Brand coding is a unique challenge, codeit is the only tool that truly gets it
Coding brand mentions from a survey seems simple, at first. But then, it throws up a whole set of painful problems: large volumes of messy data, full of typos, multiple mentions and awkward data formats. codeit knows how to make light work of this.
If you’re starting from scratch, the Extract feature contains a dedicated algorithm – unique to codeit – that understands how to handle typos, brand variants and those awkward respondents who type ten brand names in a single text box.
If you have historical coded data, this can be also applied to autocode new data consistently and accurately every time.
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Autocodes upwards of 90% of your data out of the box
A dedicated algorithm optimized for the challenges of messy brand data
Handles large volumes of data without breaking a sweat
A dedicated coding screen for reviewing and refining
Your own text rules for custom pattern matching
Works with codeit’s Machine Learning, so the more you code, the smarter it gets
Discover the common challenges of brand coding in market research and learn how codeit can help you handle this kind of data properly.
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Coding brand mentions is an entirely different challenge to coding full open ends. So, codeit contains dedicated tools optimised for the challenge of brand coding. A separate Extract process knows how to deal with this kind of messy data to extract a codeframe and autocode data. A dedicated Refine screen is designed to make it easy to work with large volumes of repetitive brand mentions from surveys.
Yes. Our dedicated brand coding AI has been designed to handle the challenges that brand coding throws up. It has fuzzy matching that can code brands even if they’re misspelled.
Yes. Just like full open ends, you can use codeit’s industry-leading user interface to easily review and refine the AI generated codeframe and autocoding. Any refinements you make will feed into the codeit AI, so it continues to learn by your example.
Yes. If you have an existing brand list you can easily import this as a codeframe for codeit to use. This codeframe can act as a fixed codeframe or as a starting point that the AI can also extend by suggesting additional codes that are missing.
Yes. Often respondents will list multiple brand names, even if you expect them to only enter one. The codeit AI is designed to handle this and will autocode each brand separately.
Yes. If you’re running a multi-country survey, the codeit brand coding system can handle brand mentions in multiple languages. For even greater precision, your codeframe can also contain translations for each brand.
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