#8: Dong Shen, Haha Cloud Technology Company
In the eighth feature of our Impact10 series, we interacted with Dong Shen, General Manager at Beijing Haha Cloud Technology Company. With a background in software and data systems. He now works in the travel industry, focusing on hotel distribution technology and SaaS-based B2B and B2C booking platforms that connect clients with global hotel suppliers. Below are some exciting highlights from our interaction: Let’s start with learning a bit about you. Tell us something about yourself- your journey as a technologist and in the travel industry. Sure! I started my career in technology, working on software and data systems. Later, I moved into the travel industry and now focus on hotel distribution technology platforms. We currently provide our clients with a SaaS version of B2B and B2C hotel booking websites, and we also help them integrate with hotel wholesalers, hotel groups, DMCs, and other supplier resources. How do you think technology has impacted the travel industry? (talk about what you think changed for good, the gaps, or what can be made better) Technology has made travel much more efficient. From the hotel booking side, for customers, it’s much easier to check real-time hotel prices around the world and book instantly. For travel professionals, it reduces a lot of manual work and makes it possible to sell through many more distribution channels. Which travel-tech, in particular, do you really appreciate? (Do not name a brand, only a tech. For example, travel APIs) I really appreciate mapping technology. Users may not really notice it, but it’s very important. Without good mapping, things like searching across different suppliers and comparing prices don’t work well. What were the challenges you were facing on your online travel platform? Hotel and room mapping are the biggest challenges. They require a lot of time and manual effort, and if the mapping is not accurate, it can lead to poor customer experience and even booking compensation issues. What led you to choose Vervotech’s mapping solution? Vervotech focuses on mapping technology and has already built partnerships with many hotel suppliers worldwide. With a simple technical integration, we can quickly access mapping data across different suppliers. What key improvements have you seen since using Vervotech? After using Vervotech’s mapping solution, we were able to quickly aggregate multiple suppliers and show different supplier prices on the hotel detail page. This allows users to get our best available price and also helps us attract more customers. Is there any feature or aspect of the product that you find particularly valuable? The accuracy of the hotel mapping is very high. So far, we haven’t had any wrong bookings caused by Vervotech’s hotel mapping errors. The room mapping data is also returned very quickly in groups, usually within a few seconds, so customers don’t have to wait too long. If you were to describe Vervotech’s impact in one sentence, what would it be? Vervotech made a complicated, manual data problem simple, reliable, and ready to scale. Would you recommend Vervotech to others in your industry? Why? Yes, definitely. Apart from their technology, they respond quickly to any issues, and the product gives great value for the cost.
The Hidden Cost of Duplicate Hotel Listings
Duplicate hotel listings hurt search results, distort pricing, and reduce bookings. Here is what causes them and how to eliminate them for good. Picture a traveler searching for a hotel in Singapore. The results show the same Marriott property three times: once as “Singapore Marriott Tang Plaza Hotel,” once as “Marriott Tang Plaza SG,” and once as “SMTP Hotel Singapore.” Different prices. Different photos. Different amenity lists. The traveler does not know these are the same hotel. Neither does your platform’s pricing engine. Neither does your analytics dashboard. This is a duplicate hotel listing. And if your platform pulls inventory from more than one supplier, you almost certainly have them. What a Duplicate Hotel Listing Actually Is A duplicate hotel listing occurs when the same physical hotel property appears more than once in your inventory under different identifiers or names. This happens because different suppliers assign their own internal IDs to the same hotel. Expedia has one code. Booking.com has another. Hotelbeds has a third. Your platform receives all three and, without a system to recognize that these records describe the same building on the same street, treats them as separate properties. The result is what travelers see: multiple entries for the same hotel with subtle variations in presentation, pricing, and content. This is not an edge case. A platform aggregating inventory from 30 suppliers will typically encounter this with hundreds or thousands of properties. The hotel mapping services market is a USD 1.42 billion industry specifically because this problem is pervasive and consequential. Why Duplicates Are More Than a Cosmetic Problem The most visible symptom is clutter in search results. But the downstream effects of duplicate listings reach deeper into platform performance. Pricing accuracy breaks down. When two records represent the same property, your pricing engine cannot accurately compare rates. A traveler may see the same room at $150 from one supplier and $140 from another, without knowing the listings are identical. Your platform loses control of competitive pricing. Analytics become unreliable. If the same hotel is counted as three separate properties in your database, every metric tied to that hotel is split across three entries. Occupancy data, conversion rates, and review aggregation all become distorted. Guest complaints increase. When a traveler books a hotel based on content from one listing and arrives to find that the photos and amenities came from a different, more flattering version of the same property, the post-booking experience fails. According to Skift research on data quality in hospitality, data inaccuracies cause service failures, billing disputes, and long-term brand damage. Supplier relationships get complicated. When a bed bank or wholesaler receives your inventory and sees the same property multiple times, it erodes confidence in your data quality and can create downstream deduplication problems for your partners. Search performance suffers. Platforms with clean, deduplicated inventory serve more relevant search results. Platforms with duplicate listings serve noise. Over time, travelers notice the difference even if they cannot articulate why one platform feels easier to use than another. The Root Causes of Duplication Understanding why duplicates appear helps prevent them from accumulating in the first place. Multiple supplier IDs for the same property. This is the most common cause. Each supplier maintains their own hotel database and assigns their own codes. Without a mapping layer, every new supplier integration multiplies the duplication risk. Inconsistent naming conventions. Suppliers use abbreviations, punctuation, and formatting differently. “Hilton Garden Inn” becomes “Hilton GI,” “HGI,” or just “Garden Inn” depending on the source. Automated text matching catches most of these but misses edge cases without multi-signal verification. Property rebranding. A hotel changes its name or affiliates with a new chain. Older supplier records still carry the previous name. The same physical property now exists in your database under two identities. Address format differences. Street addresses are formatted inconsistently across countries, regions, and supplier systems. A property with a clean match on name and phone may fail to match on address if one supplier uses a local address format and another uses an international format. New supplier onboarding. Every time you add a supplier, their entire property catalog needs to be checked against your existing master database. Without an automated mapping layer, this creates a window where duplicates enter freely. Manual vs. Automated Deduplication Some smaller platforms attempt to manage duplicates manually. Staff review flagged records, cross-reference information, and consolidate entries by hand. This approach has a ceiling. Manual deduplication works when inventory is small and supplier relationships are few. At any meaningful scale, the volume of records exceeds what human review can process accurately. A team checking 500 records per day will fall further behind as inventory grows and new suppliers come online. Semi-automated approaches combine algorithm-based flagging with human review for ambiguous cases. This improves throughput but still creates bottlenecks and depends on reviewer judgment for edge cases. Fully automated AI-powered deduplication uses machine learning to evaluate multiple data signals simultaneously: name, address, geolocation, phone number, email, imagery, amenity structure. The system continuously re-evaluates its matches as new data arrives, rather than running a one-time check. The accuracy gap between these approaches is substantial. AI-powered systems can achieve 99.999% accuracy at scale. Manual and semi-manual systems hover well below that, with error rates that grow as inventory scales. Also Read: How AI Improves Hotel Mapping Accuracy What a Proper Solution Looks Like A robust duplicate hotel listings solution has several interconnected components. A comprehensive mapping algorithm that evaluates name, address, coordinates, phone, imagery, and amenity data simultaneously. Single-signal matching (name only, or address only) misses too many cases. Continuous processing rather than batch schedules. Hotel data changes constantly. A solution that runs once a week accumulates errors between cycles. Broad supplier coverage. The tool is only effective if it knows about your suppliers. Look for solutions that cover 400 or more suppliers, with the ability to add new ones. Room-level deduplication as a second layer. Once properties are consolidated, the same logic applies at the room type level. Without room
