Craig Steele, on 15 April 2014 - 05:40 AM, said:
But it also could be so many other things.
For example, maybe the "best" cakes need certain ingredients, or certain cooking, or storage. Maybe they need more labour and he cannot put on another staff member because of an Insurance claim. Maybe he is fully capable of making the "best" cakes, and even if his market said we would love them, he couldn't actualy do it without new premises for some Health and Safety requirement? There are literally hundreds of factors that might apply.
All of this is airy fairy, I'm not saying this analogy fits with PGI.
What I am highlighting is that there's a lot more to any business decision (including PGI) than simply "do the customers want it".
Ergo: "Any 10 year old could do this" I kinda question
It's a bit of a tangent anyway, my post was just in response to that particular post.
Yes, I understand that completely that there are a lot of factors that can hinder a baker from making a particular cake and thereby be able to appeal to a less than optimal portion of the market they occupy. This is also one of the basic ideas behind commercial capitalism, somewhat akin to the evolutionary "survival of the fittest" theory. The baker in our example may not be able to make that cake, but another might and if that baker from out of town decides he has enough of the market share in town to justify opening up a branch he probably will. Competition of that kind can put a business out of business were it not for the in town bakers official monopoly ensuring that only he can bake in said town (e.g. IP License). This forces those in town to eat cake they don't like or just stop eating cake until said baker goes out of business and another baker moves in.
Numerous large, once-successful companies have failed in recent years. Some have gone bankrupt; others have been substantially reduced in size and fallen from an industry leadership position. What caused their failures? Were they not sufficiently analytical? Perhaps they had adequate information and analysis but made irrational decisions.
Failure is not necessarily due to unforeseeable events. Companies that have failed often knew what was happening but chose not to do much about it. Nor is failure always the result of taking the wrong daily actions.
Often no one challenges the status quo and asks the "tough questions". Delusion and fear of the unknown can develop, affecting how organizations handle key relationships with customers. When it comes to considering whether to adopt advanced business analytics, or whether to implement and integrate the various component methodologies that constitute analytics-based enterprise performance management (EPM), decision-makers are faced with two choices: do it or not. Many organizations ignore the fact that the choice to not act, which means to continue with the status quo and to perpetuate making decisions the way they currently are, is also a decision.
In many cases, executives believe that if a control system is in place, it will do the job for which it was intended. However, in many organizations, systems and policies are constructed for day-to-day activity but not for robustly analyzing the abundance of raw data to make sense of it all. Sustainability is based on transforming data into analyzable information for insights and decision-making. This is where business intelligence, business analytics and analytics-based enterprise performance management systems fit in.
A common misconception of information technology specialists is that they equate applying business intelligence (BI) technologies (i.e. data mining) with query and reporting techniques such as data mining (e.g. count drops from day 0, filter solo & group).
In practice, experienced analysts don’t use BI as if they were searching for a diamond in a coal mine. They don’t flog the data until it confesses with the truth. Instead, they first speculate that two or more things are related (group # cap = reduction in group player drops) or that some underlying behavior is driving a pattern seen in various data (less content = fewer players). They apply business analytics more for confirmation than for random exploration. This requires analysts to have easy and flexible access to data, the ability to manipulate the data (no, not change the numbers) and software to support their investigative process. Without initial problem framing and a confirmatory approach, mistakes are inevitable. Sadly many do not learn from their mistakes, but rather repeat them with more gusto.
We are all far less rational in our decision-making than standard economic theory assumes (x is more profitable than y = we do this). Our irrational behaviors are neither random nor senseless though, they are systematic and predictable. So wouldn’t economics make a lot more sense if it were based on how people actually behave? That simple idea is the basis of behavioral economics.
Wouldn’t getting a return on investment from an organization’s treasure trove of stored raw and activity data be greater and more meaningful if they properly applied business analytics? With today’s uncertain recovery from the global recession, the stakes have never been higher for managers to make better decisions with analyzable information. Companies that successfully use their information will out-think, out-smart and out-execute their competitors. High-performing enterprises are building their strategies around information-driven insights that generate results from the power of analytics of all flavors, such as segmentation (solo play & group play) and regression analysis (loss of customer base) and especially predictive analytics (how will this change my customer base). They are proactive, not reactive, and therefore more successful.
Executives are human and can make mistakes, but in company failures, these are no longer simply minor misjudgments. In many cases, their errors are enormous miscalculations that can be explained by problems in leadership (think in terms of your argument used to explain part of the player loss in units). Regardless of how decentralized some businesses might claim to be in their decision-making, corporations can be rapidly brought to the brink of failure by executives whose personal qualities create risks rather than mitigate them. To sustain long-term success, companies need leaders with vision and inspiration to answer, “Where do we want to go?” Then, by communicating their strategy to managers and employees, they can empower their workforce with analytical tools to correctly answer, “How will we get there?” This is the heart of analytics. Employees make hundreds, possibly thousands, of decisions every day, such as pricing and customer targeting. Incrementally better small decisions add up and may contribute more to the financial bottom line impact than the few big decisions made by executives.
I don't mean to sound lecturing, but I thought you would appreciate a more academic reply. This also seems to round up a lot of discussions going on in the moment regarding various topics.
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