Data-driven creative for sensitive verticals

With big privacy changes, creative has become even more important with verticals like health and wellness and finance. Learn how to make data the backbone of your campaigns.

The real estate industry isn’t the first industry that usually comes to mind when discussing ways to apply machine learning algorithms. The seller wants to sell the property and the buyer wants to buy it — it is just a matter of closing the deal. The stumbling block is agreeing on the price for that deal. Accurately assessing property value is a complicated process, and one that requires a lot of different data sources and scalable pricing models. The buyer can’t just reference an itemized list of all the possible factors and their associated price values and sum up all the property’s features to calculate the total value.

The automated valuable model is a machine learning model that estimates the value of a property, usually by comparing that property in question to similar properties nearby that have recently sold (“comps”). Real estate company Opendoor relies on its version of AVM — Opendoor Valuation Model — for valuation and to look up information about the comps (in order to understand the difference between the comp’s value and the property in question, for example). The company has invested heavily in data science from almost the beginning of the company’s history to incorporate different data sources and to refine algorithms to improve the model’s accuracy.

In a conversation with VentureBeat, Opendoor’s Sam Stone described why the company built the Opendoor Valuation Model and how data science fits in to the real estate industry. With the company’s plans to expand from 30 markets to 42 markets by the end of the year, and to add new types and price points of homes, data science is expected to remain a core part of the company’s strategy, according to Stone.

This interview has been edited for clarity.

VentureBeat: What was the problem Opendoor was having, and why did it decide that investing in data science in-house was the answer? What benefits did the company expect to gain with scalable pricing models and investment in data science?

Sam Stone: Since our founding, we’ve always done our data science in-house and leverage both our own and third-party data for our models. We recognized that modernizing the outdated, manual process of pricing homes could benefit consumers in terms of price certainty and the ability to more quickly take advantage of the equity in their home.

For most people, their home is their largest financial asset, and they are highly attuned to its value. It’s critical our algorithms incorporate all of the important features on a home. Since every home is unique and market conditions are constantly changing, pricing homes accurately requires constantly evolving solutions. That means we have to invest heavily in both our algorithms and our team of in-house pricing experts to make sure that the algorithms and experts work seamlessly together.

VentureBeat: What did Opendoor already have that made it feasible to build out the Opendoor Valuation Model rather than hiring the work out to another company?

Stone: Accurate and performant pricing systems are core to our business model. Our initial automated valuation model stems from lines of code our co-founder and CTO, Ian Wong, wrote back in 2014.

Since then we’ve made enormous investments on the technology and data science side. We’ve developed different machine learning model types, which includes ingesting and testing new datasets. We’ve built out processes to hire, grow and retain top-notch machine learning engineers and data scientists. And, at the same time, we’ve invested heavily in expanding our expert insights by arming our pricing experts with customized tools to track local nuances across our markets.

It’s fair to say that pricing systems are core to our DNA as a company.

We’re always eager to learn from new datasets, new products and new vendors. But we’ve yet to see any third-party that comes close to matching the overall accuracy, coverage, or functionality of our in-house suite of pricing systems.

VentureBeat: Tell me a bit about Opendoor Valuation Model. What kind of data science analysis and investment went into building this model?

Stone: Opendoor Valuation Model, or “OVM,” is a core piece of pricing infrastructure that feeds into many downstream pricing applications. This includes our home offers, how we value our portfolio and assess risk, and what decisions we’ll make when we resell a home.

One element of OVM is based on a set of structural insights about how buyers and sellers evaluate prices and decide on home purchase bids. They look at the prices of comparable homes in the neighborhood that sold recently—often referred to as “comps”— and adjust their home price up or down depending on how they think their home equates. But how do you decide what makes one home “better or worse” than another? It’s not a black and white equation and is much more complex. Homes have unique features, ranging from the square footage and backyard space to the number of bathrooms and bedrooms, layout, natural light and much more.

OVM is fed by a multitude of other data sources, ranging from property tax information, market trends, and many home and neighborhood specific signals.

VentureBeat: What does OVM look like under the hood? What did you have to build in order to get this up and running?

Stone: When we started building OVM, we kept it straightforward, relying mainly on linear statistical models. Starting with relatively simple models forced us to focus on developing a deep understanding of buyers and sellers’ thought processes. We could verify and grow our data quality, rather than getting caught up in fancy math.

As we’ve come to understand the behavior of buyers and sellers better over the years, we’ve been able to move to more sophisticated models. OVM is now based on a neural network, specifically an architecture called a Siamese Network. We use this to embed buyers and sellers behaviors, including selecting comps, adjusting them and weighting them.

We’ve seen repeatedly that a “state of the art” machine learning model isn’t enough. The model needs to understand how buyers and sellers actually behave in its architecture.

We have multiple teams, composed of both engineers and data scientists, who are constantly working on our OVM. These teams collaborate deeply with operators, who have deep local expertise, often incorporating them into product sprints. The process of developing, QA’ing, and releasing our first neural-network-based version of OVM was a cross-team effort that took many months.

VentureBeat: What is the purpose of the human+machine learning feedback loop?

Stone: Our in-house pricing experts play a key role across our pricing decisions, working in conjunction with our algorithms. We rely on pricing experts at various stages:

• Adding or verifying input data. For example, assessing the quality of appliances or finish levels, which are inputs that are important but hard to quantify algorithmically. Humans are much better at this.
• Making intermediate decisions. For example, what features of the home might make it particularly hard to value?
• Making user-facing decisions. For example, given a set of buyer offers on a home in our portfolio, which, if any, should we accept?

While we may do more or less automation on a particular area or task at a point in time, we have always believed that in the long-term, the best strategy is to marry pricing experts and algorithms. Algorithms help us understand expert insight strengths and weaknesses better, and vice versa.

VentureBeat: What would you do differently if you were building out OVM now, with the lessons learned from last time?

Stone: Ensuring high quality input data, under all circumstances and for all fields, is always top priority.

The model that is most accurate in a time of macroeconomic stability is not necessarily the model that is most accurate in a time of economic crisis — for example, the financial crisis of 2007-2008 and the COVID-19 global pandemic. Sometimes it makes sense to invest in forecasting features that don’t help accuracy during “normal” times, but can help a lot in rare, but highly uncertain times.

This past year has taught us that we can price homes using interior photos and videos shared by sellers. Prior to COVID-19, we would inspect home interiors in person. However when the pandemic began, we stopped in-person interactions for safety reasons. As a result, we turned the interior assessment into a virtual one and learned that it’s actually much easier for sellers.

Data-driven creative for sensitive verticals

With big privacy changes, creative has become even more important with verticals like health and wellness and finance. Learn how to make data the backbone of your campaigns.

Our Data, Analytics, & Intelligent Automation Summit at Transform 2021 on Tuesday took a deep dive into how data, analytics, and intelligent automation can help the greater good, the bottom line, and more.

The day, presented by Accenture, kicked off with the Big Bytes in AI & Data breakfast, presented by Accenture. Leaders from Accenture, American Express, Opendoor, Evernorth, and Google ultimately agreed that the quality of the data under their AI solutions is non-negotiable.

As Valerie Nygaard, product lead at Google Duplex, said, “You can make tons of tech innovations, but so much of the time they rely on the quality of the data, that accuracy, the normalization, the processing, and the handling.”

The American Express credit and fraud risk group uses models powered by machine learning to monitor $1.2 trillion in charges annually around the world, and return 8 billion risk decisions in real time, said Anjali Dewan, vice president of risk management, consumer marketing and enterprise personalization decision science at American Express.

“Having the discipline to make sure that the quality of that data is consistent, starting from evaluation when you put it into production, is a key competitive advantage,” she explained.

Opendoor’s valuation models, which service more than 90,000 customers, and enable more than $10 billion in real estate over 30 markets, are only as worthwhile as their data input, said co-founder and CTO Ian Wong. To ensure coverage and accuracy, they’ve built custom inspector apps that use a human expert to collect first-party data and then input it back into their central repository in real time.

It takes time to collect and manage data, ensure it is high quality and governed, and then organize it to drive insights, said Mark Clare, enterprise head of data strategy and enablement at Evernorth/Cigna. But the new agile, collaborative processes and visual-based discovery Cigna helped one financial services company implement led to the company discovering an eight-figure attrition risk in less than 30 minutes.

One big takeaway for Ahmed Chakraborty, global managing director, applied intelligence North America lead at Accenture, is that when you take a data-driven journey in the enterprise, it’s a change journey, and a big part of the change is to drive adoption.

“I call this the last-mile connection,” he said. “Literacy around data is critical. Elevating the entire acumen of your enterprise to understand data, understand what you can do with data, is so critical in the long-term journey to drive adoption and the change in your culture.”

“Cloud to survive. AI to thrive: How CXOs are navigating the path to data-driven reinvention”

The Summit’s keynote featured Hari Sivaraman, head of AI content strategy at VentureBeat, in conversation with Accenture’s Sanjeev Vohra, global lead – applied intelligence.

Post-pandemic, there’s been a massive shift toward data, AI, and cloud to create greater good, greater revenue, and greater efficiencies.

Vohra identified four key fundamental changes he and his team have seen, particularly in the past year. First, is that cloud and data have come together as superpowers. On the one side, he explained, is the proliferation of cloud which provides much higher levels of compute power and the flexibility to scale up and scale down, depending on the need. That’s combined with vast amounts of data now available both inside companies or obtained from third-parties.

“Data and cloud are a huge trend we see powering the entire planet and it has really advanced during the pandemic,” he said.

The second trend is that the C-suite from companies across industries are now actually interested in these technologies and how they can be used to derive business value. “It has  has moved out of the experimentation zone, or pilot zone,” he said, “to be used for scale.”

Speed is the third trend. As Vohra explained, “Nobody wants to spend two years, three years trying to drive value. [Business leaders] are really getting serious about saying what can be done in six months.”

The last trend is talent. It’s scarce, and the demand is coming from everywhere. So companies now are having to make important decisions about how much investment is required for building staff, and what portion is focused on building internally versus recruiting from the outside.

Later in the discussion, Vohra shared one of the projects he is particularly excited about. Along with Intel and the Philippines-based Sulubaaï Environmental Foundation, Accenture is saving the coral reef with AI and edge computing that monitors, characterizes, and analyzes coral reef resiliency. Accenture’s Applied Intelligence Video Analytics Services Platform (VASP) detects and classifies marine life, and the data is then sent to a surface dashboard. With analytics and trends in real-time, researchers make data-driven decisions that are helping the reef progress even as we speak (or as you read).

Cigna C-suite executives discuss the impact of AI and digital interactions in transforming the health of their customers

During the AI in health panel, Gina Papush, global chief data and analytics officer at Evernorth/Cigna, had a conversation with Joe Depa, global managing director at Accenture, about how they’re using actionable intelligence to make health care more predictable, efficient, and most importantly, effective.

Their major focus over the past year and a half has been been understanding the impact of COVID geographically and across different population segments.

“One of the things we’ve uncovered is that without a doubt, there are differences in terms of how COVID is impacting different groups of customers, and particularly Black and Hispanic customers,” she said.

The organization partnered with their clinical and customer experience teams, working with employers locally in those markets, to bring a concerted, data-driven efforts to drive outreach. They proactively distributed PPE and education about preventing infections, and managing illness. And as vaccinations rolled out, they worked with customer employers to get these to vaccination sites.

Once they shifted focus to studying post-COVID effects, particularly long-haul COVID, they found that in patients with long-haul COVID, many customers have pre-existing chronic conditions such as heart inflammation and heart disease, which are prevalent at higher rates in communities of color. Now they’re focused on identifying risks, and data science teams are building models and applying models to identify those who may be at risk post-COVID for severe complications.

“It’s critical that post-COVID care continues, and our predictive analytics enable us to be more pinpointed in driving that care to the right folks,” Papush said.

Understanding consumer behaviour with big data & delivering AI powered products that offer personalized recommendations

This AI in retail panel, presented by Accenture, unpacked the ultra-personalization trend with AI leaders from DoorDash, Nike, and Accenture.

“It became more apparent every day that the post-pandemic acceleration of digitization has changed the way people consume and interact with products in all categories,” said Lan Guan, applied intelligence global solutions AI lead at Accenture. “AI has leapfrogged to indulge consumer demand for exactly what they want, when they still want it. That’s what ultra-personalization is all about.”

For DoorDash, this personalization centers around what the company calls “the restaurant selection problem,” explained Alok Gupta, head of data science and machine learning at DoorDash.

Consumers come to DoorDash with a specific food in mind. Their data scientists are focused on understanding what that desire is, and identify potential new restaurant partners that can help make the DoorDash app’s restaurant and food selection as robust as possible.

With digital demand exploding at Nike, their whole model had to shift, said Emily White, Nike VP of enterprise data and analytics. The company used AI and machine learning to automate internal processes to gain speed and launch a new distribution facility to fully support their growing digital demand.

Her team created a replenishment engine to read the signal, identify available inventory across all of Nike’s distribution centers and stores, and determine which products should be allocated to the Adapt facility in Tennessee to best serve the southeast region. It’s their largest distribution center worldwide, built to distribute the company’s Nike and Jordan products to individual consumers, wholesale customers, and Nike’s retail channels as efficiently as possible in the new digital-first world.

“The outcome of this is decreasing transportation time and cost, improving our sustainability, and helping us react faster to our local demand,” she said.

One of Accenture’s clients, a fashion brand, used AI and an ultra-personalization approach to go from passively offering just a few clothing collections a year to responding to what’s still hot in the market. They collect real-time consumer feedback from across social media platforms with AI and machine learning. Within just a couple of hours, designers translate this information into product ideas and send them to micro-studios for experimental production.

“Two quick results here,” Guan said. “25% growth in yearly revenue, and 29%-plus increase in revenue-per-visit, all because of that ultra-personalization.”