Personas: the way businesses can learn to understand their end users. When faced with UX web design or when developing a new product/service, businesses need to be able to ask themselves, “What would Percy McPersona do? How would they understand it?” Note that this particular Percy is a fictional persona: any Percy McPersonas out there reading this: a) sorry for freaking you out just now and b) cool name.
They work as a reliable guide to ensure your decisions are on point because even the best idea you’ve ever had might not fit your brand positioning or company direction. However, when your company doesn’t take the time to properly research the personas that will, in many ways, determine the success of your business, you can run into problems.
Creating personas requires a huge amount of research and involves a significant time investment. It’s not a simple process and businesses go wrong more often than you’d think! The results of going wrong could be critical so allow us to prepare you with the top three reasons businesses fail with their personas.
1. The wrong people working on the personas
Let’s pretend your business is selling telecommunication equipment to corporations. Your clients will know the products and providers in your industry very well. If you don’t have an interest or any knowledge of the telecommunications industry, then you won’t get into the heads of your clients. True, field research will certainly help and provide some fairly reliable information, but having someone who understands the field in which your clients work will offer another dimension as they are able to ask more expansive questions, relate to the subject matter and better understand their motivations.
It’s also worth noting (although it should be obvious) that those who are performing the research should be experienced with interviews, data analysis and persona building.
2. Not enough personas / Too many personas
Bear in mind that the different personas you create are representative of areas of the market you will target. That means one is obviously not enough! You will want to diversify your personas to represent the full spectrum of your clients. By this philosophy, the more personas you have the better, right? Not quite.
While having more personas is good, there’s very little use in personas that aren’t the right type of persona. Be sure the personas you create are relevant to your industry. For instance, if you’re in consulting for IT companies, then it will be unlikely you have any use for a persona that lives at his mother’s house making a living with YouTube videos.
Moreover, if you make too many personas you will surely end up creating some that overlap each other. They need to be given names, backgrounds, needs: essentially an entire life. A lot of work is required to develop each persona, so to find out that two of those you have developed are both middle-aged men with high-paid jobs and a wife is a waste of time and money.
3. Working with incorrect data
Perhaps the most avoidable and damaging error one can make when developing personas is working with incomplete data. It might make sense to collect all the data for the people that visit your website or contact you via phone or e-mail, but are they really all the people who will hold an interest in your business? For B2B companies, one could assume their first point of contact for new clients would not always be someone senior and it would be understandable to deduce their UX needs to cater solely to mid-level management. But of course, they aren’t the decision-makers: the CEO or business owners are the people who make the choices. Not taking them into account could lose sales opportunities because the business owner could have a poor UX.
At the end of the day, the biggest mistake businesses make when developing personas is not understanding how much the repercussions could affect their business future. These ramifications can create a snowball effect before you know it. Be sure you take personas seriously – hire experienced professionals to undertake the research, be thorough with the data you give them and make sure you have a reliable sample size.
