Avinash Kaushik is well known to web analytics aficionados as Google's analytics evangelist, for his book "Web Analytics An Hour a Day" and his Occam's Razor Blog, but I think his insights can help anyone involved aiming to improve their results from their web business - hence this post 'web business optimisation'.
So I was delighted that he agreed to this interview for "E-marketing Essentials". In a wide-ranging interview we cover everything from trends in analytics to practical tips to measure and improve online customer engagement and retention.
Before I get to the interview, here are some pointers to further insights from Avinash:
Q1. Web analytics visualisation. In 2007, I think we saw a step change in the power of web analytics tools to visualize, to slice and dice data and to better drive out insights.
What do you see as the next major step change in the next generation of web analytics tools?
A. Avinash Kaushik:
They are going to get a lot better about what you should look at.
Visualization is great and tables are good but what is killing Analysts right now is their ability to figure out, from megabytes and megabytes of data what is actually worth looking at. Most tools still simply spew data out relying on the Analyst (or the Data Consumer) to figure thing out. That is a bad strategy, yet most tools follow it.
[Dave Chaffey: I remember commenting on the need for more "intelligence" and applying Six Sigma style statistics analysis in E-metrics in 2005 - essentially which pages / containers / referrers should I focus on which are going to give me the biggest uplift because they're underperforming. But you have put it better!]
Visualisation has gotten a lot better - but I'm not seeing any form of intelligent recommendations - it's very tricky and the web analytics companies are too busy copying other functionality! It's good you mention that clickstracks features though.
Recently I talked about one specific example on my blog about how tools are getting smarter, Actionable Web Analytics: Focus on What's Changed.
Using ClickTracks as an example I demonstrated how to look at only the data that has shifted in importance by a statistically significant amount. Your top twenty of anything never changes, but using this type of report, What's Changed, you can look at just the data that really matters. Now it is easier to take action.
I expect all tools to get much much better at applying advanced mathematics and statistics to help their users identify where to focus their attention.
The other thing I would highlight as a evolution for web analytics tools is that they are going to do a lot more than page view reporting on your site. I don't mean doing clever things like Event Logging to measure Web 2.0 experiences, that is cool of course. I am referring to their ability to measure content no matter how it is distributed (widgets, rss, etc) and where it is consumed (websites, feed readers, mobile phones, your home refrigerator or washing machine!).
Q2. Online customer engagement. In 2008, frenzied discussion of how to apply the concept of online customer engagement has continued on your blog and many others. Some have criticised it as an abstract concept that can't be readily applied in the real world. Can you give some practical examples of how a site owner using Google Analytics could apply the engagement concept to get better results.
A. Avinash Kaushik:
Engagement is a nice goal to have. Create sites that customers will find engaging and they'll stick around or come back again or maybe do business with you.
But that term has been bastardized to a point where it means nothing anymore (or everything to everyone) and is often used as an excuse to know do the hard work of figuring out what the real outcomes of the site are for the company and the website visitors.
My encouragement to website owners is to be initially sceptical when someone is trying to pawn off "engagement" on them and ask the tough question: "What do you really mean by engagement and how does it specifically apply to my business?"
Secondly I encourage people to realize that on the most glorious spring day with the birds are chirping the right song, web analytics tools can measure the Degree of engagement but they fall quite a bit short of measuring the Kind of engagement. So they can report that Visitors saw nineteen pages on your site (Degree) but they can't tell you if that was because the Visitors were frustrated with your crappy navigation or thrilled with your content (Kind).
Hopefully that helps set the context.
People use Google Analytics (or other tools) to easily measure various elements of the Degree of engagement. Perhaps the simplest example is using the bounce rate for the core landing pages to identify pages that won't even entice Visitors to make a one click (!).
In two clicks you can also get Loyalty (recency), Frequency, Length of Visit and Depth of Visit to get a solid feel for if Visitors are making repeat visits to the site of if they do it more frequently and marry that up with content consumption. Doing this by looking at trends over time is a fantastic way to understand if the site is delivering value for you customers.
For many "social" websites website owners also measure the number of people who sign up and then contribute by writing reviews or comments etc.
All really good examples of 1) measuring the degree of engagement and 2) not confusing the real metric being measured by calling it engagement.
Q3. Customer retention metrics. I've always believed that web analytics tools are much better at analysing acquisition (e.g. through Traffic Sources), conversion and content popularity than they are at improving customer retention.
Which are the best measures or reports within Google Analytics you could point us to which help marketers understand how well an E-commerce site is performing for retention.
A. Avinash Kaushik:
I touched on some of the obvious ones above, the Loyalty metrics (specifically Recency and Frequency). They immediately tell you if you are acquiring traffic that comes back again and again, and since GA will tell you Recency by going as far back in history as you have data that is a great way to know when customers come back (and perhaps also understand why).
The other obvious thing to do for shorter time periods is to look at the trends for % of New Visits, especially by the sources of your traffic.
Some retailers want to do retention analysis by looking at repeat purchases. For this Google Analytics, like pretty much every tool out there, provides a very strong complement of ecommerce reports that allow you to segment the data by the types of purchasers (new or returning) which will help you understand their purchase behaviour and by applying filters to your data you can dig deeper into sources of traffics, trends in number of visits, content consumed etc.
This in conjunction with using even simple onexit website surveys can give you a great picture of what is happening on your website and where you are missing the boat.
Q4. Conversion optimisation. Today there is a lot more talk in large organisations about using techniques like AB or multivariate testing being to improve results from landing pages for example.
Are you seeing these approaches being used in smaller companies? How would you advise a small business owner to set out on this journey?
A. Avinash Kaushik:
This might surprise you but I am seeing a lot more traction in using optimization techniques with smaller companies than with larger companies. There are a couple of interesting reasons:
1) A/B or MVT is now free with tools like Google Website Optimizer, so you can dispense with RFP's and all that "stuff" and just go try the tool.
2) Smaller companies are much more willing to try new things and have less politics and entrenched opinions (and HiPPO's) that are hard to overcome.
This is of course a tad bit sad because given the traffic and the sheer opportunities it really is a crime for larger companies to leave so much more revenue on the table, or the chance to optimize the customer experience which will improve loyalty and satisfaction.
My recommendations for any company is perhaps similar, here are a couple:
1) Start with A/B. In my experience starting simple will ensure that you will get of the gates fast and be able to start the critical process of cultural shift with easily understandable experiments. Then you can move to the 1.8 billion combination page test.
2) For the highest impact try dramatic differences in your test versions. Trying shades of blue might sound interesting but the test might take a very very long time to provide you with statistically significant differences. But try a page will only text and one with text and images might get you on the path to understanding your customers faster.
3) Run a report for your top 25 landing pages (entry pages) on your site, then look at the bounce rates for each of them. Pick three with the highest bounce rates, these are the pages letting you down the most. You'll win big by testing these first.
4) Have an active "customer listening channel".
Remote usability testing, market research, customer call centres or surveys (even a free excellent solution like 4Q, which I helped create with iPerceptions). The best focus points about what is not working on your site come from your customers (sadly not you) and likewise the greatest ideas on how to improve your site (and hence test) also come from your customers. Listen and you will prosper.
Two helpful links:
Q5. The future! And finally, a simple, but challenging question. What excites you most about potential developments in web analytics into the future
A. Avinash Kaushik:
The thing that excites me most is that no one has a clue where this is all headed. We have no idea what "web analytics" will look like in five years. That is exciting because there is a ton of change and growth to come and being a part of helping play a small part in that change is simply fantastic.
There are new data collection methods to come, there are new ways of doing superior analysis of data, there is so much more we could do with Artificial Intelligence in optimizing customer experiences, there are opportunities to bridge the various islands of data (on the web or outside) to create something amazing, there are". it goes on and on.
Opportunity is, I suppose, what I find most exciting about the future of web analytics.
By Dave Chaffey
Digital strategist Dr Dave Chaffey is co-founder and Content Director of online marketing training platform and publisher Smart Insights. 'Dr Dave' is known for his strategic, but practical, data-driven advice. He has trained and consulted with many business of all sizes in most sectors. These include large international B2B and B2C brands including 3M, BP, Barclaycard, Dell, Confused.com, HSBC, Mercedes-Benz, Microsoft, M&G Investment, Rentokil Initial, O2, Royal Canin (Mars Group) plus many smaller businesses. Dave is editor of the templates, guides and courses in our digital marketing resource library used by our Business members to plan, manage and optimize their marketing. Free members can access our free sample templates here. Dave is also keynote speaker, trainer and consultant who is author of 5 bestselling books on digital marketing including Digital Marketing Excellence and Digital Marketing: Strategy, Implementation and Practice. In 2004 he was recognised by the Chartered Institute of Marketing as one of 50 marketing ‘gurus’ worldwide who have helped shape the future of marketing. My personal site, DaveChaffey.com, lists my latest Digital marketing and E-commerce books and support materials including a digital marketing glossary. Please connect on LinkedIn to receive updates or ask me a question.
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