Big Data Monetization Lessons from Zillow
In the current tsunami of “Big Data” every business wants to get value out of the data. Here, we are sharing lessons learned by the new real estate websites who have brought together Big Data sets, home buyers, and home sellers.
The real estate industry has gone through a massive transformation as new real estate websites such as Zillow (and Trulia now owned by Zillow, and Redfin) have brought together Big Data sets on available properties, home buyers, and home sellers. These firms have, in the process, showed us that important lessons and best practices in monetizing Big Data.
While Zillow set out to disrupt the real estate market, its existence coincided with a growth in both fees paid to agents and an increase in the use of agents. In effect, Zillow transformed into a different kind of middleman – a data broker. Zillow and its competitors, Trulia and Redfin, through their data exchanges, have brought transparency to the real estate market. Because of these firms, due diligence, thorough searching, and even neighborhood comparisons are now easily available online. Zillow and its competitors brought convenience through its data products and have monetized them. Some may argue that their success, Zillow’s especially, may be more a function of the timing during which they launched: the housing market had just crashed and banks were selling homes. Suddenly, information on the direction of the housing market, neighborhoods, and foreclosures was worth a premium. Zillow fused various forms of data and embraced the realtor as a major part of its revenue source and developed data products to serve the realtor as part of their data monetization strategy.
The success of Zillow offers some important lessons in growing and managing data exchanges. Their success in creating data, analyzing data, and leveraging that data in novel ways offers lessons for all firms:
- Develop a Data Exchange with Customers on the Platform of your Customer’s Preference
Data exchanges have become increasingly important in many markets. Many exchanges like eBay and Amazon are now accessible on mobile platforms. Customers have shown a strong preference for data exchange use on mobile platforms. Leverage the movement to mobile apps to provide vicinity information. Increase customer participation on mobile apps by refreshing data at a high rate, making frequent consumer visits worthwhile and valuable.
- Fuse Data from Many Sources and Formats
Relevant data comes from many sources and formats. Fusing data from public sources with proprietary data improves convenience for consumers and creates a economic benefit in that multiple sites do not need to be accessed. It promotes scale in the number of consumers and achieves increased precision in data too. Data is also not just numeric. Develop platforms that take in text, photos, video, and even audio. Develop rankings and indices that can help organize non-numeric data. Tagging and descriptions of non-numerical data are necessary to finding and accessing such data.
- Bring Scale to Data with Indices
Having lots of data requires understanding the rank, severity, or importance of any specific entity. Indices help bring scale and communicate these natural inquiries. Just as a temperature scale allows us to understand severity and make relative comparisons, develop indices that allow users of your data to do the same. The above graphic in this post is a heat map of the “Walk Score” for various neighborhoods in Los Angeles. The index of walkability was one created by Zillow and offers consumers a great comparison of neighborhoods.
- Solve Customer Needs with Data Products
In general, customers do not want data. Customers have market questions. They seek answers to those questions. It is absolutely important that data products be created to solve the problems facing customers and answer the questions that they have. The data products permit monetization and marketplaces.
- Achieve Market Scale Quickly
Although the real estate market has given rise to multiple sophisticated data exchanges (Zillow, Trulia, and Redfin), Zillow emerged as a leader quite quickly. This was possible because it achieved scale quickly. It included more properties before the other sites. The marketplace rewards firms that achieve scale first. With more data, more innovation and more data products are possible. With that comes more users, and with more users comes more opportunities for monetization.
- Treat and View Your Data as an Asset
Zillow has a great reverence for its data. In its annual report to investors, it even describes the types of data it collects and its plans to collect more. Few firms are predicated on data as an asset, but that is changing. If you want your firm to view your data as an asset, you must begin to treat it that way. If data is the future of your firm, map your data strategy to your business strategy. It brings focus to the importance of data by your employees and by investors who see a future in your data monetization plans.
- Focus Data Products on Sellers: Reminder that Sellers (and Their Agents) are More Willing to Pay Than Buyers
Zillow shows us that in its quest to unleash information about homes to buyers, it remains that the sellers and the listing agents (real estate agents of sellers) are most economically incentivized to pay for data products. A large portion of Zillow’s revenue comes from listing agents, just like eBay earns from sellers, Google earns from advertisers (and not buyer) and the list goes on and on. Examine your business model for the role of buyers and their agents. Focus data products on sellers. Develop premium data products for sellers versus buyers.
Bio: Russell Walker, Ph.D. (@RussWalker1492) is Clinical Associate Professor at the Kellogg School of Management and the author of the book, From Big Data to Big Profits: Success with Data and Analytics, which is available from Oxford University Press.
- 2015 Predictions: What will happen to big data and data science?
- Strategies for Monetizing Big Data
- Exclusive Interview: Imran Siddiqi, SAP on the Strategic Value Proposition of Big Data
|Top Stories Past 30 Days|