Ever searched for an item online or read a captivating article and then found yourself clicking on different links and sites to then emerge an hour or two later without knowing how you got there?
That is an Internet Rabbit Hole, a fairly common phenomenon circa 2014, and getting more common every day. This is good and bad for publishers at the same.
Content FTW
The goodness that comes from this behavior is that when you plan your content taking into account this mechanism (FTW means for the win#), you can orchestrate an entertaining and informative experience that results in a better connection with your audiences.
Because leaning into the connections that one topic has to another is akin to taking readers on a deeper or wider journey, based on their availability -- e.g., are they waiting in line somewhere? -- learning style -- e.g., do they enjoy being entertained during their commute? -- and mood -- e.g., is this a stressful or bored-at-work moment?
In all three cases it is then a good use of links, images, and interactive elements to orchestrate the experience in a way that even random associations to related content by others is a welcome instance that makes the journey better.
Mobile may narrow the screen space, not the opportunity. Building within the constraints of mobility, the act of being on the go, forces creativity -- the boiling down to the must have experiential elements.
Content FTW is also relevant.
Desire to make use of W_F
Relevance rhymes with balance for a reason -- it navigates that line between providing push information and creating the space for pull by keeping us in control.
It is far more enjoyable to find something (W_F: what _you/me/we/they_ find) serendipitously than to keep getting suggestions that are off enough to be off putting.
The standard answer to this dilemma has become data.
Use data to tell what someone might be looking for based on past actions and search trends, then optimize your content to deliver relevancy based on your findings. Add a dash of machine learning to it and you have artificial intelligence agents as conversation agents.
[image via]
Yesterday #SMXEast I talked about giving people their data back in useful ways using as an example the self-quantified movement, tracking your running time with RunKeeper or Daily Mile. Say you also help people take it up a notch with guidance, like Nike+ on top of adding a community layer.
You could also take into a different direction.
What if with our ability to access data with say the geographic and keyword elements, you could add a bit of human interestingness? What if on top of that you could provide an easy presentation layer to make use of that data for discovering something about ourselves?
Enter people, and not just random people or what in marketing we refer to as "people like you;" rather people you would want to be like.
You may not have the chance to meet them and ask them how they got there; however now you have the opportunity to see the connections between where people work and where they went to school via LinkedIn#.
Go one level deeper and you could figure out who you need to meet after you complete your degree to work at the company where they work plugging into the alumni network#.
Making intelligent use of data, including the presentation layer, is the next step in search -- focus on the find experience and you flip the Internet Rabbit Hole on its head from W_F to FTW.
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Valeria is an experienced listener. She designs service and product experiences to help businesses rediscover the value of promises and its effect on relationships and culture. She is also frequent speaker at conferences and companies on a variety of topics. Book her to speak here.