Algorithms are not just for mathematicians. More and more of modern life is now run by them and they have become the instruction manuals for a whole host of everyday and business transactions. So what are algorithms? Well put simply, an algorithm is a piece of software code that operates like a decision tree, sifting through vast amounts of unstructured data, considering multiple variables before giving its decision or recommendation on how best to carry out the task in hand.

The reason that they are becoming so widespread is that firstly there is far more data available everywhere, from smart energy meters to telephone records and consumer buying habits; but also that people want to do more complicated things with this data and use it to optimise processes and deliver better – and more tailored – customer experiences. The speed and processing power of modern computers means that algorithms can execute these analyses with blinding speed using vast amounts of data, far beyond the processing capability of any human being.


So search for something on Amazon and, inspired by your browsing history, algorithms will generate recommendations based on the data for other items that you might want to buy. Buy something and algorithms will help a logistics firm to decide on the best delivery route to your house, taking into account the location of the nearest warehouse, all the other deliveries that they have to make that day and the delivery deadline. Ring to check your order’s progress and more algorithms spring into action to determine the quickest connections to and through a call centre.

Call Centres

Most people don’t realise that when they ring a large company, they’re actually being categorised, sliced, diced and parsed by a bot – a collection of algorithms. But it happens all the time. Call centres routinely decide which Customer Service Representative (CSR) to place an incoming call with, based on information such as the customer’s location, the length of queues that each operator has to deal with and the reason for the call.

Call centres can ‘guess’ the reason for a call by the options that the caller has chosen when dialling, but also through the use of predictive analytics, using the caller’s browsing and buying history to ascertain where exactly they are on their ‘customer journey’.

So if a caller has just received an energy bill, it’s likely they are ringing to query something about the bill; or if they have just bought a product, it’s likely they are ringing because they don’t know how to use it or they’re not happy with the quality.


Algorithms are also behind the optimisation of supermarket supply chains. Because a significant amount of capital is tied up in stock held in warehouses, there is pressure on supply chain management to minimise stock levels, while avoiding empty shelves. Not only that, but UK supermarkets like Tesco use them to tailor direct-marketing offers to each of its Clubcard (loyalty card) members.

Purchasing habits, patterns and preferences are logged and checked against social class, age, postcode and household earnings. Customers are even segmented on the basis of whether they have bought a large number of Tesco Finest, its premium range of foods, products in the past. Have you ever wondered why when you buy dog food for your puppy, you start receiving discount offers for a more expensive brand of dog food followed, a few years later, by offers for adult dog food (when algorithms have calculated how old your dog is)?


Airports are also very interested in algorithms. Passengers at London’s Heathrow and other busy airports are often delayed when their plane is stuck on the runway in a queue waiting for clearance to leave.

Delays happen because air-traffic controllers need to leave a gap – a safety margin – between aircraft as they take off, but the size of this gap depends on the size and speed of the aircraft in the queue. Continually re-ordering the queue can minimise the delay. Air-traffic controllers typically reordered planes in the departure queue manually, but algorithms are now being used to make this calculation more efficient.

Human beings still have role to play of course, humans need to direct the algorithms and determine what they should do. But were it not for sophisticated algorithms, Big Data would be just that, volumes of data, but with no ability to provide insight or understanding.