Foodspark

• The client owns a multinational consumer packaged goods manufacturing company founded in 1929.
• The products that the client’s company delivers are baby food, breakfast cereals, healthy beverages, soft drinks, tea, toothpaste, etc.
• To stay ahead in the niche segment, client needs a lot of data, particularly from their biggest competitors.
• The client had come up with numerous requirements related to data extraction and precise delivery.
• We understood the exact requirements, overcame the challenges hassle-free, and delivered the output in the required format.

Key Challenges 
The challenges that we faced were:

• The client’s company specializes in multiple products and hence faced significant challenges in targeting the audience effectively.
• The company’s marketing team was using various data sources, but the information they had needed to be more consistent. They needed help to gather comprehensive and accurate real-time data to understand their customer base and prepare their marketing strategies accordingly. The client realized they require a reliable source of high-quality data to revamp their strategies. 
• The major hurdles faced by the client:
• Data Accuracy: Maintaining data accuracy was a primary concern to ensure reliable market insights.
• Real-time Updates: The data must be continuously updated to keep up with the dynamic e-commerce landscape.
• Competitor Monitoring: It was difficult to collect and analyze competitor data with having accurate data.
• Creating Effective Marketing Strategies: Analyzing the data and creating marketing strategies accordingly was a complex thing.

Business Benefits
The client gained access to real-time, accurate, and comprehensive data, empowering them to make data-driven decisions with unwavering confidence. This result enabled the client’s company to adapt swiftly to market fluctuations, reduce operational costs, boost sales, and solidify its market position. Foodspark’s commitment to excellence in data scraping continues to drive success stories that shape the future of food data analytics.

Competitive Advantage: With up-to-the-minute data, the client was able to react swiftly to market changes, outpacing competitors in adapting to consumer leads.

Cost Savings: The client reduced the costs associated with manual data collection and analysis, enhancing its overall operational efficiency.

Increased Sales: By making informed decisions and altering the marketing strategies, the client’s business saw a notable growth in market share.

Product Portfolio Optimization: By monitoring competitor’s product offerings, the e-commerce giant identified gaps in their inventory. They used this information to expand their product line and attract a broader customer base.

Customer Satisfaction: Competitive prices, reliable stock levels, customer services, and an increased range of products led to enhanced consumer satisfaction and loyalty.

Client Requirement

• In order to improve their operational effectiveness and strategic decision-making in the competitive food delivery sector, the client had a wide range of specific requirements.
• They understood how important data was to remaining one step ahead of the competition and satisfying client demands.
• With a broad geographic reach and a varied menu, they required a reliable system to keep an eye on promotions, prices, and store availability across many channels.
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• Tracking store availability in real-time for competitors as well as their own.
• Maintaining competitiveness and pricing strategies requires accurate menu price monitoring across a range of goods, including variants, add-ons, sizes, and categories.
• Prompt information on stock levels and product availability to guarantee effective inventory management and satisfy client demands.
• To readily measure market trends and adapt promotional activities, a thorough monitoring of offers, discounts, delivery fees, and other changes is monitored.
• Informed decision-making can be facilitated by a tailored mapping system that compares pricing of comparable items and combos across several platforms, taking into account specific client inputs.

Challenges

• The difficulties we encountered into when trying to scrape real-time data:
• Ensuring dynamic price changes and promotions in addition to data accuracy.
• Effectively handling substantial amounts of data from several sources.
• Handling differences in data between platforms and geographical areas.
• Combining information from several platforms and channels to provide thorough insights.
• Automating the gathering and processing of data to provide timely and useful insights.

Solution

• We provided thorough and effective solutions that attempted to gather specific competition metrics from the chosen food chains in accordance with the client’s needs.
• We successfully retrieved and supplied real-time information about costs, discounts, fees, and delivery costs, as well as offers, store reviews, URL links, and tracking keyword page ranks and availability.
• To overcome the difficulties, we used advanced data scraping methods and algorithms for data management, data accuracy, and the integration of many data sources.
• Through automated data collecting and processing, the client was given structured pricing and significant actionable data insights, enabling them to make well-informed decisions.

Impact

• The business of our client experienced a significant impact from our solution.
• The client can optimize dynamic pricing plans with the use of extensive competitor pricing data.
• Our customer was able to sustain a competitive advantage by using real-time knowledge into competitor offers, pricing trends, and page ranks.
• The client may easily maintain operational efficiency through tailored reporting and the automation of data processing.
• The customer experience was improved by data-driven recommendations on price, offers, and discounts. The client was able to keep an eye on both its own and its competitors’ store availability.

Conclusion

• Due to the successful adoption of food data scraping methods, the client’s restaurant chain has been able to enhance the customer experience, manage operations efficiently, and develop competitive pricing strategies. By utilizing data-driven insights, they stay competitive and develop in the fast-paced meal delivery market.

Client Requirement

• The client wished to compile important datasets and keep a current, accurate database of vital data.
• The client has identified particular target markets, including Italy, Spain, and the UK.
• Complete restaurant data coverage, pricing, and menu audits have to be obtained at predetermined intervals. Data was needed by the brand owner for the web platform and mobile apps.
• Every week, the client requested store detail data for the online and app platforms that included market, brand, platform, related aggregators, and the full store address.
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• In addition to the product name, price, category, description, and other details, the customer requested product details, including region, brand, aggregator, and shop information.
• The client has requested a store coverage report that includes the store name and product count from the previous and current weeks.
• A summary of the previous and current week’s stores, along with a list of those that are missing.
• The required format for data delivery is CSV.

Challenges

• Discover what challenges we encountered while completing the demands of our clients.
• Data extraction solutions must be set up for particular targeted competitors and geographic areas.
• Within the allotted period, we must obtain restaurant menu details and conduct menu audits with the current specials.
• Data extraction from the web platform and mobile app must happen at the same time.
• Must obtain prior product, retailer, and aggregator details.
• Obtaining comprehensive coverage of the necessary data; ensuring SLA compliance for the accuracy, timeliness, and completeness of the data.
• Providing data in accordance with the specified format requirements.
• Fulfilling the weekly delivery schedule criteria.
• Dealing with issues related to scalability when managing massive data sets.
• Managing data discrepancies between various locations and retailers.

Solution

• Our team of experts used efficient data scraping technologies to address the issues, as listed below:
• Created unique scripts to promptly extract all restaurant coverage from specified regions, providing complete visibility of currently running offers.
• Employed sophisticated and automatic data scraping methods to guarantee weekly data delivery in the necessary format.
• Developed scalable data extraction software to manage massive data volumes.
• Extensive quality assurance procedures were put in place to guarantee the precision and entirety of the retrieved data.
• Made use of data validation procedures to guarantee that the given data complied with format and quality standards.

Impact

• Our clients benefited greatly from our all-inclusive data scraping solutions, which improved their operational effectiveness and strategic decision-making.
• Met several SLA standards and delivered data with high correctness and completeness.
• The customer was able to make better decisions because to the thorough data coverage they received for all products and retailers.
• Data was delivered in the necessary format, allowing for a seamless integration into the currently operational system.
• The customer may now access and handle enormous volumes of data.