Not long ago, I undertook a survey for a client.
We went through the results separately and came to completely different conclusions.
Data is neutral but your interpretation of it is highly influenced by your own biases which are almost impossible to escape.
For example, if your survey results show members don’t feel a strong sense of community with another you can conclude:
a) you need to take steps to increase that or
b) this isn’t the type of community where members want that sense of connection.
I’ve seen this more often than I can remember. Surveys or interviews might reveal that members don’t like a specific feature and then we battle about whether that feature needs to be improved or removed.
Even metrics are open to interpretation. Look at this graph below:
Is the number of visitors going up, down, or staying the same? The answer depends entirely upon when you start the trend line and that is where your biases creep in.
If you think data is going to win you the argument or definitively determine what you should be doing, you’re probably going to be disappointed.
So, what should you do?
First, accept that no matter what you do you can’t escape your own biases.
Second, let other people look at the same data you see and draw their own conclusions (before you share your interpretation). This will reveal your blind spots and alternative ideas. (p.s. an outside consultant can really help here).
Third, use your data to develop hypotheses you can test in your community. Not sure if members want to feel a sense of community or not? You can ask them and test activities that might help members bond and see how they go. Not sure about a feature, ask members if they want it removed. Set the parameters for success or failure of each test and stick to them.
p.s. Read this great post by Bob Hoffman.
