Keeping Millennials Engaged

How to Keep Millennials Happy During Your Digital Journey

It seems to be the case that millennials want a simple and effective user experience when utilising online services, but how can businesses ensure their 25-35 year old customers are happy whilst ensuring a secure and robust verification process to protect themselves from fraud?

Businesses should be ensuring that they are delivering a user experience that makes their customers feel secure and protected by their security measures, whilst providing a smooth digital journey.

Our 2017 big data predictions

This has been the year that big data has become, if possible, even bigger. A survey by Gartner found that 48% of companies invested in big data in 2016, and nearly three quarters of those surveyed have already invested, or plan to invest.

And this trend looks set to continue. Decisions are being driven by data in many different sectors, from marketing and pharmaceutical companies to third-sector organisations. Algorithms within the advertising industry are influenced by information about shopping habits, and the same data-driven algorithm is used for engineering both Formula 1 vehicles and asthma drugs.

But just how far can big data take us? Has it reached its peak or is there more potential to explore? We’ve had a think about what 2017 means for big data.

 

1.     There will be more data

Perhaps the most obvious big data prediction for the upcoming year is that there will be more of it. The increasing number of individuals living their day-to-day lives largely through the internet means that there is more opportunity than ever before to create and collect data, and by 2020 the amount of data that is worth analysing is predicted to double.

Everything from social media, health monitors and taxi apps collect data from the user, all with the aim to improve and personalise the user experience and make more efficient, cost-effective business decisions.

Recently, British Airways put out a patent application for a ‘Digital Pill’ – a system which intends to personalise the travel environment for each passenger. It would work as an ingestible sensor and would supposedly monitor temperature, sleep phase, and heart rate. This data would then be fed to the airline which would then alter elements of the individual’s in-flight experience, such as their meals, sleep-times, lighting, and in-flight entertainment, accordingly.

This revelation shows just how advanced the use of data can get, and raises the question of what other innovations we will see emerge in 2017 that we had previously thought to be impossible.

 

2.     There will be more demand for big data and analytical skills

Businesses should get big data analytics on their list of new year’s resolutions if they want to provide a better service to their customers and keep their competitive edge. Personalisation has become ordinary for consumers, who now expect businesses to go a step further. For example, users of TV and film subscription services such as Netflix assume that the app will understand what they’re looking for and recommend shows for them to watch. Businesses should use data to make the customer’s journey more efficient and convenient, something that traditional data, such as demographics and credit scores, can struggle to achieve.

A significant benefit of big data is that it encompasses a large number of data points from a range of sources, and can therefore paint a much more accurate picture of an individual consumer at one point in time than businesses could have dreamed of ten years ago.

It allows companies to verify identity and detect fraud, and provides insight and predictions into behaviour like travel, outings, and vote outcomes. It could even save lives – researchers have discovered a way of utilising machine learning to develop predictive models for the onset of type two diabetes.

This demand will subsequently lead to businesses needing to recruit staff with big data analytics skill sets, or work with an external specialist to keep up with industry competition and customer expectations. This may mean a rise in people with jobs like data scientists, data modellers, data analysts, developers and more.

 

3.     There will be more monetisation

Plenty of experts have predicted that the creation of this data and its use will result in its monetisation. The chief executive of Dell, Michael Dell, has predicted that big data analytics is the next trillion-dollar market. IDC predicts that that data monetisation efforts will result in enterprises increasing the marketplace’s consumption of their own data by 100-fold – or even more.

By 2020, the amount of data that is worth analysing will double. Forrester expects that as firms will try to sell their data, ‘many will sputter’, but in 2017, an increasing number of firms will look to drive value and revenue from their ‘exhaust fumes’. Forrester declares that ‘all companies are in the data business now’.

The trouble comes with identifying which type of data is useful – but it seems that businesses aren’t too far away from achieving this. IDC predicts that by 2020, organisations that are able to analyse all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.

Although this is a good few years away, we are likely to see a move towards this in 2017, as a similar figure for revenues associated with data monetisation would get us closer to Michael Dell’s trillion-dollar prediction. In the same interview, Dell described the current state of data analytics, saying that: ‘If you look at companies today, most of them are not very good at using the data they have to make better decisions in real time’. However, as the big data market continues to grow, we are likely to see companies making better business decisions as a consequence.

 

4.     There will be a deviation away from the buzzword

‘Big data’ has been one of the biggest buzzwords of the last couple of years, but as its use grows, this term will be used less. Big data itself is raw and unstructured, making it near impossible to derive meaning from it without implementing tools that are capable of analysing it and creating meaningful insights.

However, we have already started to see move away from the ambiguity of the term ‘big data’. Terms such as ‘smart data’ and ‘actionable data’ differentiate between the offerings of the different types of analytical tools available.

Ultimately, it looks like big data is here to stay in 2017. Data analytics has gone a long way towards transforming almost every sector this year, and patents for innovative products, huge growth in the amount of data generated, and increasing customer demand mean that data will likely be the driving force behind many business decisions next year.

Read the full article

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The Dark Web trade exposed

It was the cover story of The Times on Saturday, and has been an infamous point of discussion since the internet became mainstream, but what is the dark web, how can I protect data from the dark web, and why should we be worried?

“Searching on the Internet today can be compared to dragging a net across the surface of the ocean. While a great deal may be caught in the net, there is still a wealth of information that is deep, and therefore, missed. The reason is simple: Most of the Web’s information is buried far down on dynamically generated sites, and standard search engines never find it.” – Michael K. Bergman

The dark web refers to a huge area of the internet, untraceable by standard web browsers, and only accessible through encryption tools such as Tor. Tor hides the identity and location of the user, and is usually used by the website host as well as by the user, meaning that a huge amount of unlawful transactions involving fraudulent identity documents can take place covertly.

Research revealed by The Times this Saturday reported a shocking number of forged identity documents, including passports, utility bills, driving licenses and bank statements, were on sale on the dark web for anywhere from as little as £7 for a passport scan, or £752 for a physical UK passport.

Research leaders, Dr Lee and Dr Andres Baravalle, spent three months searching through the Agora marketplace, known as “the king of the dark web”, finding more than 30,000 illegal products on sale, many of which were fake or real identity documents. The academics found a trade of nearly £2 million in identity documents, including EU identity cards for £142 and driving licenses from EU countries for £419.

Recently Yahoo! Admitted that their database of over 500 million accounts was raided by hackers, leaving millions vulnerable to identity fraud and theft. Now they are being sued in California for failing to take due care of sensitive information under the Unfair Competition Act as well as negligence for poor security. Information stolen from the Yahoo! Database include users’ names, email addresses, telephone numbers, passwords, and encrypted and unencrypted security questions and answers.


 

Our Solution

Our unique software solution can be used to identify whether your customers’ data is for sale on the dark web, providing high, medium, and low risk alerts surrounding the likelihood of ID theft so that you can quickly and efficiently identify fraud and alert your customers.

With our software you can protect data from the dark web risks, and prevent millions in losses from identity fraud.

Get in touch to find out more

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The Dark Web: Is my data out there?

The Dark Web (which can also be referred to as “The Deep Web” or “The Invisible Web”) makes up at least 85% of the internet. With some sources stating that traditional search engines only see 0.03% of the entire web, we discuss the dark web data risks you need to know about and how you can prevent associated losses.

The Dark Web was launched by the US Naval Research Laboratory in 2003. It was indended for use in secret services, law enforcement, and to train political dissidents in countries with oppressive governments. Additionally, journalists in heavily censored countries could use it to communicate and exchange information.

The Dark Web requires specific software, configurations, or authorisation to access in order to ensure complete anonymity, and transactions are made using untraceable currencies such as bitcoin.

 

Dark web data risks you can’t ignore

Despite its original purpose, The Dark Web is mostly used for illicit trade including buying and selling Fake IDs and visas, stolen credit cards, weapons, drugs, child pornography, and even the services of hit men, all with hidden IP addresses of both hosting sites, and visitors e.g. Silk Road.

Cybercriminals are willing to pay good money for stolen data, making the Dark Web a huge target for illegal activity and giving people easy access to commit ID and credit card fraud through the untraceable internet.

 

Our Solution

Our solution, Fraud Web, enables you to see whether your customers’ data is for sale on The Dark Web, and which data is at risk. This means that you can identify whether a criminal could potentially access your customer’s data and know if your customer has fallen victim to ID fraud.

With Fraud Web, you will receive high, medium, and low risk alerts surrounding the likelihood of ID theft so that you can quickly and efficiently identify fraud and alert your customers.

 

The average cost of a single stolen data record for any business is $154.

Can you afford the risk?

 

Find out more by emailing us or call our office today.

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What is Bayesian Belief? A Dummies Guide

Bayesian Belief Networks are networks of connected variables that generate predictions based on assumptions. These are generally used when there is a lack of data available.

Bayesian Belief Networks – Example

Let’s say we are determining the likelihood of a person owning a Michael Kors handbag and the average UK person has a likelihood of 20% of owning one. If we know some additional pieces of information, we can update our model based on this. We can then use this to determine the specific probability of owning a Michael Kors handbag using the information we currently know.

The Average Joe has a 20% chance of owning a Michael Kors handbag

Person A: 1) Owns a dog, 2) Is male, 3) Has a semi-detached house

Person B: 1) Owns a Michael Kors watch, 2) Is aged 20-25, 3) Is female

Person C: 1) is aged 40-45, 2) Shops in Prada 3) Wears Chanel perfume

 

The likelihood that these users own a Michael Kors handbag is different for each individual based on the variables that are known because the statistical links are measured between these types of variables.

With Bayesian modelling, we can update the likelihood that a user owns a handbag despite the sparse details available.

With the known information, the model uses this to predict the likelihood of owning a Michael Kors handbag vs. not owning one, despite the fact there are certain elements about the user we do not know.

 

Bayesian Belief Networks are part of the various advanced analytics techniques we use in our software solutions to derive meaningful insights into consumers. Our solutions PROFILE and Discovery help businesses to personalise marketing to boost ROI.

Check out our blog on Natural Language Processing

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