In this guide, you'll find out how machine learning can be applied across the entire customer lifecycle, from acquisition to retention and support.

1. HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
Improve Customer Success Throughout the
Customer Lifecycle with Machine Learning

2. INTRODUCTION
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
Finding, serving and delighting customers are essential steps for any business. Whether you’re selling a
software-based application to thousands of businesses or offering services to millions of direct consumers, you have
to find people to purchase your goods, satisfy their expectations and keep them coming back for more.
The tricky part is that the value you provide changes as the stages progress. When customers make an initial
transaction with your company, they have different needs, and value success differently, than when they want to
purchase an add-on component or speak to someone in support. Each customer has more than one relationship with
your company as they move through their journey with you.
It’s impossible to have a successful, long-term relationship if you only align customer needs with initial sales and
forget about ongoing care. Likewise, if you sink resources into trying to support and retain customers who are the
wrong fit for your company or product, it can mean failure as well.
True customer success happens when you match the right individual customers with the services, products and
interactions that align with their needs at the moment they have them, building on each engagement to establish
trust and a relationship for the future. Customer success goes beyond customer support or customer service to
encompass the customer’s experience throughout their entire lifecycle.
Customer Success can be broken down into five main steps that make up the full lifecycle:
Helping customers actually use the product and obtain value immediately.
Ensuring that customers get as much ongoing value as possible from the product or service,
with an ultimate goal of increasing usage and encouraging adoption of additional services.
Monitoring for churn threats, incentivizing continued engagement, and identifying trends
that indicate retention or churn.
Reaching out to customers to offer assistance, both proactively and reactively as customers
use the product.
Finding and attracting the right customers.
Customer Success requires taking a long-range view of customer interaction with a focus on achieving the
customer’s definition of success throughout their entire lifecycle. Realistically, doing so requires knowing the
customer intimately, and that requires collecting and analyzing tremendous amounts of data.
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1. CUSTOMER ACQUISITION
2. CUSTOMER ONBOARDING
3. CUSTOMER SUPPORT
4. CUSTOMER EXPANSION
5. CUSTOMER RETENTION

3. ENTER BIG DATA AND MACHINE LEARNING
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
Big Data – the catchall moniker that has been applied to the incredible
tidal wave of data being collected and collated through human interactions
in the digital world – offers businesses an excellent source of information
they can use to optimize customer-facing engagement decisions.
However, for many, Big Data is simply a big question mark. Even if you
have a practical means of curating, storing, accessing, and visualizing large
amounts of data, the challenge remains of what to do with it in a way that
creates real value. How do you properly and profitably analyze it and turn
it into insights that can actually motivate the best course of action?
Machine learning provides a highly effective means of culling through this
seemingly endless supply of large and complex data to identify patterns,
isolate key data points, and accurately predict future activity based on
past experience. In its simplest form, machine learning takes the guessing
game out of the equation, predicting which products and services
customers may want, and how best to engage with them, based on prior
preferences and actions.
Much of the magic performed by machine learning is statistical in nature.
But, unlike a human statistician, a machine learning application can work
24/7 crunching incredible amounts of data without stopping, and it can
isolate and quantify minute patterns and disparate bits of data that would
seem insignificant if not viewed as a small part of the whole. It can then
use those patterns to predict an effect.
Analyzing complex, multi-dimensional
data about customers and your
engagement with them
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Continuously learning and improving
based on new data
Personalizing recommendations for
individual customer interactions
What is Machine Learning?
Machine learning enables computers to
automatically learn from the past to
predict the behavior of individual
customers in today’s complex and
continuously changing world. This is
accomplished through:
The end result is a predictive analytics engine on steroids:
an ever-evolving method for analyzing data and offering solid statistical
foundations for in-the-moment tactical decisions that support longer
range strategic goals with a direct, real-world application. The system
can be customized, then set on auto-pilot to continually analyze
incoming data and report its findings. As new data comes in, the system
self-adjusts to review all past and present results against the newest
information, so it’s constantly fine-tuning.

4. PUTTING DATA TO USE
In the complex customer success world, machine learning is best suited for business processes that are relatively
mature and stable, with a solid history of engagement and outcome data. Identifying the data that is used to make
decisions in the business process is key, but the good news is that machine learning can help separate what’s relevant
from what’s not. By considering data from throughout the customer lifecycle, machine learning can break down silos
and recommend an even better course of action than a single salesperson or account manager would choose. Even if
your data is scattered or you believe you don’t have enough, machine learning can start with the easily accessible
data to provide high-quality predictive recommendations and better outcomes. More data can be gradually added
over time to take advantage of the continuously improving aspect of a machine learning application.
TYPES OF CUSTOMER DATA USED BY MACHINE LEARNING:
Geographic • Country
• City
• Zip code
• Associated Census data like average
household income
Demographic • Age
• Gender
• Income
• Marital Status
Firmographic • Company Revenue
• Number of Employees
• Industry
• Types of Customers
Psychographic • Survey Data
• Social Media Engagement
Product • Clickstream Data
• Purchase History
Financial • Customer Revenue
• Purchase Frequency
• Cancellations
Personal Contact • Sales Calls
• Account Management Meetings
• Support Resolutions
Marketing • Email Performance
• Campaign Engagement
• Persona/Segment
Text • Email Copy
• Inbound Support Email
• Support Response Templates
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
As an example of how powerfully this kind of technology can affect customer
interactions, consider Amazon’s incredible personalization capabilities. The
site’s ability to recommend products based on an individual’s behavior, and the
behavior of others that have purchased similar items, translates into a valuable customer experience – and millions of
dollars in revenue on a consistent basis.
Although a large data science team is often behind the scenes of a customer experience solution like Amazon’s
personalization tool, machine learning can be applied to businesses of any size. Any company that collects data can
improve customer success and make more profitable, real-time business decisions with machine learning.
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5. PUTTING MACHINE LEARNING TO USE
CUSTOMER ACQUISITION
Customer Success starts with finding the right customers for your business and matching them with products from your
portfolio. If you don’t seek out, attract and acquire the right customers, not only will your profits suffer, but your
customers will not be set up for success.
The right customers should be a perfect fit for your product and service. These ideal customers will help you capitalize
on your marketing spend, improve conversions and set the foundation for a long-term, successful relationship. But you
have to know who they are, how to reach them, what they want, and how to position your offering to deliver value.
Imagine being able to effortlessly answer questions like:
Whether your sales process is dependent on regular phone calls or inbound leads, machine learning can uncover the most
qualified leads for your business to tackle and optimize the path to success for your customers.
If you’ve ever had the opportunity to work with a typical manual lead scoring and marketing segmentation process, you’ll
likely agree that setting up the rules, getting agreement across sales and marketing, and maintaining the system can be a
pain. The one-size-fits-all approach is non-optimal at best, and a sales inhibitor at worst as prospects frequently don’t fit
neatly into the defined boxes. Machine learning breaks that reliance on manual process and segmentation rules to deliver
better outcomes.
For example, let’s say you offer a free trial on your website. By looking back on key profile characteristics and specific signals
tied to satisfied customers who initially came in through this free trial, machine learning can help you identify which
prospects are most likely to convert in the future. This is a prime example of how machine learning helps identify and
prioritize ideal customers, so your team will not only acquire more customers, but more of the right kind of customers.
“The interesting thing is when we design and architect a server, we don’t design it for Windows or Linux,
we design it for both. We don’t really care, as long as we’re selling the one the customer wants.”
-Michael Dell
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
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Which prospects are likely to buy next month?
Which prospects are going to be the largest spenders?
What marketing content is best for a particular prospect?
4 Which sales reps are best suited to close business with which prospects?
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6. Higher sales win rate
Better marketing conversion rates
and return on marketing spend
More relevant campaigns
through personalization
BENEFITS FOR
CUSTOMER ACQUISITION
REAL WORLD EXAMPLE
Machine learning helps sales teams predict buyers, score leads and focus
efforts on prospects who are most likely to become satisfied customers.
It helps sales managers select the best sales representative to close a
deal. And it helps marketing teams microsegment offers with dynamic
content to deliver individual messages that resonate with prospects. All
of this is possible because machine learning can uncover patterns in
historical data to deliver sought after personal interactions that
continuously improve over time.
In the customer acquisition phase, machine learning can improve the
close rate for your sales pipeline and increase the return on your
marketing spend as you bring more ideal customers in the door.
US Bank, the fifth largest commercial bank in the US,
improved its lead conversion rate by over 100% with
machine learning when it deployed an analytics solution
that integrates data from online and offline channels to
provide a unified view of the customer. This integrated
data feeds into the bank’s CRM and supplies the call center
with more relevant leads. It also provides the bank’s web
team with recommendations for improving customer
engagement on the bank’s website. This data-driven
insight is used to refine website content and increase
customer engagement. As a result, the bank’s lead
conversion rate improved by over 100% and customers
receive an enhanced and personalized experience.
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
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Name
Email
Phone
Zip Code
L E A R N M O R E

7. CUSTOMER ONBOARDING
More personalized
onboarding
Reduce time
and cost
More success and
less support
Higher lifetime value
of customers
BENEFITS FOR
CUSTOMER ONBOARDING
After acquiring a customer, it’s essential to get them successfully using your
product. Depending on your product or service, this onboarding phase might
require migrating data from an existing system, integrating with a legacy
product, training users and customizing processes.
It’s not uncommon for an onboarding process to include content like video
tutorials and help emails, often delivered as a set campaign that automatically
follows a signup. The issue with this static approach is that it relies on the
same set engagement points for everyone, when in reality each customer has
different onboarding requirements.
For example, the person who signed up for a free trial and became a customer
after glancing at your home page may be in a very different place than the
person who spent hours on your site. Each would benefit from an onboarding
process optimized to their specific needs.
Machine learning can be put to use during this phase to make onboarding
more personal and tear down the static approach. It can look at engagement
in the onboarding process to identify patterns like:
• What content is most likely to ensure successful onboarding.
• If paying customers onboard differently than non-paying customers.
• Which customers are most likely to contact support during onboarding.
The onboarding phase is also a great opportunity to set a solid foundation for
reducing future churn. New customers are excited to get started with a fresh
product or service to answer their needs. However, success only occurs when
the customer actually benefits from your product or service.
By improving the onboarding process with machine learning, you maximize
the relevance of the product and marketing touchpoints for each customer.
You also reduce the chance of new customers putting unnecessary weight on
customer support.
"We see our customers as invited guests to a party, and we
are the hosts. It's our job to make the customer experience
a little bit better."
-Jeff Bezos, Amazon.com
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
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8. Faster routing
More consistent
support
Higher agent
productivity
Higher level of
customer self-help
CUSTOMER SUPPORT
"Your most unhappy customers are your greatest source
of learning."
-Bill Gates, Microsoft
Customers have a lot to say and customer support is the hub that hears every
question, complaint and ounce of feedback from the outside world. To
enhance customer success, support must not only react to requests, but also
proactively anticipate customer needs to influence future actions.
Insight from machine learning can help a support team:
For example, consider a company that provides a gaming app. Their business
thrives on “high spenders” who regularly return to game with them. It also
has a significant non-paying audience who engage but do not immediately
convert to paying customers. The support team spends a majority of its time
with these non-paying customers, who could convert to paying in the future.
To keep their audience happy, and still make money, they need an accurate
and efficient way to manage tickets.
Machine learning can deliver an automated ticket routing work flow that
analyzes and classifies incoming tickets to route them quickly to the most
appropriate support agent – or even to an appropriate FAQ.
Being able to more accurately predict what a support ticket is about gets
agents off to a running start to assist the customer and close the ticket. In
this era where support teams are being asked to do more with less, machine
learning can automate and enhance support processes so that agents can
continue to deliver great customer support even more efficiently.
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CUSTOMER SUCCESS
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Reduce response time through automatic ticket routing to
the best available agent.
Improve agent productivity through template or macro
recommendations.
Reduce ticket volume by implementing automated responses
to frequently asked questions or common concerns.
BENEFITS FOR
CUSTOMER SUPPORT
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9. CUSTOMER EXPANSION
Higher CSAT/NPS
Deeper product
engagement
More revenue from cross-sell,
upsell opportunities
Stronger
brand evangelists
“Turn a loyal customer into a lifelong loyal customer
simply by doing things that make their lives easier.”
– Peter Shankman
BENEFITS FOR
CUSTOMER EXPANSION
Recommending the right product to the right person at the right time is often
a difficult decision. However, by understanding the intricacies in customer
data, machine learning can predict what a customer needs and when they
need it to produce more value. With machine learning, a company can
proactively translate insights into actions to deliver a personalized approach
and a more rewarding customer experience.
As customer engagement data is collected across various touchpoints,
machine learning "sorts" customers by specific engagement signals. It then
works to analyze customer behavior and perform predictive segmentation
so marketers can adapt communication as the customer relationship evolves.
Machine learning helps marketers identify:
• The types of messages to inspire engagement
• Which customers are likely to be most engaged.
• How likely is a particular customer to turn into a brand evangelist.
• Which marketing approaches are most likely to yield further results
Another key component in the nurturing process that has a direct impact on
the bottom line is the cross-sell and upsell. Let’s look back at our gaming
example. They have a customer success team dedicated to their VIPs - those
gamers who return and spend money with them over and over again. But the
definition of "VIP" is inherently limited because it's based on historical
spend. As a result, they're missing out on the ability to connect with and
nurture customers who are poised to spend but haven't yet.
Machine learning identifies and prioritizes those potential VIPs so that the
gaming company can personalize their experience and maximize the chance
of converting them into true VIPs. By taking the guesswork out of
understanding customers’ evolving needs, machine learning ensures their
success and maximizes sales opportunities during the relationship.
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
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CSAT/NPS
$

10. CUSTOMER RETENTION
Churn reduction
More consistent approach
for account managers
More productive account
managers
BENEFITS FOR
CUSTOMER RETENTION
“I think the acquisition of consumers might be on the verge
of being mapped. The battlefield is going to be retention
and lifetime value.”
– Gary Vaynerchuk
9
Reducingchurnisakeyindicatorofcustomersuccess.Withthepattern
analysisofmachinelearning,youcannotonlypredictwhichcustomersare
abouttoleaveandproactivelyreachouttothem,butalsocontinually
encouragecustomerstostay.
By looking at past data, machine learning identifies factors that made
previous customers stay or cancel and enables you to apply these findings
to individual current and future relationships. This behavioral scoring
enables companies to more effectively identify at-risk customers as they
are entering the red zone and proactively save an account. After all, the
difference between a customer leaving or staying could be a special
promotion or simple email at the right time.
In our gaming example, machine learning can help the team clearly
identify what key indicators signal that a user is losing interest in the app.
The company can predict when a user will stop seeing value and
proactively re-engage them before they drop off. They can also wisely
determine which customers are not truly ideal and tailor an appropriate
approach to retention for each individual customer.
An essential benefit of machine learning is that it continues to learn and
adapt as people and patterns change, which makes customer retention
more of a reality and future revenues easier to come by.
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BLUEPRINTBLUEPRINT

11. Help ensure that your sales team is focused on leads that result in great customers, and that
your marketing team can present the best converting content based on the individual and
their buying patterns.
CUSTOMER ACQUISITION
Predict the usage patterns that will lead to successful customer outcomes, and give your
marketing and product teams an approach that they can use to test content during this
important phase.
CUSTOMER ONBOARDING
Help you automatically route tickets to the most appropriate agent or generate
auto-response messages that take advantage of your existing knowledge base.
CUSTOMER SUPPORT
Identify the usage patterns and messaging that leads to an engaged customer, ready
to expand their involvement, along with which comes additional revenue.
CUSTOMER EXPANSION
Identify the at-risk customers and the signals they're demonstrating, while predicting
what content or approach will be most likely to re-engage them.
CUSTOMER RETENTION
CONCLUSION
"Get closer than ever to your customers. So close that you tell them what they need well before
they realize it themselves."
– Steve Jobs, Apple
10
“Thecustomerisalwaysright,”maynotbealongstandingmotto.Analyticaltoolsandpredictiveanalyticsareprovingthat
customersaresometimeswrong. Theydon’talwaysknowwhattheywant.Theydon’talwaysrememberthedetails.Let’s
faceit,theydon’talwaystellthetruth.
Infact,manycompaniesfocusedonpredictingcustomerbehavior(likeAmazonandNetflix)havediscoveredthatobserved
customerbehaviorsaremuchmorereliablethancustomer-providedinformationandhavestartedmakingdecisionsbased
oncustomersactionsratherthanwhattheysay.
Withmachinelearning,youcanbuildyourbusinessbasedonthedatacustomersgenerate,ratherthanthesurveystheytake,
toleadthemdownthepathtosuccess. Datathatisproperlyanalyzedcanprovidebrandswithinsightonimprovingboth
internalandcustomer-facingprocessestogrowthebusinessandengagemoredeeplywithcustomers.
Inshort,machinelearningcanbothenableandenhanceCustomerSuccessforyourbusiness.
HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
C O P Y R I G H T 2 0 1 5 , W I S E . I O I N C

12. ABOUT WISE.IO
Wise.io provides predictive applications that allow business users to utilize the world’s most powerful machine
learning technology across the full customer lifecycle to optimize how they acquire, engage, and support customers.
Our applications connect directly to customers’ SaaS-based business applications and automatically learn from past
patterns in order to predict and optimize future behavior. We empower our customers to become truly data-driven
in their customer-facing business processes while freeing them from the burden of formulating and maintaining
human-generated business rules or static predictive models.
To see how machine learning can help improve your organization’s customer success program, contact us to discuss a
Use Case Evaluation.
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HOW MACHINE LEARNING ENHANCES
CUSTOMER SUCCESS
C O P Y R I G H T 2 0 1 5 , W I S E . I O I N C