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What is customer lifecycle optimization and how does it work?

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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

Faraday COO joins the Fintech Panel on Practical AI Use Cases at the 2018 Symitar Educational Conference

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The Symitar Educational Conference

Symitar, a division of Jack Henry & Associates, is the recognized leader in core data processing and ancillary technology solutions for U.S. credit unions.

The annual Symitar Educational Conference (SEC) showcases industry-leading technologies through educational classes, roundtables, and tech leader panel sessions.

The Fintech Panel on Practical AI Use Cases

The Fintech Panel on Practical AI Use Cases aims to cut through all the hype about AI by exploring real-world applications of AI and how they're benefiting credit unions. Here are a few discussion points that will be covered by the panelists:

  • What is AI (beyond the textbook definition)?
  • What problems is it solving for credit unions?
  • What are the requirements for AI to work well?
  • Where is AI heading?

The panel will take place at 9:45 AM on August 30th, 2018.

The panelists

We're excited to announce that Faraday COO, Rob Trail, will join the panel to share his insights on how credit unions are leveraging big data and AI to optimize target outcomes across their member lifecycles.

Rob will be joined by Clinc VP, Himi Khan, and Infosys Senior Director of Client Services, Ponsi Sundaram. If AI is on your radar, you won't want to miss this panel.

Get the case study below to see how Mid Hudson Valley Federal Credit Union uses AI to acquire new members and personalize member experiences.


mhvfcu_case_study_promo


AI for a better B2C acquisition strategy [3 advantageous applications]

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AI for B2C customer acquisition

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.

The role of AI in personalization whitepaper

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.

Customer lifecycle optimization whitepaper


PRESS RELEASE - New funding and new talent at Faraday

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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

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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