What Can Contact Centres Learn from Amazon?

The Value of Big Data: What Contact Centres Can Learn from Amazon’s Algorithms

The world of analytics is ever changing. New technologies, fresh ideas and new possibilities are being introduced all the time. Even for people who have been studying this field for years, it can be difficult to keep track of everything that could help organisations improve their online presence.

Many businesses will have their eyes on e-commerce giants to see what they can learn from their success, however some areas may seem out of reach for the average company. Even if you do manage to get your hands on Amazon's top-secret analytics, how would you make use of them? How could you turn the numbers into personalised interactions with customers?

First, a brief Amazon history lesson

The Amazon website was first launched in 1995 as an online bookstore but has since extended to cover pretty much everything under the sun. Amazon now sells anything from laptops and TVs to groceries and garden furniture. The company employs around 150,000 people worldwide, and ships to 178 different countries. As of 2021 they listed a whopping 353 million items – with over 12 million of these being sold directly by Amazon themselves.

In 2010 Amazon employed one of the big data industry's most notable figures, Dr Matt Wood. He was the former Director of Algorithms and Analytics at Teradata, a global leader in analytics services and solutions, before leaving to take control of Amazon's big data platform. Being involved in such a project is a feat in itself, but to take the reins and lead a project of this magnitude has quickly made him one of the most influential figures in big data.

So, what exactly has Dr Matt Wood been up to, and why should contact centres follow suit?

Amazon's algorithms and data science: The A9 algorithm

Algorithms seem to be the 'in thing' at the moment. Obviously they are nothing new, and now there seems to be a fresh algorithm being used everywhere you look. Whether it's suggesting TV shows on Netflix or sorting your posts on Facebook, algorithms are playing an integral part in our lives - and it is important that more businesses learn how to use them more effectively.

With the number of products on offer at Amazon, you might expect a lengthy process to make sure each item has a place within the catalogue. However, thanks to the Amazon A9 algorithm they don’t have to spend time doing this.

When a customer goes to the Amazon website and types in their search, the A9 algorithm will determine the order in which products are displayed. A9 does this by taking a variety of factors into account, with the following four being the most key components for products:

Sales performance history

Text match relevancy

Price

Availability

This use of pre-existing data gives Amazon the greatest possible chance of converting searches into sales. It’s clear that they understand their customers deeply and consequently have a brilliant selection process that lets them focus on cost-reducing logistical improvements.  

Amazon’s customer focus

Seeing as customers are at the heart of any good business strategy, it's no surprise that Amazon have been focusing heavily on their customer service elements. In 2021 they were listed as number 4 in Forbes' 100 best companies to work for and they continue to build their reputation as a friendly and efficient company that has turned customer-centricity into a valuable differentiator.

Even in their customer service, data still plays a big part in Amazon. Data about their customers is collected from Amazon's Customer Service department, who are constantly responding to queries from all over the world. This provides even more information regarding each customer for Amazon to feed into their algorithm.  

With so much data available, a focus on training, feedback and improving customer service has given Amazon the edge when it comes to using data correctly within the organisation. Data is an important part of every business, but without the right tools and people in place it is almost useless.

So how do contact centres follow Amazon's example?

Algorithms are not an easy thing to get right. They require a lot of work and effort behind the scenes for them to work effectively - but when they do, it's worth it.

Now that we've learnt more about what Amazon are doing, it's time to look at how contact centres can learn from the way they handle their data. After all, there is plenty of information on how they're running things behind the scenes and why they're proving to be so successful.

One of the keys to providing a successful service is the ability to acquire the right information from your customers and act on that information quickly to close any expectation gap.

It’s why we created the most intelligent customer orchestration platform, so it can guide agents to present customers with personalised offers at moments when they have the highest propensity to buy. The complicated data crunching is managed in the backend so your managers and leaders can easily set up and tweak conversation flow without needing expensive technical resource.

Use big data... but don't let it take over!

Amazon has relied heavily on the use of algorithms to make their business model a success. But they have balanced this out with an efficient customer service team, who vitally provide a human touchpoint for this global organisation.

Just because you have a lot of data at your disposal doesn't mean that it should be used in every circumstance. This doesn’t just remove the person-to-person contact in your business, there is an additional danger that staff members could get more bogged down in doing so. Your data should be actionable to avoid cumbersome data interpretation – and AI and machine learning of today can easily do this.

This is what underpins our Listen, Act, Learn methodology. Conversation and speech analytics platforms mean you can analyse 100% of demand and pinpoint any quality shortcomings. This allows you to easily access customer insight from every conversation in a way that offers meaningful data for your organisation to use.