More customers, more problems? That doesn't sound right

Traditionally, customer acquisition strategies were guided by industry best practices and experiential knowledge held by an organization’s executives and senior-level individuals. Sometimes these strategies were successful, sometimes they were not.

Success was primarily determined by the return on investment (ROI) for various acquisition initiatives. While ROI is still one of the best ways to gauge the effectiveness of your efforts, the “trial and error” approach can have severe impacts on revenue growth, especially for small and midsize companies.

Machine learning, a subfield of AI, enables companies to take much of the guesswork out of the customer acquisition process, and it all starts with good data.

Extracting value from your data

B2C companies have access to an abundance of consumer data — data that enable companies to draw deeper insights about what their best customers look like, which leads are worth targeting, which types of promotions are likely to keep them engaged throughout the buying process, and just about any other outcome companies want to optimize.

As companies consider more data, they require more efficient means of analyzing it. That’s where AI becomes invaluable, as it allows companies to draw deep, data-driven insights and make accurate predictions about target outcomes. However, the key is implementation, leveraging these insights and predictions efficiently throughout the acquisition process.

3 applications of AI for B2C customer acquisition

1) Propensity modeling

The best way to optimize any customer acquisition initiative is to focus your resources on individuals with the highest propensity to convert on a target outcome. Why waste your precious marketing dollars on leads that probably won’t become customers, or worse, spend more on acquiring a new customer than they spend on your products or services?

Through various machine learning techniques, data scientists can build models capable of predicting — with a high degree of accuracy — a lead’s propensity to convert on a target outcome. These propensity models are especially helpful in optimizing lead generation and customer acquisition campaigns in two ways:

• Predictive targeting: Propensity models “score” individuals on the likelihood that they will convert on an outcome. Outcomes can be as broad as “make a purchase” or as specific as “bought product X and product Y, but not product Z.” Propensity scores become actionable when they’re fed back into a CRM, ESP, e-commerce platform, or any other marketing system. As you might imagine, targeting likely-to-convert audiences helps drive conversion rates and ultimately improve ROI on acquisition initiatives.
• Personalization: You achieve the greatest performance improvements when targeting propensity-based audiences with highly-personalized campaigns. Identifying the most influential variables in the modeling process helps you draw qualitative insights, which you can leverage to personalize and optimize qualitative aspects of your acquisition strategy: product photography, ad creative, web copy, etc.

2) Real-time lead scoring

The sooner you identify an individual’s propensity to convert, the better you can allocate your resources. Real-time scoring integrations feed new lead data into propensity models, where they’re scored and returned to various marketing systems. Real-time lead scoring can help you optimize your customer acquisition strategy in three ways:

• Immediately determine whether the lead is worth further marketing or sales engagement. If not, save your resources for high-scoring leads.
• Immediately enroll high-scoring leads into the right nurture campaigns. If a likely-to-purchase lead was captured on a particular product page, you might want to enroll them into a promotional campaign for that product.
• Immediately inform your sales team when high-scoring leads enter the funnel. This is especially useful when using multiple propensity models for multiple outcomes. If a new lead has a higher propensity to purchase product X than product Y, your sales team should know as soon as possible.

3) Location intelligence

Location intelligence situates business data in a geographic context. Identifying "hotspots" of high-scoring individuals can significantly reduce customer acquisition costs in two ways:

• Retail site and showroom expansion: Physical, or "brick-and-mortar," locations carry tremendous costs. It's no secret that location is paramount to a site's success. With scored geographies and careful analysis, you can identify the best (and worst) locations for new sites.
• Service area geotargeting: Whether the goal is to drive foot traffic to physical sites or to maximize ROI on direct mail or canvassing campaigns, targeting spatially-clustered areas of high-scoring individuals will increase conversion rates and lower acquisition costs.

Where to start

Regardless of which application of AI is most relevant to your business, start by acquiring rich, high-quality data, and lots of it. Good training data fuels the models that give you the insights and predictions needed to optimize your acquisition strategy.

When you have the right data, and enough of it, you’re ready to train your AI. You’ll need experienced data scientists and a solid machine learning engine to build models that actually work, but once they’re built and predicting accurately, you’re ready to start optimizing.

For a deeper dive into data discovery and machine learning, check out AI for the customer journey. The whitepaper lays out the four-step process that hundreds of B2C companies use to operationalize revenue-building AI, from customer acquisition to retention.

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.

Faraday enables consumer brands to leverage big data and apply automated, predictive modeling at every step of the customer lifecycle to gain instant, accurate insights into each phase of the process. Faraday's B2C clients are then able to better identify and understand their prospects and customers, and optimize marketing campaigns efficiently and effectively.

Investors in this round of financing include Intercap, FreshTracks Capital, and independent investor John Replogle, former president and CEO of consumer products industry leaders Seventh Generation and Burt's Bees. Faraday was recently recognized as the best-funded tech startup in Vermont by CB Insights in January 2018.

"If my decades leading consumer brands have taught me anything, it's that knowledge is everything," said Replogle. "I invested in Faraday because they make data and AI work immediately for B2C in a way I've never seen before. They're the future."

"AI is often too complicated and expensive for companies to implement effectively," said Jason Chapnik, chairman and CEO of Intercap, Inc. "Faraday has nailed it with a highly automated and practical AI platform that any consumer-focused company can quickly implement with incredible results."

The company has hired two industry-leading experts: Rob Trail, Senior Vice President of Sales and Customer Success; and data scientist Dr. Sean Kelly.

Trail previously served as General Manager of CRM at Boston-based Bullhorn, where he helped grow the company from 14 employees to more than 700, and built and scaled SaaS platforms for B2B companies and customer-facing teams.

Dr. Kelly previously served as a forward deployed engineer at Palantir Technologies, the leader in mission-focused data analytics. At Faraday, Dr. Kelly is working with data scientist Dr. Narine Hall to further enhance Faraday's AI platform.

"I'm thrilled Rob and Sean have joined Faraday, along with our other 8 new hires over the past year," said Andy Rossmeissl, CEO of Faraday. "AI is hard but increasingly mandatory for consumer brands—and we make it easy. Our ability to scale helps our clients grow even faster."

Faraday start-up client Burrow, a modern direct-to-consumer furniture company, attests to the effectiveness and cost efficiency of Faraday's AI solution, stating AI coordinates 1 in 3 of its sales.

"Faraday showed us that AI is the future," said Kabeer Chopra, Burrow's co-founder and chief product officer. "Now we use their platform to understand critical behavioral factors we would have missed before. We've learned that details like pet ownership, hobbies, housing characteristics, and life events play a huge role in the furniture buying process."

About Faraday
Faraday is an Artificial Intelligence (AI) platform for business-to-consumer companies. Faraday uses advanced machine learning techniques to optimize revenue outcomes, from acquisition to lead conversion and retention. Faraday's turnkey AI solution includes all of the consumer data, integrations, algorithms, visualizations, and reporting necessary to roll out a powerful AI strategy in weeks, with no in-house data science team required. With over 73 billion data points on U.S. individuals, Faraday identified and reached over 90 million high-potential, high-value individuals for client campaigns in 2017 alone. For more information, please visit www.faraday.io

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:

• 240+ integrations to seamlessly upload their prospect and customer data,
• a massive national database containing over 235 million U.S. consumers in over 125 million households,
• a built-in machine learning engine that automatically builds predictive models for each and every desired outcome,
• a dedicated data-science team constantly working to validate and improve model accuracy,
• and an automated campaign delivery system enabling companies to take action on their machine learning-based insights and predictions.

"Thanks to Faraday, 1 in 3 of our sales is coordinated using AI" – CPO, leading NYC-based direct-to-consumer furniture company.

Learn more about the process here:

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.

New York-based Burrow addresses traditional consumer frustrations with furniture shopping—delivery delays, quality issues, awkward dimensions—with a streamlined supply chain and cleverly designed modular products that can be customized to fit any space. As a fast-growing company, Burrow uses AI to reach its unique customer market in the most effective, creative, and efficient ways possible.

"At Burrow, we want our marketing to be as smart and simple as our furniture," said Kabeer Chopra, Burrow's co-founder and chief product officer. "Faraday showed us that AI is the future. Now we use their platform to understand critical behavioral factors we would have missed before. We've learned that details like pet ownership, hobbies, housing characteristics, and life events play a huge role in the furniture buying process. Now we're using terabytes of data to guide everything we do. Thanks to Faraday, 1 in 3 of our sales is coordinated using AI."

Since Burrow implemented the Faraday platform in August 2017, AI has become part of nearly every aspect of the company's growth, including:

• lead scoring that customizes outreach based on propensity to convert
• nationwide predictive targeting to identify promising customer segments
• analysis linking specific product features with life stage and geography
• quantifying the positive impact of the company's showroom strategy guiding a brick-and-mortar expansion toward auspicious locations.

"Faraday makes AI work for consumer brands without the rampant cost and complexity they anticipate," said Andy Rossmeissl, Faraday's CEO. "When you can start delivering results with AI in a couple months—our clients tell us it feels like magic. It's really just science. We are honored to help Burrow grow."

About Faraday Faraday is an Artificial Intelligence (AI) platform for business-to-consumer companies. Faraday uses advanced machine learning techniques to optimize revenue outcomes, from acquisition to lead conversion and retention. Faraday's turnkey solution includes all of the consumer data, integrations, algorithms, visualizations, and reporting necessary to roll out a powerful AI strategy in weeks, with no in-house data science team required. With nearly 100 billion data points on U.S. individuals, Faraday identified and reached over 90 million of the best candidates for client campaigns in 2017 alone. For more information, please visit www.faraday.io.

Technical topics often tempt Customer Success Managers/CSMs to type up all the info they can think of and launch it at the client's inbox like a cannonball.

This is usually a mistake -- but the mistake isn't writing the draft, it's pressing send.

Here's a better path.

1. Write out an email that has all the key points but would still be reasonable to send
2. Instead of sending, send a message like this: "Hi client, would you have time to review X on the phone later today, maybe 3pm ET?"
3. Use your email as talking points

Assuming the CSM feels comfortable talking with clients, whatever the complicated or bad news is, the call will go better than the email would have.

What do you do with the email you wrote out so carefully, besides use it as talking points? Send it:

• either as a followup to the client confirming what you already discussed
• or to yourself, and give it a label like "Emails I was thoughtful enough not to send" so that you can look back and see how concise you've been