CATEGORY REVIEW

A category review is a health check of a product group or category. It uses the latest clustering and assortment planning tools combined with our expert in house consultant’s, to give you informed insight into your range and stores. This allows you to make decisions regarding your range and assortment plans.

An article by OSP Retail

CATEGORY REVIEW

A category review is a health check of a product group or category. It uses the latest clustering and assortment planning tools combined with our expert in house consultant’s, to give you informed insight into your range and stores. This allows you to make decisions regarding your range and assortment plans.

An article by OSP Retail

Category Review

Category Review introduction

A retailers Category Review process is likely to cover some or most of the following –

Financial Analysis, Customer Insight, Rest of Market Comparisons, Store Development Initiatives & Innovation, Marketing Strategy, Store Clustering, Range Assortment, Space Planning and ultimately Store Implementation.

OSP has now extended its services to Retailers and Suppliers to cover Store Clustering, Range Assortment, Macro and Micro Space Planning.

 

Clustering and Assortment introduction

Group stores by using actual customer sales at a Category or Product level then overlay demographics to determine the type of stores that will require a variance in range assortments.

Using Top Down constraints ensure you can control the stores in a cluster and the assortments assigned to them.

 

Clustering and Assortment detail

Why Use Clustering –

Clustering stores has been around for many years, this has usually been completed for mainly the whole store using “Top Down” attributes.

Modern day clustering needs to be able to cluster at Total Store and/or Category level using consumer sales purchasing patterns.

It is important whilst clustering using this method that the opportunity exists to recognise “Top Down” constraints.

Clustering at a product sales level will produce analyses that ultimately contribute to efficiencies in Logistics and the Supply Chain.

By clustering at consumer product sales levels, users need to be able to overlay demographic and geographic analyses at a store contribution level. This enables the user to define what are the different types of store clusters?

Most retailers today are only resourced to manage a limited amount of store clusters and/or assortments, so it is important that any clustering solutions has the flexibility of controlling the amount of clusters produced ensuring maximum efficiency.

The most important part of the cluster analyses is being able to understand shoppers behaviours with the ability to determine the over/under indexing of sub categories, sectors and product assortments. Once this can be established it is very easy to ensure you can identify the correct assortment for each store cluster.

One of the key areas for customer satisfaction is being able to range local products in the correct stores/clusters. The clustering analyses will enable the user to determine exactly which products are successful in which cluster.

In any Category Management Programme there are many stakeholders involved, behavioural clustering provides detailed analyses for strategic decisions to be taken in Merchandising Space, Range Assortment, Pricing, Marketing, Promotions and Seasonal areas.

Top Down Clustering versus Bottom Up –

Traditionally “Top Down” attributes have been used to determine store clusters, this may be Geographical Regions, Store Size, Sales Value, Store Locality, Demographics or Supply Chain Depots. Whilst this will achieve operational savings, it does not satisfy consumer expectations.

“Bottom Up” attributes look directly at the product sales mix through Category, Sub Category and Sector attribute levels, plus many others. This identifies product trends, consumer behaviour, shopping missions and localised/regional assortments.

Not only will this achieve operational savings, but will also satisfy consumer expectations.

Where does Clustering fit in the Category Review Process? –

Any Category Management process is likely to involve – Financial Analysis, Customer Insight, Rest of Market Comparisons, Store Development Initiatives & Innovation, Marketing Strategy, Store Clustering, Range Assortment, Space Planning and ultimately Store Implementation.

Store Clustering is usually conducted early in the process after the initial Financial Analysis and Customer Insight (e.g. Loyalty Data analysis). The results of the clustering work can then contribute to the Category Management process continuing from Rest of Market Data through Store Initiatives, Marketing, Ranging and finally Merchandising.

Clustering Theory –

“Top Down” constraints will use an average approach to assortment and space, which is accurate for stores that are close to the average, but stores that are either (for e.g.) – low/high on sales or low/high for price, these stores are not very well served.

Relex Clustering uses “Bottom Up” attributes to identify similar purchasing patterns. It uses Cohesion, Separation and the Number of Stores in a Cluster to create up to 25 cluster schemes, ranking the most accurate number of clusters. The solution will complete a hundred iterations to ensure the final ranked number one cluster scheme is the most accurate.

Cohesion:           

Ensuring that the stores within a cluster are as close together as possible in terms of behaviour.

 

Separation:       

Ensuring that the clusters are as far apart from one-another as possible in terms of behaviour.

 

Population:       

Ensuring that there are as many stores within a single cluster as possible.

The user can determine which attributes to use as an initial dry run. It is not uncommon to use a typical category Customer Decision Tree using sales value, volume and profit metrics. Once completed, different versions of this can be added and the solution will compare up to five different schemes that use the same Store, Product and Performance data. The most accurate cluster scheme and clusters can be identified for the user to select.

What Insight will Clustering deliver? –

Once the solution has determined the most accurate cluster scheme, a number of analyses can take place. These include viewing on screen the level of variance by chosen attribute or by decision tree, store data and demographics by cluster.

 

Following this the user can also map out the clusters to Google Earth to see what levels of geographical variation exist.

This example highlights in different colours the boundaries surrounding the clusters. Here, we can see a regional trend for one cluster which is concentrated in the South region whereas the other clusters overlap in terms of geographical distribution.

These levels of analyses will identify what type of clusters have been created. For store attributes this might include:

Cluster 1

Large Stores,
High Spend,
Northern

Cluster 2

Large Stores,
Low Spend,
Southern,
Food to Go

Cluster 3

Small Stores,
Low Spend,
Impulse

Using the pre-determined reports or the user report functionality store, product and demographic data can be exported to excel for further analysis. This will allow additional information to determine the type of clusters. These include –

Cluster 1 – Weekly Shop, Higher Price, Larger Sizes, Premium Products

Cluster 2 – Food to Go, Shopping Mission, Category Flow

Cluster 3 – Daily Shop, Lower Price, Smaller Sizes, Private Brand

 

Finally using demographic data by post/zip code will show –

Cluster 1 – Premium, High income, Large Houses, Older Age

Cluster 2 – Mainstream, Medium Income, Families with Children

Cluster 3 – Economy, High Ethnicity, Large Families, Low Education

 

Armed with all this information it is very easy to finally “name” the type of clusters that will be used in determining the assortment and space.

Assortment decisions by category –

All retailers will have different strategies on how they determine the correct range assortment, one example may be classifying the assortment by product positioning.

 

Hero –                  Innovation & Dynamic

Develop –            Volume growth within the category or playing catch up.

Maintain –          Medium term static products, successful but little change

Reduce –             Declining products, or ones generally over spaced.

 

Recognise the Opportunity –

There are many considerations to make in using the clustering analysis to determine the assortment, these may include – Core Range, Choice, Regional & Local Products, Media Activity, Fresh Food Wastage, Availability, Logistical Efficiency & Restraints.

In addition, working with many stakeholders in the Category Management process, retailers may want to consider a more streamlined Pricing, Promotion and Space Strategy using different options for Premium versus Economy Clusters.

Proven success from retailers adopting these opportunities using Relex Clustering has seen categories increase in sales and profits, increases in stock turn and decreases in Fresh Food Wastage.

Using OSP to analyse your sales through clustering will identify stores that operate in a similar way and enable your business to create assortments that achieve customer expectations. Lost sales will be a thing of the past.

Category Review

Category Review introduction

A retailers Category Review process is likely to cover some or most of the following –

Financial Analysis, Customer Insight, Rest of Market Comparisons, Store Development Initiatives & Innovation, Marketing Strategy, Store Clustering, Range Assortment, Space Planning and ultimately Store Implementation.

OSP has now extended its services to Retailers and Suppliers to cover Store Clustering, Range Assortment, Macro and Micro Space Planning.

 

Clustering and Assortment introduction

Group stores by using actual customer sales at a Category or Product level then overlay demographics to determine the type of stores that will require a variance in range assortments.

Using Top Down constraints ensure you can control the stores in a cluster and the assortments assigned to them.

 

Clustering and Assortment detail

Why Use Clustering –

Clustering stores has been around for many years, this has usually been completed for mainly the whole store using “Top Down” attributes.

Modern day clustering needs to be able to cluster at Total Store and/or Category level using consumer sales purchasing patterns.

It is important whilst clustering using this method that the opportunity exists to recognise “Top Down” constraints.

Clustering at a product sales level will produce analyses that ultimately contribute to efficiencies in Logistics and the Supply Chain.

By clustering at consumer product sales levels, users need to be able to overlay demographic and geographic analyses at a store contribution level. This enables the user to define what are the different types of store clusters?

Most retailers today are only resourced to manage a limited amount of store clusters and/or assortments, so it is important that any clustering solutions has the flexibility of controlling the amount of clusters produced ensuring maximum efficiency.

The most important part of the cluster analyses is being able to understand shoppers behaviours with the ability to determine the over/under indexing of sub categories, sectors and product assortments. Once this can be established it is very easy to ensure you can identify the correct assortment for each store cluster.

One of the key areas for customer satisfaction is being able to range local products in the correct stores/clusters. The clustering analyses will enable the user to determine exactly which products are successful in which cluster.

In any Category Management Programme there are many stakeholders involved, behavioural clustering provides detailed analyses for strategic decisions to be taken in Merchandising Space, Range Assortment, Pricing, Marketing, Promotions and Seasonal areas.

Top Down Clustering versus Bottom Up –

Traditionally “Top Down” attributes have been used to determine store clusters, this may be Geographical Regions, Store Size, Sales Value, Store Locality, Demographics or Supply Chain Depots. Whilst this will achieve operational savings, it does not satisfy consumer expectations.

“Bottom Up” attributes look directly at the product sales mix through Category, Sub Category and Sector attribute levels, plus many others. This identifies product trends, consumer behaviour, shopping missions and localised/regional assortments.

Not only will this achieve operational savings, but will also satisfy consumer expectations.

Where does Clustering fit in the Category Review Process? –

Any Category Management process is likely to involve – Financial Analysis, Customer Insight, Rest of Market Comparisons, Store Development Initiatives & Innovation, Marketing Strategy, Store Clustering, Range Assortment, Space Planning and ultimately Store Implementation.

Store Clustering is usually conducted early in the process after the initial Financial Analysis and Customer Insight (e.g. Loyalty Data analysis). The results of the clustering work can then contribute to the Category Management process continuing from Rest of Market Data through Store Initiatives, Marketing, Ranging and finally Merchandising.

Clustering Theory –

“Top Down” constraints will use an average approach to assortment and space, which is accurate for stores that are close to the average, but stores that are either (for e.g.) – low/high on sales or low/high for price, these stores are not very well served.

Relex Clustering uses “Bottom Up” attributes to identify similar purchasing patterns. It uses Cohesion, Separation and the Number of Stores in a Cluster to create up to 25 cluster schemes, ranking the most accurate number of clusters. The solution will complete a hundred iterations to ensure the final ranked number one cluster scheme is the most accurate.

Cohesion:

Ensuring that the stores within a cluster are as close together as possible in terms of behaviour.

 

Separation:

Ensuring that the clusters are as far apart from one-another as possible in terms of behaviour.

 

Population:

Ensuring that there are as many stores within a single cluster as possible.

The user can determine which attributes to use as an initial dry run. It is not uncommon to use a typical category Customer Decision Tree using sales value, volume and profit metrics. Once completed, different versions of this can be added and the solution will compare up to five different schemes that use the same Store, Product and Performance data. The most accurate cluster scheme and clusters can be identified for the user to select.

What Insight will Clustering deliver? –

Once the solution has determined the most accurate cluster scheme, a number of analyses can take place. These include viewing on screen the level of variance by chosen attribute or by decision tree, store data and demographics by cluster.

 

Following this the user can also map out the clusters to Google Earth to see what levels of geographical variation exist.

This example highlights in different colours the boundaries surrounding the clusters. Here, we can see a regional trend for one cluster which is concentrated in the South region whereas the other clusters overlap in terms of geographical distribution.

These levels of analyses will identify what type of clusters have been created. For store attributes this might include –

Cluster 1 – Large stores, High Spend, Northern

Cluster 2 – Large Stores, Low Spend, Southern, Food to Go

Cluster 3 – Small Stores, Low Spend, Impulse

 

Using the pre-determined reports or the user report functionality store, product and demographic data can be exported to excel for further analysis. This will allow additional information to determine the type of clusters. These include –

Cluster 1 – Weekly Shop, Higher Price, Larger Sizes, Premium Products

Cluster 2 – Food to Go, Shopping Mission, Category Flow

Cluster 3 – Daily Shop, Lower Price, Smaller Sizes, Private Brand

 

Finally using demographic data by post/zip code will show –

Cluster 1 – Premium, High income, Large Houses, Older Age

Cluster 2 – Mainstream, Medium Income, Families with Children

Cluster 3 – Economy, High Ethnicity, Large Families, Low Education

 

Armed with all this information it is very easy to finally “name” the type of clusters that will be used in determining the assortment and space.

Assortment decisions by category –

All retailers will have different strategies on how they determine the correct range assortment, one example may be classifying the assortment by product positioning.

 

Hero –                  Innovation & Dynamic

Develop –            Volume growth within the category or playing catch up.

Maintain –          Medium term static products, successful but little change

Reduce –             Declining products, or ones generally over spaced.

 

Recognise the Opportunity –

There are many considerations to make in using the clustering analysis to determine the assortment, these may include – Core Range, Choice, Regional & Local Products, Media Activity, Fresh Food Wastage, Availability, Logistical Efficiency & Restraints.

In addition, working with many stakeholders in the Category Management process, retailers may want to consider a more streamlined Pricing, Promotion and Space Strategy using different options for Premium versus Economy Clusters.

Proven success from retailers adopting these opportunities using Relex Clustering has seen categories increase in sales and profits, increases in stock turn and decreases in Fresh Food Wastage.

Using OSP to analyse your sales through clustering will identify stores that operate in a similar way and enable your business to create assortments that achieve customer expectations. Lost sales will be a thing of the past.

“OSP’s unique approach and delivery have provided Wolseley with the foundation, tools, and ability to build on and deliver continued success. Their skill to quickly build the necessary foundations based on business requirements, develop processes and coach individuals have catapulted Wolseley into a new era of true Customer Centric Merchandising. OSP’s ability knows no bounds.

Charles Milgate, Wolseley UK

“OSP has been providing essential ongoing support to Wyevale as it makes the significant transition to planogram all of its retail space, as a result, we have started to maximise our retail space. Their abilities, business understanding, and expertise across different platforms have supported the planogram capture of circa 70% of total SKU count so far”

Peter Aylott, Visual Merchandising Manager

“OSP’s merchandising solution revolutionised our space planning facilities. A thorough day’s training gave us all the tools to get started creating effective planograms. If we ever have any questions the support is always quick, detailed and friendly.”

Sarah Dixon, International Marketing Executive

“OSP stepped into the breach at short notice, quickly acclimatised to the business needs and delivered a superb merchandising outcome which is now being rolled out”

David Weaver, Retail Consulting & Interim Management

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OSP RETAIL LTD

Bakers Loft
Quay Street
Lymington
SO41 3AS
UK

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OSP RETAIL LTD

Bakers Loft
Quay Street
Lymington
SO41 3AS
UK

SOCIAL MEDIA

SUBSCRIBE TO OUR NEWSLETTER