Psychometric profiling solutions
The data a company usually collects, product usage, age, revenue, number of open tickets, tells you what a customer did. The language a customer uses when they write, in a chat, an email, a support ticket, tells you something more: why they did it.
Job-DecisionMaking
The individual may be self-confident and have good decision-making skills.
Three problems, three traits
The psychometric profiling solutions on this page cover three problems a company runs into every day with the data it already has: predicting when a customer is about to leave before the cancellation lands, splitting the customer base into groups that actually respond differently to the same message, and working out what to propose to each customer instead of reaching for a discount as the only lever available. The three pages in this hub show how Portrait reads that language for customer care, for marketing and for digital sales, with a real profile example for each department.
- Customer careAttitude-Churn0.29
How to reduce customer churn before it happens
How to reduce customer churn by reading customer language: which signals anticipate the decision to leave and how to act before they go.
- Marketingvalues_security+0.62
Customer segmentation beyond demographic data
Customer segmentation beyond age and geography: how psychometric traits split your customer base by how people decide, not by who they are.
- Digital SalesPurchaseMotivation-SeekSelfEnhance0.66
Personalized offers: what to propose to each customer
Personalized offers for customers: how to work out what to propose to each one by reading purchase motivations in their own language.
What changes with a psychometric profile
Knowing the customer's personality changes what those texts can tell you. Portrait analyzes the frequency of function words, meaning articles, pronouns, prepositions and conjunctions, and returns 50 base psychometric traits valued from -1 to +1, organized into four families: personality, personal values, observable behavior and social network.
These feed 38 aggregated traits valued from 0 to 1, ready-made correlation matrices developed with the Massachusetts Institute of Technology (MIT) in Boston, that combine several base traits into one indicator: they are the basis of Attitude-Churn for churn, the values family traits for segmentation and PurchaseMotivation- for offers, the three pages in this hub. Measured profiling reliability across more than 20,000 profiles is 72%, with an average cart increase of 48% and a conversion rate increase of 11% on projects already in production.
The calculation runs on the same texts a company already collects in the relationship with the customer, chat, email, tickets, CRM notes, with no survey and no dedicated campaign. The psychometric profile does not replace the data a company already has: it sits alongside revenue, purchase history and open tickets, and adds the layer those numbers miss, the motivation behind the behavior. Further down, the "Same profile, three departments" section shows the same profile read by customer care, marketing and digital sales, each through its own lens.
Same profile, three departments
Next best action
Portrait's suggestion
Adv-LikesLogicalCommunication at 0.74: this customer responds to rational communication. Build the campaign on data, comparisons and in-depth content rather than emotional levers.
See Portrait on your data
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