Localized Product Assortment

Chapter 1: One-Size Does Not Fit All: Why Localized Product Assortment Gives Retailers an Edge

Recently, many of your friends have been raving about a specific bag from your favorite apparel retailer, and you’ve seen influencers review it on your social media feed. It would be great for your daily commute, but unfortunately, the bag is so hot the store is sold out. The retailer just can’t keep the item in stock to meet demand.

We’ve all experienced this disappointment at some point, but in the not-so-distant future, things may look different. Why? Because the retailer of the future will tap into social media, consumer data, and their own sales data and know that sales of this bag are blowing up in Brooklyn but have been slow to move in New Jersey. These future proof retailers will be able to use their digital supply chain and logistics technology, to move bags to Brooklyn stores keeping the shelves stocked and the sales flowing.

With leading-edge technology, retailers can precisely analyze the shopping habits and product preferences of local customer bases to tailor their product assortment, thereby increasing sell-through. With customer data analytics, retailers can build an assortment strategy that is highly customized and data driven. A localized assortment strategy can improve sales by as much as 40 to 50%, according to an apparel company executive who was interviewed by Harvard Business Review.

It’s not just higher sales, however, that retailers can expect from a shift to a localized assortment strategy. Tailoring product assortment can help save on supply chain and warehousing costs, as well as improve inventory turnover because retailers are not stocking up on unnecessary products. Naturally, it may be more difficult for geographically dispersed retailers to get localized product assortment right, as this would require them to study diverse customer habits across locations. But with today’s technology, retailers have access to behavioral insights like never before.

Future proofed retailers will know what their customers need by gathering data points such as their location, shopping habits, and social media and browsing data. Retailers can also gather store data such as the sell-through of different products. Once there is sufficient data to build a few customer profiles, retailers can strategically order and market in-demand items. In the future, retailers will no longer need to ask customers what they want, but surmise what the customer plans to purchase before the customer even knows themselves.


Chapter 2: Turning Data Points Into Customer Stories

As mentioned, developing a localized assortment strategy depends on correctly capturing, interpreting, and analyzing customer data. The first step in this process is determining what customer data to analyze. An ideal place to start is by analyzing first-party customer data captured through spending behaviors, website cookies and/or user IDs, and CRM data. The right CRM system can segment this data by geographic locations for data analysts to identify patterns and trends. While data based on a customer’s region or city may generate notable insights, retailers can get even more granular if customers have granted permission to let the retailer’s app access their location. Retailers may consider grouping customers together based on “micro regions,” using address or zip code data to cluster together neighborhoods. Data visualization software can then help retailers conceptualize trends from one micro-region to another.

Third-party data can reveal new insights about customers that retailers may not be able to capture from first-party data alone. Sourcing from thousands of websites and apps, third-party data providers have access to millions of data points. Data can include where customers shop, their household income, how they spend their leisure time, recent life events such as a move, marriage or new baby, and what media they consume.

This level of detail informs a more nuanced understanding of customers, contributing to sharper targeting. Paired with their owned first-party data, third-party data can contribute to the development of robust customer profiles and associated product strategies. Once a retailer has determined the appropriate sources for customer data, data scientists will need to organize and cleanse that data, so it is ready for visualization.

Beyond this, retailers who want to future proof will increasingly leverage artificial intelligence (AI). AI and advanced analytics can accelerate the process of trend identification to inform localized assortment strategies via pattern mapping. AI can often find patterns that humans may miss.

Learn more about how AI can enhance your marketing efforts in our “How AI Contributes to Marketing” insight.


Chapter 3: Marrying Customer Data With Supply Chain Logistics to Future Proof Your Inventory

Organizing customer data and identifying trends and patterns among local customer groups is the first step before beginning to develop an assortment strategy. This includes understanding how customer preferences reveal themselves in local buying trends to determine what products to place in each location. These efforts are almost always supported by AI, which can learn from all of that customer data and their decisions to deliver actionable insights. This helps retailers better predict customers’ future purchases and understand buying patterns or trends. To take it a step further, as of late, the retail industry is increasingly tuning into Generative AI – a subset of general AI. Generative AI has the ability to create new content or images from existing data and can, for example, make increasingly tailored and targeted product recommendations for customers beyond what the industry already sees.

However, determining the “what” is only half the battle. The other half is the “how,” or aligning supply chains so the right products can be at the right stores at the right time.

GPS tracking and sensors can inform where a shipment is at any time, helping retailers to identify any slowdowns or bottlenecks and work to address them. But supply chain management doesn’t end once products arrive at their designated locations. The latest inventory management technology, which incorporates sensors, computer vision, and cameras, can quickly identify out- of-stock items and prompt employees to reorder. To become even more precise with inventory management, retailers of the future can use inventory robots to also help monitor inventory and send alerts when items are low in stock. All these pieces working together help ensure in-demand products are in stock at the locations where customers want them.

In addition to inventory management tech and understanding customer data, predictive analytics can be used to identify where supply chain adjustments need to be made. Predictive analytics can analyze supply and demand trends, as well as factors that may impact supply and demand in the future, helping retailers to forecast inventory needs. Once retailers understand what products they are selling in each location or cluster of locations, they can align assortment with demand fluctuation and adjust purchase orders as needed.

Finally, sensors, cameras, and robotics can inform where in the store products should be placed to maximize sales. Brands studied received a markedly higher proportion of sales (ranging from 8.3% to 15.7%) when displayed in an extra line-up (a large display at the front of a store), according to a study in the Journal of Applied Behavior Analysis. So, carefully consider both where and how high-priority products are displayed.

Using data analytics, AI, and inventory management technology is key to mastering a localized product assortment strategy. But knowing how to make these digital technologies work harmoniously often requires additional skills and experience. Partnering with a third-party advisor can support the development of an effective, localized product assortment strategy.


Chapter 4: How BDO Digital’s Data, Analytics and AI Services Can Help

The retailers that will succeed in the future will be able to offer their customers choice, but tailor those choices to precise customer segments. As a retailer, you may have collected plenty of customer data, but you will need to be able to organize and manipulate that data to get the insights you need.

Predictive analytics, AI-driven insights, inventory management, and forecasting technology can help you get there. Companies that can harness their data and embed analytics and advanced AI capabilities into their operations are outperforming the rest. Data-driven organizations sustain an average of 30% growth annually, according to BDO Digital.

BDO’s Data Analytics and AI services can help you assess your data and AI maturity and understand what Generative AI capabilities would be useful to develop and execute a product assortment strategy. To pull insights out of troves of data, BDO can help you adopt machine-learning solutions through the use of data mining. A more advanced type of machine learning, deep learning, can help you and other retailers continuously improve assortment strategies, taking business intelligence to the next level.

Want to know more about how BDO Digital’s data analytics experts can work closely with your business to understand what data is most important to success? Read our case study “IRC Retail Centers Leveraged Geospatial Data”.

Ready to launch your product assortment strategy to the next level?

BDO’s Retail and Consumer Products professionals are readily available to offer deep industry insight around product assortment strategy, inventory management and AI to help retailers stay competitive today and in the future.