The Sweetwater Model: How to Actually Win On Expertise and Friendliness

An Interesting marketing insight backed by a real investment in customer service

No one, incredibly, seems to have written the Harvard Business Review (HBR) case study on Sweetwater and their business model. I’ll just have to do a poor man’s version here, because I think something’s going on at Sweetwater, one of the internet’s largest online music retailers. Something that seems to be working very well.

The Challenge

Every online retailer tries to focus on customer service. They have to. In an online shopping world, especially one dominated by Amazon, you need to differentiate your service from the myriad others that carry the same products. You may try to differentiate on price, but retailers like Amazon and have such massive economies of scale that you will be racing to the bottom with an expert free diver.

So you differentiate on “heart” or on personality or especially-good customer service. And these are already tough enough to win in the click economy, where shoppers are simply putting the objects of their desire into the giant search bar and combing results for the best prices, the fastest most frictionless transactions (a giant search bar where Google can privilege its own offerings and those of its affiliates!). But they get even tougher when they’re the only differentiations left to the thousands of retailers who aren’t giant, who aren’t striking deals with the postal service or launching space programs: Everyone is doing customer service! Everyone is doing long, risk-free trials and free fast returns! There are tons of now-familiar service-first plays in online. So how do you differentiate in customer service if everyone is doing it?

The Insight

This is where Sweetwater is winning. For one thing, you need to actually have excellent customer service, and Sweetwater does. In my limited actual direct experience, the Sweetwater sales technicians really know what they’re doing, are musicians, have real advice. And their sales process also makes it easy and natural to chat with or in some other way get advice from these technicians. This is already quite a lot.

Still, in the new normal of giant e-tailers and buy-now buttons, you simply can’t show enough prospective customers that you mean it unless you market this successfully And this is where Sweetwater seems to have thought of something unique that gets their service noticed:

Product reviews that come from the Sweetwater almost invariably praise not only the product but also the Sweetwater technician, usually by name, who made the process so easy and the product so perfect. Product reviews! The following is completely typical:

review of a pedal

This is genius. Someone, maybe the founder Chuck Serack, must have decided that the virtuous circle in all this is:

  • Customers like me are searching as often as not for product reviews before we actually go to buy.
  • Search engines are syndicating more product content into the search results, including reviews, where other characteristics of the buying experience are fungible–similar price, similar one-clicks, simple galleries, similar shipping.
  • Customers come to identify Sweetwater as a site where technicians are particularly helpful, actual, praised. This is the differentiation.

I’m not sure how they actually encouraged this behavior among product reviewers. Maybe it was primed; maybe it’s self-reinforcing; maybe they paid or discounted. But the content and tone of reviews at Sweetwater–about the products–are markedly different, like the sales technician is your new best friend of a guy you’ll jam with next week. Take a look.

The Walk

You couldn’t fake the friendliness and expertise for long and have this work, of course; reviewers are ruthless. But as part of a larger, authentic dedication to customer service (“The Sweetwater Difference“) this is a great move into a novel pre-sales area, online customers reviews, that has given them a marketing edge over other music retailers online.

But Show Me *How* Jupyter is the New Excel

These are the slides, notes, and the resulting video from a presentation I gave at TekMountain on Tuesday, September 17th, inspired by the article mentioned below

I read this great article just a few weeks ago called “Jupyter is the New Excel“. I loved it, and was provoked by its premise, and wrote to the author to tell her so. This dominant and for many users intimidating part of the data science toolchain, called a Jupyter notebook, could be used for more everyday tasks. You didn’t have to do data science per se with notebooks, didn’t have to, like, crunch big data, worry about data storage, care what generalized least squares were. Jupyter was easy and useful enough to use for front office tasks, for fantasy football, dinner party invites, what have you. You could fool around with it!

I work at IBM, where I fool around with data science as a rank amateur, and I took the article as a jumping off point: Yes, how? How would Jupyter replace Excel exactly? How could you use Jupyter for your email marketing and fantasy football, for your real estate office spreadsheets?

So I thought I’d create this tech talk, a presentation where we can step through examples of everyday data crunching, the things that many of do now in Excel, see if the article’s premise checks out.

My goal is to introduce Jupyter notebooks very, very briefly, get right into just a few super-practical, everyday tasks, to not talk about data science, to welcome and un-intimidate. If I’m successful, I’ll persuade you that Jupyter notebooks are no more complicated to use than Excel, might work better for some things, can be the kind of ready-to-hand tool that spreadsheets are for many now. This might even be a gateway drug to do some better integrations of your data, which is where Jupyter starts to really outpace all these separate, versioned, weirdo macro-laden spreadsheets, or even be a useful starting point for some data-sciencing on your own :-)

Resources for learning more

Jupyter and Python and a lot of the premier data science tools are open source, which means there are a TON of resources out there for learning, trying. Here are a few good ones, focusing again not on the ocean of data science but on Jupyter notebooks:

Brython, browser-based Python

And if like a new lover or a Taylor Swiftie or a vegan you need your favorite programming language to really be everywhere, there’s Brython, which allows you to put Python code in <script> tags, where it talks to the DOM in a mostly Pythonic way, and have that code translated on the fly into JavaScript and sent to the browser.

Awkward in conception, probably pointless in production, it’s straight forward in execution. Import brython.js, call brython() on load, and you can do stuff like this

from browser import document, alert
def echo(ev):
    alert("Hello {} !".format(document["zone"].value))

document["test"].bind("click", echo)

The gallery shows some more useful things, like doing Ajax requests in Brython, sorting tabular data

But then I don’t know,…In the richer examples, like ones with decorators binding Brython functions to events, it starts to look a little more like a framework, like Flask or something. 

@bind("#get_test", "click")
def get(ev):
             data={"foo": 34})

Since Taylor Swift does country tunes, pop, politics, and emo, maybe it is time to throw out all my other music :-)