Like all scientific breakthroughs, there needs to be some sort of experiment or evidence gathering to prove a hypothesis. Sometimes these breakthroughs are unrelated to the original hypothesis and are made by accident - as long as there’s some form of information to analyse, there’s scope for discovery. With so much of an ordinary person’s life now open to analysis by the data they leave behind, we are beginning to make breakthroughs that explain everyday life experiences.

Recently a piece in The Hindu reported a study that used data analytics techniques to establish an unusual feature of Indian cuisine. It found that, whereas most other global cuisines rely on positive food pairing – the pairing of similarly flavoured ingredients - Indian cuisine instead relies on negative food pairings using dissimilarly flavoured ingredients. They also discovered, by shuffling around ingredients in a recipe to observe its effect on negative food pairing, that it was the spice that drove the negative pairing. Of the top 10 ingredients whose presence biased the flavour-sharing pattern of Indian cuisine towards negative pairing, nine were spices.

Indian cuisine is much more complex as 20 ingredients may be used for a dish compared to, say, five for a typical Western dish. When you consider this, and the variation in Indian cuisine across regions and groups, there are multiple possible combinations of ingredients which may all get a different reaction depending on who is tasting them. It demonstrates how Indian recipes could be personalised according to what spice combinations a person prefers.

Novel research

So where do novels come in? It so happens other research out around the same time revealed there are six basic plots to any novel. As reported in the UK’s The Times newspaper, Matthew Jockers, a professor of English at Stanford University, discovered this after quantitative analysis of more than 40,000 novels. Plots follow six different patterns and can be represented graphically by ‘Good Fortune’ on the Y-axis measured against time. There are six basic curves which reflect plots that are either ‘Man in Hole’, including ‘Man gets into trouble and man gets out of it’, and ‘Man on Hill’ in which the main emotional content is positive. Novels are split roughly equally between these two types, but with distinct groups within each. ‘Moby Dick’ for example, is a variant of the ‘Man in Hole’ plot, but with a longer period in the hole compared to many other novels.

So again, this indicates more information could be used to better target offers to consumers. Instead of fairly crude offers where suggestions are all crime-related novels as the reader last ordered a crime novel, offers could also draw on what plot types the individual prefers, regardless of subject-matter.

These examples highlight how it’s possible to draw on many different types of data, not just the traditional ones around age, sex, disposable income, buying history etc, to establish what tastes and preferences a particular consumer has. This big data can be invaluable to retailers wanting to deliver that personalised shopping experience needed to steal a march on the competition.

The good news is, the technology already exists to collate and analyse all this data in a matter of seconds or minutes, giving retailers the insights they need to offer the right product at the right price at the right time via the right channel.

Find out more about why personalisation is critical for today’s marketers.