Olvin

Olvin was founded in 2018 with the objective to use location data to provide a higher level of insight and analysis on retail stores. Placing a high premium of accuracy and scientific rigor, they launched their data solution in 2021.

Olvin’s retail data helps you understand the store performance and consumer profiles of over a million retail locations across the US. Data ranges from predictive insights on an individual store’s visits, to macro analysis on a national brand’s consumers.

Olvin provides a range of ground truth validated datasets on retail stores in the US.

• Store visits: Predictive and historical store visit data to analyze performance of retail stores on a monthly, weekly, daily and even hourly basis.
• Store visitors: Consumer profiles on the visitors to stores including demographics psychographics, cross-shopping and home locations.
• Brand trend: Predictive and historical store visit data to analyze the macro trend of retail brands on a monthly basis.

Key use cases include:

• Store operations: Optimize workforce management and supply chains across retail and hospitality with Olvin’s store visits dataset featuring 12-week predictive insights.
• Retail leasing: Prospective tenants can screen potential properties by assessing the performance of local retailers; while landlords and brokers can identify the ideal tenant to fill their vacant property.
• Competitor analysis: Analyze what’s impacting the success of competitors from a local to national level. Understand their performance and their typical consumers.
• Optimize marketing strategy: Personalize your campaigns by understanding the demographics, psychographics and cross-shopping habits of consumers.
• Stock performance analysis: Assess investments in retail companies by using store visits as an indicator of performance. See how brands are trending month on month.

Key data sources include:

• Location data: With a representative sample panel of 145m devices, location data is the key that unlocks a deeper understanding of consumer behavior.
• POI data: The performance and consumers of over one million retail locations and over 6000 retail brands are analyzed.
• Demographics / psychographics data: Analysis is enriched by connecting a probabilistic profile to our sample panel.

At Olvin, we know that bad data leads to bad decisions. That’s why our in-house team of data scientists and machine learning experts have spent 3 years ensuring our data is to the highest standard possible.

• Ground truth validated: Our store visits data is trained and validated against in-store foot traffic counters, enabling a correlation accuracy of up to 91%.
• Largest sample panel: The location data of over 145 million mobile devices are analyzed to power our analytics.
• Representative panel: This panel of devices closely matches the US national average on age, income, race and more, making them a representative sample of the population.
• Proprietary AI models: From clean data, to attributing store visits, to producing predictive insights - our AI models are the key to the quality of our datasets.
• Comprehensive network of datasets: As well as location data, these models are powered by census data, Safegraph’s point-of-interest data, Transunion’s consumer data and more.

Olvin’s datasets come anonymized and in aggregate in order to guarantee the privacy of our sample panel.