As retailers continue to hone their omnichannel strategies, the role of assortment planning is shifting toward creating more localized and personalized offerings for consumers. It is no longer a method to merely select merchandise. Instead, optimized, location-aware assortments are increasingly influencing strategies to strengthen consumer brand loyalty, increase revenue and drive out excess inventory and costs from the demand chain.Effective customer-centric assortment planning can be achieved by better leveraging both structured and unstructured data to pinpoint the product and customer attributes that really matter. In harnessing more insightful data, organizations should also align their business processes, governance, and measurement to improve business adoption and implement localized assortment decisions.
Personalization: Putting the customer front and center
The availability of information that enables anytime, anywhere shopping has changed the way customers make buying decisions and upped the ante for retailers to improve customer engagement. It is now more important than ever for retailers to create a personalized shopping experience across channels. One way to create this engagement is through the execution of customer- or segment-specific assortments. While not a new concept for apparel retailers, many brands fall short when creating customer-centric offerings.
Retailers tend to differentiate their assortments by grouping stores using characteristics such as region, volume, space or climate. Many companies continue to manage data and base decisions on these standard attributes (as they are accessible via existing reporting tools) rather than include data from new avenues — a practice that keeps brands’ assortments predominantly product-focused versus customer-based. But these standard attributes tell only part of the story.
Coupling this data with more contextual data — what’s trending in the market, knowledge of the customer’s lifestyle, and how the customer will use the product — allows retailers to move beyond traditional attributes and make decisions based on analytics relating to the consumer’s preferences. Apparel retailers should adopt and integrate more digital consumer-facing touch points, such as web-based kiosks, beacons, digital signage, smartphones, store associate tablets and social channel listening, and manage them on a scalable, centralized platform accessible to all key decision-makers.
However, while a centralized platform is key to this effort, that alone won’t lead to significant progress. Companies should consider an integrated data collection and analysis process across all lines of business and channels to break down data silos. By operating separate business divisions, each with disparate data sets and reporting tools, apparel brands find themselves managing redundant, even erroneous sources, that support one-dimensional assortment planning and inventory management decisions. Besides over- or under-estimating merchandise levels and planning for the average store instead of personalizing for the consumer, these decisions are increasing operating costs, leaving brands at risk of failing to respond to market trends, and more importantly, consumer demand, that may jeopardize loyalty, reduce sales and erode margin.
Yet the positive impact can be significant. As an example, a fashion retailer recently re-evaluated which attributes really represented the characteristics of their customers to drive new assortment plans across clustered locations. Instead of focusing exclusively on sales history and traditional attributes such as volume group or geographic region, the retailer incorporated a new demand forecast using unstructured data to sense and respond to real-time changes in customer demand based on what’s trending in social media. With these new insights and resulting assortment plans, the retailer quickly saw an increase in revenue by localizing just a small portion of their product assortment. Now, newer styles that are of high value to loyal customers who like, friend and tweet about them are well-stocked in locations those customers are most likely to shop, further increasing brand loyalty.
To successfully tailor local market assortments in a way that is scalable, consumer-centric, easy to manage, and still remains highly predictive, retailers must identify the driving attributes or specific characteristics that are catalysts in helping to increase sales. Every line of business needs to agree to a universal set of data points that will help define the product assortment by location. For example, this could include data around color choices, fabrication, neckline, garment weight, and price points for the specific customers who shop at a particular location.
The move from using a handful of base attributes to incorporating analytics of unstructured data requires a roadmap designed to expertly execute a brand’s consumer-centric, space-aware assortment plans. With this, integrating the systems, processes and people across organizational silos is critical. With attention to new data and the ability to manage an integrated process from demand identification to customer receipt, assortments will become more tailored and consumers will feel more connected to brands that they consider a better match for their lifestyles.
Retailers should consider the following key elements when creating an ROI-driven assortment planning strategy:
1) Achieve a Master Source for Master Data: Compile enterprise-level data from multiple data sources, across organizational silos, encouraging brands, channels, divisions, and departments to share one system of record and one source for customer and product data.
2) Analyze Unstructured Data: By applying analytics to unstructured data and social listening, brands can uncover the key attributes and associated correlations and affinities that support assortment decisions among personalized customer profiles and segments, rather than create all-encompassing plans that cater to groups of stores in similar locations.
3) Integrate Technology and Business Processes to Move Data to Action: Apparel retailers can implement a range of best-of-breed technology, from ERP systems to merchandising and supply chain optimization engines. Yet all of these technologies used in a vacuum won’t improve the retailer’s ability to react to real-time changes in customer demand. The transformation toward personalization comes from a cultural change in conjunction with the application of technology. Integrating streamlined business processes with enabling technology sets retailers up for consensus-driven, scalable supply and demand plans to respond to changing market trends much faster.
4) Establish Governance and Measure Business Adoption: ROI is improved by redefining a collaborative set of metrics that measures performance across teams instead of within teams. To increase cross-functional teams’ collaborative success, retailers should strive to optimize overall profitability versus individual financial metrics. This often requires a new organizational design and, almost always, new governance.
Putting it in perspective
To deliver a relevant shopping experience in an omnichannel world, data-determined, localized product assortments will likely continue to gain relevance. By focusing product and inventory decisions on proven consumer preferences, and pinpointing the key attributes that are truly driving sales, apparel retailers can more effectively execute consumer-centric assortment plans. To be successful, retailers should take a broader perspective to create personalized assortment plans based on the right structured and unstructured data, integrate processes and technology, establish governance and drive business adoption by measuring key collaborative performance indicators. As a result, they stand to benefit from sales lift, increased margins, inventory reductions and improved long-term customer value.
By Lindsey Mazza — April 07, 2015
Lindsey Mazza is a Senior Manager in the North American Supply Chain Technologies practice at Capgemini, a global provider of consulting, technology and outsourcing services.