In an era of exponentially increasing consumer expectations, a business’s profitability and survival depend upon the effective use of tools such as clustering which integrate the different disciplines of category management to boost sales and customer loyalty. Cluster analysis requires retailers to analyse large data sets to gain insight into the purchasing behaviour of their target market and avoid a traditional one-size-fits-all approach to satisfying their needs.
Cluster analysis requires retailers to analyse large data sets to gain insight into the purchasing behaviour of their target market and avoid a traditional one-size-fits-all approach to satisfying their needs.
What is cluster analysis?
The concept behind cluster analysis
Cluster analysis is the process of grouping variables into similar groups within the application of business analytics. Shopper data is grouped according to key characteristics such as age and LSM. The process of cluster analysis is used to reap the benefits of a centralised approach towards retail management while responding to changes in consumer behaviour. Clusters of stores/categories are identified to enable groups exhibiting similar behaviour to be managed together.
The purpose of cluster analysis
Cluster analysis is typically used to implement customer segmentation which allows retailers to group consumers according to similarities in purchase behaviour. Various clustering techniques can be used to develop internally similar groups of consumers who vary in terms of shopper behaviour, needs, wants, demographics etc. Store-related factors such as geographic location and available shelf space as well as product-related factors such as price, brand, flavour etc. can also be used for clustering. Cluster analysis allows the retailer to describe and subsequently target the main shopper segment per cluster as seen in the example below:
- Family feeders - These consumers complete regular shopping trips for their family. Their basket composition may change over time due to the preferences of the end-users of the items. They are also drawn to products from brand leaders as well as those on frequent promotions.
- Convenience shoppers - These consumers have busy schedules and purchase ready-to-eat and convenience foods regularly. They shop at a variety of retailers and are more likely to engage in online shopping
- Variety seekers - These consumers are highly responsive to new product releases and sales promotions. They are hard to retain and long-term advertising strategies are found to be ineffective due to the high prevalence of brand switching within this segment.
Why should you analyse your clusters?
The data analysis generated from cluster development aids retailers in strategic decision-making through the process of financial analysis, customer insights, market analysis, store analysis, assortment planning, merchandising and store implementation.
What are the advantages of analysing your clusters?
- Increased accuracy of demand forecasting data: purchasing patterns, sales and unit movement of different stores can be predicted by analysing the data from stores within the same cluster.
- Assists with assortment planning: product ranges that are tailored to the dominant consumer segments that shop a particular category will lead to increased sales, units movement and customer satisfaction.
- Increased customer satisfaction & loyalty: a personalised shopping experience increases customer satisfaction, unplanned purchases and repeat purchases.
What are the consequences of not analysing your clusters?
- Generalised shopper offerings: without implementing cluster analysis and consumer segmentation, product assortments offered are generalised and do not provide a tailored shopping experience to consumers
- Segmentation based on subjective instincts rather than concrete data - customer segmentation that is not based on consumer, store and product-related data will yield unreliable and inaccurate results.
- Segments are not monitored over time: When cluster performance is not measured over time, the retailer is unable to measure improvements in shopper satisfaction, profits and customer loyalty. Clustering needs to be maintained to accommodate changes in purchasing patterns and product trends.
How can you approach cluster analysis in DotActiv?
DotActiv makes use of category-based clustering under the cluster maintenance tab of the DotActiv software. The retailer can cluster categories according to:
- Available shelf space (number of drops)
- Cluster detail (store size, category turnover etc.)
- Geographic information (urban, suburban, province etc.)
- Consumer purchasing profile (% basket share)
- Shopper profile (LSM, age, sex, loyalty card holder etc.)
- Store detail (store code, number of tables, opening date etc.)
- User fields (date completed, effective date)
Using DotActiv’s all-in-one software, you are able to create, edit and maintain the clusters per category to ensure that your clusters stay up to date with the changing consumer market and retail industry.