For too long a few large corporations(aka: Big Tech) have dominated the Internet. In the process they have taken control away from users. Web 3.0 could put an end to that, with many additional benefits.

What is Web 3.0?
The Big Tech companies take user’s personal data, track their activity across websites, and use that information for profit. If these large corporations disagree with what a user posts, they can censor them even if the user posts accurate information. In many cases, users are permanently blocked(deplatformed) from using a website or platform. These large corporations use creative marketing strategies, aggressive business deals, and lobbyists to consolidate their power and promote proprietary technology at the expense of Open Source solutions and data privacy.

If you care about regaining ownership of your personal data, you should be excited about Web 3.0. If you want an Internet that provides equal benefit to all users, you should be excited about Web 3.0. If you want the benefits of decentralization, you should be excited about Web 3.0.

To understand Web 3.0, it is beneficial to have a brief history of the Internet.

Web 1.0 (1989 – 2005)

Web 1.0 was the first stage of the internet. It is also frequently called the Static Web. There were a small number of content creators and most internet users were consumers. It was characterized by few people creating content and more people on the internet consuming content. It was built on decentralized and community-governed protocols. Web 1.0 was mostly static pages that had limited functionality and were commonly hosted by Internet Service Providers(ISP).

Web 2.0 (2005 – 2022)

Web 2.0 was also called the Dynamic Internet and the Participative Social Web. It was primarily driven by three core areas of innovation: Mobile, Social, and Cloud Computing. The introduction of the iPhone in 2007 brought about the mobile and drastically broadened both the user-base and the usage of the Internet.

The Social Internet was introduced by websites like Friendster(2003), MySpace(2003) and Facebook(2004). These social networks coaxed users into content generation, including sharing photos, recommendations, and referrals.

Cloud Computing commoditised the production and maintenance of internet servers, web pages, and web applications. It introduced concepts like Virtual Servers, and Software as a Service(SaaS). Amazon Web Services(AWS) launched in 2006 by releasing the Simple Storage Service(S3). Cloud computing is Web-based computing that allows businesses and individuals to consume computing resources such as virtual machines, databases, processing, memory, services, storage, messaging, events, and pay-as-you-go. Cloud Computing continues to grow rapidly driven by advanced technologies such as Artificial Intelligence(AI), Machine Learning(ML), and the continued adoption of cloud based solutions by enterprises.

Unfortunately Web 2.0 also brought about centralization, surveillance, tracking, invasive ads, censorship, deplatforming and the dominance of Big Tech.

Web 3.0(In Progress)

Web 3.0 is still an evolving and broad ranging concept but rapid progress is being made. It was originally thought that the next generation of the Internet would be the Semantic Web. Tim Berners-Lee(known as the inventor of the World Wide Web) coined the term to describe a more intelligent Internet in which machines would process content in a humanlike way and understood information on websites both contextually and conceptually. Some progress was made on helping machines understand concept and context via metadata markup standards like Microformats. Web 3.0 was to also be a return to the original concept of the web, a place where one does not need permission from a central authority to post, there is no central control, and there is no single point of failure.

Those are idealistic goals, but recent technology developments like Blockchain have expanded the idea of what Web 3.0 could represent. The framework for Web 3.0 was expanded to include decentralization, self-governance, artificial intelligence, and token based economics. Web 3.0 includes a leap forward to open, trustless and permissionless networks.

The rise of technologies such as distributed ledgers and storage on blockchain will allow for data to be decentralized and will create a secure and transparent environment. This will hopefully put and end to most of Web 2.0’s centralization, surveillance, and exploitative advertising.

The adoption of Cryptocurrency and Digital Assets are also a major part of Web 3.0. The monetization strategy of Big Tech selling user data was introduced by Web 2.0. Web 3.0 introduces monetization and micropayments using cryptocurrencies. This can be used to reward developers and users of Decentralized Applications(DApp). This ensures the stability and security of a decentralized network. Web 3.0 gives users informed consent when selling their data, and gives the profits back to the user via digital assets and digital currency. Cryptocurrencies also use Decentralized finance(DeFi) solutions to implement cross-border payments more effectively than traditional payment channels. The Metaverse and real time 3D Worlds are also part of what is envisioned for Web 3.0.

If you understand the benefits of decentralization, privacy, and open solutions it’s time to recognize the importance of Web 3.0!

The Decline of the Traditional Real Estate Brokerage

Real Estate Brokerages have been ripe for disruption for a long time. Until recently, traditional real estate brokerages have been spared some of the enormous disruptions that have redefined other industries, but they are under increasing margin pressure, losing top producing agents, and losing market share.

The Decline of the Traditional Real Estate BrokerageI have spent most of my career analyzing market disruption, disintermediation, network effects, and flywheel effects. I have studied several different markets as new entrants or technologies took on legacy business practices, and dramatically disrupted the traditional way of doing business.

A few examples of new companies disrupting entrenched legacy business models are:

  • E-Trade vs. Stockbrokerages
  • Expedia vs. Travel Agencies
  • Amazon vs. Retail Stores
  • UBER vs. Taxi’s
  • Netflix vs. Blockbuster & Cable Television
  • AirBNB vs. The Hotel Industry

I have also observed this when new technologies took on legacy technologies. Examples include:

  • Mobile Phones vs. Land Line Telephones
  • Open Source Software vs. Closed Source Proprietary Software
  • Digital Advertising vs. Print Advertising

When possible, I try to take a position in markets that are undergoing some sort of major disruption, as it represents a tremendous financial opportunity if you play it correctly. It should be pointed out that this is rarely a ZERO SUM GAME. Yes, many legacy approaches will go away, but some end up re-inventing themselves so that they can succeed in the new competitive landscape. For example, I know of several really good travel agencies that are thriving by developing deep offerings in specialized market segments like fly fishing trips and adventure travel.

In my local real estate market(Bozeman, Montana) the large national franchised real estate brokerages have been losing market share for several years. (see chart). The lack of consolodation among these national real estate brokerges is an indicator of a market undergoing not only a market transformation, but potentially a major disruption.

Real Estate Brokerage Market Share - Bozeman, Montana
Market share is in decline for the 6 leading national real estate brokerage franchises operating in Bozeman. The 6 brands represented in the chart are Berkshire Hathaway, Keller Williams, ERA, Christie’s International Real Estate, RE/MAX, and Sotheby’s International. The goal of the article isn’t to single out any specific real estate brand or business, but analyse the broader market trends.

The large real estate brokerage franchises are not only losing market share in my local market, they are facing increasing pressure on operating margins as their leverage against real estate agents declines. Residential Real Estate Brokerages are a narrow margin business with lots of competitiion for top talent. As the number of options available to top producing agents increases, these agents are successfully negotiating more favourable commission split arrangements with the brokerage and establishing commission caps that place a limit on the amount of commission they pay the brokerage.

In order to retain top producing agents(the lifeblood of revenue for a real estate brokerage), real estate brokerages are having to offer better commission splits, nicer offices, and invest in expensive Internet technology and marketing solutions. This is negatively impacting margins and profits… and many top producing agents are still leaving to join boutique brokerages or go out on their own as true independant agents.

Real Estate Brokerages are very aware of the problem, and the competitive threats. Gary Keller(founder of Keller Williams) said in 2018 that Real Estate Brokerages have to dramatically re-define themselves in order to survive… and he said they have less than 5 years to figure it out. Keller Williams and Coldwell Banker are both making massive investments in developing new proprietary technology in an attempt to compete effectively in the new landscape of residential real estate.

There are many new brokerage concepts popping up around the country competing for top producing real estate agents. 100% models, profit sharing models, hybrid models that are a mixture of both. There are real estate brokerages like Compass that deploy capital(from lead investor SoftBank) as a competitive strategy to acquire market share and try to position themselves as a technology company. There are also the “iBuyers” like OpenDoor and Offerpad that are attempting to take away many of the “pain points” associated with selling a home. Many top performing agents are leaving traditional real estate brokerages and going out on their own, or joining co-operative type arrangements with other top performing real estate agents. According to NAR only 42% of REALTORS® are affiliated with a brokerage franchise in 2019.

Looking at the stock prices for a couple publicly traded real estate brokerage franchisors reveals how investors feel about their financial viability.

Realogy Holdings Corp. is an American publicly owned real estate and relocation services company. Realogy is the leading global franchisor of some of the most recognized brands in real estate including Sotheby’s International Realty, Coldwell Banker, Corcoran, ERA Real Estate, Century 21, and Better Homes and Gardens Real Estate among others.

Realogy estimates that for all U.S. existing homesale transactions in which a broker was involved, Realogy had approximately 16% market share of transaction dollar volume and approximately 13.5% market share of transactions in 2017.

That is an impressive portfolio of real estate brands. However, investors have punished the stock. In 2013 Realogy(NYSE: RLGY) traded at a high of $53.53 per share valuing the company at over $6 Billion. In 2019 the stock was trading at $4.93 per share valuing the company at less than $600 Million… less than 1/10th its highest valuation.

Re/Max Holdings Inc is another American international real estate company that is publicly traded and operates through a franchise system. From 1999 until 2013 the company held the number one market share in the United States as measured by residential transaction sides.

Re/Max Holdings Inc (NYSE: RMAX) traded at a high of $67.20 per share in 2017 valuing the company at $1.19 Billion. In 2019 the stock traded for $25.67 valuing the company at $457 Million… less than 1/2 what it was at its high.

At the time of this article both of those stocks are trading up from the low prices they hit earlier in 2019. To be determined is if that is a “dead cat bounce.” In finance, a dead cat bounce is a small, brief recovery in the price of a declining stock

Looking at the 2019 Residential Real Estate Franchise Report reveals the Franchise Fees for these well known real estate brokerage franchises range from $25K to $35K with ongoing Royalty Fees of between 5% and 6% of gross commissions, and Monthly Marketing/Advertising Fees of 1%. If these real estate brokerage franchises were delivering tremendous value, they would be able to command higher franchise fees, and their stocks would be trading at much higher valuations.

Technology is currently empowering real estate agents at the expense of the brokerage, but it is also predicted to reduce the importance of both real estate agents and brokerages over time. According to researchers at Oxford University, the potential for artificial intelligence computer algorithms to replace real estate brokers is estimated at 97%. The future of the real estate brokerage is under immense pressure, and is changing fast!

Why Are Traditional Real Estate Brokerages In Decline?

Over the last 15+ years almost everything about residential real estate has gone online. This has changed the consumer buying and selling behavior. Previously a real estate brokerage controlled access to information about properties for sale. Consumers used to think the real estate brokerage with the most signs around town or the most advertisements in the local newspaper was the best option.

The brand of the real estate brokerage an agent was associated with used to matter to the consumer. As a result, the value the brokerage brought to the agent was significant.

Today, consumers do not make decisions based on the brand of the real estate brokerage, they choose the real estate agent they think will do the best job for them. The brand of the real estate brokerage increasingly has little to do with the transaction other than cash the check and keep a percentage of the commission. It is the interaction with the real estate agent and the agent’s expertise that creates consumer satisfaction, not the brand of the real estate brokerage.

The National Association Of REALTORS reported in 2016 that only 2% of consumers chose their agent based on the brokerage brand they are affiliated with.

In an effort to retain top producing agents, many brokerages are still trying to convince agents that the brand of the brokerage is important, and that their brand has significant value with consumers.

The reality is that consumers do not care about the brokerage brand an agent is associated with.

The brokerage used to be the consumer’s first stop. Now the consumer’s first step is to go online, and find an agent they want to work with.

Today, information on homes for sale can easily be accessed by looking at websites like Zillow, Trulia, Redfin, Realtor.com, and hundreds of other property search related websites. Consumers no longer consider a brokerage a significant component in the value chain associated with buying or selling a property.

When it comes to data, the largest and most valuable data or information in the real estate industry is basically off limits to brokerage firms. The most valuable data is client and customer data, and that is owned by the agents and is typically closely guarded by them. Brokerages have access to data about listings, sales, revenues and costs. That information is valuable, but the customer data owned by the agents is much more valuable.

I have spent the last 5 years looking at technology solutions for real estate agents. The best solutions tend to be marketed at individual agents, or teams of agents, not real estate brokerages. When agents purchase these marketing tools and take over their own technology stack, the grip that the brokerage has on the agent gets even weaker.

As a result real estate brokerages are not only increasingly losing their influence over consumers, they are also increasingly losing their leverage with real estate agents… especially top performing real estate agents.

What Does The Real Estate Brokerage Of The Future Look Like?

I try not to make public predictions… preferring to make private investments and get positioned to benefit financially for how I predict markets will evolve.

However, you can look at the way markets have behaved in the past to predict the future. One of the Immutable Laws Of Markets is that over the long term they seek efficiency. This usually leads to disintermediation(the removal of unnecessary intermediaries in the business process). Many of the top performing real estate agents in my local market are extremely successful without being associated with a traditional real estate brokerage. They have “disintermediated” the traditional brokerage… removing an extra layer in the transaction process.

Are national real estate brokerage franchises necessary in the real estate transaction process or are they a layer of overhead and expense that can be removed? 42% of agents already feel they are not worth the additional cost.

In 2014 my wife and I identified real estate as a market we felt would be disrupted by technology, and we started putting a strategy in place to capitalise on it. We visited with friends to understand their frustration with the existing real estate process, and we started developing a technology stack and a market strategy so that we could quickly grab market share as the industry transformed. We both became real agents, and then real estate brokers so that we could run our own brokerage if we choose to. In Q4 of 2019 we started our own brokerage so that we could be more agile and rapidly respond to the changes in the market we were seeing, and so that we could take complete control over the transaction process, and take over complete control of our market strategy and our marketing budget.

We feel we have created a more financially efficient and more market effective real estate company that delivers substantial value to our clients.

I can’t say with any degree of certainty how this will all play out, but looking at the market share data for real estate brokerages in my area of the world, it is clear that change is already occurring.

Having studied market disruption for over 25 years, I have noticed one thing all of the disruption cycles have had in common. First the disruption happens slowly, then it happens quickly as Network Effects and Flywheel Effects kick in.

Traditional Real Estate Brokerages are under extreme pressure. They are suffering from declining margins, losing top producing agents, and in my local area they are losing market share.

If real estate brokerages do not redefine themselves, start providing massive value to home buyers and sellers, and figure out how to bring substantial value to real estate agents they are at risk of having the same outcome as stock brokerages and travel agencies.

How Leading Technology Companies Are Using Artificial Intelligence And Machine Learning

Artificial Intelligence looks for patterns, learns from experience, and predicts responses based on historical data. Artificial Intelligence is able to learn new things at incredible speeds. Artificial Intelligence can be used to accurately predict your behavior and preempt your requests.

Artificial Intelligence And Machine LearningArtificial Intelligence and Machine Learning are shaping many of the products and services you interact with every day. In future blog posts I will be discussing how Artificial Intelligence, Machine Learning, Neural Networks, and Predictive Analytics are being used by Marketers to achieve competitive advantage.

AI’s (Artificial Intelligence) ability to simulate human thinking means it can streamline our lives. It can preempt our needs and requests, making products and services more user friendly as machines learn our needs and figure out how to serve us better.

Here are how some of the top companies are using Artificial Intelligence.


Google is investing heavily in Artificial Intelligence and Machine Learning. Google acquired the AI company DeepMind for the energy consumption, digital health and general purpose Artificial Intelligence programs. It is integrating it into many of its products and services. They are primarily using TensorFlow – an open source software library for high performance numerical computation. They are using Artificial Intelligence and pattern recognition to improve their core search services. Google is also using AI and machine learning for their facial recognition services, and for natural language processing to power their real-time language translation. Google Assistant uses Artificial Antelligence, as does the Google Home series of smart home products, like the Nest thermostat. Google is using a TensorFlow model in Gmail to understand the context of an email and predict likely replies. They call this feature “Smart Replies.” After acquiring more than 50 AI startups in 2015-16, this seems like only the beginning for Google’s AI agenda. You can learn more about Google’s AI projects here: ai.google/.


Amazon has been investing heavily in Artificial Intelligence for over 20 years. Amazon’s approach to AI is called a “flywheel”. At Amazon, the flywheel approach keeps AI innovation humming along and encourages energy and knowledge to spread to other areas of the company. Amazon’s flywheel approach means that innovation around machine learning in one area of the company fuels the efforts of other teams. Artificial Intelligence and Machine learning (ML) algorithms drive many of their internal systems. Artificial Intelligence is also core to their customer experience – from Amazon.com’s recommendations engine that is used to analyze and predict your shopping patterns, to Echo powered by Alexa, and the path optimization in their fulfillment centers. Amazon’s mission is to share their Artificial Intellgience and Machine Learning capabilities as fully managed services, and put them into the hands of every developer and data scientist on Amazon Web Services(AWS). Learn more about Amazon Artificial Intelligence and Machine Learning.


Facebook has come under fire for their widespread use of Artificial Intelligence analytics to target users for marketing and messaging purposes, but they remain committed to advancing the field of machine intelligence and are creating new technologies to give people better ways to communicate. They have also come under fire for not doing enough to moderate content on their platform. Billions of text posts, photos, and videos are uploaded to Facebook every day. It is impossible for human moderators to comprehensively sift through that much content. Facebook uses artificial intelligence to suggest photo tags, populate your newsfeed, and detect bots and fake users. A new system, codenamed “Rosetta,” helps teams at Facebook and Instagram identify text within images to better understand what their subject is and more easily classify them for search or to flag abusive content. Facebook’s Rosetta system scans over a billion images and video frames daily across multiple languages in real time. Learn more about Facebook AI Research. Facebook also has several Open Source Tools For Advancing The World’s AI.


Microsoft added Research and AI as their fourth silo alongside Office, Windows, and Cloud, with the stated goal of making broad-spectrum AI application more accessible and everyday machines more intelligent. Microsoft is integrating Artificial Intelligence into a broad range of Microsoft products and services. Cortana is powered by machine learning, allowing the virtual assistant to build insight and expertise over time. AI in Office 365 helps users expand their creativity, connect to relevant information, and surface new insights. Microsoft Dynamics 365 business applications that use Artificial Intelligence and Machine Learning to analyze data to improve your business processes and deliver predictive analytics. Bing is using advances in Artificial Intelligence to make it even easier to find what you’re looking for. Microsoft’s Azure Cloud Computing Services has a wide portfolio of AI productivity tools and services. Microsoft’s Machine Learning Studio is a powerfully simple browser-based, visual drag-and-drop authoring environment where no coding is necessary.


Apple is the most tight-lipped among top technology companies about their AI research. Siri was one of the first widely used examples of Artificial Intelligence used by consumers. Apple had a head start, but appears to have fallen behind their competitors. Apple’s Artificial Intelligence strategy continues to be focused on running workloads locally on devices, rather than running them on cloud-based resources like Google, Amazon, and Microsoft do. This is consistent with Apple’s stance on respecting User Privacy. Apple believes their approach has some advantages. They have a framework called Create ML that app makers can use to train AI models on Macs. Create ML is the Machine Learning framework used across Apple products, including Siri, Camera, and QuickType. Apple has also added Artificial Intelligence and Machine Learning to its Core ML software allowing developers to easily incorporate AI models into apps for iPhones and other Apple devices. It remains to be seen if Apple can get developers using the Create ML technology, but given the number of Apple devices consumers have, I expect they will get some traction with it.

These are just a few examples of how leading technology companies are using artificial intelligence to improve the products and services we use everyday.

Targeted Marketing Models

Targeting the best prospective core customers within the markets that have the highest probability of success establishes clear marketing goals that can be accurately measured by customer acquisition and sales conversion.

Targeted Marketing Models
In a previous post I covered Marketing Uplift & Predictive Modeling.

This post will discuss using Predictive Modeling & Marketing Uplift combined with classic Customer Analytics to create more accurate Targeted Marketing Campaigns.

The foundation of any accurate marketing model is a precise definition of your core customer, which encompasses 4 key aspects:

  1. Who your best customers are
  2. Where your best potential customers can be found
  3. What messages those customers respond to & when to send them
  4. The value potential the customers have to you, either in terms of dollars or visits

Customer analytic solutions have traditionally been used to gather some of those customer insights(Data Dimensions).

However, a properly defined Marketing Model combined with Customer Analytics can help not only accurately quantify the benefit for your marketing programs, but build out a deeper definition of your core customer.

Predictive Modeling can be used to understand the potential benefit of a new marketing program before it is launched. It can help you predict if it is a good investment or a bad one.

It can also be used to optimize existing marketing programs by establishing and analyzing key metrics.

The ability to measure, analyze, and assess existing marketing programs can identify which programs are not meeting their potential and then determine the gap between actual performance and ultimate potential.

Correlating this marketing information with known customer data dimensions allows you to refine your Core Customer Profile.

This insight allows you to immediately reprioritize resources for management, re-assign budget and marketing from the initiatives with no upside to the ones that are just underperforming.

Insights from customer analytics and marketing models on who core customers are, where potential core customers are located within an underperforming market area, what messages they respond to and when they respond to them, and the potential value of each of those core customers makes it possible to develop a focused demographic & psychographic profile of customers to target, and a measurable marketing campaign to go after them.

BriteWire sends the right message, to the right customer, at the right time.

Marketing Uplift & Predictive Modeling

Marketing Uplift(aka Marketing Lift) and Predictive Modeling are hot concepts in marketing. In this post we take a quick look at these marketing techniques.

Marketing UpliftMarketing Uplift(aka Marketing Lift) is the difference in response rate between a treated group and a randomized control group.

Marketing Uplift Modeling can be defined as improving (upping) lift through predictive modeling.

A Predictive Model predicts the incremental response to the marketing action. It is a data mining technique that can be applied to engagement and conversion rates.

Uplift Modeling uses both the treated and control customers to build a predictive model that focuses on the incremental response.

Traditional Response Modeling only uses the treated group to build a predictive modeling. Predictive Modeling separates the likely responders from the non-responders.

Traditional Response Modeling segments an audience into the following primary groups:

  • The Persuadables : audience members who only respond to the marketing action because they were targeted
  • The Sure Things : audience members who would have responded whether they were targeted or not
  • The Lost Causes : audience members who will not respond irrespective of whether or not they are targeted
  • The Do Not Disturbs or Sleeping Dogs : audience members who are less likely to respond because they were targeted

The only segment that provides true incremental responses is the Persuadables.

Because uplift modelling focuses on incremental responses only, it provides very strong return on investment cases when applied to traditional demand generation and retention activities. For example, by only targeting the persuadable customers in an outbound marketing campaign, the contact costs and hence the return per unit spend can be dramatically improved.

One of the most effective uses of uplift modelling is in the removal of negative effects from retention campaigns. Both in the telecommunications and financial services industries often retention campaigns can trigger customers to cancel a contract or policy. Uplift modelling allows these customers, the Do Not Disturbs, to be removed from the campaign.

Big Data & Psychometric Marketing

Psychometrics, sometimes also called psychographics, focuses on measuring psychological traits, such as personality. Psychologists developed a model that sought to assess human beings based on five personality traits, known as the “Big Five.” Big data correlated with personality profiles allows for accurate psychographic targeting by marketers.

Psychometric Marketing

Psychometrics is a field of study concerned with the theory and technique of psychological measurement. Psychometric research involves two major tasks. 1. The construction of instruments. 2. The development of procedures for measurement. Practitioners are described as psychometricians. The most common model for expressing an individual’s psychometric personality are the Big Five personality traits.

5 Personality Traits (Big Five)

  • Openness (how open you are to new experiences?)
  • Conscientiousness (how much of a perfectionist are you?)
  • Extroversion (how sociable are you?)
  • Agreeableness (how considerate and cooperative you are?)
  • Neuroticism (are you easily upset?)

Based on these 5 dimensions, also known as OCEAN(Openness, Conscientiousness, Extroversion, Agreeableness, Neuroticism) we can make a relatively accurate assessment of a person. This includes their needs and fears, and how they are likely to behave.

The “Big Five” has become the standard technique of psychometrics. The problem with this approach has been data collection, because it required filling out a lengthy, highly personal questionnaire.

With the Internet, and cell phones this is no longer a problem.

The Internet allows researchers to collect all sorts of online data from users and using Data Correlation they can associate online actions with personality types. Remarkably reliable deductions can be drawn from simple online actions. What users “liked,” shared or posted on Facebook, or what gender, age, place of residence they specified can be directly correlated with personality traits.

While each individual piece of such information is too weak to produce a reliable prediction, when tens, hundreds, or thousands of individual data points are combined the resulting predictions become very accurate. This enables data scientists and marketers to connect the dots and make predictions on people’s demographic and psychographic behavior with astonishing accuracy.

Smartphones are a psychological questionnaire on their users that is constantly being updated, both consciously and unconsciously. It is not uncommon for data analytics companies to have over 4,000 data points for each person in their target data set.

The strength of their psychometric modeling was illustrated by how well it could predict a subject’s answers.

It is now possible to assign Big Five values based purely on how many profile pictures a person has on Facebook, or how many contacts they have (a good indicator of extroversion).

Psychological profiles be created from this data, but the data can also be used the other way round… to search for specific profiles.

This allows for accurate psychographic targeting by marketers in ways that have never before been possible.

BriteWire Intelligent Internet Marketing facilitates building Psychometric User Profiles with purposefully designed content interactions, and automates marketing responses based on those triggers.

PropTech Disrupting The Real Estate Industry

Real Estate is the largest and most valuable asset class in the world but the Real Estate industry is ripe for disruption. Traditional real estate brokerages are under increasing competitive pressure as technology companies make inroads into the previously walled off real estate market. This complex, multi-faceted and high-stakes industry is rapidly becoming one of the hottest markets for entrepreneurs and investors alike.

Proptech - Fintech

PropTech(short for Property Technology) refers to the sector of startups and new technologies cropping up in response to decades of inefficiencies and antiquated processes in the real estate industry. The term is being used to encapsulate the entire market space of technology and real estate coming together to disrupt the traditional real estate model. PropTech is also referred to as CREtech (Commercial Real Estate Technology) or REtech (Real Estate Technology).

The PropTech sector is generating increased buzz as more people realize the opportunities for innovation in this sector, and venture capital investments expand. Real estate tech startups around the globe raised $1.7 billion worth of investments in 2015 – that’s an 821% increase in funding compared to 2011’s total. PropTech companies have raised around $6.4 billion in funding from 2012 to 2016. It is difficult to pin down exact numbers, but it is estimated that investment in the space increased by 40% for 2016. Compass, Homelink, SMS Assist and OpenDoor Labs all saw their valuations increase to over $1 billion in 2016.

Internet technology has tremendous potential to improve transparency and efficiency in real estate transactions. Property Listings and Property Search was the initial area for improvement. Over the past several years, technology advancements in property search and listing engines have been introduced.

These advancements allow home buyers to more easily find their home by making search criteria easier to specify. Buyers can specify the obvious search criteria like price range, size, number of bedrooms, and location. Additional facets of navigation like lot size, water features, adjacent public land, conservation easements, and if horses are allowed can also be used as search criteria. Buyers can view maps of where the homes for sale are located, and find homes for sale using maps as their primary search navigation tool. Search criteria can be saved in property search engines so that when new homes come on the market that match a buyer’s predefined criteria they are automatically notified.

Advancements on property listings have allowed a listing to easily be syndicated to hundreds of websites with the click of a mouse. This dramatically expands the listing’s exposure to potential buyers.

Technological advancements in deep learning, AI and other big data technologies are driving significant innovation in all areas of the property technology sector. Advancements in video, 3D Modeling, and virtual reality are allowing buyers to virtually tour homes they are interested in. These advancements allow agents to more effectively showcase the properties they are selling. However, these advancements in moving key data to the Internet may also mitigate the role that real estate agents play in the real estate process.

Competition in the space is increasing, but many tech companies do not have access to the industry and associated data due to real estate laws and regulations on disclosure. In many states it is illegal to disclose the price of a real estate transaction, and only members of the local real estate MLS have access to all the listing information.

What is next? Investor confidence is high and reports are that a massive amount of investment capital will be pumped into the real estate sector. It is safe to assume that we will see significant changes in the technical landscape for the real estate market.