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Conversational channels Audit


AskHub detects users needs and automation opportunities by leveraging companies conversational data on every channel

  1. Marketing insights: analyze customer needs, customer journeys, conversion funnels, requests processing efficiency, etc
  2. Identify recurring requests and estimate an automation potential of the most relevant services
  3. Pre-train an AI model with user requests
  4. Detect operator responses that maximize conversion and customer satisfaction

Existing bots Audit


AskHub identifies the weaknesses of bots in terms of NLP and UX and recommends levers of improvement

  1. Identify user intentions that are not handled or misunderstood by the bot
  2. Identify new services that can be added to the bot
  3. Detect conversational UX problems that lower KPIs : bottlenecks, bounce, long runs ... 
  4. Understand users behaviors : usage frequency, retention, dialogs length,...
  5. Analyze business KPIs of the bot to demonstrate its ROI and levers of improvement

Automation bricks development


AskHub builds efficient automation bricks that are pre-trained using companies conversational data

  1. AskHub automates the recurring dialogue scenarios, on your different conversational channels...
  2. AskHub uses the best automation solutions:
  3. No blackbox effect: the bot code and your conversational data belong to you

Conversational Data-Labelling


AskHub labels millions of conversational data on your channels upon multiple dimensions to provide marketing insights and data for AI training.

  1. Purpose of contact : advice, product availability, after-sale, invoice request…
  2. Sentiment analysis : identify users negative and positive sentiments, irritants
  3. Product catalog : identify if a user talks about one of your product or service
  4. Companies and sectors specific terms

Bot improvement : NLP, UX, KPIs


AskHub turns bots into profitable channels by improving bots comprehension rates, UX and KPIs

  1. NLP training thanks to labelled conversational data to higher the comprehension rate of bots
  2. Mismatches correction to avoid false-positives in user requests comprehension
  3. AB testing of alternative dialog scenarios to improve KPIs
  4. Data-driven NLP enrichment : we automate the services that are requested by users
  5. Monitoring and improvement of bots KPIs