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Data Readiness for Retail – What to Look for in a Data Vendor

Data Readiness

Understanding the Role of Competitive Data Scraping, Standardization, Storage, and Application in eCommerce 

Table of Contents 


  • Introduction
  • Why Data Readiness Matters Today
  • The Cost of Data Error in Retail Businesses
  • What to Look for in a Data Vendor
  • Intelligence Node’s Real-time, High Velocity Scraping
  • Intelligence Node’s Full Domain Data Listening
  • Conclusion
  • Introduction

    The retail landscape has changed dramatically – more so in the last two years than it had in a decade before. The pace and complexity at which eCommerce is evolving are daunting and technology is largely shaping which brands and retailers thrive and which merely survive. The highly informed, price-sensitive, channel-agnostic shoppers have coaxed retail businesses to reimagine their future roadmaps and craft experiences that are highly personalized, thoughtful, and resonate with the shoppers’ belief system – all while meeting their price expectations. And crafting such complex and customized experiences first requires immense amounts of data. Data that is dynamic, current, comprehensive, and accurate. Data that you can trust.  

    Why Data Readiness Matters Today

    At a scope and scale that is almost unfathomable, leading marketplaces like Amazon are checking and updating the prices of millions of product SKUs every minute. They achieve this level of speed and efficiency by leveraging advanced machine learning tools that convert data into actionable insights within seconds. But merely having data is not enough. The first step is to prepare your data for excellence. Data readiness is all the tasks you need to cover to ensure your AI/machine learning algorithms are learning from reliable, clean, and accurate data sources. Your algorithms can only get as good as the data they learn from. Bad, incorrect data will hamper the accuracy of results provided by your algorithms. And while ensuring the efficiency, standardization, storage, and accuracy of your data seems like a near-impossible task, you don’t have to do it yourself. 

    The Cost of Data Error in Retail Businesses 

    Inaccurate data can cost businesses trillions of dollars in losses every year. An Experian study found that around 95% of companies saw a negative impact on their organization from poor quality data, leading to higher costs and wasted resources. 

    What to Look for in a Data Vendor

    If you are or want to be a data-driven business and are in the process of identifying a data provider who can help take your business to the next level, it is important to thoroughly scrutinize your vendor across multiple parameters, from data quality, readiness, and accuracy, to the technology, scalability, and comprehensiveness of the offerings. 

    In the following sections, we set a precedent for what you should look for while selecting a data analytics vendor with examples and use cases from Intelligence Node. 

    Real-time High Velocity eCommerce Data Collection 

    This section answers the following questions:

    1. What product attributes and parameters can I track online?
    2. Can I capture the final price of any online product?
    3. Can I capture information of specific categories and products across different geographies?

    Real-time data listening or data collection refers to our process of capturing price and other product details from competitive domains in real-time by hitting a product URL. These details are not previously-stored in Intelligence Node’s database and represent what happens on the website at that specific moment. 

    Many data providers use a process of 1 by 1 requests to collect data which makes it difficult to scale and to maintain high data accuracy levels. On the other hand, our system makes multiple data collection calls in parallel. The result is high-velocity, highly accurate information that requires little to no human inspection. We make it easy for our clients to access their competitive domains in real-time via a standard API where the client just needs to input the product URL. Machine learning-enabled high-velocity data listening helps you spot opportunities and market trends by looking into competitors’ catalogs and price movements in real-time. 

    1. Full domain data listening

    Full domain data listening refers to the capability to collect all intelligence available on an eCommerce site down to the product description level. The ability to accurately and comprehensively collect information from an eCommerce site is tricky as websites are continuously changing their layout and supporting multiple layout/responses across products. Moreover, no two websites use the same structure and nomenclature, making it an ongoing and continuously evolving process.

    To do a full domain crawl, we first scan the entire website to capture all URLs, followed by the use of an NLP based AI algorithm (classification using LSTM on BERT embeddings) to classify the URLs of the product, listing page URLs and invalid URLs, which helps us narrow down the scope and save on cost and time.  

    Intelligence Node algorithms are designed and triggered using the same techniques as used by Google, thereby enabling our technology to mimic Google search behavior. As a result, we are able to collect the same accurate and precise information that Google retrieves; however, our machine learning capabilities allow us to get into deeper granularity. 

    Intelligence Node can scrape the full domain and provide the information in a predefined format e.g., Text/CSV, JSON, XML, etc.

    2. Flexible coverage of specific categories

    If, as a user, you do not want to opt for a full domain scrape, then with the High-Velocity data scraping, you get the flexibility to choose specific categories based on your business needs. 

    ecommerce categories data vendor

    3. Identification of key product details from the product page

    With high-velocity listening, capture key product details like product variants, ratings and reviews, shipping costs and time, pricing information, and more from the product description page to decode competitor’s strategies and make product-related decisions accordingly. We render Javascript to capture items that are visible only on runtime. Additionally, we use headless browsing to load web pages and perform automated actions (such as selecting sub-menus) similar to how a human would. And in case of the CAPTCHA test, we resolve this with a human in the loop and automated CAPTCHA breaking techniques. 

    4. Tracking the final price of the product

    Intelligence Node’s sophisticated eCommerce data collection capabilities help our customers obtain the product’s final price, which is only visible in the basket and otherwise not easily accessible. The final price includes shipping fees, taxes, duties, and any other charges that may not be visible on the product description page.

    5. Capture stock level from multiple sources

    Our “smart recipe scraping” algorithms can track sales velocity by scraping inventory count from multiple sources, including product pages, shopping carts, Amazon buy box, etc., and analyzing the frequency at which the inventory levels are depleting over a certain period.

    Also read : Stockouts and Over-Stocking Affecting Your Revenue? Here’s the Solution

    6. Zip code based data collection

    Prices can differ largely from one zip code to another depending on demand, shopper demographics, supply, and geographic placement. Hence, finding a data provider that can parse data at the zip code level can help identify trends and benchmark against competition at a more granular level. Intelligence Node’s patented machine learning algorithms can compare exact, private label, or similar products down to the zip code level. These algorithms can help brands and retailers identify and compare differences in prices and store descriptions at different locations. 

    7. 1P vs. 3P seller parsing

    Intelligence Node parses 1P and 3P seller intelligence to identify sellers which are relevant for marketplaces like Amazon, Walmart, and Kroger in the near future.

    The following examples highlight the distinction between a 1st party seller and a 3rd party seller on a marketplace website like Amazon. Knowing whether the seller is 1P or 3P on a marketplace platform is critical for a retailer for appropriately monitoring the competition and taking appropriate action in terms of price as well as logistics related to order fulfillment. It also enables a retailer to accurately benchmark its assortment against various sellers on a marketplace platform like Amazon.

    Example: 1P seller – 

    Example: 3P seller – 

    Read More : All You Need to Know About The Amazon Pricing Strategy in 2022
    

    Conclusion 

    In the age of digital commerce, data plays a key role in understanding the consumer journey and expectations, analyzing the omnichannel retail market and competitors, and offering the best prices and personalized experiences to your consumers. But the accuracy, recency, and quality of data is paramount to ensuring you are making the right data-driven decisions across your consumer lifecycle. It can be the difference between the success and failure of a retail business as bad, inaccurate, or stale data can lead to inaccurate decisions that can affect your revenue, profitability, and consumer trust. Finding the right data vendor like Intelligence Node, that offers the highest data accuracy levels, state-of-art data analytics, and easy-to-use interface can make all the difference in future-proofing your business and priming it for long-term growth and market standing. 

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