Our previous article sparked interest in developing psychometric profiles using your existing data. Many of you reached out, asking how to identify psychological traits without direct customer interaction and how to apply these insights in practical ways.
We've created an easy-to-follow guide exploring psychometrics, explaining the logic behind segmentation, and offering actionable examples to try it yourself.
Let's dive into this game-changing approach, starting with an overview of the steps:
Identify data sources
Identify typology attributes
Score the attributes
Identify behavioural and interactive patterns
Segment your database
Craft personalised messaging
This article covers the first five steps. In the coming weeks, we'll tackle step 6 - crafting personalised messaging.
1. Identifying sources of data
In today's world, customers use both online and offline channels to research and purchase cars. As a manufacturer, you'll have access to some information about new-car buyers, while dealers and lenders will possess the rest. As a dealer, you'll have further details about used-car and service customers. Ideally, all this data would be unified into comprehensive customer profiles (as discussed in the article here), but most brands have yet to achieve this important prerequisite.
Good news though – you should already have enough information within your own existing systems to make progress. These sources include:
Customer Relationship Management (CRM) systems
Lead Management Systems
Vehicle Ordering Systems
Call Centre databases (including live chat and chatbot logs)
Marketing Automation platforms
Social Media and Marketing platforms
These data sources should contain information on:
Customer behaviour data
Purchase history and transactional data
Product and service choices
Social media and online activity data
Loyalty programme participation
Customer service interactions and frequency
Once you've gathered all the data, ensure it's clean, up-to-date, and complete.
Now you're ready to apply some psychometric science to your data!
2. Identifying typology attributes
In this step, you'll need to:
Analyse your clean dataset and identify specific behaviours, preferences, or interactions indicative of each Big Five OCEAN personality typology (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and
Identify the attributes representing each specific behaviour, preference or interaction.
Let's explore each personality typology and identify a few example attributes.
People displaying curiosity, imagination, and willingness to embrace new experiences may exhibit more openness.
Analysing their browsing history or engagement with marketing materials, you might find the indicators of openness, including:
Interest in new models or innovative car features
Exploration of various car types and designs
Positive responses to unconventional car designs or colour options
Sharing articles or posts about the latest car technology or trends
Engagement in discussions about new ideas in the automotive industry
These behaviours represent "inclusive" attributes, indicating the likely typology of the person.
"Exclusive" attributes help confirm which typology a person does NOT exhibit. For example, a person with low openness might:
Be interested primarily in traditional vehicle models
Not explore new car features or technologies
Rarely engage in conversations about automotive innovations
Be disinterested in electric or hybrid vehicles
Be unresponsive to marketing materials featuring new car technologies
You’ll need to combine both inclusive and exclusive attributes to narrow down a person's predictive typology further.
Responsible, detail-oriented, and diligent individuals likely exhibit high conscientiousness.
Analysing their purchase history and transactional data or participation in loyalty programs might reveal indicators of conscientiousness, including:
Comparing multiple vehicles before purchasing
Long-term vehicle ownership history
Consistent participation in loyalty and reward programmes
Organisation and maintenance of detailed car-related expense records
Opting for scheduled maintenance packages
Exclusion attributes for conscientiousness might include:
Missing scheduled appointments
Disregarding car care or maintenance recommendations
Not participating in loyalty programmes or dealership rewards
Impulsive car purchases or skipping research
An unorganised or haphazard approach to vehicle ownership
Extraverted individuals actively seek social interactions and enjoy being around others.
Their social media and online activity history, or frequency of interactions with your customer service, might show:
Frequent interactions with car manufacturers or dealers online, through call centres, or in person
A positive and friendly tone in interactions
Active participation in car-related events or meetups
Posting about car-related events and experiences
Sharing experiences or photos of road trips and car-related adventures
People with low extraversion might:
Prefer researching and shopping for cars without personal interactions
Have limited or no social media activity related to car interests
Minimise direct communication with dealership staff once they become customers
Avoid accompanied test drives, car-related events or meetups
Cooperative and empathetic individuals likely have high agreeableness.
Analysing their customer service interactions frequency, language and tone, and their social media and online interactions history, you are likely to find:
Polite language and cooperative manner in interactions
Amicable issue resolution
Sharing positive reviews and experiences with car manufacturers and dealers
Offering support and advice to other customers online
Recommending car dealerships or brands to friends and family
Exclusion attributes for agreeableness might include:
Unwillingness to compromise or negotiate during the car-buying process
Limited empathy or understanding toward others' car-related issues
Criticism or blame during discussions or interactions
Displaying impatience or frustration in customer service interactions
Not offering assistance or advice to others.
Individuals prone to negative emotions like dissatisfaction or frustration may exhibit higher neuroticism.
In their purchase history and transactional data, and customer service interactions history, you might detect:
Frequent changes in vehicle preferences or purchases
Choosing higher-risk financing options or insurance plans
Emotional language and varying communication styles in interactions
Raising concerns or issues with varying levels of urgency and intensity
Expressing concerns about car safety or reliability more than average customers
Frequently seeking reassurance from dealership staff regarding vehicle choices
People with low neuroticism might:
Display a calm and composed communication style in customer service interactions
Be consistent and stable in their vehicle choices
Show low levels of anxiety or worry about car-related decisions
Rarely seek reassurance or support from dealership staff
Maintain emotional stability during the car-buying process
These examples show a few indicators you can use as personality attributes. In your data, you'll find more. Don't overdo it; 20-30 attributes will be more than enough to start with.
3. Scoring the attributes
The next step involves developing a scoring system to quantify each person's expression of their corresponding typology.
The simplest approach is to assign numerical values to each attribute and calculate their weighted average score.
For instance, suppose you want to quantify a customer's level of extraversion based on two of the attributes we've touched on above:
Frequency of dealership interactions
Number of social media interactions
You could assign a value from 1 to 5 for each attribute, where 1 indicates low extraversion, and 5 indicates high extraversion.
Imagine a person has a high number of social media interactions (score: 5) and visits dealerships moderately often (score: 3). To calculate their extraversion score, you would find the weighted average of these two values, which might result in a score of 4. This indicates that the customer has a relatively high level of extraversion.
Once you've assigned numerical values to all the identified attributes, you'll need to calculate the average scores for all your customers.
4. Identifying behavioural and interactive patterns
Now, you'll need to analyse these scores to uncover patterns and trends. This involves looking for groups or clusters of customers with similar trait profiles. The objective is to identify distinct customer segments based on their psychological characteristics.
To comprehend these patterns, you can employ statistical techniques such as cluster analysis or k-means clustering. These methods help you group people with similar typology scores, revealing distinct clusters or segments.
Cluster analysis examines the distances between each customer's scores and groups those with the smallest distances together. This forms clusters of customers with similar psychological profiles.
K-means clustering is a more specific technique that requires specifying the number of clusters (k) you want to create beforehand. The algorithm assigns each customer to the nearest cluster centre, iteratively updating the cluster centres until you reach the optimal solution.
Applying these clustering techniques will divide your contacts database into five psychometric segments - openness, conscientiousness, extraversion, agreeableness, and neuroticism.
5. Segmenting your database
The final step is to apply a marker indicating the segment to each customer record. For instance, O for Openness, C for Conscientiousness, E for Extraversion, A for Agreeableness, and N for Neuroticism - the OCEAN.
These markers are what your marketing automation platform will need to apply the language, tone of voice, assurances, context, sequence, visuals, positioning, frequency, and channel most suitable for each personality typology.
We will provide examples of each in the upcoming article in the next few weeks.
In the meantime, if you have any questions, please use the contact details below to email, call or message us on LinkedIn.