{"id":12999,"date":"2014-09-09T17:47:12","date_gmt":"2014-09-09T16:47:12","guid":{"rendered":"http:\/\/www.beyondphilosophy.com\/?p=12999"},"modified":"2019-09-09T10:39:55","modified_gmt":"2019-09-09T14:39:55","slug":"discovering-customers-dont-know","status":"publish","type":"post","link":"https:\/\/beyondphilosophy.com\/discovering-customers-dont-know\/","title":{"rendered":"Discovering What Customers Don\u2019t Know Themselves"},"content":{"rendered":"

How many times have you undertaken Customer research to discover what a Customer wants you to do to improve their Customer Experience<\/a> only to discover when you implement this, it has no effect? What is going wrong here? Quite simply, on many occasions Customers don\u2019t know what they want and will say the first thing that comes to their head. Therefore to discover this \u2018hidden\u2019 aspect of a Customer Experience you need to undertake a different form of research.<\/p>\n

Many people don\u2019t understand the difference between Correlation and Causation. These words are often used interchangeably although the two terms mean entirely different things. The different meaning might be the reason that a company struggles with designing a great Customer Experience.<\/p>\n

Correlation<\/a> means that there is a relationship between two things statistically. When there is a correlation between two variables, it means that their data resembles each other, such as a chart or graph. It can be useful to use correlations to look for the dependency between two sets of data. It does not, however, mean that one set of data is causing another.<\/p>\n

Causation<\/a> is the act of causing something to happen. When causation is present, it has a direct relationship to the data. In the case of causation, the presence of the first set of data makes the second set of data occur.<\/p>\n

Most research uses correlation as its basis. They look at data and look for items that resemble each other. Then this points to a relationship between the two factors. A good example is the height of parents and its correlation to the height of the progeny. But causation is the reason the parents have a child that is a certain height. In this example, how DNA forms of the two parents genetic material is the causation of the actual height of the child.<\/p>\n

When we look for the hidden factor in a Customer Experience, we typically find that it is the emotional aspects of the Customer Experience. Many times we uncover that what is driving value for an organization is how they make a Customer feel. Emotions are the \u2018hidden\u2019 factor. \u00a0We call this an Emotional Signature<\/a>.<\/p>\n

In a relationship between two sets of data that does have a dependence on one another, there is typically \u00a0causation. Cause can be a hidden factor only discovered upon deeper analysis. When we do our research, we are looking for these hidden factors that cause the pattern of behavior in an organization\u2019s Customer Experience.<\/p>\n

I have a story I tell to illustrate the difference between correlation and causation. In a seaside town, data reflected that more women get pregnant when there are many seagulls in town. \u00a0Now, unless I\u2019m wrong, seagulls aren\u2019t the cause of this phenomenon.<\/p>\n

When you look at this more closely, however, \u00a0you learn that men that live in this seaside town go fishing for three months at a time. Alone, as in sans wife. So when they come in from their fishing trip they begin gutting the fish they caught, throwing the scraps into the sea, which attracts the seagulls. The seagulls follow the boats into the harbor when the men return, and well\u2026let\u2019s just say the men are happy to be home.<\/p>\n

So yes, there is a correlation between the pregnancy rates in the seaside town and the amount of seagulls that are present. But the causation of the elevated pregnancy rate has nothing to do with the birds.<\/p>\n

Businessinsider.com<\/a> had a great article that illustrated the difference between correlation and causation as well. Called Spurious Correlations, there are a great many things that look related when you compare the graphs, but clearly aren\u2019t. This one is my favorite.<\/p>\n

\"number<\/p>\n

Not only is the association ludicrous, but I also had no idea that over 800 people in the US died by getting tangled in their sheets in 2008. \u00a0I will never look at my bed the same way again\u2026.<\/p>\n

3 Stroke Model for Customer Experience<\/strong><\/p>\n

In our \u00a0assessment, we employ a 3 Stroke Model to identify the hidden factors (causation) in Customer Experiences that create an emotionally motivated response. We find this helpful in helping our clients identify key moments to enhance in the experience design.<\/p>\n

\"3<\/p>\n

The 3-Stroke Model<\/p>\n

    \n
  1. \n

    Stimulus: These are the things an organization does to Customers.<\/strong> This is the first set of data, which represents a touchpoint in the customer experience. For example, they answer the phone after a lengthy hold or send a complimentary bottle of wine to the hotel suite.<\/p>\n<\/li>\n

  2. \n

    Response: The Customer feels something about it.<\/strong> The emotions are the hidden factor in the Customer Experience. A Customer may feel \u00a0frustrated by the hold time or hotel guests might be pleased by the thoughtful surprise of a chocolate on the pillow, or a friendly greeting at reception.<\/p>\n<\/li>\n

  3. \n

    Affect: The emotions create a value for the experience and an effect.<\/strong> This is the second set of data that seems to relate to the first. It could be that the amount of hang-ups increases or in the case of the wine for the guests, re-bookings for the hotel increase after the stay.<\/p>\n<\/li>\n<\/ol>\n

    So next time before you tell all your friends that the divorce rate in Maine went down as few people in the US were eating margarine, remember the difference between correlation and causation. While the two sets of data are remarkably similar (correlation), there is no causation between them.<\/p>\n

    Besides, everyone knows that the divorce rate in Maine went down because of all the seagulls in town. \ud83d\ude42<\/p>\n

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