Predictive algorithms work the same way, on a much bigger scale.
My colleagues and I recently conducted a study using online browsing data to show the five reasons consumers use retail websites, ranging from "touching base" to planning a specific purchase.
Using historical data, we were able to see customers who browse a wide variety of different product categories are less likely to make a purchase than those focused on specific products.
Meanwhile, consumers were more likely to purchase if they reached the website using a search engine, compared to through a link in an email.
Using information like this, websites can be personalised based on the most likely motivation of each visitor. The next time a consumer clicks through from a search engine they can be led straight to checkout, and those wanting to browse can be given time and inspiration.
Somewhat similar to this are the predictive algorithms used to make recommendations on websites like Amazon and Netflix. Analysts estimate 35 per cent of what people buy on Amazon, and 75 per cent of what they watch on Netflix, is driven by these algorithms.
The algorithms analyse your past behaviour (what you have bought or watched) as well as the behaviour of others (what people who bought or watched the same thing also bought or watched).
The key to their success is the scope of data available.
By analysing the past behaviour of similar consumers, these algorithms make recommendations that are more likely to be accurate, rather than relying on guess work.
For the curious, part of Amazon's famous recommendation algorithm was recently released as an open source project for others to build upon.
But of course, there are innumerable data points for algorithms to analyse other than behaviour. US retailer Walmart famously stocked up on strawberry pop-tarts in the lead up to a major storm. This was the result of simple analysis of past weather data and how that influenced demand.
It is also possible to predict how purchase behaviour is likely to evolve. Algorithms can predict whether a consumer is likely to change purchase channel (for example, from in-store to online), or even if certain customers are likely to stop shopping.
Studies that have applied these algorithms found companies can influence a consumer's choice of purchase channel and even purchase value by changing the way they communicate with them, and can use promotional campaigns to decrease customer churn.
Although these predictive algorithms undoubtedly provide benefits, there are also serious issues to do with privacy. There have been claims that companies have predicted consumers are pregnant before they knew it themselves.
These privacy concerns are critical and require careful consideration from businesses and government. But it is important to remember that companies are not truly interested in any one consumer.
Although many of these algorithms are designed to mimic "personal" recommendations, in fact they are based on behaviour across the whole customer base.
Additionally, the recommendations or promotions given to each individual are automated from the database, so the chances of staff actually knowing about an individual customer is extremely low.
Consumers can also benefit from companies using these predictive algorithms. For example, if you search for a product online, chances are you will be targeted with ads for that product over the next few days.
Depending on the company, these ads may include discount codes to encourage you to purchase. By waiting a few days after browsing, you may be able to get a discount for a product you were intending to buy anyway.
Alternatively, look for companies that adjust their price based on forecasted demand. By learning when the low-demand periods are, you can pick up a bargain at lower prices.
So although companies are turning to predictive analytics to try to read consumers' minds, some smart shopping behaviours can make it a two-way street.
• Liam Dann's column returns next week.
