Using new technologies and building products with a clear purpose in mind can help organizations connect with users in a way that is more human and more useful.

Guest Commentary, Guest Commentary

March 21, 2019

3 Min Read

Humans share a powerful need for emotional connection. It’s what sets us apart from other species: Our ability to connect and relate to one another gives us the power to cooperate and work together toward common goals.

In the corporate world, this means that all customers share this same need. As humans, we want to feel heard and understood, and that need carries over to the products and services we purchase. This is a key challenge for businesses. In the age of outsourcing and automation, how can companies truly understand and connect with their customers? Artificial intelligence and machine learning may bring to mind images of emotionless robots and cold algorithms. But in fact, these tools can — and should — help to make businesses more human. This is a core belief at Trulia and one that is woven through our product strategy.

I believe in building tech for a purpose, and that includes how we use technologies to build our products. It’s easy to fall into the trap of using machine learning just for the sake of it, like a hammer searching for a nail. At Trulia, we’re not using AI and deep learning because they’re cool buzzwords or because we want to stay ahead of the curve. We’re using them for a specific purpose: to create a more meaningful experience and solve key challenges for our customers.

Better Recommender Systems

This all starts with using data and personalization to create a more meaningful and enjoyable experience for customers. By reworking the data they already have, industries from retail to automotive can deepen this human connection in small, accessible ways. For example, recommender systems are increasingly used to direct customers to products and information. However, they often fall short of their potential due to a lack of understanding about a customer. Trulia is leveraging deep learning and smarter data systems in its recommendations to deliver personalized, useful results to users. By analyzing a person’s digital behavior we can move from search, where consumers look for the home they want to buy, to a service where we proactively provide individualized results.

Trulia’s model is based on a deep understanding of customer preferences and intent. Through our recommender system, every element on a property detail page is assigned a score for each user, which leads to the most relevant outputs possible. By delivering personalized, useful results, the customer experience becomes more human.

Predicting User Needs

The danger of personalized recommendations and other content delivery tools is that you can end up overwhelming consumers with too much information. It’s important to be thoughtful and intentional about how and when to communicate with customers. By using behavioral data, organizations can better understand what their users want to engage with. Trulia’s trained models help us to understand the probability that a consumer is interested in something. These user engagement prediction tools curate which listings and recommendations users see, giving them the right information at the right time and improving their experience with our product.

The Benefits of Purpose

Using new technologies and building products with a clear purpose in mind can help organizations connect with users in a way that is more human and more useful. These are only a few examples of how AI and machine learning can add a more emotional connection to the customer experience. The most significant constant is to use these tools in a way that contributes to your organization’s mission and aligns teams under a clear, common goal. Buying a home is an emotional experience and we strive to add this human connection to everything we do at Trulia to help our users discover a place they’ll love to live.

Tim Correia is Senior Vice President and General Manager of Trulia.

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Guest Commentary

Guest Commentary

The InformationWeek community brings together IT practitioners and industry experts with IT advice, education, and opinions. We strive to highlight technology executives and subject matter experts and use their knowledge and experiences to help our audience of IT professionals in a meaningful way. We publish Guest Commentaries from IT practitioners, industry analysts, technology evangelists, and researchers in the field. We are focusing on four main topics: cloud computing; DevOps; data and analytics; and IT leadership and career development. We aim to offer objective, practical advice to our audience on those topics from people who have deep experience in these topics and know the ropes. Guest Commentaries must be vendor neutral. We don't publish articles that promote the writer's company or product.

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