10 Psychological Techniques for Engaging Your Users

Came across this article in my ongoing search for metrics around if and how much Easter Eggs increase Engagement. Ten solid recommendations around implementing the first step in Nir Eyal’s four step cycle in Hooked, the trigger. It’s also shocking to find-out that 65% of a sample population would shock someone electrically if they were told to do so by the correct authority figure. The comments on achievement are insightful and can be applied to both B2C or B2B site.

The Experience is the Product

Peter Merholz talk and post reminds us that in front of all the great technology and best business plans is the user experience. I’m also a believe that teams shouldn’t be organized around a code base but rather around a part of the experience as Merholz points out. This is usually illustrated in the marketplace model where there’s a team built around the buyer side and one around the seller side. This model can also be effectively applied to any other site such as a publisher where the teams might be aligned around editorial, video & photos, social and advertising. The other note that Merholz touches is that design is the key ingredient to effecting desired behaviour. For tactical examples building in the desired behaviour see the article above.

The Right Way to Use Analytics Isn’t for Planning

As one of the commenters notes this article talks about an idea that’s been around for awhile, data analysis tells us what’s happened rather than what’s going to happen and in a world of increasingly rapid change past trends are less likely to be signs of future predictors. More than ever there are constably changing perspectives from business leaders and data analysis is the tool that can support or refute these perspectives from being actioned. This point is well articulated by Jeremy Stanley in his post below, Doing Data Science Right – Your Most Common Questions Answered. Why I’ve included this read is that the authors of this article and Stanley point to the need to have people within the analytics/data science/business intelligence team that can work with a lot of ambiguity and still provide actionable recommendations. Great analytics teams produce the weekly traffic report but their real value is providing the insight on the perspective of that week, whether supporting it or not.