Pronounces stimulate decisions. A company that is 80%, certain that there is a feasible market for a new product, will act very differently than one that is only 20% sure.
Yet the chance can be difficult to understand. It’s vague – a bit like beauty. We may not be able to define it exactly, but we recognize it when we come across it. We are sure that the sun will come up tomorrow, but we are much less sure that the sky will be blue. And somewhere in between is our confidence in the weather forecast and the certainty of marketing results.
Marketing lives in a zone of uncertainty because markets are made of people. Consumers, owner -occupied groups and companies exchange billions of signals every day. These interactions create feedback klussen and introduce strangers in every situation.
Despite the VUCA (volatile, uncertain, complex, ambiguous) nature of marketing, it is still common for managers to stick to the wrong conviction that with sufficient data we can finally be the right strategy or a top analysis team of what will happen.
Why searching for certainty failed in marketing
Avoiding a security -seeking mentality is difficult. The human spirit does not think of nature in probably – precisely the opposite. We like definitive causes and effects, and our brains are looking ruthlessly for them. As small children we learned to draw simple conclusions. Uncomplicated results, such as how the dog came out, are the result of the sum of some obvious causes:
Jack opened the door while he brought in the groceries + the dog likes to be outside = the dog got out.
Problems arise when decision makers misunderstand the relationship between results and their causes in complex environments such as marketing. A reductionist thinking process, such as that from finding out how the dog gets out, assumes that all results can be traced to a chain of understandable causes. Marketing results have reasons, but it is not the clear answers that our brains crave.
Although we may try to produce a certain marketing result, all results in a VUCa world come from several contributing factors, each with a chance to happen. Reductionism leads to poor decisions in complex environments for the following reasons:
| Lack | Description |
| Causes cannot be fully discovered. | All marketing results stem from several factors and some information will inevitably be missing, despite diligent research. Hidden drivers, such as an unknown influencer, can unexpectedly change a marketing result. |
| Causes are not always linear. | Some causes can easily be linked to an outcome because we can see the connection, but other causes are set in place long ago or far away and only now have an effect. Advanced analyzes and AI help to see more patterns, but are not complete. Non-linearity, sometimes called the butterfly effect, produces connections that are impossible in advance. |
| Causes have a different impact. | The multiple factors that contribute to marketing results influence each other. Some causal effects have more weight than others and some factors will reduce or strengthen others. |
For these reasons it will try to determine a definitive, repeatable chain of causes for marketing results always lead to disappointment.
Dig deeper: why marketing benefits when it offers predicted guidelines
The better way: think like a statistician
Thinking when a statistician is to give up to seek certainty in predictions and to learn to work more productively with probably. Probabilistic think leaders to better assess risks, weigh scenarios and make better informed decisions.
Every cause that contributes to a marketing result has a certain certainty. However, because markets consist of people, this degree of certainty will be considerably lower than the trust of 90% – 95% that is common in university statistical classes.
According to a report cited in Noise: a mistake in human judgmentAn inspection of 708 studies in the cognitive and behavioral sciences, looking for patterns in human behavior, showed that only 3% of the studies produced correlations that were .50 or more. Every correlation larger than .50 is considered strong.
Thinking as a statistician requires A New mental framework. Four mental practices will help decision makers with the successful application of this shift.
Make peace with not knowing
Although more data, better analyzes and improved processes will increase certainty, some things will never be known – that’s OK. Of course, decision makers must reduce the amount of uncertainty to the lowest reasonable extent.
Even with more time, money or the best technology, complex situations such as marketing will never reach zero uncertainty – regardless of the commitment. Certainty to chase to an unreasonable extent whether people blame what is inevitable, nobody helps.
Broaden your information sources
Marketing and sales results are rarely simple. They are derived from several simultaneous factors. Tools such as Marketing Mix modeling or causal AI help to identify the right combination of variables that best explain a result. The more diverse your data sources, the better your opportunities to find the best fit.
Place bets
Betting sets a prize for beliefs and helps prevent risky opinion-based decisions. Placing various small bets, instead of one large one, is more logical if it is very insecure.
Clarify the ambiguity
Decision makers want to look for objective, verifiable data when they are available. Nevertheless, many marketing decisions, such as determining the brand values or deciding whether you want to promote someone, require an opinion.
In these cases, the precision of decision -making can be improved by using clarity tools such as scales and benchmarks. You get better results if the decision -making group corresponds to definitions.
Think as a statistician to make better decisions in the messy VUCa world of marketing.
Dig Deeper: Why Causale AI works when other prediction models fail
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