All Posts By

Bill Balderaz

Data Analytics: More than Google Analytics

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Written by: Andrew Hulse, Business Development Manager

Apples are great and probably one of the most popular types of fruit available. However, we all know that an apple is not the only type of fruit that is available to us and that we’d be missing out if we didn’t consume other kinds of fruit. I personally love apples, but I also enjoy bananas, berries, and mango.

I love analogies and this is a perfect way to describe how we see many people and organizations viewing data analytics today. We often see companies have a baseline of analytics starting with Google Analytics. While Google Analytics is a really great start, it is only the tip of the iceberg in the Data Analytics space. Think of it like this: Google Analytics, in its basic form, gives you a 2D snapshot of your customers and their interaction with your online presence. As someone in Analytics or Marketing, you shouldn’t settle for a simple 2D drawing of a circle, you should want the entire sphere and be able to hold it in your hands.

The beautiful thing about data analytics is that your organization has the other pieces to the ‘Data Analytics’ puzzle: internal data. Think of your POS data, CRM data, email marketing data, or your ERP data. As Marketers, we search for a complete picture of our ‘ideal client/customer’ and would go to the ends of the earth to find it. In reality, you already have it.

At this point, you’re probably wondering: well, this is all great, but how do I do it? My Salesforce CRM doesn’t talk to my POS and online ordering platforms, and my email campaign data is in Constant Contact. Unifying internal analytics doesn’t have to be some big secret. Through tools, for example, Google’s BigQuery, it is possible to create APIs that will connect and seamlessly integrate all these streams of data and marry them into one large, accessible data stream. Once complete, this will give your organization a complete picture of your customers and how they interact with your organization–or–how they want to be communicated with and how they don’t.

At Futurety we live and breath data analytics. We look at ourselves as a data analytics company that DOES digital marketing and marketing automation, not the other way around. Marketing doesn’t have to be a guessing game, and we’re closer than ever to creating true one-to-one marketing on a large scale to reach your customer before they need you and to make sure you’re the first brand or organization they think of when they have a need.

Image by Campaign Creators

The Two Faces of Innovation

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Written by: Travis Kendall, Account Manager

Traditionally, there are two main methods organizations use to develop new innovations: internal and external. I’m going to dive into each and give real-world examples, and I hope this may inspire you to think differently about the possibilities of innovation in your organization.

Internal Innovation
One method to develop new products, services, and strategies is through internal innovation. With internal innovation, a company acquires funds, then sends a group of highly-qualified people to a deep, dark laboratory for the development phase. The development phase focuses on research, whiteboarding, building, and testing their inventions and hypotheses until they have a working product, service, or strategy. Then it is passed on to the organization’s marketing and sales teams to form the best strategies to take the innovation to market.

External Innovation
The other method to develop new products, services, and strategies is through external innovation. With external innovation, the developing company works directly with consumers outside the organization, and they continue to develop through the proceeds of limited or unfinished versions of the final vision. External innovation allows the end user to participate in the development process from start to finish and allows for constantly evolving development, marketing, and sales cycles based on public interest in the innovation.

Big-Picture Examples
Two great big-picture examples of internal innovations are cars and phones. Scientists sit in laboratories and do their best to innovate vehicles with the safest crash-test ratings and smartphones with the most foldable screens. They’ll continue testing and innovating as long as the funding organization keeps cutting checks.

Great big-picture examples of external innovations are flight and the internet. The Wright Brothers used the proceeds from their bicycle shop to effectively develop the most advanced bicycle of the time, one that could even fly. Similarly, the internet was initially developed through the proceeds of academic and big business organizations interested in cataloging and communicating at expedited speeds. Internet innovations and infrastructure continue to be developed as long as the consumers that use it keep cutting checks.

Real-World Case Study
Both methods of innovation have worked in the past and will continue to work in the future, but one of the most exciting internal innovation vs external innovation competitions happening today is between Google and Tesla and their race to develop better-than-human self-driving vehicle technology. Both Google and Tesla are light years ahead of any other competitor in regards to total data collected, but they have gone about collecting that data in very different ways.

Google’s self-driving technology began development in the labs and parking lots hidden away on Google-owned property. The company’s self-driving technology depends on lab-developed innovations in three-dimensional space mapping, as well as limited real-world vehicles on public streets. As of 2018, Google has used this and other technology in their labs to simulate over 5 billion miles of autonomous driving.

In comparison, Tesla’s self-driving technology began development on public roads and streets across America. The company’s self-driving technology depends on monitoring real-world drivers and driving habits. Each and every customer-owned Tesla on the road is sending data back to Tesla HQ, and with each new car sold, there’s yet another source of data. As of 2018, Tesla drivers have logged over 5 billion fleet miles driven.

Will Google’s internal innovation strategy and brilliant laboratory minds carry them to the top, or will Tesla’s external innovation strategy and growing feet of consumer drivers beat them to the punch? Only time will tell.

For more reading on Google and Tesla’s very different approach to self-driving car innovation, check out this article from the Verge.

At Futurety, we use external innovation to develop full-functioning automated marketing strategies for Fortune 1,000 companies and public organizations that want to be the first. Combining marketing automation tools and advanced data analytics, we are building programs that engage consumers how they want to be engaged, all with a small, nimble team of engineers and strategists. Want to learn more about how your organization can start building the innovations of the future with us? Contact Futurety today!

Photo by Jp Valery on Unsplash

Consumer Trained Algorithms: Why You Are Already Working for Google, Tesla and Dozens of Other Organizations

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Mark and Huckle are quite the pair. For those of you who have not met them, Mark is one of Futurety’s data scientists and Huckle is our resident machine learning agent, or as I like to call him “super awesome fancy computer”.

Mark works hard every day training Huckle on behalf of our clients so those clients can use machine learning and artificial intelligence in their organizations.

A look inside Huckle, Futurety’s Data & Machine Learning Powerhouse

For example, Mark could show Huckle thousands of images and x-rays of healthy shoulders and teach Huckle that these are “healthy shoulders”. Then he can show Huckle thousands of bad shoulders and teach Huckle that these are “bad shoulders”. Pretty soon, Huckle can tell healthy shoulders from bad shoulders, even if he’s never seen that particular x-ray and Mark is so proud and he’s not crying he just has something in his eye.

There are several other ways of training Huckle. Huckle can analyze millions of emails and get the equivalent of a catnip treat from Mark when an email recipient opens an email. Huckle can get a big ol’ belly scratch when a recipient makes a purchase after clicking a link in an email.

Finally, Mark can feed Huckle a bunch of information and let Huckle figure out an answer. For example, if a real estate developer is trying to decide whether to build luxury homes, family homes or retirement housing in various zip codes, Huckle can cluster information like age, income, and marital status to help determine what homes to build and where.

One limitation with all of this is that Mark is just one guy. And even with 100 Marks, there is a limit to how much Huckle can be trained.

Unless…

What if the very consumers of your product also become its trainers? What if you could have real people, in real-world scenarios constantly training your product?

Welcome to the age of Consumer Trained Algorithms. Since 2015, Google has talked about Rank Brain, an AI platform that makes search results more relevant. Does Google do anything as tacky as survey us on how happy we are with our search results? No way. However, when we click a result, and spend 2 minutes on the site and make a purchase, we’ve just reinforced a good experience. When we click a link, spend three seconds on the site and change our search terms, we have likewise trained the algorithm. Multiply that by a billion searches a day and we can see how Google can start to think like a person.

It’s been said that when one Tesla car learns something, all Teslas learn it. We contend that Tesla isn’t a car company. It’s not a battery company, it’s a custom neural network… essentially a giant brain of the world’s infrastructure.

It’s also been said that Tesla loses around $15,000 per car sold. Flip this thought and think more along the lines of “Tesla is paying us $15,000 each to map the roads and highways you drive.” Ultimately, we are the Mark to Tesla’s Huckle. We train the algorithm.

Facebook, Apple, you name it, they can all be thought to be doing the same thing.

I happen to fall on the side of the automation debate that believes automation is a good thing. Automation will empower us to solve more of the world’s problems and we only create a better user experience for ourselves and for others when we train technology to adapt to human needs. We don’t pay a monetary fee to use Google or Facebook. We pay less for a Tesla than we should because in exchange we help develop the technology.