply send out your troops and hope for the best?
This creates friction, drains manpower and provides a lot of room for human error.
Although most organizations are embracing the change in tech and analytics, only a handful have achieved a high level of proficiency.
Most companies are too scared to let go of their old infrastructure — one of the core reasons developers of outdated technologies from before the second world war are still in high demand.
There are many common misconceptions about technical analytics one of the biggest of which is that you don’t actually have to make things complicated or be technical. In other words: you can drive the car without building it.