how machine learning enhances customer success how... · how machine learning enhances customer...

HOW MACHINE LEARNING ENHANCES CUSTOMER SUCCESS

Improve Customer Success Throughout the Customer Lifecycle with Machine Learning

INTRODUCTION


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.

C O P Y R I G H T 2 0 1 7, A n s w e r I Q 1

1. CUSTOMER ACQUISITION

2. CUSTOMER ONBOARDING

3. CUSTOMER SUPPORT

4. CUSTOMER EXPANSION

5. CUSTOMER RETENTION

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ENTER BIG DATA AND MACHINE LEARNINGBig 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.


C O P Y R I G H T 2 0 1 7, A n s w e r I Q

PUTTING DATA TO USEIn 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

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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PUTTING MACHINE LEARNING TO USECUSTOMER 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

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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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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.

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Name

Email

Phone

Zip Code

L E A R N M O R E



CUSTOMER ONBOARDING

More personalizedonboarding

Reduce timeand cost

More success andless support

Higher lifetime valueof 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

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Faster routing

More consistentsupport

Higher agentproductivity

Higher level ofcustomer 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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Reduce response time through automatic ticket routing tothe 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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CUSTOMER EXPANSION

Higher CSAT/NPS

Deeper productengagement

More revenue from cross-sell,upsell opportunities

Strongerbrand 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.

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CUSTOMER RETENTION

Churn reduction

More consistent approachfor account managers

More productive accountmanagers

BENEFITS FORCUSTOMER 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

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Reducing churn is a key indicator of customer success. With the pattern

analysis of machine learning, you can not only predict which customers are

about to leave and proactively reach out to them, but also continually

encourage customers to stay.

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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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

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“The customer is always right,” may not be a longstanding motto. Analytical tools and predictive analytics are proving that

customers are sometimes wrong. They don’t always know what they want. They don’t always remember the details. Let’s

face it, they don’t always tell the truth.

In fact, many companies focused on predicting customer behavior (like Amazon and Netflix) have discovered that observed

customer behaviors are much more reliable than customer-provided information and have started making decisions based

on customers actions rather than what they say.

With machine learning, you can build your business based on the data customers generate, rather than the surveys they take,

to lead them down the path to success. Data that is properly analyzed can provide brands with insight on improving both

internal and customer-facing processes to grow the business and engage more deeply with customers.

In short, machine learning can both enable and enhance Customer Success for your business.



ABOUT AnswerIQ 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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