Conversations: How Aiva uses Augmented Analytics in the Real Estate Industry

Today marks the first of many coming installments of our Conversations series, where we reach out to startups and existing companies to see how they use Augmented and Predictive Analytics in their businesses. The idea is to give you quick, snappy insights into how others use analytics in their businesses, and to maybe even inspire something in yours!

Our first conversation is with Aiva, an SMS-based lead engagement and qualification service for residential real estate agents, teams, and brokerages. Think Siri, but for your real estate leads and sales qualifications. We’ve had the opportunity to see it in action, and it’s one of those services that feels like the next big thing in it’s field. We sat down with Marino Orlandi, Aiva’s co-founder, to find out a little more about how things work…

Humanlytics: What’s the industry standard with regards to data science and predictive analytics?

Marino Orlandi: Real estate is largely behind the times on this, although there are some very interesting companies doing things in the space with data. That said, for every really interesting company, there are 20 companies throwing out words like “big data” and “AI” to sell products to agents that really utilize neither of those technologies.

HL: In that case, how do you see your industry adapting and changing in response to the rise of data science and predictive analytics?

MO: Slowly but surely, it is changing. Real estate will always be driven by empathy and humanity however, and successful tech companies will leverage things like data science to empower, not replace, agents.

HL: Are you “changing the game” in any particular way, especially with regards to using data science/predictive analytics?

MO: I would say that the reason our system works better than our 2 national competitors is because we’ve taken a more data-centric approach to building the product, as opposed to our competitors who seem to have structured their system around more conjecture driven processes (i.e. “Our founder was a successful real estate agent and he did lead qualification this way, so this is how the whole company does it.”

In such a fragmented industry, anecdotal wins or failures are somewhat inconsequential as compared to what the data says across a diverse user base.

HL: How do use data science and predictive analytics to make that happen?

MO: We use these types of analytical tools to determine everything about the verbiage, cadence, and tone of our texting campaigns and the scripts our concierges use in order to optimize response rates.

HL: What tools do you/your company use for data science and/or Predictive Analytics?

MO: We do a lot of this analysis on our own.

HL: How can businesses like yours use data science to make products/services better and more accessible for the average consumer?

MO: The data behind our product directly affects our ability to maximize conversion rates for our agents. In this way, it’s essential.

HL: Do you have any personal thoughts on the future of data science, machine learning, predictive analytics, artificial intelligence, or anything like that?

MO: More than I can fit here, but my philosophy is that there will be a delineation between industries where these technologies replace workers and industries where they facilitate them. Acknowledging that this is an obscene over simplification for the sake of brevity, I think it will be most interesting to see which technology companies in the ladder types of industries recognize that the true opportunity is in facilitating efficiency, and which miss the mark and aim to replace human capital. I also look forward to very possibly being wrong about all of this. Interesting times we’re entering!

A huge thanks to Marino Orlandi and Aiva for taking the time to chat with us! If you’re interested in what they do, check them out, or shoot them a tweet!

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