How We Helped a Wine Retailer Strengthen Pricing Strategy with Vivino Competitor Price Analysis.jpg
                     How We Helped a Wine Retailer Strengthen Pricing 
Strategy with Vivino Competitor Price Analysis
Quick Overview
Client Name / Industry: A mid-sized wine retail business operating in 
the competitive online beverage marketplace.
Service / Duration: Product Data Scrape implemented a structured 
marketplace intelligence solution over a recurring monitoring period to 
improve pricing visibility and product-level analysis.
Key Impact Metrics: The project improved monitored SKU coverage by 
78%, increased price-refresh frequency by 4x, and reduced manual 
competitive research time by 65%. Through Vivino Competitor Price 
Analysis, the retailer gained a more consistent view of competitor pricing, 
product positioning, ratings, reviews, and availability. The solution also 
incorporated a Vivino Liquor Data Scraping API framework to support 
scalable data collection and structured delivery for ongoing commercial 
analysis.
The Client
The client was a wine retailer operating in an increasingly competitive 
digital marketplace where customers could compare products, ratings, 
reviews, and prices across multiple online sources. As online wine 
discovery became more data-driven, the retailer needed stronger visibility 
into how competing products were priced and positioned. Frequent price 
changes, promotional offers, changing product availability, and customer 
sentiment created additional pressure on the commercial team.
Before partnering with Product Data Scrape, the client relied heavily on 
manual research and spreadsheet-based comparisons. Analysts 
periodically checked wine listings and recorded pricing information, but 
the process was difficult to scale across a growing product portfolio. 
Historical information was also inconsistent, making it challenging to 
determine whether a price movement represented a temporary 
promotion or a broader competitive trend.
The retailer needed a transformation that could improve both research 
speed and data consistency. Vivino wine price tracking provided a 
foundation for recurring marketplace observations, while the 
Vivino Review Sentiment Dataset helped the team combine pricing 
intelligence with customer-facing signals. This broader perspective 
allowed the retailer to evaluate products based not only on price but 
also on ratings, review activity, and marketplace positioning. The 
partnership created a more structured approach to competitive pricing 
and assortment decisions.
Goals & Objectives
• Goals
Build a scalable framework for monitoring wine products, prices, 
availability, ratings, reviews, and competitor positioning.
Improve the speed and consistency of competitive research across a 
growing product portfolio.
Strengthen pricing decisions through reliable and frequently refreshed 
marketplace intelligence.
Establish measurable processes for tracking product-level competitive 
movements.
Improve the retailer's ability to identify pricing gaps and category 
opportunities.
• Objectives
Implement automated Vivino marketplace price analysis across selected 
wine categories and SKUs.
Replace repetitive manual collection with automated Price scraping 
workflows.
Standardize product and competitor records for historical comparison.
Enable structured integration with dashboards and analytical systems.
Support frequent data refreshes for priority products and competitive 
categories.
Combine pricing information with ratings, reviews, availability, and 
product attributes.
• KPIs
Increase monitored SKU coverage by 78%.
Improve price-refresh frequency by 4x.
Reduce manual competitive research time by 65%.
Improve historical price-record completeness to above 95%.
Establish standardized product-level records for recurring analysis.
The Core Challenge
The retailer's biggest challenge was maintaining accurate and timely 
visibility into competitor wine pricing. Manual research created operational 
bottlenecks because analysts had to repeatedly locate products, compare 
prices, record changes, and update spreadsheets. As the number of 
monitored products increased, this process became increasingly difficult to 
maintain.
The team also struggled with inconsistent historical records. A price 
captured on one day could not always be compared reliably with an 
earlier observation because product names, formats, promotional 
conditions, and availability information were not consistently 
standardized. This reduced the usefulness of historical analysis and 
made it harder to identify recurring pricing patterns.
Another issue was the time required to conduct Vivino wine price 
comparison across similar products. Analysts needed to review 
competing wines and understand how prices varied between products 
with comparable characteristics. Without automated workflows, 
important changes could remain unnoticed between research cycles.
Data quality and speed were therefore closely connected. Delayed 
collection meant commercial teams sometimes acted on outdated 
pricing information, while inconsistent records limited deeper analysis. 
The retailer required a solution that could automate recurring collection, 
normalize product-level information, preserve historical observations, 
and make the resulting data easier to consume. Product Data Scrape 
addressed these challenges through a structured marketplace data 
pipeline designed around the retailer's pricing intelligence requirements.
Our Solution
Product Data Scrape implemented the solution through a phased approach 
that combined marketplace collection, data standardization, automation, 
storage, and analytics. Each phase addressed a specific operational 
limitation while creating a scalable foundation for future monitoring.
Phase 1: Requirements Definition
The first phase focused on defining the retailer's monitoring requirements. 
Priority wine categories, products, competitor listings, pricing fields, 
availability attributes, ratings, reviews, and relevant product identifiers 
were mapped into a standardized data structure. This ensured that 
subsequent collection cycles produced consistent records suitable for 
comparison.
Phase 2: Automated Collection Workflows
The second phase introduced automated collection workflows. Instead of 
relying on analysts to manually review individual product pages, automated 
processes gathered the required marketplace information at configured 
intervals. Priority products could receive more frequent monitoring, while 
broader categories could follow scheduled refresh cycles. This improved 
data freshness without requiring identical collection frequencies for every 
product.
Phase 3: Normalization and Validation
The third phase focused on normalization and validation. Product names, 
brands, categories, package information, prices, discounts, ratings, reviews, 
and availability signals were standardized. Validation rules helped identify 
incomplete or inconsistent records before the data entered analytical 
workflows. Historical snapshots were retained so analysts could compare 
current observations against earlier pricing conditions.
Phase 4: Price Benchmarking
The fourth phase supported wine price benchmarking using Vivino. The 
retailer could compare monitored products by price, category, brand, rating, 
review activity, and availability. This enabled analysts to identify pricing 
gaps and understand how competing products were positioned within 
relevant categories.
Phase 5: Dashboard Integration
The fifth phase connected the structured dataset with dashboards and 
reporting workflows. Business users could filter information by product, 
category, competitor, date, and pricing movement. Automated reporting 
helped commercial teams identify meaningful changes without manually 
reviewing large datasets.
Phase 6: Scalable Architecture
Finally, the framework was designed for scalability. Additional wine 
categories, products, and competitive benchmarks could be incorporated 
without rebuilding the overall architecture. The result was a repeatable 
data pipeline that supported pricing intelligence, competitive monitoring, 
assortment evaluation, and historical market analysis.
Results & Key Metrics
• Key Performance Metrics
78% increase in SKU coverage: The retailer expanded the number of wine 
products included in recurring competitive monitoring.
4x faster price refreshes: Automated workflows enabled substantially 
more frequent pricing observations than the previous manual process.
65% reduction in manual research time: Analysts spent less time 
collecting and formatting marketplace information.
95%+ historical record completeness: Standardized collection improved 
the consistency of retained product and pricing observations.
Results Narrative
The implementation gave the retailer a more dependable foundation for 
competitive pricing analysis. Wine SKU price monitoring Vivino workflows 
helped commercial teams identify price movements sooner and compare 
products using standardized historical records. Increased coverage 
allowed analysts to examine a wider portion of the assortment, while 
more frequent refreshes improved visibility into short-term pricing 
changes.
The retailer could also combine pricing information with product ratings, 
review activity, and availability to develop a broader view of marketplace 
positioning. Automated workflows reduced repetitive data gathering and 
allowed analysts to dedicate more time to interpreting trends and 
developing pricing strategies. The resulting framework supported faster 
research, stronger competitive benchmarking, and more consistent 
commercial decision-making.
What Made Product Data Scrape Different
Product Data Scrape differentiated its approach through a combination of 
scalable extraction, structured product normalization, automated 
monitoring, historical storage, and analytics-ready delivery. The 
Vivino Product Data Scraper was configured around the retailer's specific 
product and competitive intelligence requirements rather than using a 
oSnmea-rsti zaeu-fitotsm-aaltli odna teansaebt.led priority products to be monitored more 
frequently while broader categories could follow scheduled collection 
cycles. Validation workflows helped maintain consistency across recurring 
observations, while standardized identifiers supported historical product 
comparisons.
The solution also connected Vivino Competitor Price Analysis with broader 
product attributes, allowing teams to evaluate pricing alongside ratings, 
reviews, availability, and category positioning. This created a more 
complete marketplace intelligence framework rather than focusing on price 
alone.
Client's Testimonial
"Product Data Scrape transformed the way our team approaches 
competitive pricing research. Previously, we spent significant time 
manually checking wine listings and maintaining spreadsheets, which 
made frequent monitoring difficult. The automated solution gave us 
broader SKU coverage, faster price updates, and much more consistent 
historical records. Vivino Competitor Price Analysis has helped our 
commercial team understand pricing movements and competitor 
positioning more effectively. The dashboards also made it easier to 
identify important changes without reviewing large volumes of raw 
information. We now have a scalable foundation for ongoing marketplace 
intelligence and better-informed pricing decisions."
— Head of E-commerce Strategy, Wine Retailer
Conclusion
The partnership helped the wine retailer move from fragmented manual 
research to a scalable, automated marketplace intelligence framework. 
Structured product and pricing records improved visibility into competitor 
movements, while recurring monitoring created a stronger historical 
foundation for analysis.
The integration of Alcohol data with product, pricing, availability, ratings, 
and review information enabled the retailer to evaluate marketplace 
conditions from multiple perspectives. Vivino Competitor Price Analysis 
gave the commercial team a clearer understanding of pricing gaps, 
competitive positioning, and product-level movements.
With automated collection, standardized datasets, dashboards, and 
historical records, the retailer gained a repeatable foundation for future 
pricing and assortment decisions. The framework can be expanded as new 
categories and products become strategically important, helping the 
business maintain stronger competitive visibility in a rapidly changing 
digital wine marketplace.
FAQs
1. What is Vivino Competitor Price Analysis?
Vivino Competitor Price Analysis involves collecting and comparing 
product pricing information to understand how wines are positioned 
against competing products. It can include price, discount, product, rating, 
review, and availability information.
2. How can wine retailers use competitive pricing data?
Retailers can use competitive pricing data to identify price gaps, 
benchmark similar products, monitor promotions, evaluate assortment 
positioning, and support pricing strategy decisions.
3. Why is automated wine data collection useful?
Automated collection reduces repetitive manual research and enables 
more frequent monitoring. It also helps maintain standardized historical 
records that can be used for trend analysis.
4. What information can be analyzed alongside price?
Businesses can evaluate pricing alongside product attributes, ratings, 
reviews, availability, brand, category, package information, and 
promotional indicators to develop a broader marketplace view.
5. Can the solution support large wine catalogs?
Yes. A scalable data pipeline can be configured to monitor larger product 
portfolios, priority categories, and competitor sets. Monitoring frequency 
can also be adjusted according to business requirements and product 
importance.