Stop reporting absolute numbers and contextualize your data with reports to engage stakeholders in the smallest but most important data points.
This is surprising; the bad job that many marketers do to report their work to clients. This includes internal and external customers. Consider how many marketing cards and reports you’ve seen, which simply contain screenshots of Google Analytics, Adobe Analytics, Adwords, Google Console, or e-commerce reports. This is not the way to influence people with your data.
The main problem is that most marketers are not analysts. Many marketers don’t know how to collect or use all the data they need and, to a lesser extent, how to present it in a meaningful way. This is usually the job of a data analyst. Just like you buy a pound of nails, build a hammer, and don’t consider yourself a carpenter, you don’t give a data analyst access to your analytics reporting tool. Therefore, there are many reports that contain complex screens and present the data out of context, with no meaning or significance.
In many reports, facts (data) are presented simply with a number and without context. Out-of-context data is just data. Claiming that AdWords generated 5,000 sessions on a website last month doesn’t make sense without context. The number 5000 is not a good or bad data point without a reference or a cost factor. Only after adding other factors (the open window) can you indicate whether your efforts were successful or not. If the AdWords campaign only had 1,000 sessions in the last month, 5,000 sessions would be enough without any other data. But what if the cost of increasing the additional 4,000 sessions were ten times higher than the previous month? What if AdWords generated 5,000 sessions last month but at double the cost?
Only by adding additional information in a meaningful way can marketers convert their reports from a subjective presentation to an objective presentation. To do this, stop reporting absolute numbers and publish the report data. For example, if you are estimating the cost per session, enter a third factor (target conversion, revenue, and so on) and create something like “Cost per session: revenue”. This will put the data into context. For example, if each generated session costs $ 1: $ 100 (cost per session: income) versus $ 2.25: $ 100 (cost per session: income), the effectiveness of your marketing spend will become apparent. In this example, it is clear that the first result is better than the second. To normalize the denominator (to create the denominator itself), it simply shows the success or failure of an attempt.
The data is boring
Yes, sending data is boring. By simply looking at a mega track from a data set, many people lose interest and ignore the messages you are trying to convey. The best way to avoid this is to let your data sing!
Let your songs sing
Just like in the marketing world, the easiest way is to get someone’s attention and make your message sing with images. Bring all this excellent data to your mega and transform it into an easy-to-understand graph or simplified data tables, if necessary. Better yet, turn it (if you can) into interactive graphics. Don’t be afraid to manipulate your data during the presentation. The guide allows your audience to search for the data that interests them most.
Learn how to use data visualization tools like Data Studio, Tableau, DOMO, Power BI, and others. With these tools, you can take boring data and not just give it meaning, but make the data sing and make you a data hero.
Interact with your data
My company bought an electric car at the end of July 2019. We wanted to know if it was worth it. Did the savings in the cost of using electricity compared to gasoline justify the difference in the cost of ownership of the vehicle (rent insurance costs and maintenance costs)?
Below is a typical data type report with all the irritating information. This is a large amount of data and only those who are really interested in the details will find it interesting. If he gets the table, he will probably look for the total monthly savings in the right column. If I just presented the data, it would be boring, look up and start counting the holes in the ceiling panels, instead of paying attention.
To really assess the big question: “Is it worth buying an electric car?” we will have to estimate how much gasoline would have traveled the same distance compared to the average cost of gasoline in the same months. On page 2 of the report, data begins to appear when the difference between electricity and gas savings becomes apparent. The graph becomes interactive and allows the user to move the mouse over a column to reveal the details of the data.
For data to really flow, we need to compare not only operating costs but also the cost of ownership. Does the savings in operating costs justify the price difference between vehicles? We know that the difference in annual rent, insurance, and maintenance costs is between $ 85 and $ 90 per month