The right product, the right ad, the right people, the perfect location — using social media data.
This article outlines the process for using Geosocial data for marketing. This process can be used by different types of companies, but we will use retail as the primary example.
Geosocial data presents a new approach to traditional segment-building for marketers. Unlike top-down approaches such as psychographics, Geosocial segments are created bottom-up organically from data-driven approaches. Further, this data is proven to predict bottom-line business success.
Imagine you own a popular restaurant chain and are planning to run ad campaigns focused on the markets you currently operate in. What if you knew that your trade areas in the southeast over-index compared to the national market in “After Work Activities” behavior? That information should impact your strategy. Understanding location-based trends and interests near your restaurants across your entire network, regionally, and even on a site-to-site basis is research that will pay off as you create plans, propose tactics to leadership, and ultimately execute your campaigns.
For the latest generation of our segments, explore the Taxonomy.
More important than identifying segments of behavior that could be strengths in your market is avoiding mistaken assumptions of what you think your strengths in an area might be. An up-and-coming retail clothing chain may appeal to trend-setters. However, if a network analysis shows that “trendy” is overwhelmingly lacking in activity near their stores, they should refocus location-based ads away from high-fashion messaging. They may even consider choosing to spend outside their trade area or avoid location filtering as a key pillar in their ad strategy.
Without Geosocial data, you have a limited understanding of your customers and have less ability to identify what factors drive product performance.
With Geosocial data, you gain a rich understanding of your customer segments, their lifestyles, and how they each impact product performance.
Context is the key to your customers receptivity of an advertisement. If you use segmentation that isn’t based on up-to-date behavioral data, you can get into trouble by placing people in rigid groups. Someone might be a “hipster” when they go to coffee shops but are in “active mom” mode on the weekend when running errands.
You can fit the mindset of people by matching the context of the location they are in to the content of your advertisement. One of the strongest indications of intent is where people choose to spend their time. When a prospective customer for your retail electronics chain is spending time in areas known for “live music” interests, use this context to choose how you present your brand to them.
We mentioned earlier that Geosocial data is used to predict location-based performance metrics. To do this, Spatial identifies the relationship between performance and each Spatial.ai segment. Not only is this useful for real estate and distribution decisions, but the results can have game-changing marketing implications. This process identifies segments that are actually driving success or underperformance for your brand. Retailers that know “Digital Creatives” are one of their strongest drivers of success can use this data along with hyper-local targeting of areas high in this behavior to reach the optimal type of customer at the perfect time. Using Geosocial data marketing strategies in combination with performance analytics to ensure that you are reaching customers that will create the most successful outcome.
Case Study: Subway Leverages Geosocial for Marketing
If you use analytics to determine which Geosocial segments represent your best customers you are a few steps ahead of the competition. If you use this information to optimize your advertising strategy you might be ten steps ahead. The following strategy is for geo-marketing experts who want to go further.
Beyond analyzing overall performance and how it is impacted by behaviors in your trade area, you can run this same analytics process on specific products or product lines. A convenience store chain, for example, can split up their most popular products into groups and analyze performance for each one individually to understand the Geosocial segments that are driving sales for each. This information can then be used the same way we discussed before, to target location-based advertising. Ads can then incorporate promotional offers for the specific products that are correlated with any individual segment.
Marketers following this approach are using organic and unbiased segmentation data to determine the exact right promotional offers to the right people in the perfect locations.
At Spatial.ai, we always stress the value of data-driven decisions. This strategy is entirely driven by data from start to finish.
These approaches should be a helpful starting point for incorporating Geosocial data into your strategies. As a marketing leader for your company, you are in the best position to decide what will be most impactful to grow your brand.
At Spatial.ai, we are experts in Geosocial data. We’d love to collaborate with you on finding the right use case that’s customized to your current goals. Request a session with our product leaders here.