Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
We've written many blog posts on the topic of Machine Learning and how it's improving everything from fraud prevention, direct marketing in retail and the customer experience in call centres to getting us to make more impulse purchases online and making holiday and business travel more enjoyable. It's with good reason we've given Machine Learning so much focus on our blog: it is a driving force in what the founder of the World Economic Forum - Klaus Schwab - is calling the Fourth Industrial Revolution. In this blog post we answer some frequently asked questions about Machine Learning, starting with what it is and how it relates to Artificial Intelligence.
Seems like the term Machine Learning is popping up in mainstream media as the next big thing. The fact is, however, that Machine Learning went mainstream a long time ago. You don’t think so? Check your mobile phone. Chances are you’ve been using and benefiting from Machine Learning algorithms all this time without even knowing it. Read our opinion piece: Machine Learning Is Here To Stay In this blog post, I go through some of the many apps on your mobile phone that use Machine Learning algorithms to make recommendations, get you to your destination quickly and safely, improve your photos, tell you what song you’re listening to and more. You’ll see, Machine Learning is not so far away. It’s already in the palm of your hand. If you want to know what machine learning is, read our blog post on What is Machine Learning? And Other FAQs We Get...
Believe it or not, we are halfway through 2017 and if you're feeling like you're no where near achieving what you set out to achieve this year, I'm sure you're not alone. But fear not! If one of your resolutions this year was to research how to apply data analytics or machine learning to your area of specialisation - be it Marketing, Customer Experience, Debt Collection or Risk Management - you still have time! And our Data Analytics Blog is a good place to start. I've looked at the stats and compiled our Top 10 list of most read blog posts for the first half of 2017. Check out our list of blog posts below and see what topics your colleagues and industry counterparts are researching this year:
McDonalds mastered the upsell with one simple question at the time of purchase: “You want fries with that?”. A simple and relevant question at the right time that has likely generated millions of extra dollars in revenue through the years for the company. Ever since then, companies have tried to emulate their success by identifying complementary products in their offering and training sales staff to ask customers the right question at the right time.
We take pride in our ability to predict - from the results of the 2015 Rugby World Cup and the 2016 Oscars to predicting profitable customers and customer churn. However, there is no denying that 2016 was a year full of shocking, unexpected events - from Brexit and the US election results to the acrimonious break-up of "Brangelina" (shocking!) and the sad loss of some very talented artists.
The power of Customer Experience and growing competition are driving companies to take a more scientific approach to building customer loyalty.
As developed countries experience a slow but steady recovery, credit risk managers in emerging markets face growing default rates as household debt continues to rise with little relief in sight. The Institute of International Finance stated at the end of 2015 that global household debt had risen by $7.7 trillion since 2007 to more than $44 trillion, and that $6.2 trillion of that rise was in emerging markets. Household debt per adult in emerging economies also rose by 120 percent over that period to some $3000, it added.
Artificial intelligence (AI) and machine learning technology present an interesting meeting point between our fear of the unknown and our fear of being known (that is, fear of our private information being exposed and known by others). The erosion of privacy and rise of intelligent machines is actually a common theme in science fiction. But while reality still has a lot of catching up to do before we can call Skynet’s customer support or play cards with Agent Smith, many people have expressed genuine concern over the implications of modern technology – especially regarding their privacy.
Thanks to its broad applicability, data analytics has rapidly become a critical business function for modern organisations. But with expertise in the field in short supply and high demand, companies with an identified need for data analytics are looking beyond their traditional borders to monetise their information assets. Forrester Research predicts that a third of businesses will “pursue data science through outsourcing and technology” as organisations become less process-driven and look to their data to find new opportunities for innovation. And with globalisation and technological advancements making outsourcing a realistic and practical option for businesses, this trend is set to gain momentum. With this in mind, let's take a look at why an organisation would even consider outsourcing their analytics capabilities in the first place.
Machine learning is helping brands narrow the divide between their products and consumers in ways that would appear almost magical only ten years ago. From Amazon's personal product recommendations based on past purchases and browsing habits, to Netflix's uncanny ability to suggest just the right movie title according to your taste in film, data-driven insights are helping companies speak to individual customer preferences, who are demanding more personalisation in their products and engagements. This has moved data analytics from novelty status to an integral part of the marketing strategy, as brands discover new opportunities to communicate their unique selling points.
In his final keynote speech at the 2011 Apple Worldwide Developer’s Conference, Steve Jobs remarked that, “If the hardware is the brain and the sinew of our products, the software is its soul.” Jobs’ intimate understanding of and vision for his products stands out as one of the key reasons behind Apple’s success. His notoriously protective stance on his company vision and the extent of his involvement in the conception, design and development of his products right up until their anticipated release is legendary. But the man behind Forbes’ most valuable brand of 2015 also knew a little something about value creation and customer value management.
Telematics is a relatively new field, combining telecommunications and vehicular technologies with the insights of information processing. While the term was coined in 1978 in a French government report on the rapid development of computer technology, it now nearly exclusively refers to the technology that tracks vehicles in real time using GPS. Through this technology, telematics companies have access to a large amount of data that, with the help of data analytics, can be extremely useful.
Corporate silos may have been necessary in a burgeoning industrial era some decades ago, but times have changed. The era of social, mobile, analytics and cloud (SMAC) technologies has been fuelling a new wave of business transformation. Virtually every industry is being affected by the SMAC phenomenon and we’re currently only scraping the surface of what is possible.
We analysed 80 years of Oscars data to predict this year's Oscars winners. Along the way, we discovered some very interesting insights. Did you know 24% of Oscar winning films were based on a true story? Four of this year's Oscar nominees for Best Actor star in a film based on a true story. Is this a growing trend?
Following a highly successful initiative of using Machine Learning to predict last year’s Rugby World Cup results, we're trying our hand again at predicting the future and revealing some interesting insights along the way about another major event: The Academy Awards, or the Oscars.
Data has redefined how businesses understand their customer base and make decisions. For instance, it’s transformed marketing from a relatively intangible expense into a clear-cut investment with a measurable ROI and targetable initiatives. However, strategically applied data has more uses than strengthening your marketing efforts alone, especially when it comes to understanding your existing customers and better attending to their needs.
How does a credit card company differentiate itself in a market saturated with special offers, low interest rates, and convoluted rewards programs?
When you think of machine learning in action, it’s easy to imagine analytics-based marketing research or a matrix-style AI takeover. But there are actually far more grounded and practical applications that benefit hundreds of millions of people in their day-to-day leisure and business activities. Take the travel industry, for example. We’ve now reached a point in human history where over 100,000 flights are taking off every single day. With airports seeing more foot traffic than ever before – Dubai International alone saw over 78 million passengers in 2015 – they need to be run and managed like a carefully oiled machine. Alleviating bottlenecks is central to ensuring as smooth an operation as possible in the travelling experience.
The aim of a marketing director is simple: 1) to get your brand noticed by the right people and 2) to get the right people to choose it over your competitors’. As we all know, that task is becoming both more difficult and easier every day. With the rise of social media and mobile marketing it has become increasingly easy and cost-effective to reach a large audience. And the time required to do so has been cut dramatically. As a result, however, it has also become more difficult to compete for attention as the traditional barriers to building a brand, such as budget and geography, dissipate.
I fondly remember watching as a child “The Roadrunner Show” cartoon series where the coyote (Wile E. Coyote) was devising elaborate schemes to try and catch the roadrunner. Although his schemes appeared to be clever and creative (to a six year old at least), he always failed. So, in one of the episodes he ordered a giant mainframe type super computer (this was the 1950s after all) to apply “data science” to devise a more effective scheme. This time his prey was Bugs Bunny.
We take a look at the Top Ten blog posts which received the greatest number of views in 2015.
With the banking and credit industries still somewhat coping with paper and process-heavy cultures of yesteryear, innovation is fast becoming a key differentiating factor in attracting a new generation of customers. Thankfully, we live in an age where more can be done with relatively little effort thanks to the automation capabilities and extended reach technology provides us. This is why the mobile device is fast becoming the focal point of how people work, communicate, find entertainment, get informed or even transact.
Data analytics is certainly making its impact felt in our collective progress as a species, with the technology being applied across a wide range of human activity. A report by the United Nations entitled Humanitarianism in the Interconnected Age identifies four challenges surrounding data analytics in helping tackle global challenges such as access to education, natural disaster management and disease control and prevention. These requirements centre on the development, acquisition, analysis and sharing of the increasing numbers of new information channels to find solutions to global challenges. In this blog, we'll look at three ways data analytics is helping save lives by allowing us to understand, anticipate and manage events or situations that pose a threat to human life.
Last month we used predictive analytics and machine learning to predict the results of the Rugby World Cup, “out-moneyballing” the bookies themselves and placing us at the top 00.32% of humans on sports prediction site, SuperBru. Now that the dust has settled a bit after that fun initiative, I thought I’d look into other ways data analytics is being used in the sports world today. There are indeed many ways, but for the sake of brevity, let’s look at four of the more interesting ways that data analytics is changing the world of sports.
It seems to be a topic of conversation everywhere you go, and now the Internet of things (IoT) and a growing data-sharing culture is helping make the world a safer place - one missing drain cover and pothole at a time.
It was Wendell Smith, president of The Marketing Science Institute at the time, who in 1956 first advocated customer segmentation as a means to drive market demand, influence brand preference, and improve overall marketing profitability. Smith’s observations in this now 66 year old Journal of Marketing piece still holds some views that are largely relevant to today’s marketing landscape.
Many of us remember the hoopla around the predicting ability of the now deceased FIFA World Cup predictor, Paul the Octopus. For those who don’t recall, Paul was an Octopus at a German aquarium that famously predicted with 100% accuracy the results of team Germany’s six matches and final match of the 2010 Soccer World Cup.
Since the early days of commerce, competing brands have grappled with how to be the one that comes to mind first when customers discover a “need” for a product. It is also fairly common knowledge that the cost of acquiring new customers is significantly higher than retaining the most valuable ones, highlighting the need to pre-empt the ebbs and flows of existing customer lifecycle stages and their respective segments as a means to optimise share-of-wallet. In the age of big data and predictive analytics, we’re getting much closer to reaching the level of brand awareness that helps us be present at the decisive moment our customers commit to the purchase.
When we started our Man vs. Machine (Learning) initiative, we did so to have a bit of fun and to learn a few lessons we could apply to earning our bread and butter: predicting customer behaviour, customer lifetime value and credit worthiness, among other things, for our customers.
In my experience as a marketing professional, potential customers almost never just decide to walk away from a purchase - unless given sufficient reason to do so. And, if you’re wondering how a promising list of leads managed to slip through your fingers, it might be time to refocus on the basics of your on-boarding strategy.
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.
It’s Man vs. Machine at Principa HQ as our data scientists apply predictive analytics and machine learning to predict the winners and spread of each match during the Rugby World Cup. We signed up two internal teams of data scientists onto sports prediction site SuperBru.com as an exercise to put theory into play in this year’s Rugby World Cup. By applying the same principles used to predict customer behaviour for our financial services and retail clients, our two teams are vying against each other to develop algorithms and predictive models that can predict the outcome of the matches with the highest accuracy.
It’s Man vs. the Machine as South African based data analytics company, Principa, apply predictive analytics and machine learning to predict the winners and spread of each match during the Rugby World Cup. South African based data analytics company, Principa, have signed up two internal teams of data scientists onto sports prediction site SuperBru.com as an exercise to put theory into play in this year’s Rugby World Cup. By applying the same principles used to predict customer behaviour for the company’s financial services and retail clients, two teams of data scientists are vying against each other to develop algorithms and predictive models that can predict the outcome of the matches with the highest accuracy.
At Forrester Research’s Groundswell for Excellence in Social Media Awards ceremony held in April this year, winners were recognised for their innovative use of social media to drive awareness of their brands, delight customers with quality content, and reach more people than could ever have been possible in a world without tweets and shares. I’ve been somewhat puzzled by talk of social media, as a marketing platform at least, being on its last legs. The way I see it is that social is only shifting into higher gear and looking at how this year’s Forrester Research winners leveraged the medium to up their relationship marketing game, social media is still very much alive and kicking. You can read more about this year’s winners and submissions here.
I don’t think any of us like it when someone forgets our name. Although anonymity might be the companion of choice for the most socially averse, for the rest of us, the feeling that we matter as individuals carries with it a sense of identity. Marketers who bear this fundamental human attribute in mind are already halfway to creating customer retention strategies that pay dividends. Don’t believe me? Then it might be worth mentioning the Coca-Cola “Share a Coke” campaign which replaced Coke’s universally recognised branding with the personal names of consumers. The result was almost 1 billion impressions on Twitter and over 150 million personalised bottles sold world-wide.
In the field of credit risk management, few would challenge data’s role in financial forecasting, lender analysis, credit-modelling and risk aversion. In short, credit risk managers are no strangers to data. But it could be argued that the value and volume of data they have access to largely determine the quality of their decision making. The shift towards more technology and data-centric business models has created new opportunities for those in the credit risk landscape to play more collaborative roles and engage other business divisions to produce positive outcomes in shorter time-spans.
Since the days of the first radio broadcast in 1922, it took a short span of only ten years for half of the American public to adopt the new medium of the time. Marketers soon caught on to the inherent opportunities in radio transmissions, and in 1922, the first radio advertisements were broadcast to millions of listeners. Today, we’re in a similar situation with data analytics rising in prominence and marketers shifting their strategies to become more pre-emptive in the way they engage consumers. Strangely, however, it seems that not all “modern marketers” are heeding the call for data-driven strategies. A Forrester Research survey concluded that out of over 500 B2B and B2C respondents, only 11% of marketers scored well enough to be categorised as modern, data-driven marketers.
In my eyes, loyalty programs exist for two simple reasons: to motivate increased engagement with your brand and to collect data in order to build deep customer understanding. But they also exist for a third reason: customers want them. In a study by Nielsen, 84% of respondents said they were more likely to choose retailers that offered a loyalty program. Forrester Research have found that 64% of consumers agree that loyalty programs influence where they make purchases, and 50% agree that loyalty programs influence what they buy.
Virtually every retailer or restaurant chain has some type of customer loyalty program these days. In South Africa, there are over 100 local loyalty programs with an estimated 50 million memberships across them. With this said, the challenge lies in building programs that appeal to each individual customer segment that result in increased spending on products and services while boosting loyalty to your brand - a big task in these competitive times. However, what is considered “value” by one customer isn’t necessarily perceived in the same way by the next.
Human psychology is a fascinating thing. As marketers, one of the many roles we play is of psychologist and, at times, of fortune teller. A good understanding of human behaviour and a high EQ are fundamental requirements for us to develop strategies and campaigns that will influence changes in perception and behaviour, and ultimately trigger an action: a click, a download, an email, a “Like,” a “follow” or a “retweet.”
No matter how automated our processes might be, mistakes and mishaps will happen. It’s how we respond as a business in these moments of truth that can turn an unhappy customer into a customer for life. We all make mistakes. It’s what makes us human. It’s how we learn. And as businesses, we implement technology and automate processes to try to minimize the likelihood of human error. However, regardless of the advanced systems we may implement, mistakes and mishaps will happen. It’s how we respond as a business in these moments of truth that can turn an unhappy customer into a customer for life. One such way of responding is something I’ve coined the 3 Hs, which I learned from a personal experience in the early, “bleeding edge” days of e-commerce.
Out of the mouths of babes comes wisdom that can be applied to building lifelong Customer Relationships. Last week, pop musician Taylor Swift came out with an op-ed piece for the Wall Street Journal about the music industry and where it is heading. The piece left me drawing parallels between the relationship musicians form with their fans and the relationship brands form with their customers. In her musings about how social media and the Internet are impacting the music industry, she noted that the one thing that hasn’t changed is the significance of forming a long-lasting bond with your fans:
For companies to survive and flourish in the Age of the Customer, IT Departments must shift their focus from Information Technology to Business Technology to help the business win, serve and retain their customers. Customer experience is a concept that has been around for quite some time. But never before has it been as crucial to a company's success as it is today. According to IT industry analysts, Forrester Research, 92% of companies surveyed in the US say Customer Experience is a top strategic priority.
Observing consumer trends and understanding customers’ lifestyles can lead to moments of genius in meeting customer needs to create a superior Customer Experience. Case in point: Starbucks. According to US-based technology and market research company, Forrester Research, 92% of companies surveyed last year confirmed that the customer experience would be a top priority for them. We are entering what Forrester refer to as the Age of the Customer: “a 20-year business cycle in which the most successful enterprises will reinvent themselves to systematically understand and serve increasingly powerful customers.”