In-store data presents information about retail businesses in-store activities and metrics such as footfall traffic (people counting), customer behavior, sales data, customer buying patterns, and product stocks. Businesses use this information to plan promotions, optimize pricing, improve store performance to drive growth, and to analyze the performance of competitors or prospects that they plan on doing business with in the future.
In-store data for a company comes from a variety of sources.
Surveys, customer data, and purchase patterns provide insights into the customer experience and customer journey.
Location and footfall data indicate the number of retail store visitors for a given period. This information helps understand the peak and slack periods. These insights can be used for promotion planning, staff scheduling, and even store layout.
Purchase data collected using point of sales tools indicates the products, time of purchase, and price. This information delivers insights into which products sell more and which sell less, and the total revenue they generate. It also shows which periods see the highest and lowest purchases.
Inventory data shows products available at specific times, helping with supply chain planning, and planning promotions to sell less-moving products. It also provides insights on the product demand to optimize stocks.
In-store data varies by vendors and industries and has a wide range of attributes. Some of them include:
Some vendors also provide custom datasets to match your requirements.
Due to the wide range of attributes, the most essential test of in-store data is if it matches your requirements. For trusted insights from in-store data, accuracy and timeliness are also equally critical. Either get your data tested thoroughly by your internal data management team or use trusted vendors for improving data quality.
To test the quality of the data:
In-store data provides a wide range of information and gets used in a large variety of use cases.
Businesses typically use in-store data to assess store performance (their own, competitors, prospects, and potential business partners). Based on the insights from the data collection and data analytics, they can strategize to improve retail store performance, increase the revenue per store, run stores more efficiently, optimize store numbers and locations, and increase profit margins. Business can also use this data to assess the performance of their competitors, and get a better understanding of the market and industry.
Marketers use foot traffic data to analyze store visits and gain insights into conversion rates, physical store performance by location and timings, and store efficiency. They leverage these insights into designing promotions, establishing store timings, and to optimize merchandising and staff schedules.
The data provides insights into the volume and profits of sales. It also indicates customer preferences and product performance. Companies use these insights to design marketing campaigns and inventory planning. They also manage inventory stocks by leveraging inventory analysis and purchase data.
In-store data contributes to competitor and market analysis for price optimization, store location planning, and entering into new markets.
In-store data from vendors is usually recent or in real-time. But due to the data volume and several diverse attributes, data accuracy is a challenge. To derive trusted insights, data must be accurate. Other common challenges for in-store data are often related to data consistency across diverse datasets and privacy compliance.
In-store data is similar to retail data, eCommerce data, product data, brand data, shopper data, consumer review data, and other related data categories used in in-store analytics, marketing, advertising, and promotions.
You can find a variety of examples of company and consumer data in the Explorium Data Gallery.
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The most common use cases include retail intelligence, store performance prediction, promotion planning, market analysis, and shelf planning. This data also gets used for store visit attribution, shelf analytics, market share analysis, competitor analysis, and operational intelligence.
You can find more AI-powered practical uses cases for external data signals: Explorium Machine Learning Use Cases.
Industries that have brick-and-mortar stores commonly use this data for powering their in-store sales, promotions, market research, competitive analysis, and marketing strategies. They include retail, CPG, tourism, leisure, sports, entertainment, travel, hospitality, healthcare, financial service providers, insurance providers, and banking.
In-store data collected from surveys, customer feedback, customer behavior data, purchase information, location data, foot traffic, and inventory is diverse. The vendor quality is critical in ensuring the validity, integrity, and accuracy of this data. You can use the information available on the vendor websites to judge vendor quality and then interact with their reps to get your queries answered.