You've probably noticed the growing hype around artificial intelligence (AI) in marketing. From chatbots to content creation to programmatic advertising — it seems like every other MarTech or AdTech platform is baking in some sort of AI capability.

With so many applications, it's easy to lose sight of what's most important in implementing an effective, optimized marketing strategy: deeply understanding your customers.

According to Forbes Insights, only 13% of businesses express a high degree of confidence that they are making the most of their customer data.

Stepping back, what does AI even mean for marketing?

At a high level, AI refers to a computer's replication of some aspect of human intelligence — pretty ambiguous, right? AI, as it exists today, is an umbrella term for a range of computer-enabled data analysis techniques — the most relevant and widely-practiced in marketing being machine learning.

Machine learning (ML) is the process of training computers to “learn” to recognize important patterns and trends in large datasets, with the goal of developing data models that can quickly categorize new data inputs and predict likely outcomes.

So, what does that mean for you, the customer-centric marketer? When using your customer data, or training data, as the basis of machine learning models, you can start to generate deeper customer insights and make better behavioral predictions. These can be around your prospects' and customers' likelihood to convert on certain campaigns, increase their purchase frequency, churn or lapse, or something much more specific.

Leveraging machine learning in your marketing strategy is no longer a luxury — it's a necessity. As competition increases and ad space gets more crowded, consumers have more choices of businesses to engage with, making machine learning critical to efficiently reaching the right people and keeping your customers engaged.

ML-driven insights marketers can't ignore

It should come as no surprise that the world's top brands are efficiently scaling growth by leveraging machine learning to prioritize their resources and personalize experiences across their customer lifecycles.

Here are some of the most important ML-driven insights marketers are using to craft better customer experiences and optimize their performance.

Behavioral insights and predictions

A vital piece of giving your prospects and customers a memorable experience with your brand is knowing who to engage with and when.

Have you ever been in the position as a consumer where you're targeted with ads that don't align with who you are or where you are in your customer journey? Perhaps you see an ad trying to get you to buy an item you've already bought, or offering you a first-purchase discount code when you're already a customer.

As a consumer, these experiences can be annoying and frustrating. And as a marketer, your message can end up diluted instead of impactful.

Behavioral predictions are crucial to proactively engaging your customers. Rather than reacting to your customers' behaviors, you're able to anticipate them and market to your most valuable customers at the right time, with content that corresponds to where they are in their journeys. This helps you prioritize your resources, optimize your marketing spend, and cut through the noise to better reach your target audience.

How can machine learning help predict customer behavior?

Relatively straightforward machine learning algorithms can uncover predictive patterns hidden deep in your customer data. The random forest, or random decision forest model, analyzes your historical customer data, building a series of decisions trees to predict the likelihood that future inputs (e.g. new leads) will result in a target outcome (e.g. make a purchase).

If you advertise on Facebook, you've likely come across lookalike audiences, which are generated in a similar way. Using lead conversion as the example outcome, these models essentially predict the degree to which a new lead best “looks like” leads who've successfully converted to customers in the past.

Naturally, the data used to train or build your behavioral models will influence how they make predictions. Done properly, these predictive insights can have a tremendous impact on your return on ad spend, overall customer acquisition costs, and customer satisfaction.

Persona-based insights and predictions

While predicting your customer's next move is immensely helpful in reaching the right people at the right time, it's not where the road to truly optimized marketing ends. Effective and thoughtful engagement requires an understanding of who your customers are as real people, so you can create hyper-personalized experiences that evoke emotional responses.

Salesforce Research revealed that 84% of customers say that being treated like a person is very important to winning their business. If you show your audience that you understand what motivates them to interact with you, whether they're an early prospect or a loyal customer, the relationship and trust between you and your customers grow stronger.

So how can machine learning help you personalize experiences at scale?

Customer clustering

Buyer personas, semi-fictional representations of your target customers, are instrumental in creating personalized content and creative that truly resonates with them. Traditionally, personas were created using basic demographic data, some psychographic data from surveys or focus groups, and a good amount of human intuition. While this approach has worked for years, it leaves too much room for human bias.

Customer clustering leverages a different machine learning technique than behavioral modeling: unsupervised machine learning. Rather than uncovering patterns that are predictive of a known outcome (e.g. likely to convert), unsupervised algorithms, like K-means, sort data into distinct groups based on shared attributes. The resulting groups, or clusters, form the foundation of unbiased, truly data-driven personas.

As time goes on and new data is collected, running the clustering algorithm again may reveal new emergent personas, enabling you to refresh your messaging, creative, and other personalization efforts to stay relevant as your customer base evolves.

Burrow, a disruptive direct-to-consumer furniture brand, uses ML-driven personas to identify what color couches their audience segments see in targeted ads. They found that customers who are older, live in single-family homes, and have kids are more likely to buy couches in darker colors; customers who are younger, live in apartments, and have few or no kids are more likely to buy couches in lighter colors. With these insights, Burrow was able to push creative that reflected these attributes to the audiences that possessed them.

Location-based insights and predictions

Virtually all consumer-facing companies need to consider location in their marketing strategies, whether it's out-of-home advertising, brick-and-mortar marketing, or geotargeted search campaigns. However, these tactics are often expensive and difficult to do well.

Geospatial intelligence refers to a suite of geospatial analysis techniques enhanced with a combination of behavioral and persona-based insights. Whether you're considering increasing investment in your existing markets, expanding to new ones, or looking to drive foot traffic to specific retail or branch locations, incorporating predictive insights into your geospatial analyses can drastically improve ROI.

• Predictive penetration analysis aims to identify the maximum return you can expect from further investments in your existing markets. The resulting insights can help you understand the performance of your existing sites and determine whether you should increase investments or cut back entirely.
• Predictive market analysis identifies specific hotspots of existing customers and customer lookalikes to guide where you should put new sites and optimize acquisition-focused out-of-home and geo-targeted SEM campaigns.
• Predictive trade area analysis identifies the maximum distance customers are likely to travel to your existing retail sites. The resulting insights can help you optimize individually-targeted and geo-targeted campaigns focused on driving foot traffic to those sites.

Implementing machine learning

While several complex applications of AI are still years — or even decades — away from being fully developed, the democratization of machine learning is enabling nimble marketing teams to generate these predictive customer insights without having to spend millions on expensive consultants or hire large data science teams.

If you're considering adopting machine learning or looking for ways to expand your analytics team's bandwidth, our article, Is your marketing team AI-ready?, discusses important considerations to ensure successful implementation.

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!

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.

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:

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.

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.

Revenue and the customer lifecycle

For nearly every B2C company, revenue is linked to the customer lifecycle. Customer acquisition, engagement, and retention initiatives directly or indirectly impact revenue growth and sustainability by either reducing customer acquisition costs, increasing customer lifetime value (LTV), or ideally, both.

Effective marketing strategies revolve around the customer lifecycle; understanding key lifecycle stages, identifying events that are likely to trigger transitions between stages, and finding ways to optimize interactions across the lifecycle.

Naturally, the more you know about your customers and how to influence them to transition into a desired stage, the more efficiently you can grow and sustain revenue with the resources at your disposal.

Simply put, that's what customer lifecycle optimization is all about: leveraging rich customer data and predictive analytics techniques to generate insights and make predictions that measurably improve outcomes at each stage of the customer lifecycle.

Customer lifecycle optimization (a 4-step practice)

Customer lifecycle optimization (CLO) is a practice. As with other practices, CLO involves a series of prescribed steps to be done effectively. While specific lifecycle stage names, data sources, and analytics techniques will vary depending on your industry, company, and objectives, the canonical CLO process comprises four steps: lifecycle mapping; data discovery; predictive groundwork; and implementation and action.

1) Lifecycle mapping

The first step in any CLO initiative is always identifying and defining key lifecycle stages and transitions between stages. Mapping these stages and transitions to a uniform customer lifecycle is crucial to uncovering rich, predictive data.

The uniform B2C customer lifecycle:

While terminology will change from business to business, we found that this formulation is rich enough to capture important boundaries, yet simple enough to avoid stages with ambiguous transitions.

Think about your customer lifecycle. How are stages defined? Which attributes qualify individuals to be placed in those stages? What events trigger transitions between stages?

Feel free to use the following table to help organize your findings:

Note: The Customer lifecycle optimization whitepaper dives deeper into transitions and litmus tests. It's free to download.

2) Data discovery

At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage. Individuals will also have different attributes, which help determine whether or not they belong in any given stage.

Go through each cell of your customer lifecycle map and think about which events or attributes in your data could be used to trigger a transition or pass the litmus test. Then, think about where you can find that data. Is it in your ESP, CRM, or a custom data warehouse?

3) Predictive groundwork

Once you've formalized your lifecycle and its representation in data, you can start recognizing patterns and eventually predict outcomes. Imagine loading up your converted leads (customers in "retention and expansion") alongside your stale leads that never converted. What differences can you find?

This comparison analysis is especially effective when you've added depth and breadth to your existing data.

As you experiment with this type of pattern recognition, you'll quickly realize that it's the kind of thing that computers do very well. That's where machine learning comes in handy.

4) Guided outreach

Whether you want to increase conversion rates from lead generation campaigns or reduce churn from existing customers, properly targeted outreach is essential to engaging and motivating the right leads and customers to take a desired action.

Outbound communication is the strongest and most versatile intervention at an organization's disposal to compel progress and therefore expand revenue. This includes individually targeted digital advertising, a form of direct outreach.

Consider the following examples of outreach initiatives:

Having identified a stage transition you'd like to motivate with an outreach intervention, the question becomes, "Who do I reach?" Regardless of the desired transition, the general technique is called audience expansion, also known as "lookalike" audiences.

To apply the audience expansion technique, we must always identify the audience and a set of candidates with similar characteristics and attributes.

With these groups defined, the next step is to apply your predictive groundwork. This could involve using patterns you identified in your data to look for similar opportunities among your candidates, or in more advanced cases, using artificial intelligence to build a predictive model trained to discriminate between likely and unlikely transitioners.

Finally, you will be left with a well-defined group of candidates likely to transition into a desired lifecycle stage when reached with relevant content.

Putting it all together

To summarize the CLO practice, it's useful to recall the original motivation: leveraging rich data and statistical predictions to optimize revenue-building initiatives throughout the customer lifecycle. This means motivating transitions from one stage to the next.

At this point, you should have good understanding of what CLO is and the four canonical steps involved. For a deeper dive into specific data requirements and predictive analysis techniques, download the whitepaper below.

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, download our Customer lifecycle optimization whitepaper. It lays out the four-step process that hundreds of B2C companies use to operationalize revenue-building AI, from customer acquisition to retention.