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?