In a nutshell, Persona is the segmentation of your users based on their buying behavior and characteristics that are significantly different than each other segment. What it means in reality is that it is the simplification of your user base so that you can design, develop and structure your web application and campaigns around these five or six different persona instead of one type of user that is determined by HiPPO.
The idea is to understand your users better so you can serve different needs of each Persona with either different features, campaigns, copy writing and solutions. Bryan Eisenberg has written and bragged about this topic many times.
In Tel3, I started working on Persona study about a year ago. The idea was to identify the main characteristics of our users and find out the most important attributes of our users that are directly correlated with the value they add to the Company and if there were any common specific behaviors among these groups.
The very first step was to start with two consecutive Kick Off meetings with the key stake holders. The key stake holders included the top customer service reps and supervisors who are all the time in contact with the customers, plus the campaign manager and the analytics guru within the company. On our first meeting, we put everything we know intuitively about our customers to the board with colorful sticky notes. Each color represented one type of customers depending on their reason of subscribing to the service – e.g. retired couples calling domestic, or international students calling family and friends overseas etc. Then we added as many as 100 or more different attributes in terms of behavior, the devices they use to make a call, the frequency of calling customer service, using our features etc to these main categories. At the end of this practice we could be able to segment different attributes based on;
- Usage behavior – power/casual/one timer
- Device dependency – mobile user/land-line/both
- Calling destination – Latin America/Europe/Africa/Asia/Middle East/Rest + countries
- Engagement with features
- Engagement with Savings opportunities – SMS Club membership/ coupon usage etc
- Engagement with different communication channels with Company – online/offline
The second step was to understand the usage behavior and all the other attributes of each main category (the reason of using the service) which you cannot find on the database. So we started preparing a comprehensive survey – you gotta love the trinity approach of Analytics by Avinash: find the ‘what’ aspect from your revenue, database and bottom-line, find the ‘how’ from all your web analytics and CRM information and find the ‘why’ aspect by simply asking the customer.
Designing the Survey was not an easy task considering all the data we were analyzing and all the questions we had for our customers. Hence with the brilliant idea of my Campaign Manager, we asked help from one doctorate student in human psychology and she helped us designing and finalizing the layout of our Survey. It was a success at the end, and we could be able to fill in the most of the blanks with the soft touch.
The next step was the data mining which took a lot of processing and brainstorming power. Initially, laid out couple hypothesis to test and to make sure we are selecting the right attribute sets. After running many iterations to get the best and statistically different results, we ended up with two most important attribute sets that are highly correlated with the value added to the Company; usage behavior and the engagement of the user. Since engagement involves many different aspects including features, communication, savings, we had to run regressions to come up with a formula/index that will help us understand which attribute contribute how much (in proportions) to the value. Once we know the importance of each feature, we could calculate the engagement Index for all customers – Index=1.0 meaning the user is engaged with all features and engaged with online back-office and called customer service at least once. So we can map all customers depending on their score on the Engagement axis. Usage axis was easier and simply the monthly average usage over the lifetime. We found out that there were three breaking points where the behavior start changing significantly depending on certain usage amounts which defined the usage points for each Persona. The average engagement Index was 0.45 which is defined as the separator between the engaged and non-engaged customers.
At the end we come up with six Personas (see above) that each represent a very significant different behavior. The rest was simple; we ran all the attributes for each segment and ended up with the Persona white paper that I shared with all the key stake holders and even with the affiliates. It helped us in designing continuous campaign programs for each Persona that reduced the churn by 30% in the following three months. The details of the programs and all the consequent actions taken with the persona study is a subject for another post.