MikeW
Lithium Alumni (Retired)
Joined 17 years ago
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How to Design for Long-Term Behavior Change—Part 1: New Habit Formation
The subject of long-term sustainable behavior change has been the center of my work for quite some time. In fact, my recent book—the science of social 2—is entirely devoted to sustainable (long-term) social strategies. But my interest for this subject began when I was studying enterprises’ effort to engage employees internally and customers externally. Despite the abundance of technologies and strategies that drive engagement, many companies’ efforts to motivate employee engagement (or customer engagement) seem at best transient. They either don’t work, or if they did work, their effect didn’t last long. Meaning the “change” was not sustainable. So companies have to constantly repeat their engagement program with their employees (or engagement campaign with their customers). And when they do repeat, the result is usually a diminishing return.The 6 Factors of Social Media Influence: Influence Analytics 1
This post kicks off a multi-post miniseries on the topic of influencers: how to find them, engage them, and collaborate with them in word-of-mouth (WOM) marketing programs. Influence marketing today is in a state of experimentation that scientists call the pre-paradigm phase or exploratory phase. During this phase, everyone is trying different approaches based on experience. There are incomplete theories about why some approaches work and others fail, but there is no underlying fundamental principle that explains everything.Social Network Analysis 101
To understand social network analysis (SNA), you must understand what a social network is, and what a social graph is. Simply put, SNA is the analysis of social networks and a social network is just a network of entities that are connected by the relationship among the entities. This concept has existed since humans began walking the earth. In fact, social networks exist even in many social animals beside humans (e.g. wolves, lions, dolphins, bats, and even ants). Of course, the entities that interest us are people, and the relationships that are of particular interest include friendships (as in Facebook), colleagues (as in LinkedIn), kinship, communications...Community vs. Social Network
Since 2008, “social media” has become a heavily-used buzz word in the corporate world. The question is “what is social media?” Many seem to equate social media to Facebook-liked social networking sites; others seem to think that they are blogs, the Twitter family of applications for micro-blogging, Flickr, YouTube, or similar type of content sharing Web 2.0 applications. Yet, answers to this question may still range from social collaboration sites (like Wikipedia, Delicious, or Digg) to online communities (like those we host for our enterprise clients or Yahoo! Answer). Well, they are all correct to some extent, and these are functional classifications of social media. Author and blogger Brian Solis, introduced another classification of social media, based on the types of conversation. He called it the conversation prism. However, if you want to understand social media from a relational and social anthropological perspective, you will find that there are really only two major types of social media:What Drives Us—Are You Intrinsically Motivated?
A common question I get when talking about gamification is “how can you tell if people are intrinsically motivated?” Since we can’t really measure people’s motivation, it’s not easy to tell if someone is truly motivated, let alone intrinsically motivated. Yet, understanding people’s intrinsic motivation is crucially important, whether you are trying to motivate your customers or employees to do something great. This is because extrinsic motivation is not only unsustainable in the long-term; it often leads to a backlash due to overjustification. For this blog, I am going to use some real life examples to make this abstract concept of intrinsic motivation more vivid and realistic.Intrinsic vs. Extrinsic Rewards (and Their Differences from Motivations)
Last time I discussed motivation and the difference between intrinsic and extrinsic motivations. Now we can go one step further to talk about rewards and the difference between intrinsic and extrinsic rewards. Although motivation and rewards are both very critical to the design and implementation of gamification strategies, few gamification practitioners can articulate the subtle differences between intrinsic motivations vs. intrinsic rewards. Some even treat these distinctive concepts synonymously, which is ridiculously wrong. Since this post builds on the concepts introduced in my last post, if you haven’t read it yet, please take a few minutes to do so. It is critical to understand the fundamental concepts around motivation before jumping into today’s discussion. Review it here: Intrinsic vs. Extrinsic Motivation.Intrinsic vs. Extrinsic Motivation—Clearing the Fog (not Fogg!)
Despite the fact that good gamification must drive the temporal convergence of motivation, ability, and trigger, most gamification applications focus solely on motivation. Some even proposed renaming “gamification” to “motivational design.” But many people are still very confused about what is motivation, and how it differs from rewards. What precisely is the difference between intrinsic vs. extrinsic motivation? And how is that different from intrinsic vs. extrinsic rewards?Big Data Reduction 3: From Descriptive to Prescriptive
Today we will cover the last class of analytics for finding that needle of information in an ocean of big data—prescriptive analytics. Remember information << data—the information anyone can extract from big data will always be much less than the sheer volume of the big data itself. The difference is even more dramatic if we are talking about relevant and useful information. Prescriptive analytics not only predicts a possible future, it predicts multiple futures based on the decision maker’s actions. Therefore a prescriptive model is, by definition, also predictive.Big Data Reduction 2: Understanding Predictive Analytics
Last time we described the simplest class of analytics (i.e. descriptive analytics) that you can use to reduce your big data into much smaller, but consumable bites of information. Remember, most raw data, especially big data, are not suitable for human consumption, but the information we derived from the data is. Today we will talk about the second class of analytics for data reduction—predictive analytics. First let me clarify 2 subtle points about predictive analytics that is often confusing. The purpose of predictive analytics is NOT to tell you what will happen in the future. No analytics can do that. Predictive analytics are not limited to the time domain. Some of the most interesting predictive analytics in social media are non-temporal in nature.
