If I were to take a guess, I’d bet that we’ve all been in situations where we need to make an important decision quickly – where one of the possible options is presented in such a compelling way, it seems like a foregone conclusion. Perhaps the option appears to have benefits that are so hugely significant, it is seen as a ‘no brainer’ – a decision that is so ‘obvious,’ there is little point in putting much thought or contemplation into the decision making process itself. Perhaps it’s ‘obvious’ that we should launch into a new market, slash our prices, buy a new software package or restructure a particular department.
This desire to make decisions quickly is understandable and rational – after all, it’s important that we avoid ‘analysis paralysis’ – yet a real danger awaits the unprepared. In many cases, ‘no brainer’ decisions have far wider consequences than the decision maker might initially appreciate. In fact, further analysis of the data might show that you are being duped into making a bad decision with confidence. Let me illustrate with a hypothetical example.
Imagine an online retailer sees a sudden sustained drop in its sales. It knows that competition in the market is cut-throat, and it needs to retain its market share. It could be seen as a ‘no brainer’ to temporarily cut prices or offer some other type of incentive to increase sales volumes. In some cases, this might be an appropriate response – but in others, it might lead to a dangerous race to the bottom – with all firms lowering their prices until they can bear it no longer.
In the hypothetical situation mentioned above, it would be far better to look at the business situation holistically. It would be beneficial to carry out analysis and look at the business environment and establish what data and insight is available. Maybe there are other reasons that sales have dropped overnight, or other factors that should be considered. For example: was there a similar drop last year? Is there a seasonal peak in sales? Has the weather been unusually hot/cold (which might affect the pattern of people buying or ‘shopping around’)? Are people buying a completely different substitute product altogether (think Netflix affecting DVD sales)? There would, of course, be many other factors to consider too.
In situations like this, it is hugely beneficial for organisations to look at their data and analytics for insight. Carrying out analysis in this way – before making so called ‘no brainer’ decisions – helps to avoid the ‘knee jerk’ reactions that can lead to unexpected and unfavourable outcomes. Ensuring that businesses move from data collecting to curating actionable data and insight is key.
So – if you’re presented with a ‘no brainer’ decision, what should you do? Here are five key questions that you might consider asking:
1. What is our hypothesis? Put simply, what problem are we trying to solve? If our recommendation is “offer a 10% discount for the next month” perhaps our hypothesis is that “Other competitors are driving down prices, if we don’t match them we will stagnate. Offering a 10% discount will allow us to build a loyal client base.” It is also worth considering how this hypothesis aligns with the organisations objectives and strategies.
2. Where is the data and insight that supports this? It is worth examining the supporting data and considering how reliable it is likely to be. Of course, there will be some instances where little or no data is available, and we are making a decision based on pure projections – and that’s fine – but it’s important that we know what type of decision we’re making!
3. Where is the data and insight that refutes this? If none has been identified, does this mean we haven’t looked hard enough? Is there truly no data that refutes our proposed approach?
4. What is the impact of making the change? A wide ranging question, but what internal and external impact will pursuing our recommendation have? Are there any undesirable impacts? Are we prepared and can we accept all impacts?
5. Have we spoken to critics as well as advocates? It’s important to get a range of views, particularly with ‘no brainer’ decisions. It’s important to speak to a range of stakeholders – including those who may disagree. They may help us identify additional insight.
In conclusion: A “no brainer” decision doesn’t mean that “no thought is required.” For significant decisions, it’s important that organisations pause and carry out an appropriate level of business analysis and feasibility analysis. It doesn’t matter about the size of your business, analytics will help you to make insightful decisions—in fact smaller and midsize businesses that ingrain this into their thinking early are likely to thrive. This analysis needn’t be time consuming; organisations that have a good business analysis and analytic capabilities may well be able to quickly attain an answer. In the long term, these organisations are likely to have a competitive advantage, as their ability to quickly survey the landscape and make a decision based on available data will help them win.
How are ‘no brainer’ decisions treated in your organisation? I’d love to hear from you. Please feel free to add a comment below, and if you’ve enjoyed this article don’t forget to subscribe.
This post was brought to you by IBM for Midsize Business and opinions are my own. To read more on this topic, visit IBM’s Midsize Insider. Dedicated to providing businesses with expertise, solutions and tools that are specific to small and midsized companies, the Midsize Business program provides businesses with the materials and knowledge they need to become engines of a smarter planet.