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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

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

Perry McDermott on

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


Operationalize AI quickly and cost-efficiently with turnkey solutions

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Turnkey AI solutions

If you've got your finger on the pulse of your business, you know you need to adopt AI sooner than later.

While commercial AI adoption is on the rise, there's still time to catch up and get ahead of the curve with "off-the-shelf", or turnkey AI solutions.

Where do you stand in the AI landscape?

Consider the following questions:

  • Are you brand new to AI? If so, where will AI have an immediate impact on your top and bottom lines?
  • Are you experimenting with an enterprise-wide AI solution? If so, are you seeing the results you were expecting?
  • Are you already developing AI in-house? If so, what can you do to optimize your existing resources?

Regardless of your position in the AI landscape, you can quickly get started with AI or optimize your existing resources by implementing proven, turnkey AI solutions that address the low-hanging fruit.

What are the low-hanging fruit?

It's all about automating tasks that have an immediate impact on revenues and costs.

Rather than using AI to automate existing jobs, use it to improve efficiency and productivity within those jobs.

After working with hundreds of B2C companies in a wide range of industries, we found that the customer lifecycle is an ideal framework for discovering highly predictive data, applying machine learning, and developing predictive models for a variety of use cases.

Let's take a quick look at a couple popular use cases to get you thinking about how they might apply to your business.

Increasing conversion rates and reducing acquisition costs

The most effective way to optimize your spend throughout the customer acquisition process is by targeting individuals with the highest propensity to convert.

So how do we identify individuals who are likely to convert?

  • The first step is to enrich your lead and customer data with additional demographic, psychographic, and property-based attributes, giving you a vibrant picture of who your leads and customers are, not just what they purchased or where they came from.
  • The next step is to use that enriched data to train the AI that will ultimately make predictions on future data inputs.
  • Once trained, the AI can make data-driven predictions on who your most likely buyers are, where to reach them, and when to reach out.

We've seen projected sales cost reductions of over $300,000 when applying this approach.

Proactively preventing customer churn

Certain companies have a notorious problem with customer churn, especially those with subscription-based business models. Using a similar approach as the one mentioned above, companies can use AI to identify churn-prone customers before they even begin showing signs of churn.

This can help minimize marketing dollars spent on these churn-prone individuals, and focus more on customers with a higher lifetime value (LTV).

A deeper dive into use cases for turnkey AI

Hopefully, you're thinking about how you could leverage a turnkey AI solution to optimize outreach initiatives across your customer lifecycle.

For a deeper dive into the opportunities available with turnkey AI, check out our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business


For more information on to use AI to optimize your customer lifecycle outreach, download our other whitepaper, AI for the customer lifecycle: Making the most of your data.

Where do you stand in the current AI landscape?

Perry McDermott on

Artificial intelligence technologies

You've probably noticed the growing buzz around AI and ML. You've likely done some research into the field, read some AI success stories, and believe that artificial intelligence is more than just a buzzword.

Sound like you? Good, you're totally on the right path!

So is all the hype true? Is AI really as promising as it seems? Have most companies already adopted AI? Or even worse, are you too far behind to be competitive with AI?

Luckily, you're not too late.

Hype versus reality

In a survey of over 3,000 senior executives, McKinsey found that only 10% of respondents said they have actually operationalized AI at scale.

That said, another 10% claimed to have operationalized AI in at least one core business function, and about 40% are experimenting with some form of AI.

Couple those numbers with the massive increase in AI-related investments over the last few years, and we can see the time to get serious about AI is undoubtedly now.

Where's the interest, and how much is there?

Over the last five years, investments in AI have more than tripled. In 2016 alone, total investments (internal and external) ranged from $26 billion to $39 billion, with over 75% of that coming from tech giants like Amazon, Apple, Google and Baidu.

These cash-rich giants have mostly invested internally — on R&D and deployment — but they've also invested significantly in hard-to-find talent, primarily through major acquisitions.

Source: MGI Artificial Intelligence Discussion paper.

How can I possibly catch up and get ahead?

These massive investments can feel deflating, and rightly so if your plan is to go head-to-head with the giants by building up your own AI tools in-house. The good news is that you don't have to, and often shouldn't develop AI from scratch.

A wealth of AI startups have caught some serious traction by developing fit-for-purpose solutions that address focused sets of business challenges.

Whether those challenges rest in IT and security, finance and accounting, or revenue growth and marketing, organizations can easily adopt and operationalize these "off-the-shelf" solutions in a fraction of the time, and at a fraction of the cost of alternative "one-size-fits-all" solutions like IBM's Watson.

The key is to automate tasks that have an immediate impact on your top and bottom lines. AI technologies can't replace your entire marketing team, but they can arm that team with powerful customer insights and predictions, simply unattainable without powerful machine learning algorithms.

Some use cases

For more on this, and to read some specific use cases for off-the-shelf AI solutions, download our free whitepaper, AI or die: Understanding the inevitability of AI in business.

AI or die: Understanding the inevitability of AI in business