Going wider with private brand performance data
I’ve worked in the retail industry for over 30 years now and through my work at S4RB I’ve supported a wide range of international retail clients to maximize the value from the information that they hold relating to their private brand product performance.
This data was historically mainly concerned with pricing and sales volumes but in more recent years expanded into areas of quality and customer experience. This evolution ties in with North American retailers developing the identity of their private brands and beginning to create brands in their own right – think about Kroger’s Simple Truth and Private Selection or Loblaws’ Presidents Choice.
Supplementing the traditional private brand customer feedback channels
The ability to access great quality feedback metrics from call centers, testing regimes, evaluations and reviews of their private brand items is now becoming an absolute must for retailers in a world where they want to compete on level terms with key national brands within their categories to go beyond a simple national brand equivalent.
By sticking with tried and tested methods of analyzing the retailer’s own, securely held data on products, the only window into its competitors’ products ranges, quality and customer opinions has been through specific benchmarks and category reviews that can often prove quite ineffective unless as part of a well formed, consistent and independently-ran monitoring program.
As part of an initiative that we have been running alongside our technical partners, Warwick Analytics, at S4RB we have recently been able to change the picture and finally start to provide private brand owners insights into the experiences and views of their competitors’ product portfolios! And not just sentiment analysis, but customer intent.
Analyzing competitors’ private brand products using Twitter
This exciting development is all down to a combination of some superb AI technology combined with expert training of our consultants who have day-to-day experience of what private brands teams need to compete effectively in brand development.
This powerful analysis engine has been pointed at the product and customer experience data being broadcast by shoppers on Twitter. This information is of course publicly available for any set of competitors but until now, there have been several challenges in utilizing it to its full potential.
Our solution is unique in that we have a machine learning model specifically focused on the needs of private brand teams, rather than limited to the typical banner level monitoring delivered by the brand marketing department which covers everything from store parking to home delivery comments.
The result is that our retail clients can not only reach a deeper level of analysis for their own products but can also look over the shoulders of key competitors to find out specific challenges within a portfolio, be it in packaging, taste or even CSR concerns. They can pick up on reactions to those winning formulas, packaging changes and new product direction as soon as it hits the shelves.
Aggregate omni-channel feedback
The data and analysis can also be aggregated to provide industry-wide insights on private brand products. This all means that a retailer can evaluate the strength of current trends, for example in relation to animal welfare concerns across the entire sector and compare the levels and types of consumer responses to those of its competitors. Our approach allows common categorization of feedback across multiple channels, to provide more in-depth feedback analysis.
The traditional methods used by private brand retailers to assess the quality of their products vs those of their competitors still stand. However, to ‘win’ in private brands, retail teams need to look further. There is a rich seam of customer feedback which is publicly available on social media which we have now made possible to analyse and combine with other, privately-held product data and other text feedback by feeding it into our Affinity™ platform.
The AI behind our social listening tool is different from the other tools out there (which are really just text analysis tools) as it has been developed specific for the retail industry and it benefits from a human-in-the-loop to ensure that nothings is guessed. Sarcasm can now be detected!
In creating an industry specific mode for the grocery retail market, it has been able to accurately determine the root causes for dropped baskets; churn from particular stores, products or grocery brands; recommendations for future products, packaging, recipes and store layout; and early warning of previously unforeseen quality issues.