Whether you’re blonde or brunette, naturally straight or curly, here’s one thing that every woman can agree on: No one wants frizzy hair.
The issue of frizziness – how best to avoid it, what are the best products to use etc – is one that female consumers discuss frequently online, and is of obvious interest to FMCG marketers.
One such company, with specialist products in this area, routinely listens in to forums and social chat to understand more about women’s thoughts and feelings about frizzy and non-frizzy hair.
But their legacy process for gathering these insights was clunky and labour-intensive.
Before we got involved, it took the company weeks of manual effort to get any results, even adopting conventional text mining technology and a sampling approach. The amounts of data that could be examined were pretty low too, and because it had to be read manually there was an inevitable risk of bias.
Worse still, the client wasn’t really uncovering any new intelligence. And so they challenged us: could we do things any quicker, simpler – and better?
Travel is one of the most competitive sectors in the online sales arena, and agents and operators are always looking for a way to get an edge on the competition. This well-known holiday lettings company did it through language analysis – and the results were spectacular.
The company wanted to improve its conversion rate from its website. Working on their behalf through search agency novi.digital, we were asked to work out what sort of language works hardest at turning accommodation views into sales, and also what sort of language was least effective.
For a beauty company like Olay, competition doesn’t just come in the form of other products and companies – but also other treatments and therapies, including cosmetic surgery.
So it’s vital for a brand like Olay to be able to understand how its target audiences thinks and speaks about cosmetic surgery – and how attitudes to the topic are changing over time – so that it can position its own product marketing accordingly.
Traditional qualitative methods such as focus groups have struggled to come up with objective insights on cosmetic surgery because of observer bias and the obvious sensitivity of the topic. To compound the issue, there was also a lack of available research prior to 2014.
So we were challenged to find a way to come up with objective, actionable insights, going from further back in time and right up to the present.
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