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Zillow Group, Inc. (NASDAQ:Z – Get Free Report)’s stock price gapped down prior to trading on Thursday after the company announced weaker than expected quarterly earnings. The stock had previously closed at $41.89, but opened at $40.22. Zillow Group shares last traded at $39.13, with a ...

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Shares of Zillow Group, Inc. (NASDAQ:ZG – Get Free Report) gapped down before the market opened on Thursday after Citigroup lowered their price target on the stock from $68.00 to $58.00. The stock had previously closed at $41.44, but opened at $39.79. Citigroup currently has a buy rating ...

Zillow Group (NASDAQ:ZG) Shares Gap Down After Analyst Downgrade - Defense World

Zillow (NASDAQ:ZG) stock fell 5.9% after the online real-estate marketplace disappointed with its second-quarter revenue forecast, seeing a range of $525 million to $540 million, below the around $560 million widely expected.

Midday movers: Carvana and Qualcomm rise; DoorDash, Fastly, Zillow fall By Investing.com

Get the most recent info and news about Zillow on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Broker Associate and Luxury Realtor at Thế Realty.

Zillow

VR is seeing rising adoption in various industries including real estate. In this article, we gathered three recent cases where implementing VR helped raise sales in companies working with the real estate sector. Each case will highlight a different way of implementing VR. Case 1. Zillow - Buying a House Made Easier House hunting is a time-consuming process which involves meeting up with a real estate agent, visiting multiple locations, etc. Everyone has to go through this process at least once in their lifetime, but not everyone can carve out the time for it. Fortunately, some companies came up with a solution for those in need of a house, but limited in time: online real estate platforms. Zillow is one of the leaders in the online real estate business. They offer a website where a client can choose a house they like without leaving their home. However, the problem is that photos cannot reveal all the details about the house. And when making such a large purchase, clients want to be sure it will live up to their expectations. That is why Zillow has recently adopted the 3D Home tool which allows potential clients to walk around the house using VR technology. Now clients can get a much more realistic feel of the place they consider to make their home and see it as if they were physically there. Case 2. iVariant – Renovating a House Made Easier Everyone who has been through the process of home renovation knows that choosing the color of wall paint or wallpaper can be a real challenge. It often happens that the color looks great at the store but does not fit the interior or looks different at home because of lighting. Victoria Stenova is a design studio that offers a wide range of unique design solutions for apartments. They acknowledge the need to show their clients not just a sample of the wallpaper they offer, but what the apartment fully decorated with it would look like. However, demonstrating virtual designs in a real interior draws extra costs on rent and renovation. That was the problem iVariant had to solve for their client, and create a project that would allow to apply different wallpapers to virtual apartment walls. Using a VR headset the clients could walk around the apartment already decorated with the wallpaper they had chosen. The customers were provided with multiple locations to try their wallpaper on: a living room, bedroom, children's room, etc. Cutting-edge technology allowed to reproduce the wallpaper textures and interior details with superior accuracy. As a result, there has been a 30% rise in sales. Case 3. RoOomy - Furnishing a House Made Easier New houses and apartments in the real estate sector are often sold without furniture. However, studies show that people get more attached to a place that is already furnished, as opposed to unfurnished houses. Furniture makes a house look and feel more cosy, and it is easier for clients to get emotionally attached to a place and visualize their future there. Here comes the problem: how to sell empty houses so that people get as attached to them as to furnished ones? Obviously, adding furniture would be a costly solution. That is again the problem that virtual reality comes to solve. VR staging is a process of filling an empty house with furniture using VR technology. It allows using the exact area of the apartment/house on sale, but fill it with furniture, so that the client sees the finished version of it. RoOomy is an example of a VR company that offers VR staging. Such service can be useful not only to real estate professionals, but also to furnishing retailers, as VR staging allows clients to visualise how the furniture they intend to buy would look in their house. Overall, VR has revolutionized the real estate sector by offering innovative solutions for house hunting, furnishing and renovation processes. Using VR allows companies to increase sales as well as protects clients from mistakes when making an important purchase. There is little doubt that VR technologies have a great potential for further adoption in the real estate sector. Let’s see what the future brings.

3 Ways VR Changes Real Estate

A few weeks ago, Zillow as a seller in Phoenix, consummating its . Zillow joins a fleet of competitors — including , , and — that let home sellers close in days instead of months by directly buying homes at a ( ) fair price and flipping them back on the market. put its first home under contract entry into the “iBuyer” space Opendoor Offerpad Knock Redfin Now mostly There are now multiple iBuyers operating in Phoenix, including Opendoor, Offerpad and Zillow. In a , I noted some of the reasons why certain markets appeal to iBuyers: homogenous inventory and high transaction volume to maximize the accuracy of the automated valuation models (AVMs) that generate the offers, warm weather to minimize climate-related rehab surprises, and low taxes to reduce transfer and holding costs. previous analysis The corollary to this observation: we should expect to see direct competition between iBuyers sooner than the vast scale of the residential real estate market would have you believe, centered around cities like Atlanta, Las Vegas, Dallas and Orlando. These cities are the first fronts in a war to own a new paradigm in buying and selling homes that could represent multiple points of U.S. residential transaction volume over the coming years — Opendoor alone will . buy $2.5B of homes this year So: how’s it going to go down? Will Opendoor’s first mover advantage carry the day, will Offerpad’s operational chops lead to victory, or will Zillow and Redfin deploy their tremendous reach to pull ahead? In this post, I’ll propose a framework by which we can assess the competitive positioning of a given iBuyer, and apply it to the major players to predict which are best situated to win in markets where they cohabitate. Premise #1: In the eyes of the customer, iBuyers differentiate primarily on net proceeds It’s an interesting paradox of the iBuyer experience that the sheer speed of the transaction limits the degree of interaction the iBuyer has with the customer, particularly on the seller side. There are certainly ways for these businesses to differentiate on product-level experience from the seller’s point of view — a smoother flow to generate an offer, friendlier appraisals, a like the one offered by Knock, or features like a for sellers who don’t want to move right away. But these features feel relatively simple to replicate. None seems significant enough, relative to the psychological scale of selling a home, to create a customer experience moat unto itself. trade-in experience rent-back option As iBuyer product offerings mature, I believe home sellers will treat them the same way they view airlines, choosing between offers largely on the basis of the net dollars they’ll receive. In the world of traditional agents who spend months serving a client, it’s plausible to differentiate on personal or institutional “brand.” But given that the iBuyer experience is fast, impersonal and predictable by design, it’s hard to imagine brand loyalty playing a deciding role relative to thousands of dollars at stake — so long as the iBuyer has a baseline reputation as a fair counterparty, not like a “We Buy Ugly Houses” franchise. In other words, returning to the airline analogy, iBuyers will need to invest in brand to the extent they earn a spot in the United / American / Delta tier. Beyond that, I believe brand will play a negligible role in convincing a customer to choose one iBuyer over another. Without meaningful brand or product-level differentiators, the richest offer will win. Premise #2: Net proceeds are governed by cost, and the key controllable input into cost is inventory liquidity If you accept the first premise, the iBuyer with the lowest marginal cost per transaction will be able to make higher offers for homes, win the price war and capture more share. For a look at the unit economics of the iBuyer model, take a look at my . Some costs — like closing costs and taxes — are significant, but since they’re generally the same for each iBuyer (with some exceptions, which I’ll touch on later), we can net these non-controllable costs out of our head-to-head comparison. previous post The competitive drivers of cost for the iBuyer business model can be encompassed in a single concept: inventory liquidity. The with which an iBuyer can buy, rehab and sell a home drives the majority of controllable cost associated with the model, including: speed Holding costs: taxes, maintenance, HOA and insurance are proportional to holding time Financing costs: less holding time means capital can be recycled to buy more homes, lowering the cost of capital per transaction Market risk: rapid turnover reduces the risk of riding the elevator to the bottom in a downturn, minimizing the need to charge a risk premium There are other ways iBuyers can compete on cost, like developing a more accurate AVM to reduce pricing risk, or building enough scale to buy bulk materials and rehab homes for less money. Over a longer time horizon, iBuyers that offer a complete solution to buyers and sellers with owned-and-operated closing services — mortgage, title, etc — can reduce their cost relative to competitors. But beyond being the largest input into controllable cost, inventory liquidity is the best way to generate a competitive moat. The iBuyer that builds an initial liquidity advantage can charge less, which creates more seller demand, which generates listing inventory that draws buyers, which reduces holding time and improves liquidity — a virtuous cycle more durable than operational efficiencies of scale. Premise #3: Inventory liquidity is a function of flip operations and customer acquisition Once an iBuyer has purchased a home from a seller, two factors inform how fast they can turn it around: flip operations and finding buyers. By flip operations, I mean everything that happens between closing on the purchase and re-listing the home for sale: the rehab process, staging and listing on the MLS, maintaining the home, and any additional prep, like installing smart locks and security cameras to support 24/7 open houses. Using data to identify high ROI improvements for a given market, building an in-house team of contractors and handymen, and limiting the need for rehab in the first place by filtering for quality inventory are levers that iBuyers can pull to minimize flip time. But the main source of variance in turnover is customer acquisition. Traditional agents usually find buyers by sharing listings with the local MLS, where the listings are discoverable by other agents and flow through to websites like Redfin and Zillow. Tech-enabled disruptors can employ a wider variety of offline and online tools to increase conversion at every step of the buyer funnel. On the product front, several offline features could make listings more appealing to buyers, like making it easier to tour homes (24/7 open house or scans), offering a “certified home” guarantee, or even providing buyers with financing by underwriting their mortgage. But only two iBuyers have a proprietary presence at the top of the funnel — Redfin and Zillow, which have the power to highlight listings directly to tens of millions of unique visitors. Matterport Implications for the iBuyer competitive landscape If you believe that inventory liquidity is the key to lower costs, which is key to relative share gain, assessing each iBuyer’s strengths and weaknesses in flip ops and customer acquisition can help us predict the winner in markets where iBuyers compete head-to-head. Opendoor wins on ops, loses on online acquisition (and so do Offerpad, Knock, etc) Opendoor is the first mover and OG iBuyer. Given their scale and experience, I believe their edge exists in on-the-ground operations, with benefits both on the flip and in buyer acquisition. Opendoor’s shows that they hire an extensive team of coordinators, project managers and field technicians to manage their flips, and they’ve pioneered features like 24/7 open houses and a satisfaction guarantee to make their homes more appealing to buyers. They’re also launching a mortgage business, giving buyers a 1% discount on closing if they purchase an Opendoor home. jobs page Where Opendoor falls short is proprietary channels of online acquisition. Without a best-in-class search experience like Redfin or Zillow, Opendoor will need to double down on paid acquisition channels for finding sellers and source a greater proportion of buyers through the MLS — which means paying a full buyer agent commission. Offerpad and Knock haven’t been around for as long as Opendoor, but they share the same strengths and weaknesses in a directional sense. In particular, Offerpad is staffed by veterans of , which holds a gigantic portfolio of single family rental properties, and that experience will likely accelerate their operational prowess. But none of these companies have a robust online presence that organically draws customers to their home inventory. Invitation Homes Zillow wins on online acquisition, loses on ops Zillow’s advantage is clear: online reach. Boasting over a hundred million unique visitors across its flagship home search websites and apps, in theory, Zillow has an overwhelming advantage at targeting likely sellers with a pitch for Instant Offers based on their browsing activity, and finding buyers for owned inventory online. But Zillow has no experience as an operations business. They’re starting by leveraging their network of Premier Agents to manage both sides of the transaction — buying homes from sellers and listing them on the market. Both agents get a commission, so this is expensive talent, even if Zillow cuts a deal to reduce the commission and nets some of the commission payment back in Premier Agent fees. Over the long-term, Zillow will be also gated from competing on cost by its legacy business. The real opportunity for all iBuyer platforms is building enough liquidity to lower prices to parity with agent commissions (5–6% of transaction value). But this is also the moment that Zillow may run into steep resistance from its existing customer base — the real estate agents who will be supplanted by this new model. If Zillow wants to compete on price it will eventually undercut agents, and crossing the chasm from a high-margin marketplace business to a low-margin, high-volume operations business would tax even the toughest executives and shareholders. Redfin balances ops and customer acquisition Disclaimer: I’m a former Redfin employee, current shareholder and long on the business. With that said, I believe Redfin is best positioned to create a liquid iBuyer marketplace. With 25 million monthly uniques and brokerage operations in more than 80 markets, Redfin has an advantage in both online and offline acquisition. Redfin can use agents to acquire sellers by pitching Redfin Now alongside its existing listing product. Unlike Zillow, Redfin’s investments in end-to-end efficiency have resulted in a agent-listed product so affordable (1% commission) that Redfin Now won’t cannibalize it — Redfin agents and Redfin Now are natural complements. To move owned inventory fast, Redfin can highlight listings to users, and use existing vertical infrastructure (Redfin Mortgage) to create an attractive end-to-end experience for buyers. Redfin listings already than homes represented by traditional agents in most markets. sell faster Redfin’s biggest weakness relative to Opendoor and Offerpad is its lack of experience in rehab and flip operations. But given Redfin’s experience scaling a thousands-strong team of employee agents around the country, I believe they can remedy this disadvantage fast — and . they’re already starting There’s a lot at stake It’s worth noting that the iBuyer business model has gained traction even given a nationwide seller’s market, where the guarantee of a fast and certain sale is less valuable because most homes are under contract in weeks anyway. As the market turns, the iBuyer value prop will attract a wider audience of home-sellers. Balanced against the opportunity of a growing market is the unanswered question of the iBuyer model’s resilience to macro risk. In the end, perhaps the biggest advantage of inventory liquidity is reducing the chance that an iBuyer blows up during a major market correction. The battle for iBuyer market share will play out over a time horizon of years, but I believe that early and aggressive execution against flip operations and customer acquisition will kindle success in the great iBuyer game. Ultimately, a small liquidity advantage may well compound into a marketplace that makes the experience of selling and buying houses as seamless as the way we transact in everything else — and the owner of that marketplace will play a deciding role in the future of residential real estate. Sib Mahapatra is a writer and co-founder of Bureau . Originally published at www.sibmahapatra.com on July 19, 2018.

Redfin, Opendoor, Zillow and the Great iBuyer Game

An assessment of Facebook’s residential real estate strategy and the impact on real estate portals. This article was co-written with Mike DelPrete, strategic advisor and global expert in real estate technology. Subscribe to his real estate newsletter here . Facebook has always played a supporting role in the marketing portfolio of real estate professionals: historically, agents have been to advertise on a listing portal than on social media. For its part, Facebook has never built features that cater directly to real estate consumers and professionals. Earlier this year, that changed — signaling a shift in strategy with major implications for real estate portals. twice as likely A month ago, Facebook a major update to the “Property Rentals” section on Facebook Marketplace, launching a new front-end that allows mobile users to search for rentals using a variety of new filters: rental type, location, number of bedrooms, pet friendliness and more. announced Paired with this product update are new partnerships with Zumper and Apartment List — two of the most prominent online rental platforms, which collectively receive millions of unique visitors a month — which will directly syndicate “ ” of rental listings to Facebook. hundreds of thousands This is the second major step that Facebook has taken into residential real estate in three months. September saw the launch of , which lets brokerages and real estate websites live listings to Facebook users. Dynamic Ads for Real Estate promote What do these features reveal about Facebook’s real estate strategy, and what does Facebook’s growing interest in real estate mean for portals? In this article, we’ll take a closer look at Facebook’s recent moves into residential real estate, assess its motivations and forward-looking roadmap, and discuss the implications for existing players, especially real estate portals. Facebook and Real Estate Facebook cemented its status as an important marketing channel for rental managers and real estate agents well before the launch of Property Rentals and Dynamic Ads. On the rental front, Facebook has long hosted groups like , home to local classified posts from individuals and small-time landlords renting their property and rental seekers seeking the perfect match. Individual landlords have been able to post listings to Facebook Marketplace since the product’s launch last fall. Gypsy Housing But rudimentary search and lack of quality inventory has stopped rentals from really taking off on Marketplace. Though we don’t have precise data from Facebook, this qualitative observation is reinforced by the continued growth of informal housing groups. On the sale front, real estate agents have used Facebook to of their services or conduct targeted promotion of their listings, taking advantage of relatively slim competition to generate strong ROI. When Inman News surveyed agents on their use of Facebook this past September, one agent noted that she once $750 on a listing ad that generated six contracts and “hundreds” of leads. build local awareness spent Another agent that his average cost per lead from Facebook is around $5, comparing favorably to an average of $100 per lead from Zillow — although that cost per lead has jumped sixfold over the past three years as more agents bid for ad inventory. In general, Facebook has delivered to agents, but only for those willing to manually upload listings, optimize ad creative and nurture leads over a longer time horizon. reports high ROI Even without the benefit of any features specific to real estate, Facebook has already captured substantial mindshare among agents, with a September 2017 report by research firm Borrell Associates suggesting that agents are “more likely to by ads on social media than all other forms of digital media, including listing portals.” Assessing Facebook’s New Real Estate Features The revamped Property Rentals section and Dynamic Ads for Real Estate seem like simple optimizations at first blush, but their impact will be substantial. On the rental front, the product refresh and accompanying listing partnerships are game changers. Syndicating listings from established rental sites — and improving the user experience for renters — will make rental search far more useful on Facebook, attracting users who will ultimately incentivize more landlords to post inventory to Facebook. directly Facebook isn’t settling for feature parity with powerful incumbents like Craigslist, encouraging landlords to to provide a better sense for what a listing is like. post 360° photos Facebook is smart to focus on rentals, which are an ideal entry point into residential real estate because competition is fragmented: there is no MLS or single source of truth for rental inventory. By supercharging its network effect through listing syndication and user-side tweaks, Facebook has a shot at replacing Craigslist as the most comprehensive database of rental listings in America. On the sale side, the launch of makes Facebook a far more powerful tool for real estate agents. Dynamic Ads for Real Estate Dynamic Ads for Real Estate Until now, agents could only target broad audiences, capturing leads with less intent than users actively surfing a real estate portal for homes. By allowing brokerages to upload a catalog of live listings and target users based on their past interaction with specific listings, Facebook lets agents market to users who directly demonstrate affinity for their homes for sale, which should improve lead quality and ROI. Facebook’s Real Estate Strategy: All About Inventory What do these new features have in common? Both incentivize suppliers of real estate inventory — property managers and landlords on the rental side, agents and brokers on the sale side — to upload more listings to Facebook. The fundamental competency of an advertising platform is surfacing the right product or service (in our case, inventory) to the right consumer at the right time (in our case, transactional intent): in other words, identifying a consumer’s need and offering a relevant solution. With more than two billion users between its various products, Facebook is one of the few platforms that already has the right consumer in its grasp — and its real estate strategy targets the other pieces of the equation. Listing inventory is the bedrock of Facebook’s strategy to capture market share in real estate ad spend. Even if Facebook had perfect knowledge of a consumer’s intent to rent or buy a home, it can only monetize that intent if it has a “product” — a listing — to display. Accumulating a greater volume and variety of real estate inventory has another major benefit: by giving consumers more opportunities to interact with real estate, Facebook learns more about their preferences and intent, which enables more effective targeting. For example, Dynamic Ads for Real Estate already helps agents automatically target consumers who have visited their websites and browsed their listings. But a strong rental platform allows Facebook to add another powerful targeting tool to the mix, letting them advertise to folks searching for rentals with demographic characteristics that also make them likely buyers. The more precise targeting options available to agents, the stronger a case Facebook can make to brokers that they should syndicate listings directly to Facebook. This is when things get interesting: if enough agents use Facebook real estate ads in a given market, we can even imagine progressive MLS boards — who have been eager for more leverage against the portals — syndicating listings directly to Facebook. This presents the billion dollar question: will Facebook attempt to compete directly with real estate portals? We don’t believe Facebook wants to launch map-based real estate search, or turn the Marketplace into a fee-generating product for rental and sale listings. In the short run, Facebook is laser focused on improving its advertising product for real estate professionals and capturing a higher proportion of ad spend from portals. Things could get worse for portals in the long run. We discuss this in greater detail below, but as Facebook accumulates more inventory and learns how to precisely mate those listings to consumer needs, it may be able to leapfrog map-based search entirely by combining preset filters and natural language queries from the homebuyer with precise algorithms to users with the perfect home for them. match This won’t happen for years, but inventory would make it possible — and as we’ll discuss below, cultivating and protecting proprietary inventory is one way that portals can fight back. The Impact on Portals: Competition for Ad Spend The primary impact of Facebook’s move, in the short- to medium-term, will be increased competition for premium ad dollars from real estate agents. In the U.S., 70 percent of Zillow’s revenue comes from real estate agents, and the trend extends to each major international market. Facebook’s entry into real estate advertising represents clear and direct competition for this premium spend. Real estate agents will have another top-tier platform to spend money on to generate additional branding for themselves, generate leads, or promote the homes they are selling. Premium ad spend usually has no upper limit; agents spend as much as they want to promote themselves or their listings. Zillow Reacts: Premier Agent Direct Facebook launched its Dynamic Ads for Real Estate product last August. Two months later, Zillow announced a partnership with Facebook and “Premier Agent Direct,” an advertising product that pushes ads directly to Facebook. Zillow’s reaction is straightforward: the move gives it a seat at the table. With Facebook’s potential competition in the space, Zillow has decided the best way to stay relevant is to embrace the new Facebook advertising product and offer it to its existing customers. Zillow’s Premier Agent Direct Product At the time, Zillow said it “wasn’t just buying ads and reselling them.” In reality, that’s exactly what it’s doing, but with the benefit of using Zillow’s user data to improve relevance and targeting. Zillow has assumed the role of a middle man. Agents can go directly to Facebook to spend their ad dollars, or they can continue spending with Zillow and get exposure on Facebook. It’s a win-win where Zillow stays relevant, Facebook generates ad revenue, and agents maximise their exposure across multiple channels. Each option has its own benefits. Buying ads directly from Facebook gives agents more control and highlights their brand, compared to a co-branded experience through Zillow. For Agents: Buying from Facebook vs. Buying from Zillow Key Fronts In The Facebook-Zillow War Facebook’s entry into real estate will illuminate two key fronts around which the Facebook vs. real estate portal competition will focus. The first front revolves around user experience in the listing discovery process, pitting vs. . The current consumer experience on real estate portals is focused around searching and browsing. Visitors scan a map, filter it with search criteria, and browse through featured listings. It’s the equivalent of flipping through a glossy magazine or scanning the pages of a newspaper classified section. search match The very nature of Facebook lends itself to a matching experience. Real estate listings will be targeted to consumers based on what Facebook knows about them (in the same way it already targets advertising). The majority of Facebook users won’t search for real estate; they will see real estate presented to them. The second front revolves around inventory: all-of-market vs. some-of-market. Real estate portals maintain their reputation as the best place to find a home because they have of the inventory available in the market. When a consumer is searching for a new home, they want to look where all of the properties for sale are available. all Facebook’s marketplace strategy, on the other hand, is not predicated on having all of the available real estate listings. At least for the foreseeable future, the listings available will be those uploaded by its advertising clients. So while the consumer experience on Facebook will target and match listings directly to visitors, it won’t represent the entire market of possible houses for sale. This leaves a competitive opening for real estate portals. As long as the portals have a greater inventory than Facebook (which we believe will be true in the medium-term), their benefit to consumers is clear: do you want access to all of the market or only some of the market? Strategic Options For Real Estate Portals Facebook represents a clear and present danger for real estate portals around the world. It’s here, it’s growing, and it’s coming to eat your lunch. We believe a response strategy should center around the following: Deliver utility throughout the value chain and offer a more complete solution. Agents who get red-carpet treatment, free call center service, and unique online tools will be less likely to defect. Ultimately the game is about delivering ROI, so push hard to deliver more value throughout the value chain. Focus on providing value to agents. . Real estate is national. Real estate portals have the local knowledge, relationships, and brand that are necessary to thrive in the market. By building your local value to the market you compete in an area where Facebook cannot. Act local and leverage your sales team . Adopting Zillow’s strategy is controversial. Consider that Facebook already has an ad product for real estate agents, it already demonstrates favorable ROI, and agents are already spending money on the platform. In five years, will that still be the case, or will Facebook simply go away? We bet that it’s here to stay, and will only get bigger, with or without the cooperation of real estate portals. So get on board while you can and leverage your local muscle to sell Facebook’s offering to your customers. Sell Facebook’s ad product .” If and when the buyer experience moves toward a matching experience, have a product that matches buyers with homes. Don’t be left flat-footed if consumer preferences change. Explore “match If you’re considering how to compete with Facebook through social products, you’re naively barking up the wrong tree. The absolute last thing you should do is compete with Facebook where it is strongest. Don’t compete on social. Concluding Thoughts Facebook’s deeper move into real estate — both in rentals and for sale listings — represents an opportunity for agents and brokerages and a threat to a wide range of portal businesses. For real estate portals in particular, the first battleground is agent premium spend. Facebook is giving agents a new, powerful choice of where to spend their money for leads. This situation poses a dilemma for real estate portals. Do they cooperate with Facebook as “frenemies,” as Zillow has done, or do they work to stifle Facebook’s momentum at all costs? We believe that Facebook is here to stay in real estate, and will continue providing a positive ROI to agents. In the near term, real estate portals need to take action to cement their position as the best place to generate and serve high-quality leads. The good news, for portals, is that they have a fighting chance.

Is Facebook Coming For Zillow?

Usually, when you want to find a product on the internet, you face complex forms with many filters. Let’s take the example of Zillow, the real-estate marketplace website. This is how their interface looks: It seems pretty simple, which is great. Things become a little complicated when we click on “more“ and see extended form This still doesn’t look complicated. In my opinion, Zillow has a great UI/UX design. Many users find filling out multiple filters frustrating and time-consuming. My idea for this article is to provide an option to the user to have it much simpler using AI. Instead of all these fields, we can have only one, where the user can provide a free text, and all those parameters can be extracted from this field. The design for this can look like this: This is a good alternative for the complex forms. Yes, the user still needs to write all the details to provide the best match, but it will be much faster than selecting all the fields in the forms. Technical part Implementing an AI algorithm can be a challenge and can cost some time/money. But what if we keep the old structure of existing search functionality and just parse the free text into filter fields? I got this challenge for one of my clients, and I  am providing you with an adapted version of using OpenAI to do that job: openai.chat.completions.create({
  model: "gpt-4-turbo",
  messages: [
    {
      role: "system",
      content: `You are a helpful assistant that parses real-estate related text into a strict JSON format. JSON format should be just a simple string without any addition symbols, for example { "property_type": ["house"] }`
    },
    {
      role: "user",
      content: `Parse the following real-estate related text into strict JSON format with the following structure:
        {
          "location": "Extract the mentioned city, neighborhood, or region. Set empty string if none found.",
          "price_range": "Extract the mentioned price range or budget. Return as an object {min: number, max: number}. Set null if none found.",
          "property_type": "Extract the type of property (e.g., apartment, house, studio, condo). Set empty array if none found.",
          "bedrooms": "Extract the number of bedrooms. Set null if none found.",
          "bathrooms": "Extract the number of bathrooms. Set null if none found.",
          "amenities": "Extract key amenities mentioned (e.g., parking, balcony, pool, pet-friendly). Set empty array if none found.",
          "keywords": "Extract any additional keywords relevant to the real estate context. Set empty array if none found."
        }
        Text: "${userFreeTextInput}"`
    }
  ],
  max_tokens: 500
}); openai.chat.completions.create({
  model: "gpt-4-turbo",
  messages: [
    {
      role: "system",
      content: `You are a helpful assistant that parses real-estate related text into a strict JSON format. JSON format should be just a simple string without any addition symbols, for example { "property_type": ["house"] }`
    },
    {
      role: "user",
      content: `Parse the following real-estate related text into strict JSON format with the following structure:
        {
          "location": "Extract the mentioned city, neighborhood, or region. Set empty string if none found.",
          "price_range": "Extract the mentioned price range or budget. Return as an object {min: number, max: number}. Set null if none found.",
          "property_type": "Extract the type of property (e.g., apartment, house, studio, condo). Set empty array if none found.",
          "bedrooms": "Extract the number of bedrooms. Set null if none found.",
          "bathrooms": "Extract the number of bathrooms. Set null if none found.",
          "amenities": "Extract key amenities mentioned (e.g., parking, balcony, pool, pet-friendly). Set empty array if none found.",
          "keywords": "Extract any additional keywords relevant to the real estate context. Set empty array if none found."
        }
        Text: "${userFreeTextInput}"`
    }
  ],
  max_tokens: 500
}); In this example, I am using the  gpt-4-turbo model, which will return strict JSON that I can use as filters. For an MVP, using OpenAI's API is ideal. However, for a production setting with high traffic, a fine-tuned or self-hosted model may be more cost-effective in the long run. The result of the AI request can be configured to have the same as the user can select in traditional filters UI (first image). We can also improve that by adding exceptions, for example, when the user types something like this Looking for a 2-bedroom apartment in Texas under $2,500/month, pet-friendly, with a balcony and parking.
I am not considering Austin or Dallas. Looking for a 2-bedroom apartment in Texas under $2,500/month, pet-friendly, with a balcony and parking.
I am not considering Austin or Dallas. To exclude specific locations, we modify the JSON structure to include an except_location key. The AI should extract places users don’t want and store them in an array. except_location {
  "except_location": "Extract the cities, neighborhoods, or regions the user wants to exclude. Set empty array if none found.",
} {
  "except_location": "Extract the cities, neighborhoods, or regions the user wants to exclude. Set empty array if none found.",
} And here we go with obvious benefits by using AI. The user can even ask to exclude all big cities from the list, and the AI can find all big cities in Texas and does the job. Instead of traditional forms, we can give users the  possibility to use a chat, the next level of communication with services. Chat can ask additional questions in case some required information for searching is not found in the free text. This is similar to real communication with the agent when users come to the office of the real-estate agency. Having more natural interaction improves user experience. Since user preferences are different, I recommend introducing this as a beta option and allowing users to choose their preferred method. In our case, we will run an A/B test comparing AI-powered search against traditional filters. Metrics like time-to-search, conversion rate, and user satisfaction scores will determine which method performs better. In my opinion, in the future, as more people will use AI Chats, they will become more familiar with those types of interfaces and will prefer this option. The main benefit of using this approach is that we can simply add voice recognition to the input, and this will look very similar to talking to a real agent. Search results can be easily extended by adding more details to the filters. By collecting anonymized user queries, we can analyze patterns and fine-tune a model to provide better recommendations over time. I hope this article will help your business to provide your customers with a modern idea of communication.

Users Hate Search Filters—Maybe Let AI Handle It Instead

With strong demand driving average prices up to unaffordable levels and soaring interest rates that jumped from just over 3% in early 2022 to over 7% today, getting your foot on the property ladder is far harder than it once was. But, in many ways, the house-buying process has also become far easier to navigate than in the past. We have property technology, or proptech, to thank. Rather than searching far and wide for the perfect home, prospective buyers can fine-tune filters to suit their budgets and needs on property portals, view 3D models of the homes they’re interested in through virtual reality, and use platforms powered by big data to understand whether the property they’re bidding on is good value for money. As the CEO of , a new construction homes marketplace, I’ve seen the drastic difference proptech is making in a difficult market, and I believe the rapid rate of innovation will continue to see proptech thrive regardless of the volatility in today’s property landscape. NewHomesMate The Rise of Proptech: Plotting the Market’s Growth (2010-2023) Like many modern technologies, proptech found its origin in the early days of the Internet as the real estate sector sought ways to streamline time-consuming processes such as underwriting and accounting. However, it wasn’t until the 2010s that proptech really took off. The sector benefited from increased innovation and a surge of funding as investors looked to find real estate’s next big thing. Between 2010 and 2019, global investment in proptech skyrocketed from just $600m to over $20bn. In that time, we saw an abundance of proptech companies such as Compass, Airbnb, and Opendoor blossom into unicorns with billion-dollar valuations. Despite the pandemic causing the market to stall momentarily — with investment falling by 30% year-over-year — interest in proptech has risen to new heights in the current decade, most notably marked by Zillow’s 370% jump in value between January 2020 and February 2021. Of late, the issues facing the property market, such as high mortgage rates have had a knock-on effect on the proptech market. However, with interest rates having stabilized and people having accepted the new normal, there are positive signs that proptech will return to a positive trajectory in the years ahead. The U.S. Proptech Market in 2024: Trends and Predictions With the economy on the up, what does the future hold for proptech, and how can industry players capitalize on current trends to make 2024 a successful year? With the Federal Reserve having kept interest rates stable in the early stages of 2024, the worst of the economic downturn seems to be behind us. With businesses having a clearer picture of how their finances will look in the near future, they can stop focusing on survival and start investing in innovation instead. A return to growth: According to a recent Inman Intel survey, many industry players had already transitioned into a new growth phase. A year ago, securing funding seemed near impossible for tech startups. However, the Center for Real Estate Technology and Innovation (CRETI) has predicted a 15% increase in proptech seed funding and a 20% increase in Series A and B funding in 2024. Total funding is still far below previous years, but it’s a sign that confidence in the sector is growing again. Fresh funding: It’s no surprise that artificial intelligence will have the biggest influence on proptech in 2024. Latest figures show that AI-focused proptech startups raised over $4bn in funding in 2022 alone. AI everything: With over 500 companies currently developing tools that will benefit the real estate industry, plenty of useful solutions — AI demand forecasting, AI search, AI user analysis, and more — are now coming to market. With ChatGPT having shown the potential of advanced text generation tools, proptech companies will be on the lookout for ways to improve the buyer’s search experience through increased personalization. Increased personalization: Soon enough, one-size-fits-all property listings will be replaced by tailored recommendations and descriptions that help to sell homes faster. Sustainable demand: With many countries aiming for drastic emission reductions by 2030, there isn’t long left to meet these lofty targets. With real estate one of the largest polluters, producing around 30% of all global emissions, sustainability is becoming an even greater selling point for proptech companies — More so than profits and productivity for those committing to achieving net zero. 2024 and Beyond: What Does the Future Hold for Proptech? A few shaky financial years have tested the foundations of proptech and the wider real estate market. However, with the Fed expected to cut interest rates by more than 1% over what remains of 2024, mortgages will become more affordable in the months ahead. This will increase activity, which is great news for the real estate market, as well as the technology suppliers that they rely on. With emerging technologies such as AI and augmented reality becoming an essential among real estate players, proptech appears set for a period of sustained growth regardless of the challenge ahead. Valued at more than $25bn in 2022, the market is tipped to grow by 15.8% annually until 2030, reaching a total value of $81bn by the end of the decade. That isn’t to say the spell of uncertainty is over. That’s why NewHomesMate continues to experiment and implement new technologies to streamline the house-buying process, despite having secured over $80m in sales during one of the housing market’s most difficult moments in recent history. With the pandemic-inspired boom and sudden cooldown having offered a sharp reminder of how quickly the housing market can turn, providers should expect investors and customers alike to proceed with caution. Only the sturdiest of proptech businesses — those that offer exceptional value and continue to develop their offerings using the latest technology — will survive. The article uses data and materials from the following sources: “Are Home Loan Rates Ever Going to Drop? Mortgage Rates for Feb. 20, 2024”, Katherine Watt, CNET https://www.cnet.com/personal-finance/mortgages/todays-rates/mortgage-rates-for-feb-20-2024-rates-increase-for-prospective-buyers/ “Most homes for sale in 2023 were not affordable for a typical U.S. household”, Jennifer Ludden, NPR https://www.npr.org/2023/12/24/1221480443/most-homes-for-sale-in-2023-were-not-affordable-for-a-typical-u-s-household “When did the term "Proptech" start?”, Davis Wells, https://www.unissu.com/proptech-resources/When-did-the-term-Proptech-start “Value of investment into proptech companies worldwide from 2010 to 1st half 2022” Statista https://www.statista.com/statistics/951857/global-proptech-investment-value/ “Market capitalization of Zillow”, https://companiesmarketcap.com/zillow/marketcap/ “Market capitalization of Redfin”, https://companiesmarketcap.com/redfin/marketcap/ “Proptech Sector Gears Up for Growth: 2024 Market Forecast Reveals Promise”, BNN correspondents, BNN https://bnnbreaking.com/finance-nav/business/proptech-sector-gears-up-for-growth-2024-market-forecast-reveals-promise/ “AI and the evolution of proptech”, JILL https://www.jll.co.uk/en/trends-and-insights/workplace/ai-and-the-evolution-of-proptech “Has Proptech Funding Turned the Corner?”, Phillip Russo, Commercial Observer https://commercialobserver.com/2023/10/proptech-funding-2024/ “What To Expect From The Fed’s Remaining Meetings Of 2024”, Simon Moore, Forbes https://www.forbes.com/sites/simonmoore/2024/02/12/what-to-expect-from-the-feds-remaining-meetings-of-2024/ “The Proptech Revolution Is Coming. How Can Developers Know They’re Investing In The Right Tools?”, Julia Troy, Bisnow https://www.bisnow.com/philadelphia/news/proptech/the-proptech-revolution-is-coming-how-can-owners-and-developers-know-theyre-investing-in-the-right-tools-122445 “Proptech in 2024: VCs, Principals and Analysts Sound Off”, Phillip Russo, Commercial Observer https://commercialobserver.com/2023/12/proptech-2024-trends-investment/ “PropTech Market Size, Share & Trends Analysis Report By Property Type”, https://www.grandviewresearch.com/industry-analysis/proptech-market-report

Bricks, Mortar, and Code: The State of Proptech in 2024

I am a Software Quality Assurance (SQA) engineer with 12 years of experience who spent hundreds of hours writing, reviewing, and running test cases. I know that sometimes the process of creating a test design can be boring, but without a well-crafted test case design, the QA engineers will not be able to test the software efficiently. They will have little or no visibility on testing coverage, which could lead to poor-quality software filled with defects and bugs. I know that AI-powered tools like ChatGPT can become a revolution and will change the way of work for SQA Engineers. Here, in this experiment, I asked ChatGPT to write test cases for different applications and scenarios to see if SQA Engineers can fully outsource the test design job to ChatGPT or not. So, let's go! 1) Writing basic positive and negative test cases for Login functionality. The app is well known, and the functionality is common. Sample Scenario: Write Positive and Negative test cases for login into Facebook.com ChatGPT Response: My conclusion: these test cases can definitely be used for basic functionality checks. They are written good, structure is perfect. The issue I see is that they are very general. So, for example, the expected result written by ChatGPT for a negative test case, when a password is not entered, says that “the user should see an error message indicating that the password field is required.” For 50% of apps, this test case is valid, but sometimes, it is important to include more details into your test case, especially when wording is important or was changed recently, and the idea of the test case is to verify the change. Also, another issue with ChatGPT test cases, which is probably even more important, is that if you run this test case manually on the facebook.com website, you will see that the expected result can be completely different. Leaving an empty password field leads you to another screen where the avatar and name of the user with the entered email are shown. The message shown is, “The password you’ve entered is incorrect. Forgot Password?” So, as you can see, the expected result is completely different from what ChatGPT has provided. So the test cases written even for basic scenarios can’t be used straightforwardly. ChatGPT will definitely save you time, but still, deep review and rework are required. 2) Writing test cases for more complex scenarios Sample Scenario: Write test cases for the “Monthly payment calculator” functionality at zillow.com. ChatGPT Response: My conclusion: I found a couple of critical issues in ChatGPT test cases which I listed below: The expected result is too general for such complex functionality, no formulas are provided, and it is not clear how to verify that calculations are correct. The fields names in test cases are different than on the real website, Zillow.com The amount of fields in test cases is different than on the real website, Zillow.com According to the issues I see in the ChatGPT test cases, I think using ChatGPT for complex functionality is not yet possible. You probably need to write more exact prompts to be able to receive more detailed test cases. But for me, it looks like now, it is still easier to write test cases by yourself in the case of complex and unique functionality. Also, I am not sure how it is possible to write test cases using ChatGPT for the functionality that is not in production yet. Still, writing prompts, in this case, will take too much time. Again, it will be easier to write test cases by yourself. 3) Writing test cases according to best practices and test design techniques. This task is a common interview question. It is not related to any app; it is more about test design techniques knowledge. A test design technique is a standardized method to derive, from a specific test basis, test cases that realize a specific coverage. Exhaustive testing for each set of test data is practically impossible due to time and budget constraints, especially when there is a large set of input combinations. SQA engineers should judiciously design test cases that will take care of all possible test data combinations. Test case design techniques should select test cases that are most likely to find bugs, from a large number of available test cases. Boundary value analysis can be used to pick the appropriate test data and test cases. Sample Scenario: Write test cases for a numeric field “amount” using the boundary values test design technique. “Amount” field description: values less than 10 are rejected, values between 10 and 21 are accepted, and values greater than or equal to 22 are rejected. ChatGPT Response: My conclusion: According to software testing theory, the basic principle behind this technique is to choose input data values: Just below the minimum value Minimum value Just above the minimum value A Normal (nominal) value Just below the maximum value Maximum value Just above the maximum value In general, the answer from ChatGPT looks good, all the required scenarios are written, and the expected output is correct. I would definitely recommend using ChatGPT for boundary value analysis technique test design. Of course, in real life, such types of test cases are probably 1% from others. Because usually, it is more important to spend time by testing the main functionality rather than exhaustively verifying each field using test design techniques. But this is a great opportunity to prepare yourself for the interview questions. Such types of questions are often asked in interviews. Can SQA Rely on ChatGPT in Writing Test Cases? As a general conclusion and answering shortly - no, still not. Can ChatGPT save some time for SQA Engineer: yes, of course. ChatGPT can assist SQA in its work by providing insights and suggestions based on its vast knowledge base, helping to identify potential issues, and providing solutions to problems. Additionally, ChatGPT can help answer common questions and provide guidance to those who are new to the field. ChatGPT can be a useful tool to support the work of a SQA, but it cannot replace the skills and expertise of a human professional in this field.

Can SQA Engineers Rely on ChatGPT in Writing Test Cases?

239 Stories To Learn About Web3 Writing Contest

In the ever-changing modern business landscape, business growth strategies, once grounded in industry experience, competitive benchmarking, and a fair share of professional intuition, are being reshaped by the influence of . The sheer volume, variety, and velocity of data available to businesses today offer invaluable insights promising rapid growth and unprecedented efficiency. Big Data At the forefront of this revolution marches machine learning. Not just a passing trend, machine learning provides a rigorous mathematical framework to interpret patterns and make strategic decisions based on accurate predictions. But how exactly does machine learning modify and transform business growth strategies? And in what ways can its advanced algorithms help a company aiming for market leadership? In this article, I will try to answer these and other questions, exploring the connection between machine learning and efficient business development. Content Overview How Machine Learning Affects Modern Business What Are the Basic Algorithms of Machine Learning How Machine Learning is Applied to Business Growth in Practice A Step-by-step Guideline to Implementing Machine Learning for Business Growth Technology Limitations and Practical Considerations Conclusion How Machine Learning Affects Modern Business In today's business environment, data plays a central role. Every customer interaction and transaction generates data that offers insights into market dynamics. The core advantage of machine learning in business is its ability to transform this extensive data into actionable decisions. is a computational method that allows its practitioners to identify patterns and make data-driven decisions without explicit programming. In business contexts, it enhances decision-making by eliminating human biases. Instead of relying on lengthy manual analysis or basic market surveys, machine learning provides an in-depth, objective view by examining numerous variables simultaneously. Machine learning Take product pricing as an example. Instead of using conventional methods like cost-plus pricing, machine learning offers dynamic pricing models. These models factor in real-time demand, inventory, competitor prices, and even external events to recommend optional pricing. Similarly, in customer relations, machine learning refines the broad understanding of target audiences. By evaluating browsing patterns, purchase histories, and customer feedback, it assists in building personalized marketing strategies, boosting conversion rates and customer loyalty. This series of examples can be continued . ad infinitum The benefits machine learning brings to present-day business are innumerable. Today, in the third decade of the 21st century, the question is not whether companies should adopt machine learning, but rather how swiftly they can integrate it into their internal processes to avoid falling behind the competition. But first, let us ask ourselves a question, what do people mean by machine learning? What Are the Basic Algorithms of Machine Learning Machine learning leverages an intricate combination of mathematics and computation, yet it is much more than just a set of convenient tools for processing large volumes of data. Machine learning is a discipline rooted in established mathematical principles, which ensures the accuracy and reliability of the results it delivers. To give you a peek into the nature of this technology, I will provide a brief description of some basic machine learning algorithms. While this section would tell nothing new to experienced professionals utilizing the power of machine learning in their jobs, like data analysts, I hope that a reader new to the topic will find it informative. a way to predict one value based on another by discerning patterns from available data. Essentially, it determines the relationships between different variables, influencing one another. Linear regression, for instance, assumes a straight-line relationship between two variables, while polynomial regression describes curved relationships; yet another example is logistic regression which predicts the probability of a binary outcome (true or false). By understanding these relationships, businesses can predict the effect of a change in one variable on another. For example, Airbnb uses regression analysis to suggest optimal pricing for hosts based on factors such as location, property type, time of year, and other listings’ prices. Similarly, Zillow's "Zestimate" applies linear regression to predict house prices based on features like size, location, and number of rooms, leveraging vast amounts of past sales data. Regression Analysis provides play a pivotal role when dealing with massive volumes of unlabeled data. They segment data into distinct groups, with items put in groups based on inherent similarities – and differences from other groups. K-means, for example, partitions data into 'k' number of centroids, and all data points are clustered around these centroids based on similarity. The New York Times, as an example, utilizes clustering to group news articles for readers, ensuring that similar stories, whether thematic or topical, can be navigated with ease. Clustering algorithms involve analyzing available data and learning patterns to apply to new, unknown data. Often used in conjunction with clustering, classification is about assigning predefined labels to data points. One well-known application is in the finance sector. Banks like JPMorgan Chase & Co. leverage classification to discern between 'fraudulent' and 'legitimate' client transactions, drawing upon patterns in historical data. In the healthcare domain, IBM's Watson Health utilizes classification algorithms to assess medical imaging, helping doctors determine whether a tumor is benign or malignant based on specific attributes. Classification methods l: extracting meaningful, actionable patterns from data. By laying out a mathematical framework, machine learning ensures these patterns are not coincidences but have statistically significant foundations, providing reliable insights that businesses can use. This leads us to the next part of this article – a deep dive into the real-life applications of machine learning in business development. All these algorithms share a common goa How Machine Learning is Applied to Business Growth in Practice Machine learning’s theoretical foundation is captivating, but where its transformative power shines is in its ability to spark business growth through practical applications. To better understand its impact, let us dive into how it has reshaped various sectors in recent years. Nowadays, machine learning plays a key role in shaping . By analyzing vast data sets ranging from local consumer behavior, cultural preferences, and competitive landscapes to regulatory requirements, machine learning models predict the success rate of a product or service in a particular region or demographic. Such data-driven insights can guide businesses on where to open new stores, how to adjust product features for local tastes, or even when to launch for optimal impact. This predictive capability empowers businesses to make informed, strategic decisions, optimizing investments and amplifying their chances for successful market expansion. Market Expansion Strategies In the realm of , gone are the days of relying solely on traditional demographic-based methods. Clustering algorithms have revolutionized this space, enabling businesses to segment their clientele based on buying behavior, browsing patterns, and even sentiments on social media. This radical shift has paved the way for marketing campaigns that more precisely resonate with individual consumers, ultimately fostering stronger customer acquisition and retention rates. Customer Segmentation The complexity of – predicting future sales trends – has been an age-old challenge. But with machine learning, tools like regression and time series forecasting have introduced a new layer of precision. By intricately analyzing both historical data and external variables such as economic indicators or seasonal patterns, businesses now possess sophisticated tools to project revenue, enhancing their strategic decision-making process. Sales Forecasting In the dynamic world of commerce, setting the right price can make or break a product's success. Machine learning aids businesses in devising smart strategies by analyzing a combination of internal data (like production costs and desired margins) and external factors (like market demand, competitor pricing, and seasonal influences). These algorithms can dynamically adjust prices in real-time, promptly responding to changes in the market environment. By ensuring that products are priced competitively and profitably, businesses can boost sales volumes, maximize revenues, and gain a strategic edge in crowded markets. Pricing Optimization One more vital area where machine learning has showcased its value is in . By analyzing historical workflow data, machine learning can identify bottlenecks and inefficiencies holding back the business growth, or repetitive tasks ripe for automation. For instance, in manufacturing, predictive maintenance powered by machine learning can forecast when a piece of machinery is likely to fail, allowing for proactive maintenance, reducing downtime, and enhancing overall production efficiency. Operational Efficiency And last but not least, has also witnessed a profound transformation. Instead of grappling with the perennial challenges of overstocking and stockouts, businesses now harness machine learning's predictive analytics to predict future demand. This means more balanced stock levels, optimized supply chain operations, and the mitigation of lost sales opportunities. Inventory Management While these examples highlight just a few of the many ways machine learning has optimized business operations and business development, it is paramount to remember that the effectiveness of these models relies on data quality and the right algorithmic choices. A careful and informed approach to integration is necessary when deploying machine learning techniques and algorithms to develop new and facilitate existing business growth strategies. With my extensive experience in this area – harnessing the power of machine learning to benefit businesses, I can provide some practical insights on the matter. A Step-by-step Guideline to Implementing Machine Learning for Business Growth Integrating machine learning into business processes extends beyond merely deploying algorithms. It demands structured planning, teamwork, and continuous adjustment. Here are the key steps businesses should consider when embracing the power machine learning provides in their business development strategies: Before implementing data analysis or choosing a model, a company has to specify the intended objectives. Whether the aim is to expand to new markets, increase customer lifetime value, or optimize ongoing operations for better profitability, setting precise, quantifiable growth targets ensures machine learning projects stay on track with the set business goals. Establish clear business growth objectives Incorporating machine learning for growth is more than a data scientist's task. Effective integration often calls for insights from market strategists, sales experts, customer relationship managers, and tech specialists. By fostering a collaborative environment, businesses can ensure the resulting models are both technically robust and tailored to drive business expansion. Form a cross-functional team with a focus on growth Machine learning offers a variety of algorithms and tools, applicable to different growth challenges. For example, clustering algorithms can identify new market segments, while time series forecasting might predict sales trends. The choice of tools is dictated by the specific growth strategies. Select appropriate tools and algorithms After a model is created, the next step is to assess its performance with real-world data. Initial outcomes might not always hit the mark, but machine learning thrives on adjustment. Feedback and results guide the necessary model revisions for enhanced precision, and it is crucial to measure outcomes based on business growth indicators in focus – whether it is market share expansion, customer acquisition rates, or an increase in revenue. Adhere to iterative testing and refinement keeping business metrics in mind With a refined model ready, you can move to the deployment stage, integrating the model into strategic execution points. This can entail embedding insights into sales dashboards, incorporating predictions into marketing campaigns, or using forecasts in supply chain adjustments. It is critical to ensure smooth integration with current business infrastructure, enabling actionable insights that contribute to growth. Deploy and integrate As market conditions, business strategies, and customer preferences keep evolving, machine learning models must adapt. Regular monitoring is essential to track performance, with updates accommodating new data or changing market dynamics. Maintain regular oversight and adjustments By following a structured approach and implementing machine learning with clear growth-centric goals, businesses can unlock untapped growth and innovation opportunities. Still, even machine learning has its limitations, and the incorporation of its techniques and methods into business operations presents certain challenges to take into account before you start. Technology Limitations and Practical Considerations As businesses rush to harness the capabilities of machine learning, it is crucial to approach its adoption with a balanced perspective. Although the concerns I address here are common when implementing machine learning algorithms in general, if anything, they hold even more true when incorporating machine learning with the explicit goal of driving business growth. Understanding these limitations is of momentous importance to realizing the full potential of machine learning in bolstering growth strategies. One of the main things to consider while employing machine learning in your business is . Machine learning models thrive on data, and if the input data is flawed or incomplete, the output will mirror these imperfections. Businesses have to invest time and resources in data cleansing, preprocessing, and validation to ensure the reliability of the insights derived. data integrity Another critical thing one should watch out for in data sets is . Machine learning bias means that an algorithm will produce flawed results due to erroneous (biased) assumptions in the data the algorithm is trained on. If historical data carries inherent biases, the algorithm will likely reflect them. For businesses, this can lead to skewed customer insights or even discriminatory practices, and so active measures must be taken to identify and rectify such biases in the data beforehand. potential biases In the pursuit of high accuracy, there is a temptation to build complex models that perform exceptionally well on training data but falter when exposed to new, unseen data. This phenomenon, known as , can lead businesses astray with overly optimistic predictions or recommendations. Regular validation and testing against independent datasets are vital to ensure the model's generalization capabilities. overfitting Some advanced machine learning models, especially deep neural networks, are often termed "black boxes" due to their complex architectures. While they might offer superior accuracy, their workings can pose severe . For businesses, this poses a challenge in understanding and explaining the rationale behind specific predictions or decisions, especially in regulated sectors where transparency is imperative. interpretability issues In essence, while machine learning is a formidable tool in the business arsenal, its implementation demands a judicious blend of technical expertise and domain knowledge. The promise of machine learning is not in its infallibility but in its capacity to elevate decision-making when applied with due consideration of its limitations. Conclusion As the dynamics of the business world continue to evolve, the importance of staying adaptive and forward-thinking is paramount. It is evident that every day, data plays an increasingly important role in shaping modern commerce. As I hope I was able to demonstrate in this article, decision-making in business, and especially in building balanced and effective business growth strategies, has been deeply and irreversibly transformed by the integration of machine learning, adding depth and precision to once purely intuition-driven processes. Today, embracing machine learning is more than just a strategic move – it is a necessity for businesses aiming for consistent growth in a digital age. Yet, naturally, integrating this advanced technology is not without its complexities. Constructing systems that not only gather but deeply analyze data to provide actionable insights is a task that requires both innovative thinking and unyielding persistence. And while machine learning offers businesses innumerable advantages, it is the combination of this technology with human expertise that creates a robust foundation for productive business strategies. This blend ensures that decisions are both data-informed and grounded in human judgment – something that no technology has been able to replace.  At least – for now.

ZILLOW

zillow.com

51-200 employees

Since 2013

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ZILLOW

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ZILLOW

zillow.com

51-200 employees

Since 2013

COMPANY RANKING

ZILLOW

EVERGREEN INDEX #12166

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