Syndicated data, or common data if you will, used by CPGs that want to make data-based decisions, includes market data that is not specific to the customer. Syndicated data, which includes various information such as retailer sales data, customer panel data, or product data, is usually compiled by market research firms. The syndicated retail data can then be purchased by businesses or firms that have rights in the market. Syndicated data, which is one of the most needed pieces of information for companies that make decisions with a competitive approach and want to create a data-based workflow, has a critical role in creating and managing competitive intelligence and growing the brand's market.
Syndicated data provides a much better analysis of an out-of-scope product or retailer performance for CPGs, enabling statistical decisions to be made. With the syndicated data obtained from the right source and panel, new and more effective methods can be developed in addition to the existing marketing strategies of the brand. As a result, Syndicated data, which consists of only reality, depicts the common data that occurs with the presentation of mathematical data such as purchase, sale, profit, and loss in the product-retailer dilemma.
Syndicated Data for Retailers & CPGs
Whether you are marketing a new product in your company or a product that has existed for years, the most important tool you need in both is undoubtedly credible data. If you do not benefit from syndicated data containing critical information such as market data or retailer data of the product you are targeting, you will not be able to obtain effective results from your marketing work. In today's world where there is a consumption frenzy, product-retailer data is of vital importance in ensuring the supply-demand balance. Considering these and many other reasons, it can be said that syndicated data is extremely critical for CPG and retailers to survive.
In this regard, CPG and retailers must make use of Syndicated data to make effective decisions and implement these decisions. There are many reasons for CPG and retailers to use Syndicated data. The most notable among these reasons are:
- Competitive intelligence: Competitive intelligence is a situation represented by data. You can access detailed information about a product or retail traffic only with data, namely numbers. The data you have about the product allows you to make the right investment in this product at the right time, which allows you to take much smarter steps than your competitors. Real Syndicated data shows you your real growth or shrinkage. With the increase in the market share of a product you sell, your share of it may show that you are actively growing, but the further growth of your competitor retailer is that you are in a backward growth.Starting from another example, you can obtain the information that your competitor sells for 15 thousand dollars while you sell 20 thousand dollars in the store where you sell your products, with syndicated data.
- Growth Opportunity: Syndicated data shows you what you did right and wrong in product marketing. You can take steps and make strategic investments based on the Syndicated data of your retail competitor.
- Pricing and Promotion: One of the most important advantages of syndicated retail data is pricing and promotion. Based on the data you have obtained, you can develop promotion and pricing strategies for products at the right time.
Syndicated Data Panel: How It Works
Companies or large CPG firms form field teams to provide their market-product research, but this comes at an enormous cost. Instead, accessing syndicated data through brokerage firms is a much more profitable and logical move. In this context, to give an example, the syndicated data panel provided by Vispera, which consists of experts and competent people in their fields, is very useful—and it works in real-time! Analyzing the market conditions very well, Vispera syndicated retail data panel meets the vital needs of businesses in the most useful way. Check out the Syndicated Data Panel Webinar to find out more.
Vispera Syndicated Data Panel: The Future is Here
Vispera, an end-to-end retail execution platform collects retail, brand, and sales data and analyzes it in real time across all stores—all with the power that comes from our visual intelligence capabilities. To create a highly-functioning syndicated data panel, Vispera compiles the data obtained from all sites and serves the companies in need through the panel. With Vispera’s syndicated data panel developed with a user-friendly interface, you can easily access the data in the product-retailer dilemma and make quick decisions with data visualization.
What Data is Collected by Vispera Syndicated Data Panel?
Thanks to the customer-oriented Vispera Retailer Syndicated Data Panel, photos of the main fixtures, stands, and secondary sites for the selected category can be inspected if desired. Images are uploaded to Vispera in real time. Individual SKUs are identified and reported using Image Recognition, Machine Learning, and Artificial Intelligence
- Out of Stock
- Share of Shelf
- Product Location
- Shelf Standards
- Competitor Strategy
- Shelf Price
- Price Difference vs. Key Competitors
- Price Change Detection
- New Product Detection
- Web-based reports
- Weekly or monthly updates
- Management Dashboard
- Brand share of shelf comparison
- OOS %
- Price vs. key competitor variance
- New product launches
- Brand distribution
- Brand availability
- Brand share of shelf vs. key competitors
- Planogram compliance
- Filter to select the brand
- Secondary filter to select individual SKUs or the total
- SKU Report
- Excel file with SKU-level KPIs
- Store x Store Export
- Excel file with SKU-level KPIs for each store visit completed in the previous week or month
It may seem too good to be true, but with years-long experience and global partnerships, Vispera makes it possible to take your business one step further. Try Vispera, which offers you the most detailed information about products and retailers in today's consumption ecology, with syndicated data analysis.