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

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


What is customer lifecycle optimization and how does it work?

Perry McDermott on

customer_lifecycle_optimization

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:

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:

Lifecycle_mapping_table

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:

Customer_lifecycle_outreach_intervention

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.

Customer lifecycle optimization whitepaper

How B2C companies overcome machine learning barriers with Faraday

Perry McDermott on

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