Company Data

In-Store Data

What is in-store data?

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.

Where does the data come from?

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.

What types of attributes should I expect?

In-store data varies by vendors and industries and has a wide range of attributes. Some of them include:

Foot traffic information for the number of visitors over a specific period and the duration of their stay
Purchase data indicating the number of purchases for different products and prices, and total sales over a specific period
Inventory information for different products
Customer behavior and insights

Some vendors also provide custom datasets to match your requirements.

How should I test the quality of the data?

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:

Verify that the data attributes match your requirements.
Assess the data for accuracy, consistency, and completeness.
Ensure that the data is regularly updated and the datasets are available in near-real-time.
Validate that the data is privacy compliant if it contains personal or personally identifiable information (PII).

Who uses in-store 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.

What are the common challenges when buying in-store data?

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.

Data accuracy: In-store data is very diverse, ranging from customer behavior to purchase information. Along with large volumes of data, this aspect affects data accuracy. For correct insights and reliable forecasting, data accuracy of in-store data is critical.
Data consistency: In-store data may present overlapping or duplicate information, which needs to be resolved before using the data to power analysis. If the data is incomplete or has missing records, it can present an inaccurate picture of the business. Assessing the data for consistency is essential for deriving trusted results.
Privacy compliance: In-store data often contains PII and sensitive data, and it must be compliant with all the required industry and region-specific privacy regulations.

What are similar data types?

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.

What are the most common use cases?

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.

Retail intelligence: It uses technological tools to understand in-store information and gain insights into customer behavior. Some of these insights include store traffic, shelf visits, the relationship between price and sales, as well as how promotions drive sales. Analyzing this behavior helps predict customer behavior in the future and leverage it to increase sales. Some interesting outcomes of retail intelligence include understanding why sales are lost and discovering the market for new products.
Store Performance Prediction: The performance of the store depends on several factors, including store location and timings, weather, events, competitor presence nearby, pricing, promotion, as well as the in-store experience. Store performance prediction forecasts the expected sales over a period of time, taking into account all these factors. Predicting the store performance avoids under-stocking and over-stocking to optimize inventories.
Promotion Planning: Businesses run promotions to increase brand awareness and improve sales. Efficient promotion planning delivers greater brand recognition, higher conversion rates, lower costs of customer acquisition, and better ROI. Organizations today leverage ML algorithms to analyze various relevant data categories, including in-store data, for planning promotions.
Shelf Planning: The first impression of a store drives customer buying decisions. Stores ensure that their shelf planning optimizes space, highlights product display, achieves higher sales, and boosts profits. A good shelf planning model considers seasonal and promotional product demands, along with in-store sales and inventory information. Using modern ML algorithms for shelf planning minimizes the risk of under or over-stocking and maximizes product movement.

You can find more AI-powered practical uses cases for external data signals: Explorium Machine Learning Use Cases.

Which industries commonly use this type of data?

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.

How can you judge the quality of your vendors for in-store data?

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.

Demo: The quickest way to assess vendor quality from website information is demos. Often available on the site or provided on request, demos illustrate how vendor datasets can integrate with your systems and how quickly they can deliver results. Vendors may also be able to arrange a live demo for a case similar to your project.
Case studies: You may find case studies aligning with your requirements described on vendor websites. In such cases, you can quickly check the data attributes delivered. Case studies demonstrate the vendor's ability to provide high-quality datasets, the range of attributes, capacity to supply custom datasets, and commitment to the customer projects.
Customer reviews and testimonials: As reviews, ratings, and testimonials come directly from customers, they provide a good indication of the vendor's quality. You can assess the strengths and capabilities of the vendor and decide if they match your requirements. The customer information and reviews often include the industry vertical, helping you determine if the vendor is suitable for your vertical.
Interacting with vendor reps: The most reliable method of judging vendor quality is directly interacting with vendor reps. During the discussion, you can list your requirements, and the vendor rep can confirm if they deliver suitable datasets. Interacting with vendor reps helps resolve queries and start building a mutually beneficial business relationship.

Additional Resources:

BY ROLE

BY USE CASE

BY INDUSTRY

OVERVIEW

FEATURED DATASETS

Wiki Categories