Delvinia Advances AI for Virtual Moderator

We are pleased to announce an upgrade to CRIS, our AI-enabled virtual moderator. A key differentiator of the product is that it replicates a true moderated experience. CRIS behaves as a moderator would, by establishing focus, engagement, and empathy. This results in a more engaging experience for participants, and richer layered data for clients.

To do this, a discussion guide, much like what a moderator would use to conduct a traditional one on one interview, is programmed into CRIS and CRIS asks the questions. CRIS then follows up when the answer seems incomplete to get more information.

In its original iteration, the trigger for CRIS to follow up was based on expected word count, which needed to be defined for each question in advance. In addition, the follow ups were generic (e.g. can I get more detail on that? What else can you tell me about that?). Delvinia has released an upgrade that brings a more rigorous AI-driven method to the follow up process.

“We are excited for this release as it continues the evolution of CRIS to better replicate the moderator experience,” says Delvinia President and Chief Innovation Officer Steve Mast. “By having high-quality virtual tools available for qualitative research, we make it more readily available for projects of all kinds.”

Our team has worked to improve the follow up algorithm so that CRIS continues to evolve as a true qualitative moderator. Today, the AI has been improved so that CRIS now:

  1. Knows when to follow up by evaluating completeness of a response relative to responses from preceding interviews, with no intervention required
  2. Follows up more specifically, to get detail on subjects that have the most relevance. In brief, it does this by:
    • Generating keywords using the statistical measure Term Frequency-Inverse Document Frequency (TFIDF).
    • Then an NLP tagging technique is employed to tag Parts-Of-Speech (POS), and process and extract Noun Phrases, known as chunks. If any of the keywords are contained in the noun chunk(s), the ‘chunk’ is added to a follow-up phrase, “e.g. can you elaborate more on…?” Because the keyword is ranked by score, we are able to select the appropriate noun chunk if more than one is selected.
    • If no noun chunks are extracted, CRIS proceeds to follow up using the original decision tree logic.

The result is more specific follow ups that are relevant to the study’s objectives, enabling clients to gather more insightful data at scale.

Delvinia

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