Fixing the Google Analytics API (v3) examples

I'm currently working on consuming data from the Google Analytics API for one of the data sources we're using at Eysys.

So far, so normal. But what was strange was the poor state of the Google Analytics API documentation. I don't think I've ever seen one of their APIs be documented so poorly - missing source code, typos in example source code provided, a real rambling tone to the docs pointing off to different areas (a lot of this is to do with the OAuth 2.0 hoops you have to jump through before even starting to pull down the data you want to analyse).

I also couldn't believe how many dependencies the API has - I ended up with 29 jar files in my Eclipse project's lib folder! Surely this could all be a lot leaner, meaner and easier - it's just an API returning JSON data at the end of the day..

Anyway, if you're interested in getting up and running with the API examples, here's what to fix.

First of all, there is actually missing source code in the distro itself (or at least the one I used - google-api-services-analytics-v3-rev10-1.7.2-beta.zip), so you need to get LocalServerReceiver, OAuth2Native and VerificationCodeReceiver directly from the Google code repo.

LocalServerReceiver uses an old version of Jetty, so we need to migrate it to use the latest Jetty (I used 8.1.4.v20120524) which now has an org.eclipse.* package structure. So we need to update the imports as follows:

import org.eclipse.jetty.server.Connector;
import org.eclipse.jetty.server.Request;
import org.eclipse.jetty.server.Server;
import org.eclipse.jetty.server.handler.AbstractHandler;

Refreshing Jetty will also necessitate upgrading the handle(..) method to fit in with the new signature, as follows:

@Override
public void handle(String target, Request arg1,
HttpServletRequest request, HttpServletResponse response)
throws IOException, ServletException {
if (!CALLBACK_PATH.equals(target)) {
return;
}
writeLandingHtml(response);
response.flushBuffer();
((Request) request).setHandled(true);
String error = request.getParameter("error");
if (error != null) {
System.out.println("Authorization failed. Error=" + error);
System.out.println("Quitting.");
System.exit(1);
}
code = request.getParameter("code");
synchronized (LocalServerReceiver.this) {
LocalServerReceiver.this.notify();
}
}


There's also a small mod to be made in getRedirectUri(), change this line:

server.addHandler(new CallbackHandler());

to this instead:

server.setHandler(new CallbackHandler());


The logic of this class also seems pretty flawed to me - generating a random port for the redirect URI that OAuth calls back to at the end of authentication every time it's run, which by definition you won't be able to put into the APIs console. So I commented out the getUnusedPort() method and simply hard-coded one.

And after these mods, hey presto it works! :-)

Ecommerce: online conversion - simple model and toolset

Readers of this blog can wax lyrical on how to build a great B2C ecommerce site - either in JEE or .NET. First we get the technology stack right, then frameworks using that technology stack, comprehensive functional and technical specs, testing plans, coding standards + reviews with daily scrum meetings, hardware / cloud estimation and then load / penetration testing - this is bread and butter to the software architect.

What a lot of software architects don't understand (or underestimate) is what needs to happen to their site after it goes live. After the go-live of a B2C ecommerce site, a whole other team (which is fairly non-technical) takes it over. This team is really exercised by and focused on three core goals:

1. Get qualified visitors to the site as cost-effectively as possible

2. Enable those visitors to find the product they want quickly and easily

3. Convert the visitor into a customer - convince them to buy on your site

These goals are completely measurable in monetary terms, and hence you will find senior management taking a serious interest in them as well.

I work in leisure travel, and there are some very specific nuances to achieving these goals in my industry sector (every industry sector will have their own nuances). But there is also a generic model to be found and some very useful (and free!) tools that you can use to put the model in place.

Turns out the model is pretty simple. Essentially it consists of three components:

1. Analytics - where we measure what's happening on our target site - how is the user interacting with the site and can we infer what they do and don't like based on measuring and studying those interactions

2. Hypothesis testing (aka A/B and / or multivariate testing) - Analytics will give us lots of data to generate ideas on how to improve interactions, therefore we need a mechanism to test out hypotheses in a semi-automated way (if I change X, I bet the conversion rate will increase by Y%)

3. Efficient prospect capture - we want the best native SEO score possible on all of the search engines and when we spend money on ad campaigns, we want the best return for that investment.

So that's the high-level model - it's pretty simple.

Many companies (and especially Google), make an awful lot of money around online ecommerce. And that's where the "free!" I noted above comes in. It makes sense for Google to give away the tools enabling Analytics (1) and Hypothesis testing (2) for free, as they make so much revenue on selling ad campaigns in Efficient Prospect Capture (3). Unkind souls might claim that if you spend any kind of money with Google AdWords at all, then you're not really getting (1) or (2) for free, but you won't find a nefarious cheap shot like that on this blog.

Let's look at how we can implement the model then:

1. Analytics - use Google Analytics. Brian Clifton's book is an excellent treatise on the application, and the online training videos are of a high standard as well. It's well worth having a couple of developers on your team get Analytics certified to understand what the tool can do - it really is very powerful

2. Hypothesis (A/B, multivariate) testing - use Google Website Optimizer. There's less information about this tool, I guess because it's a bit simpler than Analytics, but a good overview is available. Being able to change content and see the impact on the fly is a key part of the model - that's why we use a CMS like Umbraco!

3. Efficient prospect capture - SEO, SEO and more SEO. The Art of SEO is a great read. My opinion here is that as long as you're doing a great job on your own SEO, you should begrudge a search engine every penny. By using tagging in conjunction with Google Analytics (make sure you associate your AdWords account with your Analytics account to get all this done for you automagically), you can continually check that your ROI on ad campaigns is worth the spend, and stop buying terms that don't make money.

And that's pretty much it. A three-component generic model for online ecommerce, followed by the simplest (with zero cost) way to implement that model for your B2C site. I intimated that each industry sector has its own quirks and foibles above and beyond this base model, and I'll focus on the leisure travel industry in more detail in a future post or two. For now, enjoy!