The Rise and Fall of Web Analytics (and the rise of CX)
Back in the early 2000s when you went to industry conferences such as SES, shop.org, eTail, ad:tech, and of course the eMetrics Marketing Optimization Summit, the common refrain was: “web analytics — everyone should really be using them.”
This was the time of dawn for Web analytics in terms of its use for marketing and eCommerce optimization. Back then web analytics were still a nascent niche, understood by relatively few, and used productively by even fewer.
Early on, web analytics seemed like a wondrous opportunity to learn almost everything you might ever want to know about your anonymous website visitors. So practitioners and industry analysts were telling each other that they might become the most sought after people in their companies because they know the customer better than anybody else.
So it seemed in the heydays.
Over the years, companies learned how to invest in the right people, process, and technologies to take advantage of web analytics. Today they are mainstream and nobody would doubt that they are a must-have and must-master type of analytics.
But meanwhile web analytics had to get off its high horse.
They are now only one of multiple digital intelligence sources needed in order to navigate an online business towards success.
Web analytics are and will always be a critical controlling and management tool, especially for top level numbers. But by themselves they leave so many gaps of insight about customer behavior and interests that other, newer, more nimble and more granular analytical solutions have sprung up to fill the blanks.
The Rise of CX Analytics Providing Customer Insights that Web Analytics Can’t Provide Anymore
One such example on the rise today — and in mission critical use with the early majority of adopters — are digital customer experience (CX) analytics. CX analytics show visitors’ actual behavior and experiences on your website including their in-page interactions.
They show a-ha insights that would be hard or impossible to answer with web analytics.
Today’s experience management platforms such as ClickTale do this by adding a range of visual layers to web analytics through many types of heatmaps, replays, form analytics, and other conversion optimization insights. They provide intuitive ways to gain insight from these in integration with the rest of your ecosystem of digital analytic solutions including web analytics but also VoC, A/B testing, etc.
You see everything from where the mouse is moving within pages to how specific visitor segments are interacting with drop-down menus, accordions, shopping carts and other dynamic content.
All stuff that you don’t see from traditional web analytics unless you invested a prohibitive amount of custom tagging.
Typical CX Analytics Use Cases Filling Gaps in Web Analytics
Here are a range of business use cases where CX Analytics are mission critical for customer insights and conversion rate optimization (CRO).
WHY ARE VISITORS LEAKING FROM THE CHECKOUT PROCESS?
- Are there common behavior patterns and issues that cause potential buyers / registrants to drop out?
- Are there friction points or distractions in our checkout process that we can eliminate?
WHY ARE VISITORS STRUGGLING WITH OUR ONLINE FORMS?
- Which form fields or error messages are tripping potential customers up?
- What is causing hesitation to enter text or submit the form?
- Should we make our forms shorter? More clear? How?
WHAT CONTENT IS BEST AT ENGAGING POTENTIAL CUSTOMERS?
- Are buyers vs. non-buyers scrolling down on our long pages and engaging with our many content sections under the fold? For example, are buyers scrolling down our entire home page to review our offering in its entirety?
- What content within pages is actually perceived as valuable by potential customers? For example, if we have a picture vs. a video at the top of a page, how or where does that focus customers’ attention?
HOW MUCH CONTENT IS BEST FOR PERSUASION?
- How much information is the right amount?
- Not too much to put customers into analysis paralysis when deciding to register or purchase something.
- Not too little to prompt buyers to research elsewhere
HOW CAN WE MAKE OUR SITE EASIER TO NAVIGATE?
- How effective are our drop down menus for helping visitors find what they are looking for?
- Are visitors engaging with the menus? Which ones do they browse vs. ignore?
- Should we simplify or expand our menus?
- Should we better highlight certain options that we want more visitors to notice?
IS OUR RESPONSIVE DESIGN PROVIDING EXCELLENT EXPERIENCES FOR DESKTOP VS. TABLET vs. SMARTPHONE VISITORS?
With responsive design you essentially have three websites in one. Site layout and content can change dramatically requiring dedicated analysis and optimization for each responsive design and breakpoint.
- What are our phone, tablet, and desktop visitors experiencing on our site?
- How can we make their experiences better?
- What’s the best placement for links, banners, offers depending on each device and responsive breakpoint?
HOW CAN MAKE OUR SEARCH FUNCTION LESS FRUSTRATING?
- How are visitors using site search for products, articles, or customer support tips?
- Are they using the search refiners to narrow down their search results? Which ones?
- How can we make searching a better and more successful experience in order to increase the middle of our conversion funnel?
HOW CAN WE OPTIMIZE OUR WEB APP FOR EASE OF USE?
Today’s ever more dynamic websites often include single page applications where all the interaction happens within a single URL yet you have all kinds of modal windows to interact with or quick product views etc. It’s like a desktop experience within the browser.
- Which logical screens are being noticed vs. ignored?
- How can we make it easier for visitors to navigate the application and its functionality?
- How can we increase adoption and productivity by providing greatest ease of use?
HOW MANY MARKETING CAMPAIGN VISITORS ARE BOUNCING?
The famous bounce metric in web analytics makes little sense anymore. For example think of two visitors who both had a single page view only in their session, ie a classic bounce. But one scrolled down extensively and read the entire page in detail in effect viewing multiple logical pages, eg your entire elevator pitch on the home page. That wasn’t a bounce at all.
- How many visitors from our marketing campaigns are engaging vs. bouncing?
- What experiences work for which segments?
- Why aren’t more campaign visitors converting?
HOW ARE DIFFERENT VISITOR SEGMENTS ENGAGING DIFFERENTLY WITH OUR OFFERING?
- Is there any difference between the genders how they browse and perceive our products, checkout process, or content, e.g. price conscious vs. brand focused?
- Any difference by demographics?
- Tenured vs. new customers?
WHY ARE SO MANY CUSTOMER SERVICE USERS STILL CALLING THE CALL CENTER?
- How can we avoid unnecessary customer service calls by making our self-service portal easier to use?
- Why are customers struggling (for example think of banking bill pay, account transactions, or ability to find self-service options)?
WHICH CUSTOMERS SHOULD CALL THE CALL CENTER TO AVOID FRUSTRATION AND LOST OPPORTUNITIES?
- Which of our self-service functions are so complex that we would be better off pro-actively encouraging customers to get help via live chat or call center?
It’s easy to see how web analytics by itself is way outside its element for answering these questions because it is focused on page views and how visitors go from page to page. But it doesnt’t answer what’s happening within the pages.
In theory you could custom tag granular detail also with your web analytics solution but that would be a prohibitive effort. In contrast, CX analytics are designed to capture the granular behavior out of the box.
But for every house-hold name enterprise that is using CX Analytics religiously as part of its digital intelligence eco-system today, there are others like them that still remain ignorant.
In my view of the adoption phase of CX Analytics, we are just now crossing the chasm from early adopters to early majority.
As I wrote earlier in “the Fourth Digital Analytic revolution is on!“, my prediction is that we’ll reach full majority adoption in the next 2-3 years because the business case is obvious.
And the more dynamic and more mobile the web becomes the greater the gaps in web analytics and the greater the pressures.