In this post, we are going to take a look at why Web Analytics is a better choice over Data Analytics. For some, Web Analytics and Data Analytics are used interchangeably, but for the sake of precision, Web Analytics deal with online data, while Data Analytics work with offline data. Just to be clear, this is not a post to discount the merits of Data Analytics. Data scientists are still sexy, and you can trust me on that. Every now and then, my wife would refer to me as a data scientist. When I corrected her (saying I’m not), one could see the disappointment in her eyes. She would then follow up with a hurtful comment, “Why are you not a data scientist? Can you be one?”. Sigh. Data Analytics continue to be an integral function in most companies, due to its ability to predict the future (forecasting) through statistical modelling. And at some point, Web Analytics has to depend on Data Analytics for such predictive needs. Then… why is Web Analytics better?

Why is Web Analytics better than Data Analytics?

As we all know, garbage in, garbage out. Data analytics do not generate its own data, but depend greatly on data from different sources. This can prove to be a huge issue if we are dealing with unreliable data sources.

3 areas where Web Analytics is better than Data Analytics:

1. Generate a new data source (web/online data)
2. Near real-time
3. Web data is truly representative of web users’ wants and needs

So… when I say Web Analytics is better, that is because, unlike Data Analytics which act more like an integration piece, Web Analytics open up a whole new universe and provide Data Analytics with the ultimate source of consumer truth.

1) New data source – Web data

Marketing in the past vs today

Marketing in the past was pure science. It involved an extremely deep understanding of human psychology and consumer behaviors. Toothpaste is a good example of how marketers give consumers a product they want and “need”. Marketing today is not that different from the past. Except for the fact that consumer preference changes rapidly. That, changes everything.

Time is of the essence

You can no longer rely on human brain juice to churn out an endless stream of ideas to retain the attention of your customers. A well-designed product takes years. Nobody ain’t got time for that. By the time it hits the shelf, consumers would have moved on to something else. We need something more concrete to guide our decisions. Something that can keep up with the ever-changing mind of our consumers. Something that truly represents the wants and needs of our consumers. We present to you, Web Data.

2) Web Analytics is near real-time

Struggle of offline data

Take a look at the data your company has. When were they collected and last updated? Take for example the insurance industry. We have tons of leads, captured from various sources and activities. We have tons of customer profiles, built from various surveys and assumptions. For hot leads, how long do you think they can stay warm? And for customer profiles, how likely is it for your customers to update you on their preference changes or life-stage transitions? In addition, it might take weeks or even months before data reaches the Data Analytics team. How relevant do you think those data will be at that point?

Online data to the rescue

In Web Analytics, data are collected as and when users interact with your website. In some tools (eg. Adobe Analytics), you can view the data in customized dashboards/reports, just a couple of hours after it has been captured. No need to jump through hoops, waiting for months to make sense out of your data. On top of that, with a proper one-time setup, you can capture a never-ending stream of online data. Compare that to collecting leads, arranging focus groups and promoting surveys, which are all proactive ways of collecting data, Web Analytics is quietly collecting user web behavorial data in the most passive manner ever.

How to setup Web Analytics

For those who are interested in finding out more on how to setup Web Analytics: Adobe Analytics – I won’t be linking to my own guide, as I am now focusing on the strategies and planning in my curent role, voiding me of any opportunity to access the tools involved. Having said that, in my Adobe Analytics guide, I talked about the pros and cons of using Adobe Analytics over its competitors. Google Analytics – Follow this guide of mine to get your engine started.

3) Web data represents consumers’ wants and needs

Focus group anyone? Or would you like to take a survey? How about making important business decisions based on high-level sales data? There is absolutely nothing wrong with gaining a perspective using any of the methods above, but just don’t get too attached.

Focus group

Focus group is misleading as it is difficult to get the right mix of people in the same room. Does a group of 30 represent the whole population? You be the judge.

Survey

There are a few types of survey:

• Involuntary
• Voluntary but not incentivized
• Voluntary and incentivized

Involuntary

You often come across involuntary surveys in the companies you work for or from the government. Take for example company engagement survey. How honest were you? Did the thought of getting caught leaving a bad review come across your mind? Were you afraid of getting penalized?

Voluntary but not incentivized

When is the last time you took such a survey? Do you usually just walk away, or pretend to be on the phone while holding it upside-down? For those of you who have taken the survey, was it because you had strong opinions about the topic?

Voluntary and incentivized

Everyone is guilty of taking surveys in exchange for a gift or something. Come on, just admit it. Knowing that you would be rewarded at the end of the survey, did you feel a sense of euphoria and ended up giving an unexpectedly good review? Upgrading a 3 stars to a 4? Anyone?

Sales data

Really? Nokia.

Data bias

Response bias are present in most data collection methods.

Response bias can be induced or caused by numerous factors, all relating to the idea that human subjects do not respond passively to stimuli, but rather actively integrate multiple sources of information to generate a response in a given situation. – https://doi.org/10.1037%2Fh0043424

And if that’s not bad enough, there’s a huge array of statistical bias resulting from Data Analysis.

Web analytics, nothing but the truth

User web behavior is voluntary and purpose-driven. Thanks to that user web behavior, unlike Data Analytics, Web Analytics do not suffer from response bias.

Web Analytics is quietly collecting user web behavorial data in the most passive manner ever.

Use yourself as the example. What drives you to visit a particular website? Take some time to think about it. Is there always a purpose, or are you the kind who can’t live without the almighty “I’m Feeling Lucky” option? The 4 main reasons why we visit a website:

• Entertainment
• Research materials
• Purchase
• Knowledge/Information

When you visit YouTube or Facebook, are you looking for entertainment? When you visit multiple product sites, are you looking for research materials on which best suits you? When you visit an Ecommerce site, are you looking to make a purchase? When you Google, are you looking for knowledge/information? By establishing that user web behavior is voluntary and purpose-driven, we can expect the data to be of higher quality and to be truly representative of users’ wants and needs. Hence, Web Analytics generate the most honest and updated form of data that is rarely found anywhere.

Conclusion…

As mentioned earlier, this article is in no way discrediting Data Analytics, but more of showcasing how Web Analytics can fill the data gap that has been lacking for the longest time. Rather than just being satisfied with data filled with statistical bias, seek to understand your customers’ intention better. Make your website more than just a pretty face in your company.