3 Ways To Bridge The Gap Between Adobe Analytics and Voice of the Customer
by Duff Anderson, on Mar 15, 2017
In today’s online environment, where you can leverage web analytics solutions like Adobe® Analytics to track how your visitors interact and navigate your website, there is one vital thing that these solutions can’t do: Get inside the mind of your visitors.
Every one of your website visitors is unique. They each have their own thought process and their own way of achieving what they want to do. Naturally, it’s virtually impossible to decipher all the decisions, motivations and obstacles that led them to navigate your website the way they did.
So, when you are analyzing your Adobe® Analytics data in search of ways to optimize your website’s performance, you’ll need to assume a lot of things about your visitors.
At the end of the day, the only way to really know what your visitors are thinking is by asking them directly. This is where digital Voice of the Customer (VoC) comes in.
Individually, digital VoC data and Adobe® Analytics data provide a wealth of insights about your website visitors. But by injecting your VoC data into Adobe® Analytics and bringing these two datasets together, you can dig even further into your visitors’ website experience by segmenting your web analytics data using feedback provided straight from your visitors themselves.
In this post, we look at 3 different types of digital VoC data points you should inject into Adobe® Analytics so you can bridge the gap between what your visitors did (web analytics data) and what your visitors thought about their experience and what they intended to do (VoC). These 3 types of digital VoC data points are:
Pre-Visit: Why visitors came to your website
Every visitor comes to your website with a goal in mind, which determines how they will navigate and interact with your website. As we mentioned earlier, every visitor has their own thought process and way of doing things, so while two visitors might have navigated your website the same way, they may have had two entirely different goals in mind.
For example, let’s say you manage an Automotive website. A visitor’s timeline for when they plan to purchase their next vehicle (their ‘Purchase Horizon’) impacts what they will be looking to do on the site, and you may have different conversion goals you may want these visitors to reach by the end of their visit based on their Purchase Horizon:
By injecting your Purchase Horizon data from your digital VoC research into Adobe® Analytics, you can segment your web analytics data to compare how your visitors are navigating your website based on their Purchase Horizons. Also, you can determine whether these groups of visitors are navigating your website the way you want them to based on your set conversion goals.
In-Visit: How well your visitors’ website experience went
In addition to knowing your visitors’ motivations for coming to your website, it is also very important to know how they felt about their website visit.
For example, let’s say you are an Ecommerce company. While it may appear that a visitor had a successful visit because they made a purchase, there are cases where the visitor might not feel that their task was actually ‘completed’, or that they were not able to purchase everything they wanted. Even if they did purchase everything they wanted, they may have found that it was a daunting task that might prevent them from coming back to your website in the future (more on that later). These are all aspects of the in-visit experience that can be difficult to determine from your web analytics data alone.
Just like when a cashier asks you if you found everything you were looking for when you are making a purchase in-store, these are the types of insights that can only be provided by your visitors themselves. And when you combine the voice of your customers with your Adobe® Analytics data, you can not only identify pain points in the paths you have setup on your website, but also shed light on the processes that lead to the highest level of satisfaction and Task Completion for your visitors.
Post-Visit: What visitors plan to do after their session
As we’ve seen, being able to tie your visitors’ motivations for coming to your website, as well as their in-visit experience, to your Adobe® Analytics data becomes very useful when you want to want to find ways to optimize the performance of your website. But also as useful to know is what your visitors plan to do after their website visit.
Like In-Visit Experience, your visitors’ next steps after leaving your website can indicate whether your website is doing what you want it to do, whether you’re trying to drive them down your sales funnel, or to drive them to spread positive word of mouth about your brand and company.
For example, companies nowadays are providing more self-service product support resources on their websites to reduce call center costs. Being able to segment your web analytics data by those support seekers who indicated that their next step was to call customer support can help you investigate the experiences that warranted these visitors to phone your call center. Also, you can compare these visitors’ experiences to those who indicated that no next steps were required after their visit.
Understanding the differences in these experiences, and identifying the barriers on your website that may be preventing your visitors from taking your desired action post-visit, is knowledge that you can only get from bringing your digital VoC and web analytics datasets together.
Bridging the VoC and Web Analytics Gap
Digital VoC and web analytics data both tell you two different sides of the same story for your visitors. By injecting the context that you get from your digital VoC research into Adobe® Analytics, you can start getting the full picture of your visitors’ website experience. This allows you to segment your web analytics data using actual feedback provided directly from your visitors, and reduces the need to make assumptions about what your visitors are trying to do, how well their visit went, and what they plan to do next.
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