A Pillar of Strength - Private Label Brands & Local Ranging Part 2
A Pillar of Strength
In Part 1, we looked at the origin and evolution of Pillar Brands and ended by asking what the future holds for them.
Customer data has been essential to the recent evolution of private label brands. Shopper data in the the major supermarkets tends to be aggregated at a national level; at this level the data tends to say the same thing across the major grocery retailers, leading to limited differentiation. This obscures local customer needs and results in a large yet boring product range in store. Rather than listing the right products in the right stores for the right customer, shoppers end up with an impossible selection to make from thirty seven types of honey, which do not make for a better ‘honey experience’ at home. Instead it’s annoying to choose and buy honey because they can’t get the one they want in their local store. This doesn’t just apply to regional brands, as the famous P&G Head and Shoulders study outlined back in 2008. So how can retailers really create product differentiation for customers in their offer?
Continuing to manage a range at a national level, using aggregated national data sets, substitutability and derived decision trees, perpetuates a blandness of assortment which doesn’t allow for local customers’ needs to be turned into a competitive advantage. In addition, the products that the (local) customer cares less about can be rationalised, so they don’t clutter up the fixture, or in the case of fresh lines, turn into waste. One of the secrets behind Morrison’s revitalisation over the past 12 months has been to learn from this and focus instead on local products and ranges; ensuring that the offer in each store resonates with their shoppers, by understanding instantly where local and national brands and own label products really matter for customers.
To explore and make decisions at this level of granularity (by Region, County, City, Town, Post Code), data needs to be analysed by store (and product), generating hundreds of thousands data points. This scale of data is far beyond the immediate understanding of the Buyers who typically make ranging decisions; they are experts in negotiating and building supplier relationships, but tend to be less skilled as analysts in spotting patterns, manipulating dendrograms, and then identifying what to do next. They need simple and compelling insight that helps them choose quickly, prevents silly mistakes when selecting their range and decided whether to delist a product, to grow their sales and margin.
Buyers’ category knowledge is essential to interpreting this information, which needs to be presented in a way that doesn’t require a degree in data science to make sense of it. This is where data visualisation comes in; by visualising trading patterns, exploring and their knowledge, these choices come to life. The best tools save buyers time and show them and their merchandising teams what decisions to take, by product, by store, by brand, by region, in an instant.
Attachment : Transforming your Regional and Store data into simple visual actions
Armed with tools like this buyers avoid mistakes such as simply delisting a product or brand in all stores, rather than where it has been over-extended, only to be inundated with complaints and queries from stores and customers and risking a GSCOP infraction. Now they can ensure the right products and brands are in the right stores, with the right space, so they are available to meet customers’ needs. The result is a more relevant range, more suited to local needs; the brands themselves are more valued, and they act as a true point of difference in both neighbourhood stores, and versus the overall retail competitor set.
For a customer, if your local store in Staffordshire stocks the bread from your favourite Staffordshire bakery, the store feels personalised, relevant and more neighbourly to you. Retail sales data will bear this out; while buyers often look at sales performance at a national level, this “averages of averages” approach conceals the local variations in preference.
Taking a local first approach, the bread buyer realised that a local brand was the number 1 performer in its category, in its locality, outperforming the big national brands. It was equally obvious that outside of its locality the brand was over-extended and had high waste, because it had no local relevance to the customer. The buyer therefore adjusted the range accordingly, increasing sales and reducing waste.
Whilst the initial focus was on local brands it soon became apparent there were many other opportunities to localise the offer. Brands behave differently by geography: some known, such as cider in the South West; some suspected, such as crumpets in the North/North West; some completely unknown. One such example was pork pies, which are a much more popular in the Midlands (and North of England), and far less popular in Scotland and Southern England. So to resonate with local customers the question should not be “Do we need a best tier own label pork pie across the country?”, but “Where do we need a best tier own label pork pie (and where don’t we)?”.
Interactive visualisations allow data exploration rather than producing static lists of text and numbers. When data is mapped across the UK, it is possible to surface a deeper level of geographic understanding beyond traditional regional or TV boundaries; for example, preferences based on environment, such as urban vs rural stores. The patterns jumps out of the map, so you can see where urban conurbations drive behaviours - especially versus national stereotypes on affluence and other attributes.
Some of these observed preferences represented shopper missions, such as food on the go for breakfast items like croissants, however other product groups showed a preference that was rooted in the social culture/trend in the urban environment.
‘Free From’ (which has been growing in the UK for several years), is a great example of this. Traditionally this private label brand extension has been focused by retailers on their more affluent stores and these stores listed a larger range of ‘Free From’ products. However, visual analysis of shopper behaviour demonstrated the importance of ‘Free From’ products to many urban stores, irrespective of the affluence of the local residents. The customers of stores in large urban conurbations are more transient than rural and suburban areas and tend to be more cosmopolitan in their attitudes.
Visually combining affluence, geographic, environmental and other forms of data, allows buyers of any skill level to take action quickly and effectively build and change the range, based on observable customer behaviour, rather than on gut feel.
The questions to ask yourself, whether retailer or supplier, are:
· Do you know where you brands really matter and resonate with customers, by region and store?
· Can you see which stores really drive demand and contribute to waste?
· Can you identify future brands and suppliers based on true local knowledge?
If you’ve answered no to any of these, then come and see how Atheon can help you make the right choices, and make your private label brands a real pillar of strength.