Top-Management Focus Group „Marketing Decisions“, 13./14. October 2016
Six key messages to think about
1. Be aware of biases: they might influence the way you decide!
Please consider cognitive biases when it comes to decision-making, since they are ubiquitous in management. Amongst other important biases, there are bandwagon effects and clustering illusions you should be aware of.
Figure 1: Potential cognitive biases in decision making
In terms of the bandwagon effect, it is not a good idea to enhance your belief in something with regard to the increasing believe of other people. No doubt, it is easier to justify, but it can generate misleading and bad outcomes in the end. Think of price-setting mechanisms: the majority of companies stick to a cost-plus pricing strategy. However, even if you are inclined to do the same, it is not smart to do so simply because it does not create value to your customers. Another important bias that has to be mentioned in this context, is the clustering illusion which causes you to see patterns in random events. With reference to this, just remember: Correlation does not mean causation. This is of special importance concerning the ongoing Big Data discussion. Sophisticated algorithms often find interesting correlations in huge databases, but the identified variables have nothing in common in terms of deriving marketing activities. Thus, you should critically reflect found correlations regarding their usefulness to develop marketing activities.
2. The more data the better is not always true – heuristics beat data … sometimes!
The common wisdom “the more data the better” should be critically questioned. Without any doubt: it is good to have more data in most of the cases, but there is a sweet spot. Please keep that in mind when it comes to your own decision-making. If you collect too much data, it could be the case that there is some information overload or that you generate unnecessary transaction costs in doing so. In the end, the accuracy of the decisions made will not be improved any further … on the contrary: it might be the case that you will diminish it if you collect too much data. With reference to this, heuristics might be a useful alternative. They ignore parts of information in order to make decisions more quickly, frugally and accurately. One prominent and illustrative example concerning the usefulness of heuristics is the emergency department. It would not make sense to extensively collect data regarding the medical conditions of a person. In contrast, the medical staff generally analyses three vital functions in order to decide whether the person is in serious condition or not. Beyond that, heuristics are useful in marketing as well.
Figure 2: Classification of active/inactive customers
Recent research has found, that a simple heuristic (you classify a customer as inactive when there has been no purchase within the last six months) beats sophisticated analytical models. To sum up, consider managerial heuristics when it comes to decision-making because it might improve the accuracy of your decisions made.
3. An outside-in and an inside-out view is helpful to develop strategic decisions
Marketing decisions can either be operational or strategic – both tasks are demanding and need a clear structure and preparation in order to be successful. Dr. Carsten Petry, Global Product Manager Human Nutrition at Bühler AG, has clearly pointed out that you need an outside-in (external analysis) and an inside-out (internal analysis) view to successfully develop strategic marketing decisions. Amongst other things, an external analysis should include a market size & trend analysis, the description of the customer landscape as well as a competitor analysis. On the contrary, an internal analysis should consist of a portfolio analysis, the derivation of a clear value proposition and an honest discussion and evaluation of strength and weaknesses. Based on this knowledge gained, you are able to derive a competitive strategy which enables you to make successful strategic decisions.
Figure 3: Visualization of the outside-in/inside-out view
4. Marketing decisions are wide-ranging – they can change traditional businesses!
Marketing management plays a key role regarding the overall company performance due to its huge impact on adjacent fields. Sometimes, decisions made in marketing departments can change the traditional business models of firms. Dr. Carsten Schrijver, Head of Customer Solutions Billing and CRM at Vattenfall, pointed out convincingly how marketing decisions have been disrupted traditional utilities – in other words: the transformation from point of deliveries into customers. In 2012, Vattenfall fell into difficulties resulting in a dramatic decrease of the operating profit.
Figure 4: Sales and operating profits, SEK millions, 2008-2015
Thus, Vattenfall had to accomplish some modification in order to be competitive. The transformational process of Vattenfall is based heavily on important marketing decisions which will be briefly outlined. At first, Vattenfall’s management do no longer see customers as points of delivery and therefore they attempt to optimize the consumer experience rather than internal processes. Beyond that, they try to achieve a kind of local differentiation meaning the leverage of current customer behaviour to anticipate preferences of tomorrow. With reference to this, you can see that marketing decisions might have a huge impact on firm development. Thus, it’s not just an academic topic due to its huge impact on your daily business. Take that into consideration!
5. Big Data is useful to improve decision making – but not to accelerate innovations!
Currently, there is a huge discussion regarding the value added potential of Big Data in business. For sure, some business cases proof the usefulness of Big Data and underline the fact that it helps to make better decisions (especially in an environment with relatively low uncertainty, for instance direct mailings etc.). However, you should be skeptical when it comes to innovation management because Big Data is not suitable to generate disruptive innovations. Remember, every algorithm is based on historical data and thus it is almost impossible to predict events that never happened before (as it is the case with disruptive innovations). This is of special importance as recent research of the institute of marketing reveals that top managers do rely on facts and figures generated by Big Data Analytics due to the higher perceived credibility compared to other data sources (in this case, personal experience and market research). To conclude, Big Data might be an extremely useful tool to improve existing decision making mechanisms. But don’t use it in an innovation management context because an exclusive reliance on decisions derived by Big Data might stifle disruptive innovations.
6. Data-driven decision making can improve the value added potential of your company!
Data-driven decision making is “en vogue”. It seems that data could become the oil of the 21st century due to its hidden value potential. Thus, most companies strive to implement a kind of data-driven decision making. However, the value added potential has not been proofed in most cases. Andreas Jess, Tactical Manager at John Deere, has outlined that a clear focus on data and the respective information leads to an improved company performance.
Figure 5: John Deere FarmSight
The so-called “MyJohnDeere Operationscenter” is advantageous for customers on different levels: At first, it enables the optimization of the machine in terms of efficiency and effectiveness. For instance, customers will be informed about the upcoming maintenance quite early (predictive maintenance). Furthermore, it is also useful to optimize special job tasks and to provide decision support concerning the farming operations. In this case, the respective agricultural machines are connected with the application and provide a lot of sensory data. As a consequence, the farmer/customer is able to plan his farming operations more properly resulting in a higher customer satisfaction and – in the end – a better firm performance.