Is the Customer Always Right? Should You Fire Some of Your Customers? 3 Ways CX Analytics Can Help

Recent customer rage reminded me of a TiE (The Indus Entrepreneurs) panel
10 years ago when the three of us were asked “Is the customer always
right?” After the other two panelists said “No, the customer asks for
ridiculous things” or “No, we know what’s right for our customers” or “We
need to fire our worst customers!”, I jumped into the fray and challenged
them with “Of course the customer is right! Plus, if you don’t do the right
thing for your customers, they will walk away.”

OK, those are two different
thoughts, but there’s a through line so bear with me.

FT Weekend’s recent Business column “An unruly toddler offers a lesson in customer
care” recited a story from Memphis, Tennessee in the US where an upset café
customer posted an online review after “her meal had been interrupted when
the owners’ toddler appeared naked and “bent over” to show her its bottom.”¹
This post escalated with the owners threatening “to ban people
leaving bad reviews”, in both cases not talking directly to each other but
resorting to social media to vent.

However, this story also seems to be
part of a nasty trend with CEOs berating customers (as the United Airlines’
CEO initially responded when his passenger was dragged off the aircraft),
harkening back to one of his predecessor’s autobiography “believing it
important to side with employees facing passengers who could be
“unreasonable, demanding jerks”.”²

“The Customer is Always Right.” Really?

Let’s go back to my first question, the one from the TiE panel. I always
thought that “It was Henry Gordon Selfridge, founder of London’s Selfridges
store, who said “the customer is always right”” (the opening line in
another FT article from 2010³), but on NPR two weeks
ago I learned that Marshall Field, Selfridge’s boss in Chicago before
moving to London, coined the expression.

Seems that the retail trade is
following this path over the years (John Lewis, also in London; Costco,
Nordstrom, REI, and now Amazon in my hometown Seattle; Neiman-Marcus in
Dallas; and others). One of the other panelists that day was CEO of an
online retailer (later fired, and his company was gobbled up,
coincidence?).

Before I state my case for “Yes, the customer is always right” let’s
quickly summarize the “No” camp, starting with a HuffPost column that
pretty much sums it up with these “Top 5 Reasons Why ‘The Customer is
Always Right’ is Wrong”⁴:

1. It makes employees unhappy (cites the same airline CEO)
2. It gives abrasive customers an unfair advantage
3. Some customers are bad for business
4. It results in worse customer service
5. Some customers are just plain wrong

Just reading this list makes me cringe, both as a customer and based on the
research that my co-author David Jaffe and I did to produce our second book Your Customer Rules! that describes how
renowned customer experience leaders do place the customer front and
center. We called this being “Me2B Leaders” instead of the classic B2C or
B2B that places the business first.⁵ It turns out that these
companies also place their employees front and center, harkening back to
the classic “service profit chain”⁶ In other words, you can have
it both ways, and you need to have it both ways: employee satisfaction and
loyalty, and customer satisfaction and loyalty.

What if some customers are “leaving bad reviews” or being “abrasive”? Are
they “wrong”, and should they be “fired”? Or are policies, procedures, and
training getting in the way of customer (and employee) experience? Are
their weak links in the “chain” (pun intended!) that upset customers are
trying to tell you?

CX Analytics to the Rescue

Here are some ways to use customer experience analytics to frame these and
other essential questions:

1) Personalization and “segments of one”

I’ve always agreed that “all customers are not created equal”, and today
with Big Data and algorithms you can extract insights from images, email,
and social posts; apply dynamic offer-relevancy scoring; and then adjust
your offers’ or services’ relevance dynamically against ROI. Here we’re
talking about a lot more than Platinum-Gold-Silver levels (or “Bronze”, as
one of my clients described its least profitable customers); instead, you
can approach “one to one marketing” as espoused 24 years ago by Don Peppers
and Martha Rogers.⁷

Now, using machine learning and other tools you can read how well these
offers work, if personalization is indeed leading to greater sales and
profitability, and what happens if customers are not happy with the offers
or services (posting negative comments, reducing their purchases, etc).

It could turn out that some customers are chronically unprofitable (e.g.,
only using deeply discounted products or services, calling you all the time
for the same issues, returning products all the time) and/or very
displeased. If it’s not possible to convert these customers, perhaps they
will leave on their own accord; if not, it might make sense to “ask them to
leave” via the offerings themselves, certainly not by “dragging them off
the plane”.

Where has this worked?

One of the US-based mobile carriers applied precursors to Big Data 10 years
ago to address higher than expected customer attrition. I suggested to them
that they mine all of the recorded calls for the last 12 months of lost
customers, find the patterns, and then apply those findings to calls with
current customers. By overlaying segmentation levels, not down to “segments
of one” at that time, they discovered key insights that reduced the churn
and improved agent to customer interactions.

More recently, a large
US-based restaurant chain has piloted this full program and will soon roll
it out across its entire customer base, with rapid feedback to test the
acceptance of offers, re-purchase rates, and satisfaction.

2) Predictive experiences.

Building on personalization, it is now possible to predict your customers’
experiences without resorting to costly and biased surveys. I described
this exciting new area in one of my earlier CustomerThink columns “Don’t
Ask, Know! What Are Your Customers Not Saying? Not Doing?”⁸ with
these four tips:

“Listen to what your customers are already telling you online (via social
listening), in your contact centers (recorded call speech or data
analytics), or in home (installer or repair crew post-visit voice
capture).”

“Ask your front line what customers are saying as you wander around their
cubicles or hold roundtables or sit with them to listen to customer calls
(you do this already, right?!) or via an online form.”

“If you could simply “staple yourself to an order” that your customer
placed and discovered how complicated the process is and how much customers
had to wait, or if you listened to your company’s IVR tree and then got
connected to an agent who asks the same questions, or if you got copies of
all of the customer correspondence that you team sends to explain how to
easy it is to use your products or services – all of this would reveal more
than a survey that garners a 5% response rate.”

“Predict customer wants and needs, loyalty and ease-of-use” using Big Data
predictive analytics.

This final tip applies expectation thresholds during “the customer journey”
with a recommendations engine providing “course corrections” to improve the
customer experience and models to read the customer response and “learn”
what works, and what doesn’t work. The result = fewer customers leaving;
more “attachment” to your products, services, and brand; and happier
employees since they are able to influence customer satisfaction more
clearly. Some customers decide to leave, too, but even that goes into the
model to determine which customers are the best prospects in the first
place.

Where has this worked?

Disney has done an excellent job with customer experience analysis over the
years, recently adding predictive experiences and personalization with its
MagicBand system:

“If you’re wearing your Disney MagicBand and you’ve made a reservation, a
host will greet you at the drawbridge and already know your name – “ Welcome Mr. Tanner!””⁹

3) Integrated voice of the customer (I-VOC).

In a more recent CustomerThink column “Using Big Data to Build an
Integrated Voice of the Customer Program: A 6-Step Guide”¹⁰ I
described how customer experience analytics and Big Data can now “mash up”
all of the silo’d “voices” that your company collects in marketing, sales,
field support, customer service, trade shows, and much more. This combined,
scored, and parsed I-VOC, by “’mashing up’ different data, ‘cleaning’ those
data, and ‘learning’ from models to improve the accuracy of the solution”,
allows you to then “use Big Data tools to pull in all of the VOC sources,
associating the VOC sources with the objectives and modeling all of the
factors to determine best weights.”

The result = finding needles in
haystacks, those insights usually lost in dull averages or “top 10” lists
of issues; deep frustrations that you and address to turn around customers,
and apply ahead of time to others at risk; and concerns that you can tackle
directly with the customer.

Where has this worked?

A leading US fast food retailer mimicked its store performance central data
repository and insights with an I-VOC program that synthesized over 12
different “voices”, or sources of customer comments and requests. This has
enabled them to delight their customers with newer and more friendly
offerings, anticipate issues and remove barriers to that delight, and build
stronger relationships with its franchisees.

So what do you think? Is the Customer Always Right? Should You Fire Some of
Your Customers?

Notes:

¹
“An unruly toddler offers a lesson in customer care”, FT Weekend
15 July/16 July 2017, Pilita Clark on Business, page 9 (US edition).

²
Cited in “An unruly toddler offers a lesson in customer care”, FT Weekend 15 July/16 July 2017, Pilita Clark on Business, page 9
(US edition). Refers to Gordon Bethune’s book

From Worst to First: Behind the Scenes of Continental’s Remarkable
Comeback, Wiley, 1999.

³
“Right or wrong, the customer always matters”, Michael Skapinker, FT 23 March 2010.
http://www.ft.com/cms/s/0/832c0c32-3619-11df-aa43-00144feabdc0.html?ft_site=falcon&desktop=true#axzz4nCgGWgKr, accessed 17 July 2017.

⁴
“Top 5 Reasons Why ‘The Customer is Always Right’ is Wrong”, Alexander
Kjerulf, HuffPost 15 April 2014.
http://www.huffingtonpost.com/alexander-kjerulf/top-5-reasons-customer-service_b_5145636.html, accessed 17 July 2017.

⁵

Your Customer Rules! Delivering the Me2B Experiences That Today’s
Customers Demand

(Wiley 2015). Here are the 7 Customer Needs that Lead to a Winning
“Me2B”Culture; each Need breaks down into a total of 39 Sub-Needs.

1. “You know me, you remember me”

2. “You give me choices”

3. “You make it easy for me”

4. “You value me”

5. “You trust me”

6. “You surprise me with stuff that I can’t imagine”

7. “You help me better, you help me do more”

⁶
“The service–profit chain establishes
relationships between profitability, customer loyalty, and employee
satisfaction, loyalty, and productivity. The links in the chain (which should be regarded as propositions) are as
follows: Profit and growth are stimulated primarily by
customer loyalty.” From “Putting the Service-Profit Chain to Work” by James L. Heskett
, Thomas O. Jones, Gary W. Loveman,
W. Earl Sasser, Jr., and
Leonard A. Schlesinger in HBR July-August 2008.

⁷

The One to One Future: Building Relationships One Customer at a Time, Don Peppers & Martha Rogers, First Currency 1993.

⁸
“Don’t Ask, Know! What Are Your Customers Not Saying? Not Doing?” by Bill
Price 12 November 2015.
http://customerthink.com/dont-ask-know-what-are-your-customers-not-saying-not-doing/

⁹
Cliff Kuang, “Disney’s $1 Billion Bet of a Magical Wristband”, Wired March 2015
https://www.wired.com/2015/03/disney-magicband/, accessed 26 July 2017.

¹⁰
“Using Big Data to Build an Integrated Voice of the Customer Program: A
6-Step Guide” by Bill Price 16 March 2017.
http://customerthink.com/using-big-data-to-build-an-integrated-voice-of-the-customer-program-a-6-step-guide/