web analytics.pdf

7/27/2019 web analytics.pdf

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Web Analytics Process -Measurement & Optimization

This article is a review of a model proposed by me and Avinash Kaushik in

a two-part paper we wrote for the Search Engine Marketing Journal on2009 and 2010. If you wish to read the original papers, you can downloadthem on pdf: Part I and Part II.

Below I propose a Web Analytics Process framework that shows the stepsand the flow that should be present when implementing Web Analytics inorganizations.

The objective of Web Analytics is to first and foremost improve theexperience of online customers, it is not a technology to produce

reports; it is a virtuous cycle for website optimization. The processproposed above will enable companies to create a powerful data-drivenculture, to measure customer interaction with the website, to segmentvisitors and understand how each group behave, to analyze campaignReturn On Investment (ROI), and to optimize the website in order toincrease profitability.


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1. Start with a clear definition of business goals2. Build a set of Key Performance Indicators to track goal achievement3. Collect accurate and complete data4. Analyze Data to extract insights5. Test Alternatives based on assumptions learned from data analysis.6. Implement insights based on either data analysis or website testing

Following, I discuss each step in detail.

Defining Website Goals

This is the first step on any website optimization initiative, you mustunderstand your goals in order to improve your website. The answer tothe following question is critical in defining a websites goals: whydoes your website exist? An anecdote that represents the importanceof objectives can be found on Alice's Adventures in Wonderland, when shemeets the Cheshire cat in a crossroad:

"Would you tell me, please, which way I ought to go from here?"

"That dependes a good deal on where you want to get to" said the Cat."I don't much care where-" said Alice.


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"Then it doesn't matter which way you go," said the Cat."-so long as I get somewhere," Alice added as an explanation."Oh, you're sure to do that," said the Cat, "if you only walk long enough."

Each website will have its own unique objectives. For some, the objective

will be to increase pages viewed in order to sell more advertising (mediasites); for others, the objective will be to decrease pages viewed becausethey want their visitors to find answers (support sites). For some, theobjective will be to buy as fast as possible (increase revenues); for others,the objective will be to sell only if the product fits the needs of thecustomer (decrease products returns). But, as Jim Sterne said in hisSocial Media Metrics book:

"Your focus should always be on either increasing revenue, lowering costs,or improving customer satisfaction. Doing all three would be just fine."

As we can see in the Web Analytics Process proposed above, theobjectives are absolutely necessary in order to start the process, onlyafter they are defined we can proceed to build the Key PerformanceIndicators. It is also very important to constantly revisit the goals in thelight of website analyses and optimization to fine tune them.

Building Key Performance Indicators (KPIs)

In order to measure goal achievement, the marketer should create KeyPerformance Indicators (KPIs) to understand whether the website is goingup or down. KPIs must be like a good work of art: it wakes you up.

Sometimes it makes you happy and sometimes it makes you sad;but it should never leave you untouched, because if that is thecase you are not using the right KPIs.

And good works of art are rare, you have just a few truly touching worksof art per museum; and not every work of art touches the same people.The same applies to KPIs, there are just a few truly good KPIs percompany, and each person (or hierarchy level) will be interested in adifferent set of KPIs, the one that relate to their day-to-day activities:upper-management is touched by the overall achievement of the websitesgoals; mid-management is touched by campaign and site optimizationresults; and analysts are touched by every single metric on the world!


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Good KPIs should contain four attributes:

1. Un-complex: decisions in companies are made by people in severaldepartments with different backgrounds. If the web analyst is theonly person that understands the KPIs, it is unlikely that decision

makers across the company will use them.2. Relevant: as we mention above, each company has its unique

objectives, therefore they should also have their own set of KPIs tomeasure improvement.

3. Timely: great metrics must be provided promptly so that decisionmakers can make timely decisions. Even excellent KPIs are uselessif it takes a month to get information when your industry changesevery week.

4. Instantly useful: it is vital to understand quickly what the KPI is,so that one can find the first blush of insights as soon as s/he look

at it.

Following the definition of the website objectives and the metricsthat will be used to measure them, we will be in a much bettercondition to collect the data that will be needed.

Collecting Website Data

When a company starts to collect website data (or reviews its datacollection), two questions should be asked:

o Is my data accurate? If your data is not accurate, it is likebuilding an empire in the sand, your foundations can beshaken too easily

o Am I collecting all the data that I need? If data is notcollected, you will not be able to understand customerbehavior properly.

I will not go into data collection methods (Web Logs, JavaScriptTagging, Web Beacons and Packet Sniffing) as this has beenextensively documented. You can find an explanation of each of themain four data collection methods currently used on Part I of thearticle I wrote with Avinash.


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Analyzing Data

Following I provide a few ideas that can help on the conversion ofdata into insights, an essential step when optimizing any website.

o In the same way that a 100m freestyle swimmer won'tcompare his time to a 100m sprint runner, a website can't

compare itself to different websites. The best way toproceed is to trend your metrics over time, to understand ifthe website is improving or not, like a swimmer is constantlystruggling to break his own record.

o There is no absolute truth when it comes toengagement, a high number of pageviews can be a goodsign (for a content website), a bad sign (for support websites)or no sign at all (for Flash and AJAX websites). However, timeis probably a good indicator of visitor engagement.

o Bounce rates measure the quality of traffic you areattracting, if visitors are not interacting with your site at all,you are doing something wrong: either attracting the wrongaudience or not leaving up to you promises.

o Keywords driving traffic to your site tell a lot about yourvisitors intent, the visitors are telling why they are comingto the website, and keywords with high bounce rates showwhere the intent is not met. It could be that the website isranked for the wrong keywords. It could be that the pagesthese visitors are landing on dont have the right calls toaction.

o Internal site search tells you exactly what visitors wantfrom you, so look for which searches provide engagingresults (look at 'time on site after search' metric), and whichsearch results are driving them away (look at '% searchexits'). Maybe visitors are looking for a product you currentlydo not sell? Maybe your search is badly configured and theyget 0 results for an important product search? Google hasproved that search is really important for people!

o Data Visualization is like a car's windshield wiper on therain, it enables the driver to see more clearly. You can chosefrom an endless pool of graphs and tools to visualize numbers;if you like to read numbers, good for you, but don't assume


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everyone does, in the majority of cases people just hatenumbers. So use line graphs, pie charts, density reports,heatmaps...

Important to note that data analysis can lead to three different

outcomes (as seen in the Web Analytics Process chart above):

9. To a discover of immediate insights for implementation suchas website bugs or pages that do not convert for an obviousreason.

10. To a hypotheses regarding a low converting customertouch point - this will lead to a split test.

11. To an understanding of a data collection failure - eitherimportant data can be missing or inaccurate.

Testing Alternatives

In the spirit of the African proverb above, it is very unwise tochange a website completely without first trying with the tip of yourfingers. When we test, we lower the risk of a loss in revenue due toa poor new design and we bring science to the decision makingprocess in the organization. But the most interesting outcomeabout experimenting is not the final result; it is the learningexperience about the customer, a chance to understand whatthey like and dislike, which ultimately will lead to more orless conversions.

The web analyst must try endlessly and learn to be wrong quickly,learn to test everything and understand that the customer shouldchoose, not the designer or the website manager. Experimentingand testing empowers an idea democracy, meaning that ideas canbe created by anyone in the organization, and the customers (themarket) will choose the best one; the winner is scientifically clear.

I have described the Website Testing Process before, and theadvantages of A/B and Multivariate Testing and I also discussed thechoice between testing and analysis, but here are a few tips when itcomes to website testing:


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12. Testing is not limited to landing pages: It should beimplemented across the website, wherever visitors areabandoning the website and wherever the website is leavingmoney on the table.

13. Try your tool (and your skills) with a smallexperiment in a page that does not require the CEO approvalto change. Sometimes it is wise to start small and then grow.Once you are familiar with your tool, try a test in an importantpage but for a small (or less profitable) segment. Then headfor the jackpot!

14. Measure multiple goals: while you improve primaryconversions you might be decreasing registrations ornewsletter signups which might have a negative impact in thelong run.

15. Test for different segments: as I have described onTest Segmentation for Higher Conversion Rates, segmentssuch as GEO location and operating systems (Hi, I am a Mac,and I am a PC) can have completely different behaviors, sothe tests should also be segmented in order to understandthose differences.

Implementation

Brazilians have a popular saying that can be translated as "to die onthe beach". It is used for situations where you are almost gettingwhat you want (the sea) and then you lose it. A Web Analyst thatovercomes all previous steps successfully and then gets

stuck on the implementation of insights is dying on thebeach. No implementation is a synonym of no Web Analytics. Beloware some tips that can help you overcome implementationbottlenecks:

o Get C level support: this will be essential if you come to apoint where organization priorities must be set and resourcesallocated.

o Start small: as I mentioned above, starting small helpssetting the expectations, people understand the tools andwhat is required from them.


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o Be friendly: being a nice person and smiling always help yougetting things done, that's the way human nature works.

Conclusion

The big question is: how can a website manager convince surfers tobuy a product or read an article? And the answer is: look at the dataand understand what is happening in the website, listen tocustomers voices and optimize the website to better serve them;after all they are the reason for the websites existence. Customersshould tell us what to do, not consultants, friends or feelings;data and online surveys are the place to look for customersneeds.

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