The Future of Identity Verification

Recent research reveals that an astonishing majority (84%) of financial services in the UK are concerned about their identity verification capabilities, and rightly so.

No consumer is likely to sing praises about the verification processes currently in place in a lot of institutions, a common example being providing a recent utility bill.

With sharp advancements in technology across a vast majority of sectors, it’s a wonder why identity verification in many still rely on traditional methods such as official documents.

Financial institutions need to consider the evolving needs of consumers, predominantly Millennials and generation Z, both typically lacking in traditional data such as credit history. These populations are in danger of finding themselves stuck in cycles where they put off big purchases such as cars or houses because they lack adequate credit for financial support, and therefore fail to build up a credit history.

An age-old problem

Millennials reportedly earn 20% less in salaries than the Baby Boomer generation, and as a result, many are putting off significant purchases that would actually help them to build credit such as houses and cars. The lack of credit history means that a large amount of this generation – and possibly even more of Generation Z – class as thin-file and, as a consequence, find themselves unable to access services such as traditional loans, rental agreements and mortgages.

So what does the future hold?

Big Data = Big Possibilities

There’s a lot of talk about ‘Big Data’ but this simply refers to any large amount of unstructured data and is entirely meaningless without the adequate analytics tools to derive meaning from it.

There are huge amounts of digital data created daily, therefore a continued reliance on the same traditional sources as twenty years ago risks restricting access for consumers who could otherwise be verified.

Balancing Customer Convenience & Security

Customer’s data is extremely important to keep safe, and with fewer and fewer consumers finding themselves adequately covered by traditional sources alone, it is important to take into account alternative ones that we have available to us in the digital age. Consumers have become used to being able to access goods and services within a few simple swipes and clicks, and any requirements for more vigorous checks adds friction and can result in high drop-off rates throughout the process.

This is how leveraging a multi-pronged approach to analyse digital data in real-time (assessing the quality and quantity, as well as looking for corroborative data) can robustly verify identity quickly and efficiently all online with less friction than traditional methods. Harnessing alternative data sources throughout the verification processes can help boost inclusion and increase access for those currently excluded or restricted, helping them to invest in business and property and, in turn, the economy.

To find out more about how you can bring your identity verification processes up to speed, visit our product page or get in touch for a chat/ free personalised demo.

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Soda’s new starters and quarterly round up

The Pizza-eater of the quarter is… our new Business Development Director Phil!

Get in touch with him to congratulate his efforts (or to find out how you can benefit from Hello Soda’s solutions)…

New Starters, New Products, New Clients!

Hello Soda’s sales team is expanding! After more than doubling in size in the past year, and opening an international office in Austin, Texas, we are excited to continue our rapid growth with 3 new additions to our sales team.

Bet on Brazil bet on big data with PROFILE 

It is increasingly vital for bookmakers to use big data as it is in other industry, and Bet on Brazil (an Argyll Entertainment brand) is now more focused on it than ever. Find out how Bet on Brazil benefit from PROFILE in the gaming industry.

Discover our New Product

Discovery turns your limited contact information into a plethora of detail. Using just a single unique identifier as a starting point, Discovery builds confidence in a user’s digital identity and paints a better picture with in-depth interest insights, verified contact points, and employment verification.

Get in touch to find out more

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The travel data goldmine

Holidaymakers and travellers alike are leaving huge data trails in their wake. Whenever a destination is researched, a trip booked, a place visited or comments and reviews left online, insightful travel data is being created. Travel companies have cottoned on to this and are now utilising big data to their advantage. The challenge lies in deciphering this unstructured data to determine relevant insights that have the potential to boost revenue. Like a goldmine, the raw materials for these insights already exist, and travel companies now need to drill down in order to extract and apply them.

Times have changed

Big data analysis lends itself particularly well to the travel sector. Smartphones are ubiquitous amongst millennials, who are now the largest generation in history and constantly connected. This combination makes for incredible volumes of data production and the data insights that can be gleaned from one aspect (such as geo-tagging) alone are vast.

“Bored of the same old French Ski resort? Excitement isn’t far away – get 10% off a 2 night stay in Grenoble Ski Resort if you book before Friday”

It should come as no surprise that fewer people are heading into a high street travel agent in person to discuss their travel options. The global online travel industry was responsible for a staggering $498 billion of revenue in 2015. The percentage of adults who purchased travel arrangements and accommodation online was almost 40% in 2014. The rise of online travel websites and platforms has brought convenience to consumers, as well as greater choice and the ability to make quick and easy comparisons. By extension, this has granted more power to consumers but also results in more effort being required on their part. And without the human element within the buying cycle (eg. a travel agent who can make suggestions based on your likes, interests and requirements) the dilemma that online travel companies now face is how to establish a customer’s perfect holiday and to suggest it to them before they’ve even opened Google.

The holy grail of personalisation

True personalisation to the consumer’s wants and needs brings with it greater customer loyalty. In the current comparison-driven market, building brand loyalty has become a greater focus than ever before. 46% of travellers stick to using just two or three different websites when making a purchase online. And whilst 34% agree that online travel companies have nailed down the basics of search and shopping, more than half (53%) think that they can do more to improve.

With the aid of the right tools, big data can allow travel companies to be proactive rather than reactive to their customer’s wants and needs, and the personalisation of travel offers and services is becoming a hallmark of good customer service­­­­. Tailoring how you market your business’s goods or services to particular interests and needs is the key to obtaining the best conversion rates and ROI and – while traditional data sources can segment audiences by factors such as age and gender – in an ever connected world, this data quickly becomes outdated.

Using advanced text analytics, our flagship software solution PROFILE establishes a person’s likes and interests with astonishing accuracy. Utilising this technology opens up a wealth of marketing possibilities, and – in a busy marketplace – this can provide online travel companies with a much needed edge over the competition. The same technology also enables you to establish a customer’s personality traits, allowing you to make more informed decisions about how you promote or advertise your carefully tailored offer.

PROFILE uses the scientifically-backed Big Five method of scoring an individual’s personality. For instance, an individual who scores highly in conscientiousness is much more likely to respond better to comparative adverts which show how one deal compares to another. Perhaps they score highly in agreeableness, in which case the emphasis of your campaign should focus on the enjoyment and feelings that the consumer is likely to experience. Unique insights including these can empower your marketing campaigns by enabling you to identify cross-sell and up-sell opportunities, boost customer loyalty, and in turn uplift revenue.

Why wait until everyone else does it first? Get in touch to book a demo and revolutionise your marketing with PROFILE Personalisation.

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Big Data in the Gaming Market

Big data verification in the gaming market

The gambling industry is estimated to be worth 50.7 billion US dollars as of 2017. Identity checks are a vital element of any gambling registration process, but relying on current, outdated data sources can lead to you missing out on potential users and millions in revenue. Is big data identity verification the future?

There are consumers who will (and do) visit your website or your app, go through registration, and then drop off due to an inconvenient and drawn-out signup process such as requesting copies of driving licenses. Even losing 10% of potential players because of this can radically harm your revenue, something which is avoidable by implementing the right tools to optimise registration processes.

Smart phone penetration is predicted to reach 70% by 2020, and, by 2018, the mobile gambling industry is expected to be worth $100bn. The mobility that these devices provide to customers, and the rise of publicly available Wi-Fi, enables them to play anywhere, resulting in an increase in user demands for convenience and efficiency, with quick verification processes being a particularly vital part of the user experience for time-dependent bets or offers.

Technology is sufficiently advanced to no longer have to rely on traditional data sources alone. Digital data is expected to reach 180 Zettabytes by 2025 (up from 4.4 in 2013) and but, at present, less than 0.5% is ever analysed and used.

With 2.5 Exabytes (or 2.5 billion gigabytes) of digital data is produced every single day, why continue to rely on outdated data sources alone when there are solutions available that leverage this digital data to enable quicker, more user-friendly verification processes?

Big data analytics software can be utilised to verify identity and detect fraud to ensure that safe and genuine customers are seamlessly and quickly authenticated. Our solution PROFILE provides an efficient and modern form of ID verification by harnessing a customer’s existing digital footprint to drastically improve the user experience. Following consent from the user, in simple terms PROFILE assesses both the quality and quantity of data available about an individual, ensuring it is consistent, meaningful and real. This use of big data can determine whether an individual is who they claim to be, in real-time, without affecting the experience of the gamer.

With PROFILE, you will supplement traditional data with the user’s digital footprint to enhance existing verification processes. PROFILE will be just as accurate and work just as well regardless of where in the world the player is.

PROFILE is revolutionising the way that the gaming industry verifies identity. With clients already seeing 63% uplifts in ID verification on thin-files versus traditional processes, PROFILE will drastically improve your odds of success in a crowded and competitive market.

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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How to leverage big data to improve customer experience

Enticing new customers and understanding existing ones in order to encourage brand loyalty is essential in every sector, and has arguably never been more important than it is today. Consumers are saturated with marketing when it comes to products and services of all types, and the financial services industry is no different.

Fintech start-ups have been appearing at an increasingly rapid rate, leaving consumers with plenty of options for lending, insurance, banking, and much more – rather than having to rely on the handful of traditional players.

With so much choice, differentiation is key in order to win new business. One reason for the popularity of many fintech providers, including online banks and lenders, is their seamless user journey. From using a smartphone to make contactless payments and being able to chat to an expert on demand, modern consumers demand quick and easy access to services they require. Too many steps before a payment, too many details to input into a form or a complicated user interface can each prevent a customer from using a service or completing a payment. Word travels fast in the digital age, and if a fintech company can make the customer’s life easy, it is bound to be popular.

Read the full article

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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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Using consumer personality insights to maximise marketing success

In the USA alone, $170billion is spent on direct marketing each year but it offers little ROI.

A recent study revealed that 3% of recipients of physical mail bought something as a result, while the conversion rate for online ads into sales was a mere 0.01%.

Rather than direct marketing itself being the issue, maybe it is the persistent habit of using demographic data. Using age and sex to target advertisements can result in your effort-laden ad being interpreted as impersonal junk-mail.

Not only is understanding personality a vital aspect of maximising sales, it’s hugely relevant to the effectiveness of marketing styles.

The study

A study by Jacob Hirsh found that advertisements targeted to personality were rated as more effective than universal advertisements.

Using 324 participants, they created five advertisements, each designed to target one of the Big Five human personality traits. These are: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness. Individuals high in Agreeableness value familiarity, compassion, and belonging. Those high in Openness value intellect and aesthetics, and people high in Neuroticism tend to worry and value security and safety.

The advertisements contained a picture of a phone beside text which was altered to target different personality traits. Extraverted participants received the tailored ad reading “With XPhone, you’ll always be where the excitement is”. Neurotics had “Stay safe and secure with the Phone” and participants were asked to rate the ads on their effectiveness.

The findings

Targeted advertisements based on consumer personality insights were rated more effective than the universal one.

Hirsh stated “This research has broad implications for the development of tailored communication strategies across industries. Personality-based message design may be useful not only for advertisers, but also for fostering any number of outcomes, from health promotion, to civic engagement, to environmental responsibility.”

Huge amounts of money is spent every day on advertising tailored for demographic groups. Using consumer personality insights to target ads can improve effectiveness and help you understand your customers needs and motivations as well as how your products should be sold to them.

How you can gain consumer personality insights

PROFILE gives businesses consumer personality insights as well as insights into hobbies, spending habits, due diligence and more. With this, you can target your advertisements based on the things that truly matter.

Dig deeper into your consumer base. Find out more about PROFILE today.

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