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Machine-learning takes on ecommerce

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While machine learning is a relatively new concept for many, there are an increasing number of platforms and services that are adopting this construction of algorithms. ROBERT SCOTT, SafariNow CCO, talks about the relevance of supervised and unsupervised machine learning.

Given the massive increase in the amount of data that companies – of all sizes and across sectors – are generating, it is no surprise that data analytics and machine learning are fast becoming key components of every innovative company’s toolkit. For the uninitiated, machine learning refers to the way in which companies can now leverage computing power to find important patterns within their data – and then use these patterns to improve their service or product offering.

Because of the sheer volume and complexity of the data being created today, it is often far beyond the capacity of any human – no matter how analytically gifted – to find any relevant trends or insights within what has been tagged ‘Big Data’.

Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, “what is my top selling item?”) and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

Although it is a relatively new and novel concept for many, the technology has already been applied to platforms and services that we use daily. Take Google Search, for example. When we enter a search term, Google uses elements of machine learning to analyse our behaviour once the first results have been served up (i.e. did we need to type in the same search term again, or did we follow some of the top links provided?) and then refines and improves its service according to the data. Other examples include Google’s self-driving car, how Netflix suggests which movies you should try next, and how a dating site suggests which people are most likely to be a suitable match for you…

Unexpected Insights

As with most technological tools today, almost any company or sector can leverage machine learning to better serve their customers. The challenge for companies is to recognise where – and how – certain insights and trends can improve their product or service offering.

Within the travel sector, we have identified various areas in which machine learning can be applied in order to fine tune our offering and help travelers locate their dream destinations. One of the great benefits of this tool is that it often finds relationships between factors that are completely unexpected and unplanned.

Machine learning has led us to the insight, for example, that some accommodation providers have a preference for prioritising requests from customers who would like to stay with them in the next few days – whereas other providers would much rather prioritise requests far in advance (for the school holidays, for example). Often, it is these unexpected – or unplanned – insights that can be the most beneficial for customers.

Continual Improvement

As an online travel aggregator, there are in fact infinite possible use cases for machine learning – and we are at the tip of the iceberg in terms of harnessing its potential to improve our offering to consumers looking for the next adventure.

Looking ahead, machine learning will perhaps become a standard application within the travel and e-commerce environment. Companies that are open to innovative ways of finding insights in their data can ultimately serve their customers more efficiently – and even develop closer relationships with them in the long-term. The key for companies is to keep an open mind as to whether or not their long-held beliefs about what customers want is actually supported by the data.

By always remaining alert to new patterns and insights, companies can make adjustments – both big and small – to enhance their offering.

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VoD cuts the cord in SA

Some 20% of South Africans who sign up for a subscription video on demand (SVOD) service such as Netflix or Showmax do so with the intention of cancelling their pay television subscription.

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That’s according to GfK’s international ViewScape survey*, which this year covers Africa (South Africa, Kenya and Nigeria) for the first time.

The study—which surveyed 1,250 people representative of urban South African adults with Internet access—shows that 90% of the country’s online adults today use at least one online video service and that just over half are paying to view digital online content. The average user spends around 7 hours and two minutes a day consuming video content, with broadcast television accounting for just 42% of the time South Africans spend in front of a screen.

Consumers in South Africa spend nearly as much of their daily viewing time – 39% of the total – watching free digital video sources such as YouTube and Facebook as they do on linear television. People aged 18 to 24 years spend more than eight hours a day watching video content as they tend to spend more time with free digital video than people above their age.

Says Benjamin Ballensiefen, managing director for Sub Sahara Africa at GfK: “The media industry is experiencing a revolution as digital platforms transform viewers’ video consumption behaviour. The GfK ViewScape study is one of the first to not only examine broadcast television consumption in Kenya, Nigeria and South Africa, but also to quantify how linear and online forms of content distribution fit together in the dynamic world of video consumption.”

The study finds that just over a third of South African adults are using streaming video on demand (SVOD) services, with only 16% of SVOD users subscribing to multiple services. Around 23% use per-pay-view platforms such as DSTV Box Office, while about 10% download pirated content from the Internet. Around 82% still sometimes watch content on disc-based media.

“Linear and non-linear television both play significant roles in South Africa’s video landscape, though disruption from digital players poses a growing threat to the incumbents,” says Molemo Moahloli, general manager for media research & regional business development at GfK Sub Sahara Africa. “Among most demographics, usage of paid online content is incremental to consumption of linear television, but there are signs that younger consumers are beginning to substitute SVOD for pay-television subscriptions.”

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New data rules raise business trust challenges

When the General Data Protection Regulation comes into effect on May 25th, financial services firms will face a new potential threat to their on-going challenges with building strong customer relationships, writes DARREL ORSMOND, Financial Services Industry Head at SAP Africa.

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The regulation – dubbed GDPR for short – is aimed at giving European citizens control back over their personal data. Any firm that creates, stores, manages or transfers personal information of an EU citizen can be held liable under the new regulation. Non-compliance is not an option: the fines are steep, with a maximum penalty of €20-million – or nearly R300-million – for transgressors.

GDPR marks a step toward improved individual rights over large corporates and states that prevents the latter from using and abusing personal information at their discretion. Considering the prevailing trust deficit – one global EY survey found that 60% of global consumers worry about hacking of bank accounts or bank cards, and 58% worry about the amount of personal and private data organisations have about them – the new regulation comes at an opportune time. But it is almost certain to cause disruption to normal business practices when implemented, and therein lies both a threat and an opportunity.

The fundamentals of trust

GDPR is set to tamper with two fundamental factors that can have a detrimental effect on the implicit trust between financial services providers and their customers: firstly, customers will suddenly be challenged to validate that what they thought companies were already doing – storing and managing their personal data in a manner that is respectful of their privacy – is actually happening. Secondly, the outbreak of stories relating to companies mistreating customer data or exposing customers due to security breaches will increase the chances that customers now seek tangible reassurance from their providers that their data is stored correctly.

The recent news of Facebook’s indiscriminate sharing of 50 million of its members’ personal data to an outside firm has not only led to public outcry but could cost the company $2-trillion in fines should the Federal Trade Commission choose to pursue the matter to its fullest extent. The matter of trust also extends beyond personal data: in EY’s 2016 Global Consumer Banking Survey, less than a third of respondents had complete trust that their banks were being transparent about fees and charges.

This is forcing companies to reconsider their role in building and maintaining trust with its customers. In any customer relationship, much is done based on implicit trust. A personal banking customer will enjoy a measure of familiarity that often provides them with some latitude – for example when applying for access to a new service or an overdraft facility – that can save them a lot of time and energy. Under GDPR and South Africa’s POPI act, this process is drastically complicated: banks may now be obliged to obtain permission to share customer data between different business units (for example because they are part of different legal entities and have not expressly received permission). A customer may now allow banks to use their personal data in risk scoring models, but prevent them from determining whether they qualify for private banking services.

What used to happen naturally within standard banking processes may be suddenly constrained by regulation, directly affecting the bank’s relationship with its customers, as well as its ability to upsell to existing customers.

The risk of compliance

Are we moving to an overly bureaucratic world where even the simplest action is subject to a string of onerous processes? Compliance officers are already embedded within every function in a typical financial services institution, as well as at management level. Often the reporting of risk processes sits outside formal line functions and end up going straight to the board. This can have a stifling effect on innovation, with potentially negative consequences for customer service.

A typical banking environment is already creaking under the weight of close to 100 acts, which makes it difficult to take the calculated risks needed to develop and launch innovative new banking products. Entire new industries could now emerge, focusing purely on the matter of compliance and associated litigation. GDPR already requires the services of Data Protection Officers, but the growing complexity of regulatory compliance could add a swathe of new job functions and disciplines. None of this points to the type of innovation that the modern titans of business are renowned for.

A three-step plan of action

So how must banks and other financial services firms respond? I would argue there are three main elements to successfully navigating the immediate impact of the new regulations:

Firstly, ensuring that the technologies you use to secure, manage and store personal data is sufficiently robust. Modern financial services providers have a wealth of customer data at their disposal, including unstructured data from non-traditional sources such as social media. The tools they use to process and safeguard this data needs to be able to withstand the threats posed by potential data breaches and malicious attacks.

Secondly, rethinking the core organisational processes governing their interactions with customers. This includes the internal measures for setting terms and conditions, how customers are informed of their intention to use their data, and how risk is assessed. A customer applying for medical insurance will disclose deeply personal information about themselves to the insurance provider: it is imperative the insurer provides reassurance that the customer’s data will be treated respectfully and with discretion and with their express permission.

Thirdly, financial services firms need to define a core set of principles for how they treat customers and what constitutes fair treatment. This should be an extension of a broader organisational focus on treating customers fairly, and can go some way to repairing the trust deficit between the financial services industry and the customers they serve.

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