All entries for Monday 11 March 2013
March 11, 2013
After studying robust decision making, I have become increasingly aware of the importance of making effective decisions. I have learnt that good decision making involves putting together all the relevant information in a timely and useful manner. Sometimes, there is a need for quick decisions while other times, particularly if there are high stakes involved and time is available, one can deliberate more on the issue and assess the different alternatives. Moreover, I have learnt how bad we can sometimes be in making decisions but I have also been encouraged to see how the use of some tools can greatly assist us in the decision-making process. In addition, I now appreciate the judgement bias that exists in almost all tools much more than I used to before. However, this begs the question: How do you know if you are making a foolproof decision that guarentees good results? Can you ever be sure that your decision is going to work? I guess there will always be some uncertainty. The moment you delve into the world of probability and likelihood, you can never be certain if you have made the right call. Does this mean that making a robust decision, i.e. one that is immune and unaffected by circumstances and uncertainty, is impossible? I would leave that one open to debate, however, I would argue that it is besides the point anyway and we shouldn't be too critical of ourselves. At least, by developing an understanding of these tools and using them effectively, we can certainly increase our chances of making a decision as robust as possible.
Hence, it is important that we should not be quick to point fingers at the tools if the decision goes wrong. We should simply assess the outcome and try to analyze why the decision didn't work. It could be that we used an inappropriate tool or maybe we didn't apply it properly so it could be an error in our application of the tool.
To help us in the marketing medium choice, we used Grid Analysis and Pairwise comparison. Since I have already written about Pairwise comparison, I will focus on Grid Analysis in this post.
Grid Analysis is used when you have a number of options to choose from, and you want to figure out the possible implications of each choice. The table was relatively simple and straight-forward to construct. We wrote down all the options such as Internet, TV, Newspapers, etc on the left hand side and all the factors such as cost effectiveness, etc along the top row. The selection of factors to compare can be slightly tricky as you have to judge their impact on your decision. We were able to confirm our factors after a bit of research and once we had done that, we assigned weights to each factor. This is obviously a subjective decision and involves judgement bias. However, we tried to minimize it by talking through it as a group. It could be tricky to get everyone to agree on the weights but if the reasoning is sound, then a consensus can be reached. All the options were then scored for each factor. This again involves a bit of informed guessing. It is important that the reasoning behind the decision is logical, understandable and easy to follow. Next, the individual scores were multiplied by the weights of corresponding factors. Finally, after a simple calculation, we found out the overall scores for each option and we decided to go with our 4 highest scorers, namely Flyers, Astroturfing, Tissue Pack marketing and Celebrity endorsement.
All in all, Grid analysis is an effective tool to use when making this sort of decision. I hope my experience of using it for this presentation helps me if I am required to use it for any decision in the future.
We came across AHP in the leadership and excellence module, and we have covered it in RDM as well. Personally, I think that it is a great tool for getting the team to arrive at a consensus. It allows the team to build a hierarchy of decision factors. The factors are compared and ranked. The different options are then analyzed in order to make a decision. This approach is helpful as it gives a clear trail which can be followed to see how the decision has come about.
There are many other benefits of using AHP. For instance, it has an intuitive appeal and it is also flexible (Ramanathan, 2001). Furthermore, it can assist user to make complex decisions even when Quantitative data is not available and Qualitative approach is needed. This, however, could lead to judgement bias so one should, as ever, be careful when using the tool.
Reference: Ramanathan, R. (2001). A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management, 63, pp. 27–35