CIPA - Candidate Identity Psycholinguistic Analysis Trace Data Science, with its unique analytical process and expertise of psychologists, help enterprises across the globe to find the right employees for the job. TDPA - Team Development Psycholinguistic Analysis The team development analysis improves the overall organizational health and facilities management in making informed decisions.
Audience Intelligence We dissect audiences into smaller clusters, identify mega-nodes and then analyse patterns, habits and behaviour. The result is actionable and defendable insights that you can use to make data-informed decisions. CLM - Cognitive Load Mapping Our exclusive process combines AI with the expertise of clinical psychologists to provide high levels of insight that can identify when your key people are in an overload situation.
Customer Journey MappingWe create customer journey maps based on psychographic characteristics, achieving a better visual history of your customers' interactions with your brand, product, service or experience. Data Storytelling We build a narrative around a set of data and its accompanying visualisations to help convey the meaning of that data in a powerful and compelling fashion.
Most analytics companies settle for overall abstractions of customer data like age, location, or where they're from. These are useful segments only when you don't have the technical resources or you’re trying to draw some massive generalization.For us this is just the beginning.
We study people’s values, desires, goals, interests, and lifestyle choices using Active Data consciously provided by an individual (surveys) and Passive Data collected through online tracking technology and actual conversations (insider analytics).Once data is gathered, it is combined to reveal hidden truths about your audience.
We process through our system and confirm with our human team what we call Contaminated Data Sets to detect and delete high magnitude contamination, shape contamination and partial contamination.Preserving only normal curves and discarding outliers.
This final form is built around observed preferences, based on -not so much the way people say they feel or the way they think- but what they've actually done and what we actually observe of their behaviours, using first-party data obtained via state of the art Big Data Analytics, Machine Learning and Artificial Intelligence.
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