Localization and Clustering: Electronic product tags and internet stores are providing retailers with deep insights on local buying habits. This data can then be integrated with data on leases, costs, store performance, and maintenance to find an optimal location to open a store on. The offerings of the store will be unique to the preferences of the local customers. This will increase sales and prevent retailers from keeping too much stock.
Store grading started many years ago, as retailers saw the need to look at their business based on store groupings — particularly for retailers who had hundreds of stores to manage.
Usually, stores were ranked in terms of sales and grouped, usually by a per cent of average sales across all stores.
The “A” stores were the stores that performed at a certain % above the “average store”; “B” stores were within a mid-range index of the average store, and so on. A stores were the most important stores, because they represented the largest volume for the retailer. Retailers can also take their store grading approach one level deeper; retailers can create category-specific segmentation. Different categories get their own clusters of stores, using the same “A”, “B”, “C” methodology, but at
lower classification within the store.
This is an antiquated and outdated approach that does not consider the shopper and thereby limits the retailer’s ability to focus on shopper. But there are still many retailers who continue to cluster their stores in this limited way.
2.Multiple Attribute Clusters:The next level of clustering gets retailers looking beyond volume and store size. Some attributes may be more physical — like store size, geographic locations and climate conditions. Other attributes are based on the consumer — based on consumer purchase behaviour like loyalty and conversion. Consumer demographics can also be used when store clustering, for example based on income levels, age or ethnicity with a focus on the most loyal or heaviest buying consumers.
Once these attributes are defined and are measurable for each store, store clustering can be done based on these multiple attributes. Because each of these attributes can be significantly different at a category level, store clustering can also be done by category for the most important categories for the retailer. This
drills one level deeper to understanding that all important consumer. Attribute clustering enables retailers to quickly identify clusters of stores with similar demand patterns, which also allow the retailer to focus on the most important target consumer segments for them.
Retail Store Clustering
A starting point for shopper understanding.
The many changes in our competitive Canadian landscape have driven a need to adapt the category management approach to become even more collaborative, flexible, forward looking and shopper centric. A first and necessary step towards shopper understanding is for retailers to group their stores beyond store size and volume and expand to consider shared demographics and customer purchasing patterns.
From here, the opportunity is to create cluster-specific assortments, shelving, promotions and pricing strategies that meet the different shopper needs. Retailers who try to move to shopper-focused solutions will be limited in their approach until they cluster their stores to reflect their shoppers and their needs.
Store clustering and geodemographic data analysis is really the starting point for understanding shopper marketing, or shopper insights. Shopper marketing refers to all manners of influencing the shopper from when the consumer perceives a need and is motivated to start the path to purchase, to creating a shopping list or initiating a shopping mission, to researching a prospective purchase, to considering which retail avenues to shop and choosing a store or web site to shop and ultimately looking at the product choices they present, to making a purchase decision. It also includes all research associated with understanding the shopper path to purchase.
Retailers need to build a new set of strategies for store clusters (groups of stores that have similar shoppers, performance, and traits) or even for unique stores, and then assemble the right resources with the right ideas and competencies to take advantage of the different opportunities. They need to consider the types of consumers that they are trying to get, and how they will meet their needs. Are their target consumers large families with young children; seniors; the environmentally conscious; ethnic consumers; single parents; health-conscious consumers; or consumers with lower income levels?
Store clustering can take on new value as retailers look to localization to differentiate themselves and improve performance. It also helps them to define their target consumer within different store clusters, based on the differences in who is shopping in their stores in each cluster.
When deciding to move to store clustering beyond store groupings, you need to identify and implement a clustering approach that is right for your business — without the need for new systems or major organizational changes. This will limit your investment, add less complexity and give you flexibility to make changes and
adjustments as your store clustering approach evolves. Leave the huge investment for later.
adjustments as your store clustering approach evolves. Leave the huge investment for later.
There are different types of store clustering, starting with the least and moving to the most sophisticated approaches.
1.Store Grading:Store grading started many years ago, as retailers saw the need to look at their business based on store groupings — particularly for retailers who had hundreds of stores to manage.
Usually, stores were ranked in terms of sales and grouped, usually by a per cent of average sales across all stores.
The “A” stores were the stores that performed at a certain % above the “average store”; “B” stores were within a mid-range index of the average store, and so on. A stores were the most important stores, because they represented the largest volume for the retailer. Retailers can also take their store grading approach one level deeper; retailers can create category-specific segmentation. Different categories get their own clusters of stores, using the same “A”, “B”, “C” methodology, but at
lower classification within the store.
This is an antiquated and outdated approach that does not consider the shopper and thereby limits the retailer’s ability to focus on shopper. But there are still many retailers who continue to cluster their stores in this limited way.
Once these attributes are defined and are measurable for each store, store clustering can be done based on these multiple attributes. Because each of these attributes can be significantly different at a category level, store clustering can also be done by category for the most important categories for the retailer. This
drills one level deeper to understanding that all important consumer. Attribute clustering enables retailers to quickly identify clusters of stores with similar demand patterns, which also allow the retailer to focus on the most important target consumer segments for them.
Clustering Helps Retailers Support Merchandise Localization Featured
- Written by Fatima D. Lora
As retailers seek to find innovative ways to draw shoppers into individual stores, clustering has become a focus for Merchandise Planners, as noted in the Boston Retail Partners (BRP) 1st Annual Merchandise Planning & Allocation Benchmark Survey. Nearly one quarter of retailers interviewed are using clustering for Bottom-Up Merchandise Planning.
Clustering also supports store assortment planning for more than 80% of retailers, allocation (approximately 60%) and developing store plans (approximately 35%).
Merchants are using a variety of types of data to develop clusters, including (in order): sales volume (close to 75%), store size (close to 60%), store attributes (approximately 55%) and merchandise characteristics (approximately 45%).
“Clustering at the store level is critical as retailers work to meet customer expectations through localization and customer-centric merchandising,” the report stated. “It appears that retailers are focused on improving this area of planning…without major organizational changes. This follows retailers’ general thinking to enhance the organization through technology without adding more staff.”
Merchandise Planning & Allocation Trends
The BRP study noted a number of trends in the area of Merchandise Planning & Allocation (MP&A) among retailers. For example, more than 80% of retailers reported using Forecasting as part of the Merchandise Planning process, but may not roll that data into actual Planning.
Unit Planning has reached saturation among retailers, at approximately 87%, yet BRP noted that “the usage (of Unit Planning) is generally not fully taken advantage of…because unit are generally an afterthought generated from applying historical average unit retail to the planned sales dollars.
In Store Planning, the most successful retailers are developing Store Plans in units and dollars, BRP reported, “which allows for more accurate plans than retailers who plan against just units or just dollars.”
When conducting Assortment Planning, “most retailers are planning against units, with 64% able to tie the plans back to dollars,” BRP noted. “But to fully tie back to the Merchandise Plans, and thus ensure that the product offering and ‘buys’ support the financial goals, Assortment Plans should be converted to dollars.”
One of the key opportunities for many retailers is to implement Space Planning, according to BRP. Just over 33% of retailers currently use Space Planning, and more than 75% of that group do not link Space Plans with Assortment Plans. “Without that, retailers often fall into a less-efficient ‘assort to space’ model,” according to BRP.
Store Clustering is the grouping of stores based on common store and demographic characteristics. There are two types of store clusters: performance and non-performance based. Performance based store clusters are grouped according to how they perform. For example, store locations with similar sales performance would be placed in the same store group. Non-performance based clusters consider store characteristics such as climate, store size and/or store type etc. Non-performance based clusters also consider customer demographics such as ethnicity, income level, age group, fashion preference etc.
Store clusters are usually developed at a Department and/or Class level. Store Clustering is very important as retail chains begin to consider customer-centric merchandising and tailor their assortments based on localized customer need. Assortment quantities and merchandise mix will be defined based on the characteristics of the various store clusters.
Store clusters are usually developed at a Department and/or Class level. Store Clustering is very important as retail chains begin to consider customer-centric merchandising and tailor their assortments based on localized customer need. Assortment quantities and merchandise mix will be defined based on the characteristics of the various store clusters.
Effective store clustering often considers a combination of at least two types of clusters For example, it is very common to consider both store size and sales volume together as a method of clustering. As shown in the chart below, this approach allows the planner to consider various combinations that will impact the assortments and products provided. A small store with high sales volume will be merchandised quite differently than a small store with low sales volume. The result of good clustering is an improved ability to provide a customer-centric merchandise environment.
Store Clustering Process and Methods
An overview of the pre-season Store Clustering process is shown below.
Store Clustering Process and Methods
An overview of the pre-season Store Clustering process is shown below.
Retail Clustering Methods
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Retail assortment planning is a topic that has increasingly gained attention and momentum within retail executive suites and merchandising solution vendors' future development and enhancement plans. With the wider interest and use of "big data" and analytics – as well as more robust tools for supporting these capabilities and increasingly demanding and fickle customers – wider attention to assortment planning is inevitable. A foundational element of effective assortment planning is the ability to appropriately cluster stores and channels to maximize sell-through and margin potential. However, this key capability is rarely given top priority – often viewed as a mundane, analytical effort and is assumed to be "built in" to the assortment planning solution.
Effective clustering provides the ability to unleash the true potential of assortment planning capabilities, bringing with it significant financial benefits in terms of sales, margin, and inventory utilization – as well as improved customer satisfaction, due to being better able to provide the "right" mix of products for customers, across locations and channels.
In this Point of View, The Parker Avery Group discusses how clustering for assortment planning is an intricate undertaking with a variety of approaches and elements to consider. Granted, there are simple, straightforward clustering methods, but these tend to have significant shortcomings, and typically fail to create assortments that drive meaningful results. Conversely, more sophisticated approaches usually require more skilled resources, solid data integrity, and appropriate supporting solutions to take advantage of the potential these methods can deliver. We will explore ten different clustering approaches in depth and highlight the advantages, disadvantages and under which circumstances each should be used. This understanding, coupled with clearly defined assortment planning objectives, will help retailers understand which clustering approaches are most appropriate to employ.
Assortment Planning is a hot topic, especially amongst retailers, wholesalers and the software developers that offer solutions to these industries. Yet despite lots of conversation, we hear very little discussion about the various clustering methodologies that lie at the heart of most assortment planning approaches. Parker Avery would like to help remedy that situation by examining the various clustering methodologies that we've encountered through working with a variety of retailers, with the aim of providing some insight into which technique or combination of techniques makes the most sense for your business model. Subsequent Parker Avery publications will address other aspects of clustering and assortment planning.
Before we dig into clustering, we need to briefly discuss assortment planning. Assortment planning is a term that has been in widespread use throughout the industry, yet does not have a clear, consistent definition. The meaning can vary depending on the perspective of the user and the situation. The term has been used variously to mean quantifying SKU-level sales and purchases, developing targeted assortments, assortment / space optimization and more. In looking at industry and academic literature dealing with assortment planning ranging over 40 years, nearly every aspect of merchandise planning and space planning has been included. One early attempt at definitive work on the subject[1] included the design of the product hierarchy and layout of the display space as part of the scope of assortment planning. Clearly, that definition is too broad for this current exercise.
For purposes of this conversation, we will define assortment planning as "the practice of developing different assortments for targeted groups of customers." There still may be other functions of the assortment plan. It may, for instance, be used to quantify purchases for each item or help determine the amounts of inventory to be distributed to each store and held back for direct sales. Yet, for this discussion the primary purpose of assortment planning is the development of tailored assortments.
Following this definition, clustering is the mechanism that is used to develop those targeted groups of customers. The ideal state of assortment planning would allow the targeting of a collection of products to each individual customer, based on his or her particular preferences. We may eventually be able to deliver on this ideal state through digital channels, but for the foreseeable future it will not be attainable in the bricks-and- mortar or catalog channels. This is because a multitude of customers and customer types patronize any individual store location, making individual targeting impossible. Clustering seeks to overcome this challenge by grouping together sales outlets (stores, website, catalogs recipients, etc.) that demonstrate similarities in customer shopping behavior.
We can now turn our attention more fully to the topic of clustering. The term clustering refers to "the process of grouping sales outlets together based on similarities or patterns in their underlying customers' behavior." These similarities are most often gleaned from data related to historic or forecasted sales, or information that is descriptive of the customers or the store. Examples of the latter include demographic or climatic information. Clustering is frequently accomplished using a set of statistical algorithms that assemble a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.
The most frequently used statistical method for developing clusters is K-Means clustering, which requires the user to specify a target number of clusters. The algorithm then creates the specified number of groupings, such that the statistical distance between the clusters is maximized. This procedure can be done multiple times and the results compared to determine the optimal number of clusters for the user's purpose. For retail applications, clusters are typically formed by grouping stores and other sales outlets (such as a website or catalog).
Clustering methodologies have a number of applications in retail merchandising. Statistical grouping of sales outlets can be very useful for allocation, macro-space planning / space brokering, size optimization, determining pricing zones, etc. For the rest of this conversation, though, we will concentrate on the application of clustering to assortment planning activities.
In our discussions with clients, we sometimes encounter confusion between assortment clusters and customer segments. Customer segmentation involves the division of a customer base into groups that are similar in ways that are more applicable to product development and marketing. Segmentation uses factors such as age, gender, interests, attitudes and spending habits to classify customers into behavioral or psychographic groups. Examples of these groups might be "Tech-Savvy Millennials" or "Golden Age RV Enthusiasts." While there are many similarities between assortment clustering and segmentation approaches, most attempts to use customer segments for assortment planning don't succeed. This is due to the fact that customer segments do not map cleanly onto sales outlets. Any given store will have some representation of most or all customer segments within its customer base. While it is possible to construct assortments based on customer segments, it is very difficult to determine how to assign those assortments to sales outlets. As a rule of thumb, sales outlets should be clustered for assortment planning, while customers should be segmented for product development and marketing purposes.
Retailers use a variety of tools to create clusters for assortment planning and other uses. These tools include reports and spreadsheets, specialty statistical analysis software packages (such as SAS and Minitab), clustering solutions tailored specifically for use by retailers, and clustering capabilities that are integrated into a broader assortment planning solution. Depending on the organization's clustering and assortment planning philosophy, any of these approaches can work well. When considering the best toolset to deploy at your organization, it's important to consider integration and availability of clustering metadata. Once clusters are created, the cluster assignments typically need to be made available to an assortment planning tool. Your clustering methodology should allow for easy integration of cluster data between your clustering and assortment planning tools. Also, the characteristics of sales outlets that have been placed in the same cluster provide important clues about the product preferences of the underlying customer base to the merchant or assortment planner as they undertake the development of targeted assortments. This cluster metadata may communicate basic information such as the number of stores or geographic location of sales outlets. It may also convey more complex insights, such as demographic information or product preferences of the cluster's customer base. Your toolset should be capable of presenting this type of characteristic cluster information to end users as they make product decisions.
There are many approaches to assortment clustering in use today; some approaches are quite basic, while others require advanced statistical analysis capability. The approaches also may be mixed and matched to meet the particular assortment targeting needs of your organization. We will describe the major ones in this section. One major consideration in determining the ideal clustering method for your company is the complexity that is added to the merchandising process. Some of these approaches require very little ongoing maintenance, while others demand that new clusters be created for each collection or floorset. Some basic methods use the same cluster structure for all categories of product. More complex approaches necessitate the development of different clusters for each category or class of products.
This approach may work well for companies that have a focused, concise product offering that clearly represents the brand image. It also may be applicable to retailers with few outlets or outlets that are situated in very similar markets. Certain premium brands, such as Prada or even Apple, may thrive with this strategy.
On the other hand, retailers with broader product offerings and more diverse store bases may have great difficulty in maximizing sales and margin with this approach. We have frequently heard the lament, "How can we manage multiple assortments when we can't get one right?" The reason is that a single assortment is ill suited to fulfill the needs of a diverse customer and store base.
This represents a good preliminary approach to differentiating assortments. It allows the retailer to take advantage of the unique display characteristics of each sales channel, particularly the "endless aisle" offered by websites. It also increases the probability that the retailer can meet a customer's needs by allowing fulfillment from multiple assortments across multiple channels. Typically, the on-line channel has the broadest offering, with stores and catalogs being culled down from there. Sometimes, retailers will have "retail only" items as well, usually in cases where products are impacted by state regulations (e.g. liquor or firearms) or have physical characteristics that make them impractical to sell on-line.
The downside of this approach is that it can sub-optimize the bricks-and-mortar channel. It is too simplistic to reflect regional and local differences in customer preference and demand. Since it doesn't allow for the tailoring of assortments within a channel (only across channels), it is likely that retailers following this approach are suffering from slow moving choices in some locations and excess demand in others.
This is a very common clustering approach, whose main benefit is that it is relatively easy to understand and implement. Frequently, some type of store volume-based attribute is already available in the location data, having been created for use by allocation or replenishment tools. Supporters will use this approach to expand the breadth of the product offering in high volume stores and edit it in low volume stores.
Unfortunately, sales volume-based clustering isn't very useful for developing differential assortments. Store sales volume typically is driven by population density, traffic patterns, co-tenancy, local competition and other factors not related to the product offering. Stores located in Miami and in Minneapolis coincidentally may be in the same sales volume cluster, but it would be a mistake to assume that they would require the same items. And what to do with high sales volume stores with small selling floors? This approach does not help much in tailoring assortments to those needs.
This clustering approach is helpful in determining the number of choices to house in each cluster, as it is based on the display capacity of the outlet. It also has the benefit of being relatively easy to understand and execute.
However, it does little to aid in determining how to make up the actual content of those choices – i.e., the assortment of products. As with sales volume-based clusters, stores in Miami and Minneapolis (as an example) may have the same selling square footage, but might require dramatically different assortments. Also, following the pure capacity- based approach does not take into account the sales velocity generated by the outlet. This could result in sending an excessively broad assortment to a large square footage store with poor sales potential.
While we mentioned earlier that all of these approaches could be mixed and matched, this particular combination is common enough to merit being discussed separately. It has the benefit of taking into account both capacity and sales volume, so it provides a decent chance of getting the size of the assortment correct.
Unfortunately, once again this approach doesn't help much with determining the content of the assortment to be assigned to each cluster. Also, this combination of factors has the effect of multiplying the resulting number of clusters, which dramatically increases assortment planning complexity. Most retailers that follow this method end up with a cluster of high capacity / low volume stores, presenting a major challenge:
• Does the retailer provide these stores with an extended assortment to help fill the display space? In so doing, they will be sending many below average performing items to a low volume store, creating markdown jeopardy.
• Or do they send an abbreviated assortment, commensurate with the sales volume, but leave a significant portion of display space empty?
• Does the retailer provide these stores with an extended assortment to help fill the display space? In so doing, they will be sending many below average performing items to a low volume store, creating markdown jeopardy.
• Or do they send an abbreviated assortment, commensurate with the sales volume, but leave a significant portion of display space empty?
Neither option seems to fit the bill.
This approach is frequently used by retailers who carry items with pronounced seasonality, such as swimwear, winter coats or patio furniture. While we wouldn't propose that winter boots appropriate for Alaska should be carried in Los Angeles, this method can be less straightforward than it seems. Parker Avery has performed multiple studies on seasonal selling patterns that have shown counter-intuitive results. In one example, a national apparel chain discovered that in January, its bestselling swimsuit stores were located in frigid Minnesota. In another study, no evidence could be found that sales of winter jackets spiked earlier in the North than in the South.
Before embarking on a climate-based clustering effort, we would advise performing in- depth analyses of the regional sales performance of seasonal merchandise to validate the approach. Once the underlying data confirm the validity of a climate-based scheme, these clusters can be used to tailor assortments or adjust the timing of item introductions to closely match local demand patterns.
Store type-based clusters frequently arise from local store managers' or district managers' requests for specific types of merchandise, based on direct customer feedback or perceived market needs. Examples of store type clusters include "campus stores" (that require more back-to-school items and appropriate team merchandise) or "resort stores" (that require beach towels, sunscreen and flip flops throughout the year).
While store types are often identified using input from the store operations organization, this information is sometimes combined with data analysis. Store type clusters tend to be created and maintained manually as a location attribute, but still may be interfaced into an assortment planning solution to allow visibility and planning by end users. This approach can be very effective at capturing some limited localized demand. On the other hand, the manual nature of this approach usually precludes deploying it on a broad scale. Also, assortment requests from store operations can be based on a few anecdotal customer interactions, which may not be representative of true underlying demand. These cases can result in a lot of effort, but ultimately drive few incremental sales.
This method is not broadly used, but may have some application for retailers that face strong, differentiated, regional competitors. As an example, a broad-line mass merchandiser may choose to beef up their assortment of hunting, fishing and camping gear if they compete in a market against an outdoor specialty superstore. Many retailers face a distinct set of competitors for their ecommerce channel, and may elect to use this approach to offer special or extended assortments. This approach does not provide merchants and assortment planners any information about the types of products they should add to or edit from their assortments. Instead, competitive shopping and other forms of research must be used to help determine the optimal product mix. Competition- based clusters are also quite useful for price management (but that's a topic for another day).
This approach has some benefit, particularly if products within an assortment have a clear appeal to a particular demographic group, such as with ethnic foods or specialized products for the aged. Clusters are created based on characteristics that might include average age, ethnicity, income level, population density, educational level and others.
There are several challenges with the demographics-based technique, however. The first is that the demographic data associated with any particular store may not actually represent the actual shoppers. Most demographic data that is available to retailers is based on U.S. Census data. It represents the characteristics of the population of a certain radius around the store, typically 5 miles. Unfortunately, the population that shops at a particular store is not necessarily representative of the population surrounding the physical address of the store.
Let's examine a retail outlet located adjacent to Penn Station in New York City. The demographics-based clustering approach would suggest that the population shopping that store would resemble that of Manhattan. Yet, since Penn Station is the terminus of the Long Island Railroad, which carries millions of commuters to the city each year, the stores' actual shoppers might more closely resemble the more middle and working class folks from Nassau and Suffolk counties.
One way around this problem is to make use of data about the actual customers of each outlet. This is sometimes obtained through credit card companies, but this approach only captures credit card customers, who may not be representative of the overall customer base. Increasingly, retailers are relying on Customer Relationship Management (CRM) data from loyalty programs to characterize and cluster sales outlets. This method is promising, but may still exclude a significant portion of shoppers that have not joined the loyalty program.
Most importantly, knowing the underlying demographic characteristics of a group of stores does not mean that a merchant knows how to assort to that customer base. The relationship between product preferences and demographics isn't always obvious. For example, consider markets with a high penetration of Hispanic people. The product preferences of such a market in Miami (with its strong Cuban and South American influence) may be entirely distinct from those of Arizona (much more akin to those of neighboring Mexico). In most cases, this kind of information, while accurate, may be misleading.
In our opinion, this is one of the most valuable clustering approaches and demands a lengthier discussion. This approach has the benefit of the clusters being explicitly tied to the make-up of the assortment. It removes the guesswork on the part of the merchant about which products satisfy the customers in which clusters. To illustrate, an attribute that may be useful for jewelry might be "Material," with distinct values including Gold, Silver, Stainless Steel, Platinum, Hematite, etc. Outlets are clustered based on their relative sales of products exhibiting these attributes. Should a store exhibit an affinity for silver jewelry (perhaps because it is based in the Southwestern United States), then the merchant can simply assign more silver items to the assortment for that store.
Multiple attributes can be used to describe the same assortment; for example, jewelry could also be described by "Price Point" or "Gem Type." To identify the best attributes, we recommend undertaking a statistical analysis of the relationship between the available product attributes and sales to determine which attributes drive differential sales performance from outlet to outlet. The attributes with the most impact should be the ones used for clustering.
To further illustrate, let's examine a real world example from the category of "Beverages." In this case the most sales-impactful attribute happened to be "End Use," with attribute values including Isotonic, Energy, Vitamin, Tea, Kid's, Soda, Sparkling Water, etc. Stores were clustered based on the penetration of each of these attributes in their sales history. Below are graphical representations of the penetration of each of those attributes values in two of the resulting clusters.
As you can see, customers in the stores that make up Cluster 1 have a clear preference for New Age, Teas, Vitamin, and Energy drinks. These same customers are not as interested in traditional beverages, such as Still Water, Sparkling Water and Juice. Cluster 2 seems much more oriented toward thirst quenching, over-indexing on Still Water, Soda and Isotonic (such as Gatorade), at the expense of New Age, Vitamin and Energy. The strength of those preferences can even be gauged by the magnitude of the index number. In Cluster 1, customers have bought 1.4X the overall average amount of Energy drinks, while they have purchased half the overall average amount of Sparkling Water. This kind of precise preference data can directly inform the number of choices assigned to each cluster that bear each attribute.
Once attribute clusters are formed, demographic data for each cluster can be analyzed to determine if there are any significant relationships between population characteristics and cluster membership. If such relationships exist, there is now some compelling insight into the make-up of the customer base of that cluster. If demographics reveal no significant population characteristics for the cluster, all of the necessary information still exists to make intelligent assortment decisions. Demographic cluster characteristics can also be used to create a model to predict which cluster a new store might fit into.
One significant drawback of this approach is that it demands the use of different clusters for each product category, which tends to increase the complexity of creating and maintaining store clusters – especially when faced with a rapidly changing store base. Another potential source of complexity comes with categories that have many different seasonal assortments, for example in apparel. If an apparel retailer has six seasons or collections a year and drops six distinct assortments with different attributes, then the clustering and assortment planning processes may have to be performed six different times.
Another shortcoming of this method is that it does not take into account the display capacity of the stores within each cluster. To overcome this problem, a hybrid method could be employed that includes both the penetration of product attributes and the capacity of the store. Perhaps a preferable method would use attribute-based clustering to determine the content of a "master assortment" for each category. Items within that "master assortment" could then be ranked by sales importance and culled down to fit the available display space in each store.
But the era of standardization is ending. Consumer communities are growing more diverse—in ethnicity, wealth, lifestyle, and values. Many areas, moreover, are now saturated with big-box outlets, and customers are rebelling against cookie-cutter chain stores that threaten the unique characteristics, such as architectural styles and favored brands, of their neighborhoods. When it comes to consumer markets, one size no longer fits all. In response, smart retailers and consumer goods companies are starting to customize their offerings to local markets, rolling out different types of stores, product lines, and alternative approaches to pricing, marketing, staffing, and customer service. They’re moving from standardization to localization.
The era of standardization is ending. Consumer communities are growing more diverse—in ethnicity, wealth, lifestyle, and values.
Combining sophisticated data analysis with innovative organizational structures, they’re gaining the efficiencies of centralized management without losing the responsiveness of local authority. The greatest benefit of moving from standardization to localization is strategic. Standardized offerings discourage experimentation and are easy for competitors to copy. (Sam Walton openly referred to Kmart as the “laboratory” he copied while growing Wal-Mart.) Customization encourages local experimentation and is difficult for competitors to track, let alone replicate. When well executed, localization strategies can provide a durable competitive edge for retailers and product manufacturers alike.
Reinventing the Big Box
Although standardization has been a powerful strategy in consumer markets, it’s reached the point of diminishing returns. Customers are becoming more diverse, according to studies by geodemographers, people who study the population characteristics of specific geographic areas. Measuring ethnicity, age, wealth, urbanization, housing styles, and even family structures, the demographic company Claritas determined in the 1970s that 40 lifestyle segments were sufficient to define the U.S. populace. Today, that number has grown to 66, a 65% increase.
Diversity is not the only nail in standardization’s coffin. Many large chains have erected so many stores that they’re literally running out of room to expand. They can’t open new outlets without cannibalizing old ones. Standardized chains are also meeting with other constraints: Where attractive locations are still available, attempts to build stores often face fierce resistance from community activists. From California to Florida to New Jersey, neighborhoods are passing ordinances that dictate the sizes and even architectural styles of new shops. Building more of the same—long the cornerstone of retailer growth—has been tapped out as a strategy.
Finally, standardization can do the most strategic damage by forcing products and practices into molds. The resulting homogenization of business tends to undermine innovation, all the way up the supply chain. Managers become so focused on meeting tight operational targets—and stamping out exceptions—that they begin to consciously avoid the experimentation that leads to attractive new products, services, and processes. In the end, standardization erodes strategic differentiation and leads inexorably toward commoditization—and the lower growth and profitability that accompany it.
The good news is that there’s a way out of standardization’s dead end. Technological advances, from checkout scanners and data-mining software to Internet stores and radio frequency identification (a wireless technology that uses small electronic tags to identify and track objects), are providing retailers and their suppliers with deep insight into local preferences and buying behaviors. For the first time, mismatches in supply and demand at individual stores can be pinpointed immediately. The new data make it possible to “localize” stores, products, and services with unprecedented precision. (For an example of the new insights technology can deliver, see the sidebar “Mining the Internet.”)
Mining the Internet
Our analysis of 30 localization leaders, including Best Buy, Tesco, and VF, documents these benefits. Even Wal-Mart, the sultan of standardization, is moving toward localization. The company has made customization the cornerstone of its “store of the community” strategy, announcing that it plans to tailor formats and products to the local clientele in every store in its chain.
Wal-Mart uses a rigorous process to ensure that customization does not undermine its traditional efficiency. That process begins when a store is still on the drawing board. Company real-estate teams deeply research the local customer base when scouting for locations. Designers then create the store’s format by combining suitable templates—stores near office parks, for example, with prominent islands featuring ready-made meals for busy workers. Templates allow Wal-Mart to maintain considerable economies of scale. The company has also developed a sophisticated logistics system, encompassing 110 distribution centers in the United States alone, to manage complex delivery schedules quickly and efficiently.
Through its Retail Link program, Wal-Mart works with suppliers to tailor store merchandise with similar precision. Built on a vast database, Retail Link provides both local Wal-Mart managers and vendors with a two-year history of every item’s daily sales in every Wal-Mart store. Using the Retail Link Web portal, Wal-Mart and its suppliers can create maps of local customer demand, indicating which merchandise should be stocked when and where. For example, Wal-Mart stocks about 60 types of canned chili but carries only three nationwide. The rest are allocated according to local tastes. Five years ago, Wal-Mart used just five planograms (diagrams showing how and where products should be placed on retail shelves) to adapt its soup selection to local preferences. Today, with the help of Retail Link, Wal-Mart and its suppliers use more than 200 finely tuned planograms to match soup assortments to each store’s demand patterns—raising soup’s growth rate by several points in the process. Product companies also use the system to track their sales and inventory levels in Wal-Mart’s stores and distribution centers and to develop pricing and marketing programs to boost sales.
Thinking in Clusters
As Wal-Mart and other leaders have discovered, successful localization hinges on getting the balance right. Too much localization can corrupt the brand and lead to ballooning costs. Too much standardization can bring stagnation, dooming a company to dwindling market share and shrinking profit.
Striking the right balance means understanding which elements of a business should be considered for localization, how costly they are to customize, and how much impact they will have from one store to another. Far from being an all-or-nothing game, localization can take place in myriad ways (see the exhibit “What, Where, and When Should We Localize?”). For one retailer, it might make sense to have a highly localized staffing approach but a standardized product mix, while another retailer may warrant the opposite. Similarly, a manufacturer might localize product features in one area and retailer incentives in another. While it may be prohibitively expensive to customize a product to many locations, it may be possible to gain similar benefits by tailoring the product’s packaging or promotions at a far lower cost. Wal-Mart found that while ant and roach killer sells well in the southern United States, consumers in the northern states are turned off by the word “roach.” After labeling the pesticide as “ant killer” in northern states, the company has seen sales increase dramatically, according to John Westling, senior vice president.
What, Where, and When Should We Localize?
Of course, customization has its limits. Even with rich data, a company can’t customize every element of its business in every location. The sheer complexity would be overwhelming, leading to spiraling costs, if not paralysis. That’s why leading localizers have begun using clustering techniques to simplify and smooth decision making, focusing their efforts on the relatively small number of variables that usually drive the bulk of consumer purchases.
Rather than letting local managers’ decentralized decisions fragment economies of scale, the pioneering companies have developed a science of analyzing data on local buying patterns to identify communities that exhibit similarities in demand. For example, American Eagle Outfitters, a retailer of fashionable casual wear with 740 U.S. stores, found that customers in western Florida exhibited seasonal purchasing patterns and price elasticities that closely matched those of certain communities in Texas and California. By tailoring assortments and promotions to such clusters of locations rather than to individual stores, companies like American Eagle can benefit from customization while holding on to most of the efficiencies of standardization.
The customization-by-clusters strategy, which Bain first applied to grocery stores in 1995, has proven effective in drugstores, department stores, mass merchants, big-box retailers, restaurants, apparel companies, and a variety of consumer goods manufacturers. Clustering sorts things into groups, or clusters, so that the associations are strong between members of the same cluster and weak between members of different clusters. Clusters enable manageable, modular operations—think again of Wal-Mart’s store templates—that capture most of the benefits of customization while also simplifying decisions and protecting economies of scale. Consider a merchandise manager who has to decide how to stock 100,000 items in 1,500 stores for 365 days each year. If she wanted to customize the mix, she would have to make about 54.8 billion decisions (100,000 x 1,500 x 365), many of which would be based on such small sample sizes that the predictions of even sophisticated models would be meaningless. If, however, the merchandise could be clustered into 2,500 classifications, the stores could be clustered into 20 similar types (for example, Latino border locations or upscale suburban places), and the timing (back to school, winter holidays) could be broken into 52 weeks, the number of decisions would be reduced to 2.6 million, which a modern computer model can optimize fairly easily. (For a discussion of a particularly powerful statistical technique used in sorting through many variables, see the sidebar “CHAID: Clustering by the Numbers.”)
CHAID: Clustering by the Numbers
Best Buy is using clustering to move away from a standardized big-box strategy. It has revamped close to 300 of its 700 U.S. stores, introducing “customer-centric” formats to appeal to local shoppers. The company identified five representative types of customers. First, there’s “Jill,” a busy mother who is the chief buyer for her household and wants quick, personalized help navigating the world of technology. In Eden Prairie, Minnesota, the company designed a store that caters to the needs of this busy suburban moms segment. The company found that this group of previously untapped consumers offered the best opportunity for expansion in the region. To attract this group, the store has an uncluttered layout with wider aisles and warmer lighting, and technology-related toys for children. Personal shopping assistants educate technology neophytes about products, and there’s more floor space allocated to household appliances. Although the store still serves other, more traditional electronics shoppers, the company hopes the store can boost its sales by attracting a set of local customers that have felt overwhelmed inside a Best Buy store.
Other stores are being designed around the remaining four types of customers and are based on local demand patterns. For example, there’s “Buzz,” a technology junkie who wants the latest gear for entertainment and gaming. Stores catering to Buzz have lots of interactive displays that allow shoppers to try out new equipment and media. Then there is “Barry,” an affluent, time-pressed professional is looking for high-end equipment and personalized service. Stores tailored to his needs feature a store-within-a-store for pricey home-theater setups. Stores made with “Ray” in mind emphasize moderately priced merchandise with attractive financing plans and loyalty programs for the family man on a budget who wants technology that can enhance his home life. Finally, for small-business customers, there’s a set of stores with specially trained staffs, extensive displays of office equipment, and mobile “Geek Squads” of service technicians.
While the chain plans to phase out these individual names beneath its banner, the terminology helped Best Buy crystallize the vision of each target customer for each cluster of stores.
By customizing stores in clusters, rather than individually, Best Buy has been able to maintain many of the scale economies that have long underpinned its success. So far, the new strategy is delivering strong results. The 85 Best Buy stores that had been localized as of early 2005 posted sales gains two times the company’s average. Encouraged, the company is accelerating the conversion, with plans to change over all its U.S. stores in three years and localize outlets in other countries as well.
So how do you get started with clustering? Begin by collecting as many data as possible on key elements of your business for each store. (Use the exhibit “What, Where, and When Should We Localize?”) If some information is missing or hard to get, don’t wait for it to be collected. Use what’s readily available to launch the analysis, recognizing that clustering always gets better over time. Use the data to develop clusters and identify customization opportunities. Then estimate the economics (including both sales and costs) of localizing the most promising elements of the customer offering—using as few clusters as possible. A clothing retailer, for example, might find that localized markdown policies offer attractive returns and that climate is the key variable influencing markdown decisions. Further analysis may determine that a small number of store clusters—three, say—will be sufficient to gain the optimum economic benefit. For merchandise mix, by contrast, the key variable might be customer lifestyle, which may require a dozen clusters to get the maximum payoff.
Diversity in the Product Line
As big retailers shift away from standardization, the ripple effects will reshape the entire consumer supply chain. Consumer goods companies will need to introduce more variations into their lines, collaborating closely with retailers to put the right products in the right places at the right times with the right pricing and promotion programs. Manufacturers in general have been slow to make this change. Although they conduct extensive consumer research to develop specialized products for unique segments, they have little confidence that rigid retailers will sort, merchandise, and market custom products to the right customer clusters. Products developed for senior citizens will pile up in college communities—slowing inventory turns, forcing costly markdowns, and often leading retailers to drop potentially profitable niche products.
Nevertheless, as growing numbers of retailers are rolling out their own versions of Wal-Mart’s Retail Link—including Lowe’s (LowesLink) and Target (Partners Online)—a handful of consumer product companies are seizing the advantage by learning to localize. When one food company introduced low-calorie versions of some of its snack foods, it shipped additional cases to stores near Weight Watchers clinics. Cadbury added kiwi-filled chocolate Cadbury Kiwi Royale in New Zealand. Kraft developed Post’s Fiesta Fruity Pebbles ready-to-eat cereal especially for Hispanics. Coca-Cola has developed four canned, ready-to-drink coffees for Japan, each formulated for a specific region. Procter & Gamble introduced Curry Pringles in England and, later, Spanish Salsa flavor in England and other parts of Europe and Funky Soy Sauce Pringles in Asia. Frito-Lay developed Nori Seaweed Lay’s potato chips for Thailand and A la Turca corn chips with poppy seeds and a dried tomato flavor for Turkey.
One of the leading localizers is consumer products giant VF, a $6 billion apparel maker that owns such popular jeans brands as Lee and Wrangler as well as upscale labels including Nautica and North Face. VF integrates many data sources to identify customization opportunities—to the delight of retailers and consumers. “It is not unusual for localization to improve sales by 40% to 50% while simultaneously reducing store inventories and markdowns,” says Boyd Rogers, VF’s president for supply chain. “We consider our localization capabilities to be one of our most powerful competitive advantages.”
VF combines third-party geodemographic and lifestyle data with daily store-level sales data, extensive consumer research, and competitor analysis to develop localization strategies with retailers, such as Kohl’s. VF has found, for instance, that while many buyers now desire lighter-weight denim, male Hispanics still prefer heavier weights. Women in southern California tend to buy shorter denim skirts than those in northern California. Even stores in the same metropolitan area can exhibit very different demand patterns for jeans and other clothes. A store in a community with a large immigrant population, for example, will tend to have greater demand for smaller-size clothing than a store surrounded by nonimmigrant Americans—a subtle testament to America’s obesity problem.
For one U.S. chain, VF created 40 clusters, based largely on consumer lifestyle segments and purchasing patterns. Product assortments, marketing strategies, and supply chain systems are tailored to each cluster. VF uses rapid data exchanges to study each store’s daily point-of-sales data—not just to replenish shelves but also to discover new demand trends in colors and styles and foster innovation. Through such efforts, VF and its retailers are boosting sales substantially while also avoiding markdowns and returns.
Central Control, Local Touch
A shift to localization raises big management and organizational challenges. The early movers are, in fact, breaking through the old “centralization/decentralization compromise.” But it’s tricky. Executives’ first instinct is often to empower local managers, giving them control over, say, the selection of products on store shelves or major promotional programs.
Such decentralization often backfires, for two simple reasons. First, local managers lack the depth of data, and often the skill, to make consistently smart decisions about buying, merchandising, and operations. Second, giving local managers too much leeway can introduce costly complexity and inconsistency into a business. Indeed, our research found that large manufacturers are less willing to collaborate with, or offer their best terms to, highly decentralized retailers.
J.C. Penney discovered this the hard way in the late 1990s, when it ran into problems by allowing store managers to determine order quantities. Local managers turned out to be too conservative. Seeking to minimize risk, they would buy a wide variety of goods rather than concentrate on hot items. As a result, the stores ran out of popular products quickly and were left with swollen stocks of slow sellers. And because headquarters lacked information on what was in each store, central managers couldn’t even see the problems. Between mid-1998 and the end of 2000, Penney’s stock price plummeted from $54 to $8.
Then, in 2000, Penney’s embarked on a successful turnaround program under the direction of its then-new CEO, Allen Questrom. Penney’s went from a decentralized company whose buying and markdown decisions were made at the stores to a centralized, data-driven organization. The management team classified stores into seven clusters on the basis of size and customer demand patterns, developed merchandise and fixture modules, and consolidated purchase orders. It also developed demand-based optimization techniques—allowing product and price ranges, replenishment policies, as well as the timing and depth of markdowns to be tailored to store clusters. Over the next five years, Penney’s stock price more than tripled. Comparable department store sales (sales of stores open for 12 consecutive months), having eroded 2.3% in 2000, rose 3.4% in 2001 and 5% in 2004.
As Penney’s discovered, efficient localization requires that most decisions be coordinated centrally, by managers with a broad view of demand patterns and sufficient store-level data to distinguish real insights from random noise. To support headquarters decision makers, leading retailers are building sophisticated information systems that draw from many sources—census and other demographic research; data from store scanners and loyalty cards; consumer surveys and unsolicited comments; Internet sales data; data from third-party syndicators like ACNielsen; and intelligence on competitors. Local managers and personnel are also critical sources of information—often picking up signals that computerized systems can’t see. When Wal-Mart, for example, introduced kosher food to its store in Berryville, Arkansas, it was acting on a recommendation from the store manager. The company’s other data sources had not uncovered the nearby Jewish community.
Central coordination is also essential to forging close relationships between retailers and product suppliers. Product manufacturers have deep knowledge about how goods sell across all stores in a region. Retailers have equally deep knowledge about how products sell across their networks of stores. Combining those two troves of information allows for a much more comprehensive understanding of both local demand patterns and the way they may cluster across regions.
Leading from the center does not mean that local managers become unthinking robots. In fact, by centralizing data-intensive and scale-sensitive functions such as store design, merchandise assorting, buying, and supply chain management, localization liberates store personnel to do what they do best: Test innovative solutions to local challenges, engage with store guests, and forge strong bonds with their communities. Wal-Mart’s store managers are legendary for highlighting hot items and responding to local pricing challenges. Best Buy encourages store employees to create and test hypotheses and share what they have learned throughout the chain. One Best Buy employee recently hypothesized that she could raise store sales by making iPods easier to find. She moved a display to the front of the store, created a shirt that said, “iPods here,” and raised the store’s sales ranking from 240th to 69th. 7-Eleven knows that corporate headquarters could never predict a busload of football players arriving on a Friday night, but the store manager can. Combining the efficiencies of a national chain with the entrepreneurial touches of a mom-and-pop convenience store, 7-Eleven has created a system that it calls “centrally decentralized.”
A World of Difference
Localization isn’t free. The shift requires greater investment in data collection and analysis. And however sophisticated the clustering effort, some economies of scale will need to be sacrificed—in purchasing, marketing, manufacturing, and store construction. Most companies will want to focus their initial efforts on areas offering the greatest and quickest return. For example, the investment is typically lower and the payback faster on localizing markdowns (typically less than one year) than localizing base prices (often two years or more). But as localization skills grow, so do localization opportunities. The systems, data, and organizational processes that first enable a company’s leap to localized markdown strategies greatly ease subsequent steps to the localization of pricing, promotion, and marketing programs. (For examples of retailers pushing the frontiers of localization, see the sidebar “Extreme Localization.”)
Extreme Localization
Ultimately, all companies serving consumers will face the challenge of local customization. It’s often been assumed that globalization implies ever-greater homogenization of businesses and their products and services. The world, in this view, will be packed with indistinguishable big boxes selling the same goods and services to everyone. But a look at the emerging localization strategies of the leading companies in consumer markets—companies that once shunned customization but now embrace it—reveals how mistaken this assumption is. We are advancing to a world where the strategies of the most successful businesses will be as diverse as the communities they serve.
For decades, sultans of standardization, like Wal-Mart and McDonald's, have ruled the consumer markets. But their formula of rigorous consistency is now reaching a point of diminishing returns. Communities have grown more diverse-in ethnicity, wealth, lifestyle and values. Many areas are saturated with big boxes, and consumers are rebelling against cookie-cutter stores that threaten the uniqueness of their neighborhoods. Attempts to build new chain stores often face fierce resistance. More of the same has been tapped out as a strategy.
In response, smart retailers and consumer goods companies are starting to customize their offerings to local markets, rolling out different types of stores, products, pricing, marketing and even customer service strategies. National chains, including Tesco and Wal-Mart, and manufacturers like apparel maker VF are replacing standardization with localization. But the shift requires companies to achieve the right balance. Too much customization drives up costs and complexity; too little leads to stagnation, dooming a company to dwindling market share and profits.
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In retailing, Bain consistently finds a strong correlation between local market share and profitability.
-Darrell K. Rigby |
Bain & Company's analysis of 30 localization leaders identifies three factors critical to obtaining an optimal balance: These leaders understand which elements of a business should be considered for localization, how costly they are to customize and how much impact they'll have from store to store. Combining sophisticated data analysis with innovative organizational structures, the leaders are preserving the efficiencies of centralization while increasing responsiveness to local needs.
It's all in the data. Technological advances-from Internet stores to data mining software to electronic product tags-are providing retailers with deep insights on local buying habits. Companies can now customize store content with unprecedented precision. Wal-Mart uses its Retail Link database to track each product's buying history, down to daily sales in every Wal-Mart, then creates maps of demand indicating which merchandise should be stocked where and when. For example, of the 60 brands of canned chili Wal-Mart sells, only three are available nationwide; the rest are allocated according to local tastes.
Think in clusters. A company can't customize every element of its business; the sheer complexity would be overwhelming. Pioneering retailers have made a science of analyzing data to spot clusters, communities with similar buying habits and demographics. For example, American Eagle Outfitters, a national retailer of casual wear, found its customers in Western Florida share buying patterns with shoppers in areas of Texas and California. American Eagle tailors assortments and promotions to such clusters rather than individual stores. Wal-Mart applies a similar approach to store design by creating templates that can be combined in a way that meets local needs: Stores near office parks, for instance, feature prominent islands with ready-made meals for workers. Clusters enable manageable, modular operations that capture the benefits of customization while simplifying decisions and preserving economies of scale.
Collaborate on adding variety. As localization increases, consumer goods companies will need to introduce more variations into their lines and work closely with retailers to put the right products in the right places. One leading localizer is VF, the $6 billion clothing maker. It integrates third-party geodemographic data with store data and extensive consumer research to identify customization opportunities and new trends-to the delight of consumers and retailers. It's not unusual for localization to improve VF's sales by 40% to 50%, while reducing store inventories and markdowns.
Localization does raise organizational challenges. Empowering local managers too much may backfire; most decisions still need to be coordinated centrally by managers with a broad view. That doesn't mean that local managers become unthinking robots. They play a critical role in picking up signals computer systems can't see, testing innovative solutions to local challenges and forging strong bonds with their communities. That generates a powerful competitive advantage-one that all companies serving a diverse consumer base may soon be pursuing.
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