"It’s a little like dating. A smile across the room is nice, but there’s only a modest amount of engagement involved. However, taking a two week trip to New Zealand together implies a much higher level of engagement."
- Sitecore, From Web Analytics to Engagement Analytics: Quality over Quantity
Customer engagement is a crucial part of the sales process—it’s also notoriously difficult to measure. Ask a digital marketer how to measure engagement on your website and you’ll most likely end up talking Web analytics. A visitor is “engaged” if she subscribes to a newsletter, watches a promotional video, or comments in a discussion board; a landing page is engaging if it has a low bounce rate and a large number of visits.
This means that engagement is measured by the frequency of conversions or aggregate average visits that happen on a website over a fixed time period (day, month, year). A higher frequency of either of these data signals increased engagement.
Or does it?
Well, maybe not. As described above, engagement is typically calculated using quantitative data like conversion rates or average visits. But there’s growing concern among digital marketers that the quantitative data is not properly qualified. In other words, it’s hard to know whether or not a segment of data is meaningful. Consider how our partners at Sitecore describe the problem in their white paper From Web Analytics to Engagement Analytics: Quality over Quantity.
"Web analytics won’t give you insight into which traffic sources or type contributes to visitor commitment and marketing effectiveness.
Web analytics is all about measuring aggregate average quantities; for instance, number of visits, percentage of new visitors, page views, bounce rates, top assets downloaded, most popular entry pages, most visited pages, and so on…. First, we should be measuring quality, not just quantity…. Secondly, we should be connecting the dots."
In other words, without context, or something that defines the quantitative data qualitatively, it’s hard to determine the value of a conversion (or a visit) with confidence. You may meet 30 people in an hour at a speed dating event (lots of promising smiles from across the table!), but unless you can qualify the value of a date it’s unclear who has long-term relationship potential.
The problem with contextualizing quantitative data is that, like defining engagement, it’s a difficult task. Even with qualitative models for how to interpret the multiple data points around aggregate average visits on your site, most content management systems are relatively simple and out of the box. It can require advanced segmentation and customization to turn that model into a working technical solution (which is expensive!).
One of the best-in-market solutions is Sitecore’s Customer Experience Platform (CEP). It has a robust engagement analytics system based on engagement value, a method in which differential points are assigned to user actions or aggregate visits. The point-based model allows marketers to qualify the quantitative data as more (or less) valuable and therefore determine the effectiveness of marketing efforts, in concrete terms. The advantage of Sitecore’s CMS is that the CEP architecture is out of the box—the digital marketing team can manage the engagement analytics without a programmer.
In my next post, I’ll continue to assess the challenge of calculating engagement in digital media, and explore Sitecore’s engagement analytic model in more depth.
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