Forecasting the influence social networks on demand: The Matrix case
Published on 11/2/2020
Published on 11/2/2020
Today, it has become increasingly complicated to estimate market reaction to a new product and it is extremely tempting to simplify consumer behaviour as much as possible. As a result of the effect and influence of social networks, traditional forecasting models have become less and less reliable and managers need to develop a different approach to anticipate demand. Red pill or blue pill?
Imagine two rooms, one red, one blue, each full of 10,000 people. In the blue room, each person is to toss a coin, heads or tails. In the red room, only one person is to toss a coin and the result will apply to the other 9,999 people.
This simplified process in the red room represents the impact social networks have on the purchasing process. Indeed, the social networks have a significant impact on purchasing decisions through the “good buzz” or “bad buzz” effect and their ability to generate a following and imitation, particularly among young people, making demand extremely volatile.
There are many different ways a consumer may be influenced, although two main trends that predict their reactions have emerged: according to a number of studies, the influence of social networks is clearly illustrated, but the nature of the product or service offered to the consumer can also have an important impact.
With this in mind, two categories of goods or services can be distinguished: “utilitarian” goods or services and “hedonistic” goods or services, i.e. those that provide pleasure and provide the end-user with a positive image of themselves. The latter are examined more from a subjective perspective (I like / I don’t like) and as such are highly subject to individual perception. So-called hedonistic products are highly influenced by fashion, which is greatly reinforced by the social networks. For example, the process involved in buying a watch or lipstick is nowhere near the same as buying a sponge to do the washing up or a windscreen wiper.
Giovani J. C. da Silveira, co-author, provides the following illustration published in the Washington Post on 7 March 2019. A hipster[efn_note]Hipster refers to a person or subculture defined by the stereotype of young adults residing mainly in gentrified neighbourhoods-Wikipedia[/efn_note] named Flynn thought he had recognized himself in a photo that appeared in a newspaper, when in fact it was not him. This situation is an example of a true paradox, since in trying to stand out from the crowd by adopting an original look, the man ended up becoming a typified “hipster” to the extent that he could no longer recognise himself with certainty. Such strange behaviour is strongly influenced by social networks, “fashion magazines, instagram influencers and fashion Vloggers”, as Flynn himself admits.
By developing theoretical influencer networks, the authors were able to simulate the purchasing behaviour of two consumer types: those whose purchase is based on their own opinion (intrinsic preference) and those whose purchasing decision is subject to an external influence.
In the first case, where buyers are considered to be independent of one another, the larger the market, the less uncertainty there is concerning demand and therefore the more certain and easier it is to anticipate sales.
In the second case, where consumer purchasing decisions may change as a result of external influence, uncertainty concerning demand remains, whatever the size of the market. Here, the well-known Matrix sci-fi trilogy can shed some interesting light on how demand forecasts can be made.
In Matrix, humans can opt to stay immersed in a wonderful, unreal world by taking a blue pill. But they can also choose to return to a harsh reality by taking a red pill. This choice is extremely similar to the one managers take as they base their demand forecasts on two possible calculations: an average or a probability.
Managers can, as in the blue pill world, estimate that consumers will exercise an intrinsic preference, which will allow a linear prediction and therefore an “average” market estimate to be made. Or, on the other hand, take a red pill and try to account for a potential influence on consumer purchasing behaviour, then make an attempt to estimate “probable” demand and face potentially extreme cases of demand evolution.
It is precisely by taking these influences into account that managers can succeed in improving their knowledge of customers and their preferences. In terms of decision making, they will become more precise and make better choices in terms of operations: production, storage, logistics, supplies, etc.
Passing from the simplistic world of the blue pill to the complexity of a world viewed through the prism of the red pill is far from an easy transition. This passage can easily become a source of conflict and great apprehension within an organisation.
Advice 1. The first piece of advice is to approach this transition step by step, taking into account the evolution of the demand and gradually counterbalancing the importance given to either method of calculation used to predict demand.
Advice 2. Pay close attention to your customer’s social network habits. Depending on their connection time, buying preferences may be more or less influenced and their demand vary to a greater or lesser extent.
Advice 3. In order to predict the influence of external factors on consumer purchasing decisions, determine the nature of the product (hedonistic or utilitarian) as precisely as possible. Products related to fashion or leisure can be greatly affected by the opinions of social network influencers.
Advice 4. Finally, provide your consumers with as much information as possible. Buyers who have easy access to official information (descriptions, technical data sheets, SPECS,…) tend to rely more on their own judgement and therefore are less likely to be influenced by external opinion. This makes demand forecasting much easier.
This article is based on two publications:
• International Journal of Production Economics, “Estimating demand variability and capacity costs due to social network influence: The hidden cost of connection” by : Mozart B.C. Menezes, Giovani J.C. da Silveira, Renato Guimarães.
• Industrial Management, Sep/Oct 2020, Vol. 62 Issue 5, p17-21, “Deciphering social network influence on consumers” by : Mozart B.C. Menezes and Giovani J.C. da Silveira