Points of Interest (POI) data refers to the digital representation of physical locations that people may find helpful or interesting.
Commercial places like restaurants, shops, and hotels can be considered Points of Interest. Also, a POI can be a public place such as a park, museum, school, or a hospital.
It is important to mention that POI data has become essential to many applications across industries like retail, real estate, logistics, tourism, urban planning, and more.
Why is POI Data so beneficial for businesses?
As mentioned, Point of Interest data is like a “digital fingerprint” of a location, providing unique details about that place. The information it delivers can go beyond just an address or GPS coordinates. Using the power of Location Analytics, this data can tell us so much about consumers, competitors, and the overall market.
This data is essential to optimize marketing performance, develop expansion models, analyze market share, or optimize supply chain logistics. Some use cases can be:
Store performance analysis: For businesses, especially retailers, POI data can be a goldmine of insights. Data like these are useful to understand how well a store or outlet performs.
Consumer analytics: POIs are also valuable when understanding how users behave. POI data provides insights into what places our target consumer visits, how long they stay in a specific place, or how they move. This kind of information makes all the difference for marketing and sales teams. Learn more about customer analytics
Improve logistic processes: By providing information on specific locations, their characteristics, and the activity patterns around them, it can help improve route planning, optimize delivery times, and assist in strategic decision-making.
Site selection: Finding the “perfect” place for a new store, office, or branch is key to success. Whether it is an expansion strategy or a new brand, POI data can provide the necessary insights to identify the right location according to specific needs.
Analyzing an area: Trade area analysis can be simplified using specialized tools based on POIs. Learn more about our Trade Area Analysis approach.
Geofencing: Businesses can send potential customers personalized messages and content through push notifications and ads. Learn more about Geofencing
Real estate investment: Brokers and investors can analyze an area’s development to find the ideal places to invest. Learn more about Real Estate Analytics
Where does POI Data come from?
Point of Interest (POI) data can come from different sources, each bringing unique insights (And unique challenges). Here are some examples:
User-Generated Data, Web, and Media Platforms
Location data collected from social media check-ins and other user activities are also a source of POI data. However, this data’s accuracy is not guaranteed and may be influenced by hardware, application settings, and user errors.
Using web scraping tools, POI data can also be extracted from web sources like Google Maps. The data acquired usually requires preparation before analysis.
Onsite Data
Gathering data on location through field representatives provides high-quality and accurate information. This method is commonly used for creating first-party POI databases but can be time-consuming and expensive.
Official Government Sources
Official records from government institutions offer valuable data, though access might be limited due to privacy regulations. This source might not cover new, temporary, or upcoming POIs, thus requiring supplementation with other data sources.
Geocoding
This provides location information for a comprehensive overview of all POIs in a particular area. However, this data must be increased with other POI data to offer a complete picture.
Public Directories
Postal addresses provided by businesses during their registration process can form a POI dataset. The data might become outdated if companies change locations and fail to update their information.
Manual Verification
Some providers hire individuals to manually maintain their POI databases by physically adding or verifying locations. While ensuring accuracy, this method avoids privacy infringements associated with user data collection.
How is POI data represented?
The core elements of POI data include the location’s geographical coordinates (latitude and longitude) and street address.
In addition, it includes other valuable information like the venue type (For example, “store” or “restaurant”), or industry classification, which can be categorized using standard systems such as the North American Industry Classification System (NAICS) or Google Maps POI categories.
But POI data isn’t just about the venue itself. It can provide context about the surrounding area. For example, it can include details about local traffic patterns, foot traffic, nearby infrastructure, businesses, events, and attractions.
In a more advanced stage, these data can even provide insights into the visiting volume during specific times and their staying duration.
When combined with customer analytics techniques based on mobility data, businesses can answer questions like:
- How many people are in the radial vicinity of a POI?
- What are consumers’ preferences for living or residing near or within the POI?
- How is pedestrian mobility within a POI?
- What is the relative wealth index level of people living in or visiting a specific POI?
The main challenges of data quality
POI data is not “standard” around the web. Datasets can vary in their accuracy, relevance, standardization, and consistency.
Accuracy: Depending on the sources, collected POI data might be incomplete or imprecise. These inaccuracies can lead to misguided analysis, affecting critical decisions and long-term planning. That is why it is important to validate information across multiple sources.
Relevance: Data can be influenced by seasonal factors and sudden external factors (Such as the pandemic). It is crucial to ensure data remains relevant and accurate; otherwise, it could lead to inaccurate insights.
Standardization: Datasets from different vendors could be incompatible with each other or a particular data model. Verifying attribute compatibility and format requirements is essential.
Consistency: Not all sources similarly identify a location as a Point of Interest. For example, a manufacturing plant might be significant in an industrial listing but not a retail one.
Further, categorization can also vary across datasets, adding another layer of potential inconsistency.
So, how to make the most out of these data?
PREDIK Data-Driven uses trustable and high-quality data sources from authorized global vendors in compliance with GDPR, CCPA, and all other legal regulations.
Also, our unique methodology combines AI, Machine Learning, Data Science, and an expert team of experts to ensure our POI-based solutions can provide the best location intelligence solutions for you.
