"Honey, we can't afford to buy a house here in Palo Alto. We should move back to Austin." I sprang that on my wife the other day, after a frustrating afternoon of home shopping - well actually not shopping - because the cheapest house for sale in Palo Alto is over $2.5M.
The fact is that great algorithms require great data sets. Getting actionable data into your data science workflow is a frequent stumbling block that prevents otherwise motivated teams from getting out of the algorithm-building starting blocks. At Terrain, we've experimented with various data import and transformation processes, from an uploaded CSV to a direct connection with a customer's Elastic cluster. The result of this experimentation is that today, we revealed a major upgrade to Terrain: a world-class data management solution for data science and machine learning.
The best real estate professionals are knowledgable, responsive communicators who constantly multi-task across many clients, properties and proposals. They need to be always informed of market conditions, client needs, property details, and still keep an eye on the competition. It's a hectic but rewarding career.
We are all familiar with drip marketing campaigns that send a steady set of messages to prospects with the hope that repeated exposure to your brand and message will trigger a further interest in your product or service. Heck, you might be reading this blog post because of one. But now you can make your marketing campaigns smarter by implementing a drip algorithm campaign.
Today, every company needs hundreds of algorithms. How can you build, manage and optimize them?
There is an arms race brewing in the world of intelligent algorithms that drive smarter results for users. Over the past decade, the information available to make informed decisions has grown by 100x while the relative screen size apportioned for this decision making has shrunk by a factor of 10.
One of the questions we get from savvy marketers is how does an algorithm management system like Terrain compare to personalization software vendors. After all, both types of tools are sold to marketing organizations and the tech teams that support them so they can have better, more personalized results presented to site visitors. So why would you consider one versus the other?
You don't have to look past the home page of Amazon.com to find literally dozens of algorithms, all hard at work personalizing results for you. Behind the scenes Amazon has nearly 1000 data scientists working across their sites and those experts manage uncounted thousands of algorithms.
Trust is a well-researched and considered topic. Few who have been in a trusted team or relationship doubt it’s importance – but we all know that trust does not come easily. One of my favorite frameworks for trust was developed by Joel Petersen while at the Stanford GSB and I urge you to read about it and then go get the book.
I sometimes get asked how we recruit as a startup in the Bay Area. This article is not about that, maybe next time. It did, however, make me think of career fairs and marketing messaging. . .