Recently I received information about an interesting new
vendor called GoodData – it appears that they are specializing in BI-type user interfaces
for function-specific dashboards (of course, in the cloud and involving
cross-database information display). One
of these was GoodSupport Bash, a “bash-up” of customer-support metrics and KPIs
(Key Performance Indicators). And that
caused me to reflect on the idea of applying these types of “comprehensive”
metrics to customer support.
Customer-support technology has a relatively long
software-vendor history: I remember
first seeing the idea in action back in 1982, when Computer Corp. of America’s
email system was used to track customer inquiries. Our forms capability allowed
us to enter the call info in an email, send it for resolution to the
appropriate techie, and then see the “email trail” as the problem was
resolved. Like many small companies, we
had a reputation for excellent customer support because the techies were
developers who had spare time to actually solve problems and the clout to
change the code base to do so. In fact,
I was once told that one “guru” would go to install at new sales and when the
customer asked for changes, he would make them without documentation or
changing the main code base, so that the next version overrode the
customization – it made the customer very happy initially, and very annoyed a
year later.
However, in the early 2000s, when offshoring came in,
major-vendor customer support was one of the first to move, and the semi-mature
customer-support software of the day now had to define support personnel tasks
very carefully, as the new Indian and Filipino respondents had some difficulties
with American English and more difficulties with being up to date with software
technology. The resultant upheaval in
customer support has never really settled down again, imho. Companies like Microsoft would really like to
move away from live-person customer support entirely, but the complexity of the
technology means that users keep having to ask questions of live
representatives who can be forced to pay attention. For example, it was not until I got to a live
representative that I realized that the Windows 8 version of Windows Media
Player does not support DVD playing out of the box (according to PC World, you
need the $10 Media Pro or some such).
Well, this in turn meant that phone-based customer support
had to be “optimized” in some way, else waits of 2 hours and unresponsive
representatives would cause a significant black eye for the company providing
the support for its products. And so,
solutions like GoodSupport Bash allow companies to “take the temperature” of
their customer support according to all sorts of criteria, like speed to reach
a representative, time taken by the representative, and whether the
representative is taking the opportunity for up-sell or cross-sell.
Except that I believe that the results of this kind of
fine-tuning according to various management theories can sometimes actually be
counter-productive – and the fault lies not with the customer-support software
vendor.
Grey’s Anatomy and Ice Removal
I must confess that Grey’s Anatomy is one of my guilty
pleasures, and I often find it difficult to watch without rotfl. To me, GA is the ultimate in actor
torture. In just about every episode, at
least one and often several actors and actresses must somehow make credible a
complete 360-degree turn in their characters, with the words they are given to
do this stretching the limits of belief that anyone could talk in this
way. As character after character
changes partners or sexual orientation on the verge of a marriage and in the
middle of a surgery, I can almost imagine the reaction as that actor or actress
sees the week’s script – bracing themselves, and yet unable to anticipate the
next contortion. Although there are
limits: no one has taken a sudden
interest in animal husbandry – yet.
Anyway, in this episode, doctors facing a hostile takeover
of the hospital went to find out what this new highly-efficient corporation was
doing, and posed as patients. The doctor
started reading from a scripted set of questions, and when the “patients” attempted
to derail them, started the script over again until they could get it
done. This was done, we are assured, in
the name of standardized efficiency. You
will recognize the similarity to some customer support experiences.
However unlikely the situation, the GA actors and actresses,
trained to make the unlikely plausible, made it very clear how customer support
driven by metrics appears to the customer.
The caller reporting something that often is unusual must be fit into a
series of questions that attempts to cover only the usual. Callers who do not fit are effectively
ignored – you could share the frustration as the “patient” attempting to
vaguely describe symptoms is asked to spend much time talking about things that
are not problems, and the feeling that no one is listening. At one point, the “patient” confesses that he
is a doctor, pretends he’s already a new part of the company, and asks why
these procedures; the “representative” assures him in a scripted manner that
the procedures are more effective – except it’s clear that he must answer that
way, no matter what he thinks, or lose his job.
Sound implausible?
Well, I actually went through a similar experience the other day, when I
tried to see if a national gutter specialist could provide snow and ice removal
on an emergency basis. Sure enough, I
wound up with a representative who took some time to realize that I was not
calling about gutter cleaning, finally said that the company did not provide
the services, and then spent three minutes attempting to upsell me on a bigger
gutter cleaning contract, despite the fact that (a) I said twice that I was not
going to do so, and (b) I had just been told that they didn’t do something else
I needed. It was clear that this was one
of the metrics by which he was judged:
did you run through all the questions?
Did you upsell? You can imagine what I felt about the company afterwards
– and it wasn’t the representative’s fault.
Sloan Management Review, iirc, had some interesting data on
the effect of this type of thing on customer loyalty. Often, the most loyal or biggest-spending
before the disappointment were the quickest to jump ship – they felt “entitled”,
as it were. But even the truly loyal
needed customization that truly paid attention to their needs, else they too
found it difficult to stay. Branding
only goes so far.
My point here, however, is that none of this was the
customer-support software vendor’s fault.
On the contrary, whether the company’s support strategy is good or bad,
the typical indication from users is that something like GoodData Support Bash
will make it more efficient. If the
strategy is bad, however, it may also make it less effective, or more harmful.
The Bottom Line: Metrics Is As Metrics Does
It is now a truism of management theory that metrics create
behavior – employees game everything to their own advantage, or to minimize
their own disadvantage. And so, users of
customer-support software like GoodData Support Bash badly need to remember
that the most effective use of any customer-facing tool is not to create
representatives who will deliver the company’s idea of customer happiness via
efficiency, but to find analytics to understand the customer better. Only after that knowledge is gained and the company
has used that knowledge to improve customer support responsiveness to real
customer needs should efficiency metrics be considered.
To put it more concretely:
among the metrics in your dashboard, is at least one telling you honestly
the degree of customer satisfaction with the interaction? And are you mining data from that metric
telling you what’s good and bad about your present solutions? If not, are you really making things better with your improvements in customer-support
efficiency?
Or are you making them worse?
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