S4RB we take some publicly available Tweets looking specifically at packaging. It’s a timely example to use as it is a growing topic as consumers voice more and more environmental concerns, as well as the usual quality issues relating to packaging.
In a generic text analysis model, you might be lucky to pick up packaging issues at all, or at best to pick them up and assign positive or negative sentiment. However, this doesn’t help to action anything, not without further reading and coding. One must also figure out what the themes are to code in the first place.
With ‘human-in-the-loop’ software like PrediCX and the S4RB model specific to grocery retailer, the new signals are referred to a human as they appear so that nothing is missed, nor does it have to be guessed. The data truly speaks for itself!
Here is just one example from the blog post:
Beyond sentiment this richness allows brand owners to understand competitive advantage or disadvantages, which can feed into either marketing or product development. The ease of opening on Aldi’s product will be for more than just bacon!
Also, there’s a hint of long-standing Tesco customer so can add label: “loyal customer”. These tags can both be used to help improve packaging, avoid serious issues, and also improve the brand’s standing to competitors in terms of the features that customers mention. What’s interesting is that a longstanding, loyal Tesco shopper has made an unsolicited comment to Tesco about a competitor. Have they switched? Imploring their favoured brand to improve?