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Coding open-ended text: The rise of the coding robots?

Machine learning as an idea has been with us for decades. In recent years, active research and raw computing power have led to significant advancements in this field. It now feels like machine learning is coming of age and promises to revolutionize the way many industries work. The impact of all this on Market Research is uncertain. However, it is becoming clear that routine and repetitive tasks are the most open to automation through machine learning. One such repetitive task is the process of coding open-ended text responses. In his book “The Rise of the Robots”, Martin Ford asks: “Could another person learn to do your job by studying a detailed record of everything you’ve done in the past? If so, then there’s a good chance that an algorithm may someday be able to learn to do much or all of your job” Does this mean that computers can learn to do Market Research coding instead of humans? For the last year, Tim has (with help) tried to find out the answer to this question. Can a machine really code open ends as well as human? Can modern algorithms magically make sense of what respondents say? Will we need coders in 5 years’ time?

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Coding open-ended text: The rise of the coding robots?

  1. 1. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Coding open ended text: The rise of the coding robots? Tim Brandwood Digital Taxonomy
  2. 2. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Coding: Transforming unstructured text into quant data
  3. 3. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Can machines do the work instead?
  4. 4. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A?empt #1 Off-the-shelf services
  5. 5. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A:empt #1 – Off-the-Shelf Services Verbatim: ‘Because I’m poor’ Answer: ‘No other people’, with confidence 0.012 Does not suit the needs of the MR Industry
  6. 6. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A?empt #1 Off-the-shelf services A?empt #2 Rule-Based Matching
  7. 7. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A:empt #2 – Rule-Based Matching Code 18 – Fewer Distractions Example: “I can concentrate better.” “There are no distractions” Code 18 – Fewer Distractions “Less inclined to reach for my phone” “No interruptions from the kids” Code 18 – Fewer Distractions Code 18 – Fewer Distractions
  8. 8. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A:empt #2 – Rule-Based Matching Rule 2: “popcorn” + “don’t” = Code 2 Example: “I like popcorn” Complicated setup Difficult to maintain Not scalable “I don’t like popcorn, I love it!” Rule 1: “popcorn” = Code 1 “I don’t like popcorn” …
  9. 9. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence A?empt #1 Off-the-shelf services A?empt #2 Rule-Based Matching A?empt #3 Machine Learning Approach
  10. 10. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Learning by Example The AI learns by example as Coders do their normal work Assistance improves over Nme AI assists Coders where appropriate A:empt #3 – Machine Learning Approach
  11. 11. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence “There are no distractions” Code 18 – Fewer Distractions “There are no distractions” “Fewer distractions” “It’s not as distracting” Code 18 – Fewer Distractions
  12. 12. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Codeit: Result from a NPS Survey 3 different Modes based on the confidence of the AI: 1. Autocoded Mode: The AI is certain 2. Assisted Mode: Less certainty - needs verificaNon 3. Manual Mode: Really difficult verbaNms AI switched off
  13. 13. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Using AI to support the coding process ArNficial Intelligence
  14. 14. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence High AutomaNon Where are we now? Manual Coding Text AnalyNcs AI Assisted Coding Full AI Coding ?? Precision Low High
  15. 15. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Conclusion: The rise of the coding robots? AI can help the coders but not replace them New markets More value More volume Be?er AI
  16. 16. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Thank you for your a:enPon! If you’d like to know more please contact: Pm@digitaltaxonomy.co.uk or pat@digitaltaxonomy.co.uk h:p://www.digitaltaxonomy.co.uk
  17. 17. Coding open-ended text: The rise of the coding robots? Tim Brandwood, Digital Taxonomy Artificial Intelligence Q & A Sue York The Handbook of Mobile Market Research Tim Brandwood Digital Taxonomy

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