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. Having 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 a 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 the 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.
If Artificial Intelligence ever achieves complexity to rival human then we will be contemplating which one is superior. Maybe, we will be forced to reconsider our beliefs should it become clear that humans’ own creation is more intelligent. Or, else, we might be tempted to play God with AI.
Thankfully, none of these outcomes are near. For the time being, people are in control of their own future. AI is taking baby steps in assisting people with narrowly scoped tasks in business. Human judgment is still the key to success.
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 a bar, line or some other chart. In my career, I saw people spot and correct charting mistakes and make “clever” misrepresentations designed to help someone get ahead.
The 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
Back in the early 2000s, Business Intelligence professionals were talking about the novelty of automated cross-advertising. An example could be advertising greeting cards to someone who just purchased a gift or vice versa. If you bought a present then you must need a greeting card, so how about some of the choices right there, presented to you before or after you check out. While this wasn’t predictive analytics, it was still based on past shopping behavior. Market baskets are a more sophisticated version of shopping behavior analysis.
Modern predictive analytics is more comprehensive – it is an analysis of a pattern across a time span aimed at predicting what you are likely to do or need next. There is, probably, an infinite number of ways to analyze various data to try to predict human behavior – past shopping data, weather pattern data, seasonal data, income level data, credit score data. Creative analysis of combinations of these data and many more factors can uncover what’s about to happen before it happens.
However, many contemporary predictive analysis examples still amount to little more than simple cross-advertising that can, potentially, be achieved without the associated expense. It’s hardly necessary to go through extensive data analysis to “predict” that I am likely to need a greeting card to go with that gift I just purchased. It would be wise to understand the real business need for analytics. If the goal is to develop a competitive edge in the modern marketplace, then predictive analytics is likely the way to go, and it is a journey that takes methodology and patience.
Predictive analytics and advertising: the science behind knowing what women (and men) want
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