SYSTEMS AND METHODS FOR IMPROVED USER EXPERIENCE PARTICIPANT SELECTION
Systems and methods for selecting participants for a user experience study are provided. In some embodiments the systems and methods first receive at least three features for each participant profile. The participant profile is scored by quantile-based discretization. Next the participants are grouped into clusters using an unsupervised machine learning (ML) clustering algorithm(s) for each participant profile. The clusters are ranked using a number of models. These models are a function of geography and study type. The participant profile is assigned to a single cluster for each model. Participants are sampled from the clusters by their ranking.
1 . A method for selecting participants for a user experience study comprising:
receiving at least three features for each participant profile;
scoring each participant profile by quantile-based discretization;
the grouping participants using an unsupervised machine learning (ML) clustering algorithm to generate a cluster from a plurality of clusters for each participant profile;
ranking the plurality of clusters based upon a model of a plurality of models, wherein each model of the plurality of models is a function of geography and study type; and
sampling participants from each cluster responsive to the ranking.
2 . The method of claim 1 , wherein the scores include: 1) time since last participation, 2) total number of participations of the given participant profile, 3) time response score, 4) quality response score, 5) burnout ratio, and 6) exclusion variable.
3 . The method of claim 1 , wherein each cluster has a single score for each model.
4 . The method of claim 3 , further comprising receiving a numerical weight for each of the scores.
5 . The method of claim 4 , wherein each participant profile is assigned to a single cluster for each model of the plurality of models.
6 . The method of claim 4 , wherein the sampling proportion from each cluster is correlated with the cluster score.
7 . The method of claim 6 , wherein the sampling includes ponderation from lower ranked clusters.
8 . The method of claim 1 , further comprising clustering new participant profiles using supervised modeling.
9 . The method of claim 1 , further comprising intentionally sending an invitation to the selected participants which are a better fit to engage in a user experience study.
10 . The method of claim 1 , further comprising asking the filtered participants at east one question to determine at least one missing feature.
11 . A method for streamlining tailored screening questions for recruiting targeted participants for a user experience study comprising:
receiving an unstructured description of a recruiting sample requirements;
interpreting the unstructured description to relate the unstructured description to a concept;
extracting from each concept a subject relating to at least one sampling target;
correlating the subject to an attribute for the at least one sampling target; and
selecting a template question from a plurality of template questions for the attribute responsive to the subject.
12 . The method of claim 11 , wherein the subject includes a class, a function and a value.
13 . The method of claim 12 , wherein the determining the template includes filtering a plurality of templates by the class, and then selecting a template from the filtered templates using the function.
14 . The method of claim 13 , further comprising generating a question using the template, the class, the function and the value.
15 . The method of claim 14 , further comprising presenting the question to a subset of sample targets of the at least one sample target of a user experience test.
16 . The method of claim 13 , further comprising extracting a requirement from the generated question.
17 . The method of claim 11 , wherein the description includes at least one of text, audio and video.
18 . The method of claim 11 , wherein the interpreting includes parsing the unstructured description, normalizing the parsed description, lemmatizing the normalized description and conceptually clustering the lemmatized description.
19 . The method of claim 11 , wherein the correlating uses at least one ML model.
20 . The method of claim 11 , wherein the attribute is correlated with a score for a participant.