AI, startup hacks, and engineering miracles from your friends at Faraday

PRESS RELEASE - New funding and new talent at Faraday

AI Leader Faraday Secures New Funding and Adds Industry Experts to its Team

BURLINGTON, Vt., May 2, 2018 /PRNewswire/ -- Artificial Intelligence (AI) platform provider Faraday secured more than $2 million in new funding and added top talent to its team of leading data scientists and business software experts. The latest round of funding, from new and existing investors, supports the company's unique approach to AI, which is cost effective, easily implemented, automated, and delivers meaningful results that drive revenue for B2C companies.

Read more here



How B2C companies overcome machine learning barriers with Faraday

Overcome machine learning barriers

Diminishing barriers to entry for machine learning

Thanks to major improvements in computing power and network speed over the last decade, the barriers to leveraging machine learning have diminished significantly. We’re now seeing a wealth of companies fundamentally changing their industries with innovative data-driven processes optimized with advanced machine learning algorithms.

Common challenges in successful operationalization of machine learning

While machine learning is more accessible than ever before, several companies still struggle to successfully operationalize the technology for a number of reasons.

  • Machine learning requires huge datasets to be successful. Companies often lack the volume, breadth, or depth of data needed, so they have to purchase 3rd party data which gets pretty expensive.
  • Companies that have the right data still need data scientists and machine learning experts to clean and organize the data, define desired outcomes, and write queries to tell the machine learning engine what to look for. These individuals are in high demand and salaries are through the roof.
  • Building accurate models is just the first major hurdle. Once models are built, engineers must develop systems to feed predictions to various destinations and track their accuracy to further refine the models.
  • Due to the scarcity of talent, companies struggle to apply their resources to all business functions. Data science teams have lengthy backlogs and tend to prioritize optimizing complex back-office processes like demand forecasting and supply chain management. Consumer-facing functions like marketing, sales, and customer experience are generally lower priorities when allocating machine learning resources, despite their immediate impacts on revenue.

How Faraday helps B2C companies overcome these challenges

We understand that acquiring, managing, and implementing the resources and processes needed to operationalize machine learning can be daunting, so we bundled it all up into a simple, user-friendly platform designed for non-data scientists. With the Faraday platform, B2C companies have access to:


"Thanks to Faraday, 1 in 3 of our sales is coordinated using AI" – Chief Product Officer, Burrow.

Learn more about the process here:

How to grow revenue with AI in 5 easy steps



PRESS RELEASE - DTC furniture disruptor, Burrow, powers growth with AI

Faraday's AI Solution Transforms Marketing for Furniture Leader Burrow

BURLINGTON, Vt., April 11, 2018 /PRNewswire/ -- Burrow has redefined the furniture industry's customer experience by combining style, sustainability, affordability, and convenience for today's shopper. Now, the 2-year-old startup is using the Faraday artificial intelligence (AI) platform to grow even faster, quickly gaining insight on who and where their customers are, as well as when—and why—they are likely to buy.

Read more here



Operationalize AI quickly and cost-efficiently with turnkey solutions

Turnkey AI solutions

If you've got your finger on the pulse of your business, you know you need to adopt AI sooner than later.

While commercial AI adoption is on the rise, there's still time to catch up and get ahead of the curve with "off-the-shelf", or turnkey AI solutions.

Where do you stand in the AI landscape?

Consider the following questions:

  • Are you brand new to AI? If so, where will AI have an immediate impact on your top and bottom lines?
  • Are you experimenting with an enterprise-wide AI solution? If so, are you seeing the results you were expecting?
  • Are you already developing AI in-house? If so, what can you do to optimize your existing resources?

Regardless of your position in the AI landscape, you can quickly get started with AI or optimize your existing resources by implementing proven, turnkey AI solutions that address the low-hanging fruit.

What are the low-hanging fruit?

It's all about automating tasks that have an immediate impact on revenues and costs.

Rather than using AI to automate existing jobs, use it to improve efficiency and productivity within those jobs.

After working with hundreds of B2C companies in a wide range of industries, we found that the customer lifecycle is an ideal framework for discovering highly predictive data, applying machine learning, and developing predictive models for a variety of use cases.

Let's take a quick look at a couple popular use cases to get you thinking about how they might apply to your business.

Increasing conversion rates and reducing acquisition costs

The most effective way to optimize your spend throughout the customer acquisition process is by targeting individuals with the highest propensity to convert.

So how do we identify individuals who are likely to convert?

  • The first step is to enrich your lead and customer data with additional demographic, psychographic, and property-based attributes, giving you a vibrant picture of who your leads and customers are, not just what they purchased or where they came from.
  • The next step is to use that enriched data to train the AI that will ultimately make predictions on future data inputs.
  • Once trained, the AI can make data-driven predictions on who your most likely buyers are, where to reach them, and when to reach out.

We've seen projected sales cost reductions of over $300,000 when applying this approach.

Proactively preventing customer churn

Certain companies have a notorious problem with customer churn, especially those with subscription-based business models. Using a similar approach as the one mentioned above, companies can use AI to identify churn-prone customers before they even begin showing signs of churn.

This can help minimize marketing dollars spent on these churn-prone individuals, and focus more on customers with a higher lifetime value (LTV).

A deeper dive into use cases for turnkey AI

Hopefully, you're thinking about how you could leverage a turnkey AI solution to optimize outreach initiatives across your customer lifecycle.

For a deeper dive into the opportunities available with turnkey AI, check out our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business


For more information on to use AI to optimize your customer lifecycle outreach, download our other whitepaper, AI for the customer lifecycle: Making the most of your data.

Where do you stand in the current AI landscape?

Artificial intelligence technologies

You've probably noticed the growing buzz around AI and ML. You've likely done some research into the field, read some AI success stories, and believe that artificial intelligence is more than just a buzzword.

Sound like you? Good, you're totally on the right path!

So is all the hype true? Is AI really as promising as it seems? Have most companies already adopted AI? Or even worse, are you too far behind to be competitive with AI?

Luckily, you're not too late.

Hype versus reality

In a survey of over 3,000 senior executives, McKinsey found that only 10% of respondents said they have actually operationalized AI at scale.

That said, another 10% claimed to have operationalized AI in at least one core business function, and about 40% are experimenting with some form of AI.

Couple those numbers with the massive increase in AI-related investments over the last few years, and we can see the time to get serious about AI is undoubtedly now.

Where's the interest, and how much is there?

Over the last five years, investments in AI have more than tripled. In 2016 alone, total investments (internal and external) ranged from $26 billion to $39 billion, with over 75% of that coming from tech giants like Amazon, Apple, Google and Baidu.

These cash-rich giants have mostly invested internally — on R&D and deployment — but they've also invested significantly in hard-to-find talent, primarily through major acquisitions.

Source: MGI Artificial Intelligence Discussion paper.

How can I possibly catch up and get ahead?

These massive investments can feel deflating, and rightly so if your plan is to go head-to-head with the giants by building up your own AI tools in-house. The good news is that you don't have to, and often shouldn't develop AI from scratch.

A wealth of AI startups have caught some serious traction by developing fit-for-purpose solutions that address focused sets of business challenges.

Whether those challenges rest in IT and security, finance and accounting, or revenue growth and marketing, organizations can easily adopt and operationalize these "off-the-shelf" solutions in a fraction of the time, and at a fraction of the cost of alternative "one-size-fits-all" solutions like IBM's Watson.

The key is to automate tasks that have an immediate impact on your top and bottom lines. AI technologies can't replace your entire marketing team, but they can arm that team with powerful customer insights and predictions, simply unattainable without powerful machine learning algorithms.

Some use cases

For more on this, and to read some specific use cases for off-the-shelf AI solutions, download our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business