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

The DTC movement, AI, and snowshoes

Macallan Atkins on

Last week the Faraday sales team took some time off from the usual day-to-day grind to reflect on the last year, talk about emerging trends in the consumer landscape, and get some much-needed exercise in the Green Mountains of Vermont—I'll admit they're a little greener in the summer.

faraday-sales-retreat-2019

We briefly talked goals and tactics (would it really be a sales retreat without some numbers?), but the bigger discussions revolved around Faraday's why — specifically, why data-driven companies will eventually dominate every consumer market. I'm not going to pitch you here, but I will share my biggest takeaways from the day...

  1. We've all noticed the direct-to-consumer (DTC) movement that's rendering traditional marketing channels obsolete. Bypassing distribution channels is better for the bottom line and gives brand-manufacturers greater control over end-customer data—that is to say, meaningful data—which smart brands are using to optimize everything about their business. Here's the big takeaway: the DTC movement is expanding beyond retail and goods to literally every single consumer market (i.e., consumer finance, real estate, transportation, home improvement—the list goes on). My fellow colleague, Riley, just published a great article about how the DTC movement is transforming financial services. Definitely worth checking it out for a deeper dive into this DTC shift.
  2. To echo Riley, companies who embrace this movement will thrive, while others slowly fade away. Intelligent use of data will distinguish the thrivers and faders, making it clear that AI/ML adoption is no longer a luxury—it's an absolute necessity.
  3. Here's my third and final takeaway: if your team ever plans a snowshoe expedition (which is probably just a Vermont thing) make sure to wear snowshoes that actually fit, or you'll end up tripping yourself constantly, as I did (Dad's gigantic snowshoes).

Regardless of your company's industry, we can all agree on one emergent truth: the future of consumer marketing requires AI/ML to stay relevant.




PRESS RELEASE - NYSERDA chooses Faraday AI Platform to reduce customer acquisition costs

Perry McDermott on

NYSERDA Chooses Faraday AI Platform To Reduce Customer Acquisition Costs For Clean Energy Technologies

BURLINGTON, Vt., Oct. 10, 2018 /PRNewswire/ -- Faraday, Inc. has been chosen to provide its multichannel data-driven customer targeting tools and complementary consulting services to contractors participating in New York State Energy Research and Development Authority (NYSERDA) programs base on their unique artificial intelligence (AI) platform and experience in clean energy.

Read more here



A big takeaway from the Symitar Fintech Panel on Practical AI Use Cases

Rob Trail on

I recently participated in the Fintech Panel on Practical AI Use Cases at the 2018 Symitar Educational Conference (SEC). It was great to see that there's a lot of excitement about AI and how it's being used by credit unions and other consumer finance organizations.

On top of that, Jack Henry did an amazing job of organizing the panel, providing valuable insights to attendees in varying stages of AI adoption by bringing together participants from companies operating in very different areas of the field.

  • Infosys consults on large-scale AI projects, helping credit unions and banks develop complex, in-house capabilities to be applied across numerous business functions.
  • Clinc specializes in advanced conversational AI, helping banks develop, train, and deploy superior conversational AI solutions.
  • Faraday, yours truly, specializes in AI-powered customer lifecycle optimization, helping credit unions, banks, and fintech platforms better understand their consumers, personalize experiences, and improve member interactions from acquisition to retention.

The big takeaway

We covered a lot, but the biggest concern in the room was how credit unions with little to no experience with AI can operationalize the right solutions quickly.

There are seemingly endless ways AI is being used by innovative consumer finance organizations. Every day, we're hearing about new, creative ways organizations are applying data science and machine learning to extract more value from their data. Frankly, it can become overwhelming for credit unions that are just getting started.

You're not going to transform every process with AI overnight (or even over the next year). Start with one or two solutions that are relatively easy to implement and will yield significant results in the short term.

If you're considering using AI to draw deeper member insights, personalize experiences, and optimize outreach, Faraday can get you up and running in 6-8 weeks. Here are a few ways to get started:


cs02_mhvfcu_promo


How Peacock Alley began their journey to an AI-driven growth strategy: an interview with Peacock Alley's Head of eCommerce and Digital Marketing

Perry McDermott on

Over the last 45 years, Peacock Alley has established itself as a leader in high-end, luxury linens and bedding by placing their valued customers first, from product design to enjoyable, evolving shopping experiences.

Peacock Alley recently signed on with Faraday to leverage artificial intelligence and nationwide consumer data to continue to deepen their understanding of their current customers and intelligently target and acquire new customers.

Ryne Higgins, Head of eCommerce and Digital Marketing, spearheaded the efforts to incorporate AI into Peacock Alley's marketing strategy. We had a chance to speak with Ryne about Peacock Alley, their decision to implement an AI solution, and what their onboarding experience has been like so far.

We'd like to thank Ryne for sharing his thoughts and experience, and hope you enjoy the interview!


Peacock Alley AI Interview

Can you provide a brief history on Peacock Alley?

Ryne:

"Our founder, Mary Ella Gabler founded Peacock Alley in 1973 (yes, we are 45 years old and working with an AI / machine learning software!) on the "Little Black Dress" theory to bedding and bath linens; own the best basics and then thoughtfully work everything else in.

About 9 years ago, she handed the reins over to her sons – Jason and Josh - and they are continuing to run the business today with her watchful eye still on product development and brand.

The company has been through a lot of iterations throughout the years but our core business channels are perhaps not that much different than you'd expect – we have a growing wholesale, retail, and eCommerce presence still today."

Who is a typical Peacock Alley customer? Is your customer base changing?

Ryne:

"The typical Peacock Alley customer is a difficult question and one of the reasons why we have paired with Faraday.

Across our channels, you may get a lot of answers. For example; our wholesale channel sells to a number of specialty stores, online dropship channels, and direct-to-designer. Our retail stores, while open to the public, mostly focus on the professional, accredited designer who is looking for the touch and feel experience in Dallas, Austin, Atlanta, or Nashville.

For my purview of the business (eCommerce), we are mostly focused on striking the cord with the do-it-yourself designers (think of what you see on Pinterest and beyond), whether they need coverlets and bedspreads, bath towels, or luxury fitted sheets.

I wouldn't say that our customer base is necessarily changing – we're still committed to the people that have made us successful over the past 45 years – however, as a brand, we are always interested in understanding what makes our customers tick and finding ways to attract new customers."

What does a typical Peacock Alley customer lifecycle look like? For example, are a lot of your customers repeat buyers? How do you communicate with past customers?

Ryne:

"Like a lot of 45 year old brands, our changes in the online space have been more revolution vs. evolution. In the past year; we've redesigned / replatformed our website, paired with companies like Faraday, and have really been trying to reshape our marketing mix between traditional and digital channels. For digital channels, we are seeing a high rate of new customers as we've built an experience that they are interested in interacting with.

We see very strong customer loyalty and to be frank with you, our product and brand has really stood on its own in bringing customers back historically. Our messaging and retention strategies have really been based more on the personal relationships we've built with our customers over the years.

As a brand with high-touch customer service and interaction, we are always looking for ways to supplement the old-fashioned "how are you doing" with digital communication that adds value to our customer's experience with the brand."

How has your marketing strategy evolved over the last 5 years? Where have you found success? Where have you struggled?

Ryne:

"Without getting into too many details, I will say that it is definitely in a place of evolution. Our business was built on the wholesale channel and sales tools, swatchfolios, etc have always been a staple of our marketing efforts.

That is still true today but we are also trying to find ways to improve those assets while also mixing in digital marketing (be it SEO, paid search, paid social, etc). As with most traditional media efforts, tracking can be difficult compared to the relative ease of digital. Faraday's toolset will allow us to continue to close that gap."

To what extent has data guided your marketing strategy throughout that period?

Ryne:

"Data and the ever-evolving digital landscape has been paramount in our evolution of our marketing strategy. When Mary Ella started the business, you could have never imagined directly tying a dollar spent to a dollar earned in marketing.

As these tools and technologies continue to evolve, for us it is about finding ways to marry the digital tools out there today with the traditional efforts that have really built and grown this business over the years."

Can you describe your current tech stack from your e-commerce system to customer data management? Has this changed significantly over the last few years?

Ryne:

"Sure. The fun thing about digital is it is pretty easy to find out what tools and technology people are using so it's pretty much public information.

Last year, we made a transition from Magento to Shopify, we signed on with a tool that allows us to visualize the true color of a product without having to shoot our products in a dozen different colors, and have signed on data and machine learning partners like Faraday.

We believe that to scale the marketing efforts of a luxury business through digital – we had to have a partner that could help us solve for the black box of channels like Facebook and Instagram.

It is pretty unbelievable that, with as great of a platform some of these channels are, they can only build look-a-like type prospecting audiences with 2.1M+ people in them, making it very difficult to understand why something is performing or not.

By leveraging a partner like Faraday, we have the ability to pull back the curtain and communicate with members of their account management, leadership, and theoretically their engineering team if ever needed.

For us to make significant adjustments in our marketing budgets, we need the ability to find the right people and send the right message at the right time. It is a fairly cliché statement but partners like Faraday should help us close that gap."

Prior to learning about Faraday, had you considered leveraging AI in any aspect of your business? If so, how? If not, why?

Ryne:

"I mean, sort of? I come from a background that is extremely digital in nature and I'm constantly trying to follow the trends that will help us build our brand. I will say – before talking to Faraday – I didn't think that AI was accessible to brands like ours quite yet (we have a small, scrappy team).

I was extremely impressed with what a relatively small team in Vermont can produce, and I liked their ability to "flex" their messaging to someone who considers themselves digital savvy but would have never considered scaling an in-house data science team in the short-term."

Why did you choose to leverage Faraday's AI solution?

Ryne:

"I've said a lot in the rest of the interview that I think answers this question but I would also say that the ability to take our use cases and personalize their product to what we needed to accomplish was key.

The Faraday team (shout out Robbie) is the type that listens, and I genuinely got the feeling that, in a world of technology partners who say a lot, they actually had the ability to walk the walk."

Can you describe your onboarding experience with Faraday?

Ryne:

"I've been through countless onboarding meetings and they are often met with a disconnect between sales and account management. It seemed like sales and account management was in sync. They had communicated our efforts, asked for refreshers as needed, and without the need for constant follow up have been meeting the quick deadlines that we needed them to.

As a business manager, I'm constantly met with "how" or "why" type questions and the team has been very responsive and thorough. It really sets the stage for our entire engagement."

We're excited to be working with you! Do you have any other comments or suggestions you'd like to add for companies looking to get started with AI?

Ryne:

"This might sound weird from a customer of the company, but don't just sign up for AI because you'd like to say you work with an AI firm. Take the time through the process and see if you can link Faraday's AI solution to your business outcomes.

Consider your ability to execute with the platform. We've been waiting for the solutions that we think Faraday can solve for and it seemed like a great fit for our business."

Thanks a bunch, Ryne!

Curious about how your team can use AI? A quick demo and consultation is a great way to find out! Click below to get started.


Faraday AI Platform Demo



Is your marketing team AI-ready? 3 considerations for consumer marketers

Perry McDermott on

No doubt you're hearing about the power of AI to transform marketing. But if you're like many marketers, the prospect of integrating artificial intelligence (AI) into your strategy may be daunting. It's a rapidly evolving field with seemingly endless applications, and you may have had relatively little experience with AI to date.

However, if you're committed to a data-driven marketing strategy, it's worth getting acquainted with AI and machine learning (ML); doing so will allow you to further monetize your existing data, and put the immense power of big data to work for your business.

According to Capgemini Consulting, 3 out of 4 organizations using AI and ML increase their sales and enhance customer satisfaction by more than 10%. Marketers already use AI to optimize various initiatives and automate repetitive, time-consuming tasks. Some applications include:

  • Audience expansion (AKA lookalike targeting)
  • Personalization at scale
  • Programmatic advertising
  • Automated content creation

If you're curious about what AI can do for your marketing team, but still trying to decide whether you're ready to implement the technology, here are three crucial points to consider:



3_considerations_ai_ready-3

1) Goals and strategy: Which outcomes do you want to optimize?

Establishing clear goals will help you make decisive choices about which applications of AI are best for your team, which data sources to tap into, and how to implement the technology with the resources at your disposal.

Are you more focused on optimizing customer acquisition, turning one-time buyers into repeat customers, or perhaps something much more specific?

If your goal is to optimize ad spend across your digital campaigns, you might consider leveraging applications like propensity modeling and programmatic advertising. If your goal is to drive upsells or cross-sells amongst your existing customer base, propensity modeling and personalized offers will be more helpful.

It's important to clearly define your target outcomes before making decisions about how to apply AI within the context of your particular marketing strategy. Then, research and strategic planning will help you decide which solutions are worth prioritizing in pursuit of your established goals, and which will involve more trouble and cost than they're worth.

2) Data: Is your data AI-ready?

Data is the fuel that powers AI. Quantity and quality will ultimately determine the effectiveness of your AI applications. This means it's critically important to ask whether you have enough historical data to train your AI — typically, at least 1,000 "past examples" — and whether your data is clean and rich.

Does your data have sufficient breadth and depth? More specifically, how many attributes and events are being considered per record, and are those additional data points predictive of the outcomes you want to optimize?

Thanks to big data vendors, consumer marketers don't need to rely purely on first-party data to train their AI models. Enriching existing lead and customer data with third-party consumer data can improve the predictive accuracy of your AI applications by an order of magnitude.

Here are a few ways to ensure your data is AI-ready:

  • Identify predictive data sources: If the goal is to increase lead conversion rates, your ESP and e-commerce platform will likely contain the most predictive data. Lifecycle mapping is a useful exercise to help you discover which data sources are most predictive of a target outcome.

  • Do a data audit: Clean your existing data by eliminating inconsistent, incomplete, or duplicate records. You can do this in-house or hire a consultant. Alternatively, certain AI platforms and third-party services can help automate the process.

  • Add depth and breadth: Enrich your first-party lead and customer records with third-party demographic, behavioral, and property-based data. While licensing third-party data can be costly and time-consuming, certain AI platforms and third-party services include third-party data and automate the data enrichment process. The Faraday Identity Graph is a prime example.

wp01_clo_promo-2

3) Implementation: Which approach makes the most sense for your team?

The resources needed to operationalize AI depend on how you choose to implement your solution. Business Insider's AI in Marketing report lists three common approaches: building in-house capabilities, using a third-party service, or using a platform. Each approach has its pros and cons, so it's important to carefully consider whether a solution's benefits outweigh implementation time and costs.

In-house capabilities

This is the most customizable, yet most resource-intensive approach to operationalizing AI, and should only be considered if your entire company is committed to an AI-first mindset. At minimum, building in-house capabilities from scratch requires:

  • Rich training data
  • Data storage capabilities
  • ML algorithms
  • Data science automation systems
  • Deployment features and integrations
  • Experienced talent (data scientists, ML experts, and engineers)

Third-party services

For companies that require numerous AI applications across different business functions, but don't necessarily need to feed insights and predictions between applications, third-party services like IBM's Watson can be a good alternative.

These services help facilitate AI implementation by including data science automation systems and built-in ML algorithms that in-house data science teams can use to build AI applications for marketing, accounting, SCM, or any other function worthy of optimization and automation. At minimum implementing AI with a third-party service requires:

  • Rich training data
  • Deployment features and integrations
  • Experienced talent (data scientists, ML experts, and engineers)

AI platforms

Platforms are designed to streamline AI implementation for relatively specific sets of use cases. ML capabilities and features are built in to platforms and supported by data science teams, enabling marketers to easily interact with, and act on AI-powered insights and predictions through the platform's user interface.

While specific requirements will vary depending on the platform, at minimum, operationalizing AI with a platform requires:

  • Basic training data

So, is your marketing team ready to implement AI?

If you have enough customer data, there's probably a solution that will fit your needs, but choosing the right implementation approach for your specific objectives can be challenging. If you're having trouble, consider the following questions:

  • Is your company committed to an AI-first strategy? If so, where do your marketing objectives fit into the strategy?
  • Does your company already have an in-house data science team? If so, do they have the bandwidth to focus on your marketing objectives? If not, do you have the resources to recruit and hire the right talent?
  • How important is customization? Will your AI-powered marketing insights and predictions help optimize AI efforts in another business function?
  • What's your timeline? In-house capabilities generally take at least 18 months to build from scratch, third-party services can take between 12-18 months depending on customization requirements, and platforms will take 6-12 weeks depending on predictive modeling requirements.

How to build your own consumer marketing AI stack

If customization is a priority and you have the resources to build in-house capabilities or use a third-party service, we've created a guide to building your own consumer marketing AI stack.

Alternatively, if it seems like your team should be using a platform, the guide will give you a good understanding of the data, systems, and features included in Faraday's AI platform.

faradiy_promo