1. Applying the methods and objectives
You have just seen the basic framework that is used in the rest of course. Let's help a fellow data practitioner in his analysis.
2. Poor Dale
Meet Dale, a data-driven executive at Busy-ness Corp. They are a conglomerate selling all types of hot dogs. Dale is miserable because he doesn't yet know our framework for data-driven decision making.
3. Hot dog research & development
Dale's boss, the CEO came up with an idea. The CEO's opinion is that a banana ketchup dog will be a success. Dale doesn't know where to begin exploring this idea with data.
Let's review, there are some accepted facts from the HIPPO, people like bananas, people like hot dogs. We're a hot dog company so it's a natural fit.
4. Where's the data?
Reviewing our three methods, let's figure out which fits best.
A banana dog probably hasn't been tried before so historical data for predictions doesn't exist.
Similarly, explaining purchasing drivers for the banana dog isn't realistically feasible. Dale could perform qualitative analysis with focus groups but the market has never really seen this product. Thus, explaining purchasing characteristics such as high income or age isn't possible.
Although the CEO may think this is a great idea, and no one can dispute the prior facts, Dale should seek out an exploratory analysis because there isn't enough information. So that's our method, now let's choose an objective.
5. What is the point?
Let's examine the banana dog in the context of cost versus benefit. Dale knows the hot dog factory can't make any banana dogs at the moment. Examining the cost of expanding the plant for fruit storage and product assembly is the cost. The benefit, number of banana dogs made, is straightforward based on the factory expansion. However this doesn't answer the fundamental question. Answering the cost/benefit objective may still leave the factory full of unsold banana dogs.
What about risk versus reward? Certainly there is a risk to setting up a new product that can be quantified. The reward may be a bit trickier, because profitability of the new product may be excellent yet the company may not sell any so the variable reward may not materialize.
Hindering both cost versus benefit, or risk versus reward, demand is the larger question. With demand Dale can quantify the supplies needed, the risk of the project not going well, and finally the costs of the innovation weighed against benefits. Knowing demand unlocks the other objectives. Dale must use data to support or oppose the CEO's idea.
6. Exploratory market demand
Dale's challenge is finding data that illuminates the banana dog's potential. Dale decides to use a social media listening tool, searching common platforms and even blogs. He searches for banana, hot dog, banana dog, and banana hot dog, recording his results in this table.
Hot dog has the highest number of mentions and brands associated with it. So consumers are mentioning hot dogs more often and when they do there is brand awareness. People may be using positive language to describe bananas, but these posts aren't aligned to any specific brand. This is supported with 70% sentiment score, where higher is better, yet only 17% brand mention. Things look more dire for the banana dog. Banana dog had fewer mentions and no brand awareness.
While this data isn't conclusive it points to a small market potential, few people publicly wishing for the banana dog nor asking a company to produce it.
7. Phew... avoided that nonsense!
With only a few product mentions, no brand mentions and an unclear positive or negative sentiment, Dale's exploratory analysis leads him to oppose the CEO's idea. Since he is armed with data, the CEO changed her mind and Dale helps the company avoid catastrophe.
8. Let's practice!
Now that we've helped Dale through this madness, let's do the same in the upcoming exercises.