As opposed to customer segmentation which divides the market into groups based on demographics and purchase history, persona detection and segmentation creates more personal profiles in order to better understand potential customers using information about behavior, attitude and personal journeys.
Personas are difficult to create as they are based on a lot of information that is not necessarily divulged in interactions with customers or prospects. However, they can prove very important because in many cases, they help to identify which products or services are most likely to interest a potential customer using information like life stage, special interests, and more. The difficulty of creating accurate personas and the potential that they hold make it very important to create a good and dynamic model.
Using internal data to create user personas and to segment your customers accordingly can be challenging, since your past interactions, sales history, and demographic information will not necessarily accurately reflect your users’ personalities, goals, attitudes, etc. External data will be crucial for a good persona segmentation model but personal information gathered by registration forms and CRM databases can prove useful.
The best way to analyze people and create personas is to observe their daily behavior, thoughts, and actions. Therefore, social media information is crucial for persona building and segmentation. There is also widely-available software that analyzes and assesses personalities using these social media accounts, such as Watson Personality Insights, MR Simmons Brand Catalyst, and more.
Surveys and questionnaires could be distributed in order to help create personas and identify concentrations of certain personas within your set of customers and prospects.
A significant challenge for persona segmentation is a lack of data, leading to reliance on stereotypes that can lead to incorrect assumptions about your customers and prospects, even alienating them at times. It is also challenging to correctly segment the personas and fit your marketing strategy and advertising to each persona.
Resulticks: Using Machine Learning to Create Persona Segments
Dynamic Yield: Turning Personas into Data-Driven Profiles for Segmentation
Persona Detection shows promise for the fraud detection field, as well.
“Artificial intelligence helps develop different personas based on behavior patterns. The algorithm already knows which persona the user will exhibit, so even if the correct password was typed on the associated device, the system knows when bad actors are featuring a persona that does not belong to the actual user.”
Biometric Update: Behavioral biometrics and persona-based security intelligence as a key fraud prevention layer
Prosper Insights & Analytics Data crafts customer personas with intent & sentiment data as well as modeling, predictive analytics, & more
Semasio Audience Targeting uses the Semasio semantic approach to optimize marketing strategies. This approach uses records of keywords and phrases used by site visitors to create Semantic User Profiles. Then Semasio takes keyword and phrasal similarities in the browsing habits of established customers to create Seed Audiences that you can use to plan your marketing campaigns.
In each case, Semasio provides companies the ability to tailor their marketing approach with either specific or more general keywords.
Demografy’s dataset – ‘Demografy’s Consumer Demographics Prediction’ provides Demographic Data, Individual Data and that can be used in and Persona Detection/Segmentation
Demografy’s dataset – ‘Demografy’s Consumer Demographics Prediction API’ provides Demographic Data, Individual Data and that can be used in and Persona Detection/Segmentation
Kido Dynamics’s dataset – ‘Kido Dynamics Smart Destinations for Tourism – POI Data’ provides Foot Traffic Data and Tourism Data that can be used in and Persona Detection/Segmentation