Persona creation and development
Answers questions like:
Which groups typically use my product? What are their preferences and desires?
Innovative and flexible
Data based creation of personal descriptions.
Connection of different methods for optimal results.
The target group
The biopinio panel includes consumers of organic products from all over Germany and Austria. For each project we narrow these down to tailor your exact needs.
Personas can function as virtual focus groups for further analysis and studies
Understand specific needs of your existing and potential customers
Prioritize product requirements from the perspective of target groups
[wp-svg-icons icon=“copy-2″ wrap=“span“] The Task: Who is actually consuming vegan, vegitarian, and flexitarian products? Consumer segmentation using the persona approach.
[wp-svg-icons icon=“loop-2″ wrap=“span“] The Method of Testing: A cluster analysis with the help of the ‚k-means‘ algorithm for eating behavior subgroups, followed by further investigations into the different breakfast and natural cosmetics habits of the designated subgroup’s personas.
[wp-svg-icons icon=“lightning“ wrap=“span“] The Result: A more detailed approach to a specific user group’s eating and cosmetics behavior. We identify clear target groups, respectively, which can be used for in-depth analysis projects.
What are Personas?
Personas represent specific users and allow an especially customer-centric focus on audiences.
When using personas, quantitative and qualitative methods are combined to build a more detailed approach of analyzing consumers. Personas help to understand and to make data applicable, and also offer the possibility of in-depth analysis based on the identified user types.
Persona characteristics exemplified by two flexitarians
What kind of segmentation analysis do we undertake?
Data based clustering and segmentation analysis is the finding of natural groupings within a dataset. A group, or cluster, should be as homogeneous as possible and at the same time different from other clusters.
An Algorithm called ‚k-means clustering’ randomly determines a predefined number of the top divisions and assigns them to the observations of the so called Euclidean distance to the respective centers. Subsequently, a new center is formed on the basis of the average values of the in cluster observations and the algorithm checks again how observations change the group by the updated centers. .
This is repeated until an optimal solution is found and thus the similarity within the cluster is optimized. Corresponding groups are then supplemented with qualitative insights and combined to form representative profiles.
Contact for project enquiries
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