Hard as it may be to master data analysis, honest unbiased reporting is even more difficult.
We all make mistakes, and quite a few of us give in to the temptation to mislead others for personal gain. While mistakes are par for the course, deliberate manipulation is not.
Charts are one of the most common data visualization choices. It’s not uncommon for people to “distort” reality either by mistake or intentionally when presenting bar, line or other charts. In my career, I saw people spot and correct charting mistakes and make “clever” misrepresentations designed to help someone get ahead.
Labeling of X and Y axes can be misleading. The context of what’s being presented can be made unclear. Cherry picking is a common practice for making things look better than they really are. Misusing charting conventions can “trick” people into seeing something that isn’t there such as a pie chart seemingly showing a favorable picture overall when, really, the chart is focused only on a small subset without an accompanying explanation.
This brings me to the point I am trying to make – be extremely vigilant with analytics. It’s just as dangerous as it is helpful. If you know what you are doing or if you are not very good at it, the result can be a disaster just the same.
Is a chart lying to you? This video has some tips to figure it out. – Vox
Gone are the days of spreadsheet data analysis and “blanket” marketing, or so we hope. In are the days of more individualized and more timely marketing through predictive analytics.
US Cellular found out what users of their mobile app liked and didn’t like to see. Naturally, the company gave the users more of the good stuff and less of the bad. Happier users means better retention and, ultimately, more $ for the company.
A professional photo market service provider figured out which customers were likely to stop using the mobile app. They had a clear “stopped using the app” definition, or churn – 30 days of no app activity. This made it possible to look for various app usage patterns that suggested possible churn. High churn risk correlated to a high number of certain types of app usage events – in other words, if I did “this,” “that,” and “the other,” I am highly likely to churn. But if I only did “this” and “that” then I am medium risk, and just “this” alone is a low risk. The service provider then decided to target high and medium risk customers by sending them information about photo competitions to their mobile phones. It worked – plenty of customers re-engaged with the mobile app and, eventually, continued to spend $.
Data these days can reveal wants and un-wants of individual customers. That means I get my very own marketing material tailored to me instead of the generic stuff, or I am stimulated to re-engage sooner rather than later. The likelihood of me responding is higher, and that’s what you want as a marketer.
Mobile phones are everywhere, including my pocket, and, therefore, it’s an important channel. Mobile app usage data can tell a lot, and a smart marketing team can make good $ from it. The challenge is to understand the data.
How Do Brands Use Predictive Analytics for Mobile?
We must strike a balance between relying on data and emotion when making decisions. A glaring example of this is our last Presidential election here in America. Clinton relied on data and lost. Trump played on people’s emotions and won.
Tools for predictive analytics took a hit in presidential election