For the past 15 years, web analytics have been used to measure the effectiveness of web strategies. But web analytics can be misleading and can drive marketing efforts in the wrong direction. Web analytics is based on the volume of visitor traffic and whether visitors stay on a web page. It is a measure of attractiveness; it is not a measure of engagement, relevance, or action. We need to know how relevant our marketing is to our visitor's needs. And we need to see where and how we can improve marketing for greater bottom line impact. With all the channels of web marketing, web, email, and social media, we now have more than 12 online marketing channels that need as many as 47 metrics to measure. What we really need is a short set of metrics we can use to compare effectiveness across these marketing silos.
First, we should be measuring marketing quality, not just visitor quantity. But current web analytic tools aim to measure and increase the quantity of people coming to a website. 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. Secondly, we are not connecting the dots. Email is tracked independently from the web which is tracked independently from the mobile web, social media channels, and mobile apps.
When you meet with a customer online, you are essentially engaging in a conversation. An exchange of communication and trust takes place. As this communication and trust builds the level of commitment also builds. This combination of communication, trust, and commitment exhibited by your customer is a measure of engagement value. Marketers can now understand what drives engagement and produces bottom line results, not what drives crowds.
Engagement analytics measures quantity, quality, and the relationship between the two.
Quantity is everything you know from web analytics today. Quality is the level of communication, trust and commitment achieved by your customers. Online marketing analytics tools somewhat express quality as conversion percentages, such as visitors having signed up for a newsletter. There are two problems with conversion: Not all conversions have the same level of trust, communication and commitment and as a website has more conversion points, understanding the complexity becomes overwhelming.
Marketers need this barrier of complexity distilled down to a simple measure of marketing quality; which we call the engagement value. Another measure used by engagement analytics is relevance, which is the relationship between engagement value earned by a marketing event and the number of visitors. If, for example, you run two identical AdWord campaigns on Google and Bing, and the Bing ad has twice the engagement value for the same number of visits, then you must conclude that the ad running on Bing has more relevance for the customer's needs. Marketers using engagement analytics can learn exactly which part of their marketing mix creates the greatest impact, which parts of their website earn the greatest Engagement Value, and where they should spend their next marketing dollar.
Measuring engagement on the webTo measure engagement you need to look for transaction points on your website where visitors show their communication, trust, and commitment. This is true whether your website is for B2B, B2C, or non-profit.
Here's an example of a B2B company that has hundreds of these transaction points. Each of these transaction points has one of three levels of engagement with each level having a different engagement value. The actual numbers are not important. The ratio between the numbers, however, shows the relative weight between the levels of engagement.
This is very different from measuring conversions. When someone registers for a white paper that's not just a one-point conversion as it would be in most web analytics, it is recorded as 25 points. When the visitor later requests a quote an additional 50 points is added to the engagement value they have accumulated.
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