Last week Metro Bank became the first financial institution to offer a gender neutral option for both its staff and customers.
The move was made in response to an interview with a Scottish teenager who couldn’t open a bank account because they didn’t identify as either male or female, which was a required question on the form.
Image from Buzzfeed.
Metro Bank now offer a title of ‘Mx’ and a gender option of ‘nonbinary’ in addition to the standard ‘male’ or ‘female’ options. While the response to the change has been polarised, these changes are something that will impact marketing data and how we use it going forward.
We seem to have accepted that ethnicity, and more importantly how people identify themselves is varied, so why is gender different? Metro Bank may be leading the way here, but I can’t imagine it will be long before other large organisations follow, particularly with the various anti-discrimination acts in place.
When developers are dealing with large amounts of data, they tend to ensure that storage is as efficient as possible. You may remember the Millennium Bug where problems were perceived due to the year 2000 being denoted by only the final two digits, making it indistinguishable from 1900.
This has been the case for gender data storage in practically every database and application I’ve seen over the years, where a binary (0 or 1) field is used for gender, with 0 being mapped to the most common gender for that application.
Even where gender isn’t explicitly gathered, it is sometimes inferred based on title. I regularly get emails and post addressed to Mr Janet Bastiman – almost certainly because my data has been passed from a system where my Dr title is allowed into a system where it isn’t, and the mapping rules are less than intelligent.
The first thing to consider is whether you are gathering and storing the data appropriately.
While I may not care whether a company gets my title or gender incorrect, there are a large number of people who would be offended by such a mistake and will not do business with you if you get it wrong. If these customers are important to you, then you need to take action and get it right.
Speak to your developers and add the options into your websites, databases and any other means of getting your customers’ details. Make sure any third party that collates and processes your data doesn’t override these changes back to a binary format.
Any data processor should be adaptable to your fields and not assume what you want to store, or impose any rigid data structure that decreases the detail.
Sometimes these mapping rules can be harder to change than adding a new option to a web form. So review whether you assume a person’s gender and the rules you are using. Software changes like this can take time and need to be planned.
One thing you should ask yourself is why you are segmenting based on a macro trait such as gender at all. Surely a better approach would be to target customers based on their interests, whether they are implicitly or explicitly expressed?
Does it matter that a cis female likes tech and power tools, or someone who is gender neutral is interested in romance novels? If you stick to stereotypes you will be missing out on opportunities.
Understand your data and what it is telling you. Find the correlations between what interests your customers and what purchases they make, and personalise your campaigns based on that and not traditional absolute categorisations.
“Only a Sith deals in absolutes” – so let’s all be a bit more Jedi.
Dr Janet Bastiman is the Chief Science Officer at SmartFocus and a contributor to ClickZ.