In recent discussions with industry leaders and as outlined in our recent thought leadership piece “Evolution of Real Estate Network Planning in the Age of AI”, a key barrier to any organization investing in AI is the absence of a focused data strategy. Furthermore, a recent Forbes article, “2024 Is The Year of AI, But Data Will Steal The Show” by Afif Khoury, CEO of SOCi, Mr Khoury expects the top trends to be focused on data:
“While the AI revolution is real, promising significant changes in various industries, the transformation will unfold in stages—much like the digital, social and mobile eras. Given your use of AI is only as good as the breadth, quality and security of your data, the initial impact of AI will be most evident in the first wave of technologies affecting how organizations collect, ingest, analyze and secure data.”
Data comes in many forms, and any retail or real estate professional knows that good data is the cornerstone of making sound site selection decisions. Most professionals begin with identifying a retail trade area and compiling demographic information about the potential customers in that area. Although this is a good start, it lacks specificity about the actual customer profile of the trade area, including their preferences and shopping behaviors.
Customer data fills this gap, providing a rich, holistic view of the people who shop in the trade area. Here, we look at the different types of customer data, why customer data is so powerful, and how easy it is to leverage customer data to inform your retail and site selection strategy.
Types of customer data
Different types of customer data tell you different things about your customers, from where they live to their shopping behaviors and spending patterns. Customer data moves the foundation of your retail strategy from simple demographics of a trade area to more nuanced and granular customer profiles.
Postal Codes
Many retailers collect the postal codes of their customers at the point of sale. Postal codes are an easy and useful piece of customer data that allows retailers to identify with greater accuracy the size and shape of their trade area.
Geosocial Segmentation Data
Geosocial data is a newer type of customer data that provides even more insight into customer preferences and behaviors in a particular location. Geosocial data is another term for location-based social media data, and it works by analyzing publicly-available social media posts in a particular trade area to build customer profiles that include what types of products they spend their money on.
PiinPoint has partnered with geosocial data provider, Spatial.ai, to help retailers and real estate professionals identify whether a particular store concept – a vegan bakery, for example – will perform well in a proposed location based on the customer profiles of the trade area. Spatial analyzes the data from billions of social posts and organizes them into more than 70 social segments, such as Pet Lovers, Dating Life, and Fitness Obsessed. These segments are then spatially profiled either in heatmap form or statistical indices in standard trade area reports to give you a feel for the density of a particular segment within a trade area, provide rich profiles and character upon which to better understand your customer base.
Mobile Location Data
Mobile location data is another type of customer data that can be used when other types of data aren’t collected or readily available. Mobile location data allows retailers to see when a customer enters a store, how long they stay, and what other stores or locations they visit in the same shopping trip. Mobile location data helps retailers approximate customer travel patterns to determine their willingness to travel a certain distance to get to a specific brand or type of store.
Loyalty/Rewards Programs
Customer loyalty and rewards programs are a veritable gold mine of useful customer data. While the main purpose of these programs is to improve customer engagement and ultimately increase sales, they can also tell you where your customers live, how often they visit your stores, how much they typically spend, and what they like to purchase. This customer data can identify customers who haven’t visited a retail location in a while and may need an incentive – a coupon, for example – to come in and shop.
The best part about this type of customer data is that loyalty and rewards programs also collect customer addresses, which means businesses can tie specific customer spending patterns to geographic areas increasing the specificity of information in a given retail trade area.
Why is customer data so powerful?
Customer data allows retailers to understand their clients on a much deeper level so they can better pinpoint the most profitable locations in a trade area. It moves beyond the simple demographic profile of age, income, and education and includes hyper-specific and, more importantly, up-to-date information, including their interests, where they shop and how often, how much they spend, and what they buy.
Often, businesses make assumptions about their customers, but without solid data to back those assumptions, retailers may find themselves “missing the mark” on who their customers really are and what they really want. Vague and even aggregated information leads to too many missteps and miscalculations that threaten the health and viability of any business. Using customer data allows organizations to consistently “hit the mark” enabling businesses to optimize and ultimately thrive – particularly in competitive markets.
Who benefits from using customer data?
Customer data is a powerful tool for all types of businesses, from smaller niche markets to large retailers with a wide trade area – but it’s utilized in different ways and for different reasons, depending on the business.
A small, niche business, like a vegan bakery, can’t open a store just anywhere, because it has a much higher chance of failure if it’s not located in a neighborhood where vegans live. Using customer data to understand which neighborhoods have high concentrations of people who would be likely to patronize a vegan bakery – and even the hours their customers are most likely to visit – ensures the bakery sets up shop where it has a high likelihood of success.
A much larger retailer, such as a wholesale club store, has a wide variety of customers and a much larger retail trade area. Customer data can tell this retailer where to focus their expansion efforts, what hours of operations are optimal, and what products their customers most often purchase. For large retailers accustomed to high volume and traffic, this information is invaluable for informing how they should stock their shelves, minimizing waste and moving more products out the door.
The beauty of customer data is that its usefulness isn’t limited to one type of retailer or one use case. The same customer data that tells one business where to locate its brick-and-mortar tells another business what their customers are most likely to purchase. What matters for any retailer is that robust customer data contributes to its ultimate success.
How easy is it to collect customer data?
Customer data is easier to collect than you may think. In fact, you might already be collecting informative customer data and not even realize it. If you do any sort of online retail or offer curbside pickup services, you already have access to customer addresses – combine that with mobile location or geosocial data and the publicly available demographic information of your trade area, and you can easily construct detailed customer profiles. Add a loyalty or rewards program, and integrate it with your Point of Sale system, and you’ll gain dynamic, real-time insights into your customers’ buying behaviors.
The PiinPoint Way
The more data points you collect about potential customers, the greater your edge in understanding their habits, their preferences, and your trade area as a whole. In the PiinPoint platform, customer loyalty data is uploaded as a data layer to show your customer data in a heat map display to measure the performance of your operations across markets for trade area size, stores with higher traffic, store sales contribution, and measuring cannibalization across your footprint. Furthermore, it can help delineate natural boundaries of where your customers travel from in order to calculate market penetration and visualize store cannibalization by looking at multiple stores on one map.
You can also upload multiple samples to see how the spread of your customer changes across different periods of time (e.g. seasons, months, years).
For example, this Sample Store 1480 saw a huge increase in customer distribution from 2016 to 2017, perhaps due to closing a store in North York and increasing the number of targeted ad campaigns run in Etobicoke.
There are so many things you can do with your customer data - it's unique to the business!
PiinPoint’s mission is to ensure our clients and users get the most out of the data they bring to the table by integrating it with our in-app data sources for more powerful and insightful analysis and site selection efficiency.
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