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How Netflix And Data Analytics Are Making Movie Magic

October 8, 2015 at 4:49 PM

Kevin Spacey may not have given data analytics the nod at his acceptance speech this year at the Golden Globes, but that doesn’t mean the star doesn’t understand the depth of data’s role in the success of his hit show, House of Cards.

In fact, Spacey has been talking “analytics for a while now and his keynote speech at the James MacTaggart Memorial Lecture at the Edinburgh Television Festival reveals that Hollywood’s storyline is set to take a twist of its own thanks to data analytics. And Netflix’s recent announcement that they’ve signed up 50 million new subscribers globally further challenges traditional production houses to re-think their business models in the age of data and the empowered consumer".

Data has long been a strategic asset for Netflix


Since the early days of its DVD-mailing business model, Netflix has been aware of the potential that lay dormant in consumer data. In 2006, the company offered $1 million to developers to create an algorithm that could best predict how viewers would rate movies based on their previous ratings. With the arrival of video streaming, thanks to internet maturity and consumer buy-in, data became abundant and Netflix were poised to capitalise on it based on the success of their initial foray into analytics. Today, House of Cards is a multi-award-winning TV-series that is conceptualised, cast, produced and written in partnership with the insight gained from massive volumes of consumer data. Traffic to the Video on Demand provider accounts for over one third of internet peak time in the USA.

What lessons does the Netflix story hold for us marketers?


Interestingly, Spacey notes in his talk that, “…through this new form of distribution we have demonstrated that we have learned the lesson that the music industry didn’t: give people what they want, when they want it and in the format they want it.” As a marketer, this hits home on many levels, and as the internet-age continues to redefine life, work and play, we should seize opportunities to bridge the divide between our brands and our changing markets. In a report titled ‘The Rise of the New Marketer’, Forbes notes that “Within organizations that have achieved high levels of customer intelligence, there is a data-centric culture that is supported from the top down, and decision makers at all levels are provided training and support in mastering the power of data to better reach their markets.” This highlights the need for marketers and others within their organisations to pay closer attention to what their data is telling them and draw inspiration from companies like Netflix who have taken the data plunge in such exemplary fashion.

Netflix now knows how to rope in series fans


Recent analytical endeavours have revealed that Netflix viewers are more likely to get hooked on a series if they are given access to the entire season at once, rather than forcing subscribers to wait a week for the next episode. Pooling data from 20 shows across 16 countries, Netflix was able to determine at which point viewers would become loyal followers of certain TV shows. Analysis revealed that viewers preferred having the control in their hands about when and how they consume content, making the relationship with the content and the provider a more personalised one.

It seems the stage is set, and data is at the centre.


All bets are on that Netflix is set to expand its global reach and are conducting quality of service studies to markets currently outside of its reach. This bodes well for consumers, who will enjoy more volume, variation and personalisation of the content they consume. However, this comes at the expense of sharing your personal information with content providers - but if consumers feel they’re getting value in exchange, my hunch is that they will surrender it without too much deliberation. The race to produce content that speaks to an ever-increasing and diverse viewership through a new and powerful medium is certainly on, and it appears that big data will be cast in the role of kingmaker.

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Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

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The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. 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Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. 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Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. 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In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. 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