Brand Coding in codeit

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.

codeit’s unique solution

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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Key Features

Autocoding

Autocodes upwards of 90% of your data out of the box

Optimized for brands

A dedicated algorithm optimized for the challenges of messy brand data

Big data

Handles large volumes of data without breaking a sweat

Dedicated UI

A dedicated coding screen for reviewing and refining

Customizable

Your own text rules for custom pattern matching

Machine learning

Works with codeit’s Machine Learning, so the more you code, the smarter it gets

What our clients say...

codeit has made our large coding projects much quicker and easier to manage. The brand coding functionality is particularly valuable, as it handles real-world brand mentions without the manual work that we previously had to do. The platform is user-friendly and can deal with large volumes of open-end responses efficiently without sacrificing accuracy.

Jacob Walker Strat7 Audiences

The challenges of coding brands in Market Research

Discover the common challenges of brand coding in market research and learn how codeit can help you handle this kind of data properly.

Frequently asked questions

How does codeit work with spontaneous brand mentions?

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.

Can codeit cope with typos and misspellings?

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.

Can I refine the AI generated output?

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.

Can I use an existing brand list?

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.

Can it handle multiple brand mentions?

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.

Does it work in multiple languages?

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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