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Own-label needs its own story

Text analytics reveals what an online clothing retailer needs to do before it can raise its prices

The problem with most customer satisfaction surveys is that the person setting the survey has already chosen what the customer should talk about. If your customers were free to tell you anything, what would they say?

We were asked by the head of Customer Insight at a multi-national online clothing retailer to use text analytics to discover whether the business could easily increase prices.

New Insights from old methods

The client wanted to continue with their quarterly, emailed customer satisfaction survey. We readily agreed, and suggested that if they included more opportunities for customers to express their views freely, rather than via tick boxes, then they could reduce the length of the survey, increase response rates and discover new insights.

We wrote and ran an email survey to more than 10,000 customers in 3 continents

We started our work on the replies with analysis of the structured data to determine satisfaction scores, and then used 3rd party software to spot how themes in the customers’ comments corresponded with these scores.

“Fair” pricing and the “I’m fine, thanks” conundrum

Text analytics revealed a dominant theme in the customers’ attitudes to the pricing of the site’s own-label range: they thought it was “fair”.

Just as any of us should be wary when we return home and our partner says they’re “fine”, we should be careful about what “fair” means.

We used our linguistic skills to look deeper and realised that “fair” was being used to describe pricing that was (begrudgingly) acceptable.

Text analytics had shown us ‘hot spots’ of concerns among customers and we looked at the other attributes of the own-label brand. For the first time, we identified that most customers thought the brand was hollow: it didn’t have a story of its own, and was regarded as merely a copy of other, branded products.

Learning from competitors

In a highly competitive market, with well-supported brands, it was clear that own-label needed its own story and its own values in order to substantiate a price rise.

The final role of our text analytics was to look at customers’ comments about branded, competitive products sold online and identify which of their attributes were most motivating for customers.

From here, we were able to recommend to our clients which of these values could drive their own label and give it more substantive fashion creds.

Download our white paper: “Cold, hard numbers and bright lite insights from text analytics”