Big Data (but not a Big problem solver)

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You don’t know who your customers are? You don’t know who your customers could be, what they want and how to seduce them? A common problem for many companies in today’s consumer society and here’s everybody’s all-round solution:


But do you really need to track and save all this private information about so many people to increase your ROI significantly?

Big data as the bad guy of consumer behaviour research

Using big data methods to improve interaction and communication with your customers is controversial and often portrayed as unethical and total surveillance. While there is definitely potential and when used with responsible know-how it offers great opportunities, turns out, it often isn’t worth the hassle. Capturing and storing a huge variety of information about people does not automatically give you answers. Data is likely to be noisy and it’s a complicated, expensive, time consuming process to develop a model that makes sense of all this information.


So why choosing this pain when all you need to do is ask three magic questions?


Just like you don’t need to know the full background story and detailed personality report of a person you randomly meet in a café. In order to empathize and have an engaging conversation, you just ask a few questions and listen to what and how they respond.


The good guy of consumer behaviour research

To create positive interaction with a stranger in a café, you need a rough frame of who you’re talking to and rather obvious information like gender and age group are not necessarily sufficient. Seeing it’s a guy and knowing he is 27 years old still leaves endless topics to pick from that could possibly make the conversation great or awkward at the same time. Is he a stay-at-home dad of two kids, part-time musician, carpenter, semi-famous gay actor, or a top league consultant?


For our companies, this means that consumer behaviour research is relevant – to a certain extend. In addition to monitoring demographics (age, nationality, gender, etc.), collecting some psychographics (including personality traits, activities, interests, and opinions) makes a big difference. While two potential customers could have similar demographics, their interests and way of thinking might be completely different affecting their purchase behaviour. You would certainly want to separate confident risk-takers from deliberate stability-lovers and hedonic from functional shoppers, because they wouldn’t be interested in the same sort of conversation, they communicate differently.
A small data set including the most relevant demographics and psychographics can get you far.


I mean, knowing the guy in the café does extreme sports and lives for taking risks is a conversation kick-off, right? What would his religious affiliation, average annual household income, or five most frequently visited websites this month matter?


So how do you decide between relevant and irrelevant information? How do you find out about your customers’ personalities without invading their privacy but still ensuring truthful answers? Simply and explicitly asking about opinions has shown to be problematic since people are not good at predicting their own attitudes and behaviour.


Three magic questions

Your solution is Osgood’s Scale of Semantic Differentials (and our Semantic Pairs survey which uses the method). This psychometrically controlled scale was designed to measure the connotative meaning of objects, concepts, attitudes and values. The development of this scale goes back to the first half of the 20th century and a lot of shaping and confirming has been done on it since. Even Nobel Prize winning psychologist Daniel Kahneman did his dissertation on the subject of the Semantic Differential.


Participants are asked to rate presented words, phrases or objects using bipolar adjective pairs. The objects to be rated as well as the rating adjectives are individually variable which makes the scale very flexible and brought about its nickname ‘the Eveready battery’ of the attitude researcher.


However, three recurring attitudes have shown to be cross-culturally universal and are most commonly used: evaluation, potency, and activity. The corresponding adjective pairs are ‘good-bad’, ‘strong-weak’, and ‘active-passive’. Evaluation mostly concerns utilitarian and explicit attitudes, potency explores rather implicit attitudes and activity gives an idea of people’s attitude-behaviour gap. This combination ensures a full picture of people’s opinions and gives you a good idea about what part to adjust in order to match you customers’ communication preferences even better.


A rating scale of bipolar attitudes compared to a classic Likert scale also delivers more accurate feedback as it is not dependent on the subjective understanding of the rating terms. The way a participant understands the word ‘strong’ when asked ‘How much do you agree with the following statement: ‘Just do it’ is a strong slogan?’ will impact the rating and cause unwanted variation in the results. A scale of opposites solves this problem.


So in order to check your customer interaction now, don’t plant bugs in their living rooms. Don’t track them down to the tiniest detail and don’t capture loads of data you will never (need to) use. Take a piece of your messaging apply Osgood’s scale of Semantic Differentials and let them tell you how they really value your communications.





References

Ajzen, I., & Driver, B. L. (1992). Application of the theory of planned behavior to leisure choice. Journal of leisure research, 24(3), 207-224.
Batra, R., & Ahtola, O. T. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing letters, 2(2), 159-170.
Marketing is Evil. (2017, January 17). Retrieved from https://www.psychologytoday.com/intl/blog/how-do-life/201701/marketing-is-evil
Matz, C. G. (2018, May 02). What Marketers Should Know About Personality-Based Marketing. Retrieved from https://hbr.org/2018/05/what-marketers-should-know-about-personality-based-marketing?autocomplete=true
Personality Matters: How one company doubled its ROI by customizing ads based on personality. (2017, June 08). Retrieved from https://marketingexperiments.com/digital-advertising/ads-based-on-personality
Slater, S. F., & Narver, J. C. (2000). The positive effect of a market orientation on business profitability: A balanced replication. Journal of business research, 48(1), 69-73.
We Think You Think You Will Buy That. (2018, May 15). Retrieved from https://www.dmnews.com/customer-experience/article/13034533/we-think-you-think-you-will-buy-that


Image c/o https://inform.tmforum.org/features-and-analysis/2015/04/introducing-the-analytics-big-data-repository/





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