Archive for category Analytics
For 30 years I have been a supporter of equal pay for women. My reasoning is very selfish and I have shared it with everyone who has asked. As I was married to a working woman, I benefitted as she made more money. It’s a simple and straight forward as that.
It’s also rather obvious and it’s quite similar to clean air and clean water. Do you hear anyone arguing the other side. Are there presidential candidates who argue in the debates that we have to lower woman’s pay. Of course not. And yet in every speech that Hilary and Bernie give they bring up ensuring that women receive equal pay for equal work.
But where are the companies that aren’t doing this? If either of them can name just one company that does not pay women the same as men then a lawsuit should be filed immediately under the two laws that are currently on the books.
Equal Pay Act of 1963
Lilly Ledbetter Fair Pay Act
But they can’t do that because there is no such example. So why do they keep up this pretense?
Of course it’s about politics. If they can keep up the narrative that there is a war on women it will only help them in the general election. (And if they can get a new law passed that uses the same shoddy standards of evidence that the 77% claim uses than the trial lawyers will be happy to file lots of lawsuits).
To fight this narrative, there needs to be more focus on the facts. Not the shoddy analysis done by the Obama Administration.
A very good analysis was discussed by the folks at Freakonomics. Although Steven Levitt, one of the founders, is a professor at the University of Chicago and therefore you might say leans right, However, Stephen Dubner really runs the place and if you have read his stuff or listened to his podcast you know that he leans more left. This is the link to their podcast on the subject.
The analysis that he discusses was done by Harvard University Professor of Economics, Claudia Goldin. She was the first woman to get tenure in the Harvard Economics department and a former President of the American Economics Association. In other words, pretty credible.
What she found was that when you control for factors such as education, profession, experience and hours worked the gap virtually disappears. Additionally, there is another factor that could be called flexibility selection that can explain any remaining delta. Flexibility selection is employees “selecting” jobs that give them more flexibility for their personal lives. Some factors of flexibility could be to work from home occasionally, set their own hours, less travel amongst other things. This may be to take care of kids or parents or just because the person doesn’t want to have their life consumed by their work. One example of this is the lawyer who leaves the high pressure law firm to become a corporate council. Lower paying but giving the person more flexibility with their schedule. Yet both positions show up on census data as the same job. These elections are made by women more often than men and although they show up in the data as the same job, one is paid less. Should this be illegal.
A major problem in continuing this narrative is that by constantly repeating it we are creating an entire generation of victims. I don’t want my daughters thinking that they are being taking advantage of by “the man”. To quote President Obama “that’s not who we are”. I want them to believe that they are in control of their future and with hard work can achieve whatever their goals are. That is the American Dream. That is who we are and it applies equally to men and women.
I loved this Blog posting from the On Startups Blog. It gives you 17 lessons from the movie Money Ball that apply to startups. Very thought provoking.
For centuries retailers have been trying to understand what the customer wants (product and price) so that they could win their business. We have used Category Management tools, RFM Models, Customer Segmentation and Customer Personas amongst other efforts. Unfortunately we have been handicapped by imperfect and incomplete data due to limitations in the technology available at the time.
Now, because of advancements in storage and analytics we can read all of the signals that the customer is leaving for us, across all channels and social networks.
Turning these signals into action is a 3 step process:
1 – Gather – We need to gather and clean all of the data. It may be determined that there is additional data that we currently don’t gather that may be useful so we need a strategy for gathering that also.
2 – Analyze – The data needs to be analyzed for trends and actions to understand what influences the customer to take action. We need to understand the customers triggers whether it is product, price, promotion, pacing, peer pressure or some combination.
3 – Deliver – Deliver the results back in an actionable way so that we can influence the purchase decision. This can be on line, in the store or in a call center. This is the most important part yet the hardest to execute.