Category: Work

Service and Hospitality are Different Things

Let’s talk about service and hospitality.

Relying on one-on-one experiences, on service, to delight your customers when you serve tens of thousands (sometimes millions) of people daily is untenable and inefficient.

Service and hospitality. These are two words that we throw around a lot when we discuss the work of delighting our customer once they have our product. After all, the essential function of every support team in a software company is to be the front line of your post-launch products.

My aim today is to convince you that 1) those two words have important and distinct meanings, 2) that traditional examples of customer service or hospitality do not serve us well, and 3) that we need to shift our focus in a meaningful way.

Let’s get started with some definitions.

Service is any interaction that occurs between an employee of the company and a customer (or potential customer, in some cases.)

Hospitality is the sum of all of the other environmental factors that impact a customer’s experience of your product.

Imagine you’re visiting a new town, and, as someone who cares about coffee, you’ve done some research and are looking forward to taking in an especially well-considered espresso bar. You arrive, and enter the place with a sense of anticipation.

It’s busy inside – music is playing quite loud over the house speakers, and the line stretches nearly to the door. It’s surprising to you since it is an off-hour for a cafe. Upon further inspection, it looks like they must be short-staffed, as the folks behind the counter are moving at a breakneck pace, and with very serious expressions.

Still you wait patiently, and when you arrive at the counter the young man who greets you smiles widely, and the interaction is perfectly fine.

You remember that you have to shout to be heard over the music and din of the other customers, and speak up. You place your order, pay, tip well, and pick up your drip coffee.

Moving to the condiment station, it’s a train wreck – all of the milk dispensers are empty, one tipped over on its side. The trash is overflowing, and there is white sugar covering easily half of the surface. You decide to enjoy your coffee black and make your way back to the street.

This example is probably not so foreign. Despite receiving solid service from the company’s representative, the overall experience skews negative. A long line, a poorly written schedule, a failure to keep the shop tidy; these are small things but they add up to lackluster hospitality.

By the time you’ve tasted the coffee, you already have a bit of a bad time. Not due to any individual’s behavior exactly, but due to the sum total of small decisions made well before your arrival.

When we think of really outstanding examples of both service and hospitality, we usually think of high-end hotels or expensive restaurants; white tablecloths and Egyptian cotton sheets.These examples have guided hospitality and service very well for a very long time – largely through a focus on the way that representatives of a company interact with customers. This works very well for business that serve hundreds (sometimes thousands) of customers in a day.

Every business from a steakhouse to a social network presents a picture of hospitality to its customers. By virtue of being a customer you collect a sum of experiences with a company that impacts your view of that company and its products.

A hotel, restaurant, or cafe, gets (at the very least) one opportunity to provide excellent service. Often these businesses get more than one opportunity – the host at the front door, the bartender while you wait for your table, the sommelier, the waiter – excellent restaurants have many chances to balance out missteps in hospitality with outstanding service. In this way, investing heavily in those one-on-one encounters can pay dividends that outsize the investment.

For every high-end restaurant we have dozens of small cafes and pubs. Let’s say, conservatively, that traditional companies like this enjoy about one service experience per customer. That is, they have an opportunity to impact their product’s overall hospitality with outstanding service about one time per customer.

Consider WordPress.com – we receive only one support request for every four thousand blog posts published. For every four thousand uses of our product, we get only one opportunity to color that experience with outstanding service. I would imagine that for most software-as-a-service companies, that ratio is not far off.

If I was feeling bombastic, I’d say that for software companies, hospitality is four thousand times more important than service. Since these numbers are fuzzy, and I am nothing if not level headed and measured in my opining, we’ll instead settle on this:

For software companies, hospitality is three thousand, nine hundred and ninety nine times more important than service.

To delight our customers, we need to discard our traditional ideas of how service and hospitality operate, because we are navigating in a new and exciting space. Our traditional ideas will not lead us to success.

It’s time to find some new ideas – I’m excited. Are you?

This post originally appeared on Support Driven, a blog about hospitality on the internet

DVC Day 30: EL FIN

(This Post is part of my 30 day Data Visualization Challenge – you can follow along using the ‘challenge’ tag!)

Thoughts:

– Boom.
– Longer post-mortem in the works about both this challenge in particular and doing a thing every day for 30 days in general.
– Thanks for tuning in 🙂

Code:

> library(ggplot2)
> qplot(day, n, data=dh) + scale_y_continuous(limits=c(0,3)) + geom_smooth() + ylab("Number of Posts Published") + xlab("Day of the Challenge") + theme_bw()

DVC Day 29: Almost There!

(This Post is part of my 30 day Data Visualization Challenge – you can follow along using the ‘challenge’ tag!)

Here we have the final graph that I presented to the rest of my colleagues in discussing the difference between our chat durations with Paid customers vs. our Business customers.

Thoughts:

– This is more effective than the box-and-whisker graph because it illustrates that while Paid and Business chats may have roughly the same median duration, the breakdown of the chat duration field is not the same – note how the Business chats bump out on the longer end. Very interesting.
– Note also that the duration piece has been changed to a log scale – this is to handle some of those huge outliers.

Code:

&amp;gt; library(ggplot2)
&amp;gt; mydata = read.csv(“~/olark_april_2015.csv”)
> q = ggplot(mydata,aes(log(chat_duration))) 
> q + geom_density(aes(fill=factor(group_title, labels=c("Business","Paid")) , alpha=1/4)) + ylab("% of Total Chats")

DVC Day 28: Enhance!

(This Post is part of my 30 day Data Visualization Challenge – you can follow along using the ‘challenge’ tag!)

Following the highly smushed boxes of yesterday, my next step was to limit our y-axis only to the lower end, where the vast majority of our data points were:

Thoughts:
– Now we can see that our Business folks (on the left) and our larger cohort of all Paid users (on the right), have roughly the same median chat duration.
– In the interest of curiosity, though, it seems like this deserves more consideration, especially with the monster number of outliers. Box-and-whisker graphs are also not largely well understood, so bringing this before a broad audience wouldn’t work well if the goal is to communicate a difference (or lack of difference) in an effective way.

Code:

&gt; library(ggplot2)
&gt; mydata = read.csv(“~/olark_april_2015.csv”)
&gt; p = ggplot(mydata, aes(group_title, chat_duration)) 
> p + geom_boxplot() + scale_y_continuous(limits=c(0,5000))

DVC Day 27: Practical Applications

(This Post is part of my 30 day Data Visualization Challenge – you can follow along using the ‘challenge’ tag!)

As we finish out the 30 days, I’ll actually be using an example of work that I did to test a hypothesis at Automattic. We currently provide live chat support to two cohorts of our customers, the folks who purchase WordPress.com Business, and our customers who have purchased any upgrade at all (mostly domains and WordPress.com Premium). There has been a longstanding assumption that our live chats with Business customers were longer in duration – they have access to Ecommerce options, as well as no-cost access to our entire library of Premium Themes.

So, I ported our live chat data out of Olark and into R, and threw together a box plot:

Thoughts:
– If this looks wrong somehow, that’s because it is: our box is so small as to be flattened. All we really see are the massive upward outliers.
– This clearly does not do anything to help us decide which style of chat tends to be longer in duration – our Business folks are on the left here, and our Paid customers are on the right.
– Clearly the next step is figuring out how to change this display so we can see what those boxes look like in a zoomed-in view.

Code:

> library(ggplot2)
> mydata = read.csv(“~/olark_april_2015.csv”)
> p = ggplot(mydata, aes(group_title, chat_duration)) 
> p + geom_boxplot()

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