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by Graham Cookson
in Big Data
on 05 December 2014

How big data drives holiday sales

Whether you celebrate the holiday season or not, it’s hard to avoid this time of the year and not get swept up in the camaraderie and general good atmosphere that is seemingly pumped into our lives. In particular, for retailers it’s a time of year that you would be foolish not to use to your advantage.

Since the birth of online retailers, shopping during the festive period has grown exponentially – even with the global financial crisis, shoppers have been spending more year-on-year.

In 2012 during the three days of December 24th, 25th and 26th UK shoppers made more than 304million visits to online retailers – a little over 100million each day. [1]

In 2012 it was also estimated that on ‘Mega Monday’ (the busiest shopping day of the year) shoppers made 115million visits to websites, spending £10,000 every second between the hours of 9am-9pm and spending an estimated £465million throughout the day. That’s nearly half a billion pounds in just one day. [2]

In 2013 YouGov estimated UK sales during the festive period to reach a total of £22.3billion – with UK households estimated to be spending an average of £822 each on food, drink and gifts for friends and family. [3]

Easy shopping means easy sales

It’s no surprise that since online shopping was introduced into our lives that the spending at this time of year has risen. Consumers no longer need to physically leave their homes or offices to spend days walking around shops for the perfect gifts for loved ones; instead a whole world of presents is available at their fingertips.

Especially thanks to mass-online retailers, selling almost any item a person can think of, with very little effort anyone can order their entire list of presents from a single website, pay and wait for the gifts to be delivered – sometimes even gift-wrapped already.

And then, to entice customers more, retailers often slash the prices of popular items before, during and just after the holiday shopping rush. This move in cutting prices became such a massive trend in America that every year the Friday following Thanksgiving was dubbed ‘Black Friday’, where retailers (both physical and online) cut prices in a bid to bring in more customers and is now seen as the start of the holiday shopping season in the US.

And while Black Friday is technically not classed as a public holiday in America, many states have started treating the day as one - allowing government employees, schools and other non-retail workers to use the day for shopping.

In the UK, it has taken longer for Black Friday to become a big event. This year (2014) is the first time that UK stores really took advantage of the sales and prominently advertised the legend: 'Black Friday'.


However, Black Friday has been around in the UK and other nations for several years now (not just the US), but in the past it's only really been large online retailers and companies based in, or originating from, America which have used the day to drop prices and push for sales. Companies such as Amazon, Apple and ASDA (part of Wal-Mart Stores Inc) used the event to mark the beginning of their holiday sales period – advertising deals on their homepages and using limited-time offers (including count-down clocks) to encourage consumers to grab a bargain.

In the US, the National Retail Federation has published recent ‘Black Friday Weekend’* sales figures. In 2005 the weekend saw sales reaching $26.8billion, with that number being more than doubled by 2013 – reaching in excess of $61.4billion.

Another prominent day for shopping at this time of the year, which has spawned from Black Friday, is ‘Cyber Monday’ – the Monday immediately following Black Friday.

It has previously been noted in several studies that Monday’s are recognised as the busiest online shopping days throughout the year and, in particular, the Monday following Black Friday saw a huge increase in sales as consumers who were unable to take advantage of the weekend’s sales try their luck to grab some bargains online on the Monday at home or in the office.

In 2012, Amazon announced that it sold over 3.5million items on Cyber Monday alone; approximately 41 items per second.

In 2013 the credit card firm, Visa, predicted that Cyber Monday spending would reach 450m in the UK alone, with 7.7million transactions taking place and more than £312,000 being spent per minute on its cards online.

A huge boost in terms of online sales in recent years has been the huge adoption of smart devices. In 2013, John Lewis said that more than 42% of its online traffic came from smartphones and tablet computers. Mark Lewis, online director at John Lewis, said: "With shoppers becoming more comfortable when purchasing via mobiles and tablets, coupled with better connectivity and the rise of smartphones so people can browse online through the day, it's not surprising the way we shop has changed." [4]

Socially driven sales

However it’s not just the ease of access or the wider range of methods in which consumers can now tackle their seasonal shopping sprees that has contributed to increased revenue. Social media is a big player when it comes to companies understanding their audiences. The conversations between consumers, friends and family over social media has grown over the last few years, in particular mentioning products or sharing direct links to items of interest.

But it’s not only online friends that consumers take notice of. According to a recent study, more than 54% of Twitter users said that advertised promotions they learn about through the social media channel motivate them to buy, with 52% of users saying that they have purchased products they originally learned about on Twitter.

The research went on to show that 39% of Twitter users now use the platform as their holiday shopping list and 54% of users check Twitter while shopping in a retail store. [5]

And it’s not just Twitter that consumers use to find out about promotions or new gifts to buy loved-ones.

Social media giant, Facebook, is an avenue that retailers can (and have been) using for the last few years to target their audience with holiday deals. As a consumer follows or likes a company’s page on Facebook, that company will know that when it posts a product promotion on its wall, that the majority** of people following said company will see that promotion and therefore the company knows exactly what its reach is and how well that particular campaign is working through its correlating sales figures.

But what about other social channels?

Facebook and Twitter might be the most popular social media channels at the moment, but they aren’t the only public channels people communicate through or have conversations about gifts and purchases on. And while a promotion on Facebook or Twitter is somewhat easier for a company to gauge in terms of success, the other channels aren’t as easy to judge.

And how do companies really know what products/services people actually want deals on during the festive season?

Everyone loves a bargain, but if a sportswear retailer started discounting beach towels and swimming costumes during the festive shopping season you can bet that those sales won’t be as profitable as if they were to discount thick winter jackets or ski/snowboard gear.

The subtle sale with big data

The answer is simple and it should come as no surprise: more and more companies are now turning to big data analytics to help predict the most sought-after products and understand their consumers better, in order to clinch vital sales during this competitive period of the year.

Big data analytics enables retailers to take advantage of multi-channel retailing to help boost profits and sales figures. But, crucially, it also subtly ensures that consumers are getting the products they want, when they want it and through any medium/source they might use.

And it is crucial that consumers are getting what they want, because that means that the customers are satisfied and do not later regret their purchasing decisions. In the days before big data or the Internet, retailers would have to rely on good judgement, good research or more under-handed tactics to ensure high sales margins.

The more under-handed methods would involve pressurising customers to buy and often to buy something they hadn’t thought about purchasing before. By pressurising sales onto customers, retailers would soon find more and more disgruntled consumers leading to a decline in future and repeat sales. This is why you never see door-to-door sales people anymore – customers don’t like being told what they want to buy, they like to choose.

And that is where the subtlety of big data analytics really benefits both sides of retail. Big data analytics allows the retailer to find out what their customers are interested in, through how they shop on their website to what is being said on social media, forums and blogs. The retailer can understand an individual shopper’s needs and can then target products that they know that person is interested in.

From the customer’s side it makes shopping easier. There are millions of websites in the world and not enough time to look at them. By someone looking on two or three websites for a particular item, search engines, such as Google or Bing can (and do) track the user’s browsing and can then use their advertising system to target similar products – meaning that now that user is seeing adverts appear on websites they visit for similar products that they were just viewing; instead of the consumer having to search for products, those particular items are now being shown to them.

It’s a subtle distinction between door-to-door or pressurised sales tactics – because those older methods meant that the consumer was being targeted with products or services that the retailer had chosen to sell to them; now that product is being selected by the consumer and the retailer is simply saying: “We have that in stock. Why not shop here?”

And while the example above mentions big data advertising through search engines, retailers are able to do exactly the same by using big data analytics platforms directly – which can deliver more in-depth and accurate results.

One-click buy

Amazon is probably the best example one can give about a retailer utilising big data analytics to help boost sales. Everyone with the Internet has heard of Amazon, even if you haven’t used it yourself, you will know about it and someone who has used it. But few know about Amazon’s origins.

For those that don’t know, Amazon initially started out as an online book retailer out of founder, Jeff Bezos’, garage in 1995. It is now the largest Internet-based company in the United States, listed on the Fortune 500, has a revenue in excess of $74billion and employees more than 132,000 people worldwide. [6]

So how has a relatively humble online book store from Seattle gone from a single employee to become one of the biggest global-operating businesses?

You could put it down to luck, excellent business acumen or, more likely in Amazon’s case, the company’s ability to use data and any insights gained to its advantage.

Amazon is widely regarded as being one of the key innovators in big data technology, since being founded, the company has used a variety of big data methods and tools to help secure sales and guarantee customer satisfaction:

Customer first

One of Amazon’s first uses of big data has been its use of ‘customer recommendations’. When a user logs into their Amazon account, they will see a list of recommendations on the homepage specifically catered to them, based upon previous browsing history, additional recommendations, sale items based upon what the customer previously bought or searched for. The majority of online retailers use this method today, but Amazon was one of the first to implement it.

In a similar way to what was mentioned previously with Google and Bing advertising, Amazon’s customer recommendations aims to deliver a more personalised shopping experience. And it has been reported that this personalisation has led to customers buying more than they would otherwise.

It might sound obvious, but for retailers looking to implement big data, the number one focus needs to be the on the customer. You can’t approach sales with a mentality of: “This product will make me X amount if I sell it at Y” then try and push the sale and disregard the fact that the customer might not even want ‘product X’ at all. It’s a cliché, but you don’t want to be 'that' company trying to sell snow to an Eskimo or sandpits to a Sheikh.

It goes back to customer satisfaction and with the Internet every customer has a voice that can influence others in their buying practices. If a customer has a bad experience, they can (and often do) shout about it on social media - persuading friends, family and (importantly) complete strangers to steer clear of your products and services. One person can have a potential reach to thousands of prospective customers – a poor sales practice doesn’t just affect future sales from an individual anymore, it affects the decisions of a small army.

If companies want to succeed, the consumer has to come first and big data can help gain those necessary insights into understanding your customers, their needs and requirements.

On-going developments

Amazon’s use of big data doesn’t stop at the customer recommendations. While the company hasn’t publically advertised how it uses big data, it has been a contributor to the on-going development of Apache’s Hadoop project - an open-source software framework for distributed storage and distributed processing of big data.

Part of this contribution to the development of Hadoop has come out of necessity. As one of the most visited online retailers, achieving millions of transactions per day, the company has had to find a way to process all these transactions, store its listing of hundreds of millions of products, while maintaining server strain at the particularly busy periods.

A big part of Hadoop’s infrastructure is an application called MapReduce*** which, in a nut shell, is a system that allows the parallel processing of large data sets.

While most companies dealing with big data use MapReduce in its standard form, Amazon has gone on to further develop the application to meet its ever-growing demands, creating ‘Amazon Elastic MapReduce’ (aka Amazon EMR) – a web service that is said to make it easy to quickly and cost-effectively process vast amounts of data.

Amazon has multiple web services, all of which help the company handle big data in multiple ways, including: log analysis, payment tracking, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics – essentially the various things Amazon’s retail platform is working on in the background while you do your shopping.

Direct social shopping

In May 2014, Amazon made it even easier to shop through social media by connecting Amazon accounts with Twitter accounts. [7]

This process enables Twitter users to add items directly to their Amazon shopping carts without needing to leave Twitter, by users following the @Amazon Twitter account, which promotes top deals and discounts and users then reply with the ‘action hashtag’ #AmazonCart (in the US) or #AmazonBasket (for UK customers).

But Amazon is far from the first company to trial this method of social shopping. Action hashtags have been used in multiple ways, with varying degrees of success. In 2013 American Express launched its own action hastags, as has Chirpify with competitions for its clients (plus many other websites and companies). And if Amazon and America Express’ use means even easier shopping for consumers, there’s a good chance this method of advertising will evolve and grow – and big data would become even more key in understanding what products companies need to entice their audiences with.

Predicting purchases

Amazon is also taking things to a new level with big data – it is so confident in its big data analytics processes that the company has patented and is working on establishing something that it refers to as “Anticipatory Shipping”. [8]

Essentially this is a new method which takes into account varying factors for each shopper and anticipates what a consumer will be likely to buy and organises those items in advance.

Now, to clarify, the idea is not that you will suddenly find boxes of products suddenly appear on your doorstep and you be expected to pay for them.

What Anticipatory Shipping does is understand what individual customers in different regions are likely to buy and organises its shipping system to accommodate. So those products will be set aside in a distribution centre local to the consumer in anticipation for the customer to buy the items.

This should help bring down the costs of logistics for the company and ensure faster and more efficient deliveries for customers – and potentially bring down costs.

Will it work? That’s something we will have to wait and see to find out.

From big enterprises to small companies

Another reason why Amazon is a good retail company to look at in regards to big data, is because it has shown how both small and large companies are able to use big data to its advantages.

Unlike some larger companies you hear about, which are already big corporations that now need to look into big data, Amazon started out as a small company and using big data and insights to its advantage it grew into the multi-national enterprise we have today, and its use of big data has evolved along with the company.

It helps to show that any company, regardless of the size, can use big data to help boost profits. Will every company become as big as Amazon? Probably not.

Amazon was lucky/shrewd enough to be one of the first retailers to really take advantage of online retail, by expanding its operations from books to almost any product a consumer can possibly want or need. Now that it has firmly established that foothold and prestige in the market, it’s hard for anyone to expect to become quite as big – but never say never.

But can big data analytics help to increase profits and market share? Absolutely.

If utilised correctly, the insights gained from big data can help almost any retailer grow and evolve its business.

That time of year again

This brings us back to the festive period. As it was highlighted earlier, this time of year can be a retailer’s dream, if it is approached correctly.

While the Internet is constantly evolving and growing, the market during the festive season is becoming increasingly competitive for opposing companies trying to effectively ‘win’ customer sales from their rivals.

It’s here where companies, like Amazon, have been able to use big data analytics to gain a significant advantage over other retailers.

Through the use of big data analytics tools, such as Digital Contact’s, companies can monitor conversations across multiple sources, such as social media, blogs, websites and forums to gain a better understanding of what is popular.

That information can also be broken down into very specific target audiences, such as gender, age range and location, so you can be sure that you are targeting your promotions at the right consumers, who are genuinely interested in what you have to offer.

The insights gained from big data can lead to an advantage over the competition that is hard to ignore – allowing successful companies to grab a hold of a larger portion of the market and dramatically boost revenue over previous years.


During the festive period, every year companies fight over customer sales and it’s been proven that newer channels of communication, such as social media, have helped businesses attract more customers.

With the constant growth of the Internet and the continued (and expanding) use of social media, big data management is becoming more common place and a more important tool to help make sense of the huge amount of information out there.

The application of big data management and its potential is vast, but the true benefits can only really be seen when the data is collected and handled efficiently by the end-user.

The key to achieving success with big data in any industry is to ensure that you have set goals and targets that you wish to accomplish before you set out. In retail, simply understanding your customers’ needs is crucial for success: as soon as you know what your customers want to buy, you can not only target campaigns to the right people, but also to let consumers actually choose what they want to buy, not force products upon them - easily fulfilling their desires, boosting your revenue and guaranteeing repeat custom. The smart companies are not forcing sales, they are just letting those customers know that they are able to provide what is required. When looking to boost revenue with big data, it is up to you (the user) to understand what your goals for big data management are and how you want to best achieve them. Digital Contact’s platform has been designed for use in all industries, gathering its information from millions of sources through a wide variety of channels – delivering further accuracy and insights to its results.

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* The Federation's definition of ‘Black Friday weekend’ includes Thursday, Friday, Saturday and projected spending for Sunday.

**Facebook’s ever-changing algorithms and back-end features means that you cannot always guarantee your full target audience will be reached, as not everyone receives every post a page puts out.

***For more information on MapReduce, please see our white paper: “An introduction to Hadoop”.


Sources: [1] BBC News | [2] Daily Mail | [3] YouGov | [4] Perspicacious | [5] Official Twitter Blog | [6] Amazon company details – Wikipedia | [7] CNet | [8] Wall Street Journal / TechCrunch / HuffingtonPost / Forbes |


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