Predictive_AnalyticsIt can often feel eerie when you view a certain product online and end up getting recommended something that would complement it perfectly. But what’s really going on here is complex algorithms are being used to predict, or in some cases influence your behavior as a consumer.

There is now an unprecedented amount of data about your shopping preferences available for companies to get a hold of and use. This data goes on to get analysed by predictive algorithms to make deductions on what is most likely to happen in the future.

For example, when you visit a fast food restaurant, the server may notice you always order a medium meal with a certain soft drink. They could then look at the data to predict you will order the same tomorrow, and already have it ready for you when you get there.

How are Predictive Algorithms Used?

Insights extracted from collected data shows valuable information about shopping habits – such as consumers are more likely to make a purchase if they reached the website through a search engine, compared to a link in an email, and customers who browse a wide variety of different product categories are less likely to make a purchase than those that are focused on specific products.

This kind of information allows websites to be more personalised, based on the motivation of each visitor, whether that be taking them straight to the checkout or supplying more inspiration to those that need more time browsing.

Amazon and Netflix also use similar algorithms to make recommendations on their site. It is estimated by analysts that these algorithms drive 35% of what people buy on Amazon and 75% of what they watch on Netflix.

The way these algorithms work is by analysing both your past shopping behaviour and the behaviour of others. Making sure that there is a wide scope of data available is the key to success, as by analysing the past behaviour of similar consumers, the predictions that are made are more likely to be accurate than relying just on guess work.

Behaviour is not the only thing that can be used. For example, in the lead up to a major storm in the US, Walmart stocked up on strawberry pop-tarts based on analysis of past weather data and how that influenced demand.

It can also be used to predict the future of purchase behaviour – whether a consumer is likely to change purchase channels or even if certain customers are going to stop shopping. This can then go on to reduce customer churn by targeting these customers with certain promotional campaigns.

Should We Be Concerned?

While predictive algorithms obviously provide benefit, there are also big concerns around privacy with them. An example of this is with claims that companies have predicted that consumers are pregnant before they know themselves. Careful consideration of these privacy concerns from businesses and the government is necessary.

Despite these concerns, it’s important to remember that recommendations that these algorithms predict is based upon behaviour of a whole customer base, rather than just one customer. Companies are not truly interested in just one customer. Any promotions targeted at a customer are generated from an automated system rather than being picked out by an individual member of staff, so the chances of an any member of staff knowing about an individual customer is very low.

The way that companies target consumers using predictive algorithms isn’t necessarily a bad thing either, it can be of great benefit to the consumers. For example, when you’ve looked at a product, you will more than likely receive ads for that product over the next few days, sometimes this can come with a discount code – saving you money.

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