How marketing analytics works for banks

Ewan Dunbar our consultant managing the role
Posting date: 10/3/2013 1:30 PM

Martin Brennan, Customer Insight Manager with Permanent TSB discusses how Customer intelligence software helps Permanent TSB offer customers what they want, when they want it.

Bankers often use marketing analytics to figure out how to sell their products and services. At Permanent TSB, analytics also drives the kinds of products we offer in the first place. By “reverse engineering” the process, we’ve created products and services that better meet our clients’ needs.

Historically we took the blanket approach to marketing – everyone got the same message. We might send marketing messages on mortgages to a retired couple with a paid-off house and to a 25-year-old with no need for one. When we started using analytics, we began to segment customers so that we could target the messages. This dramatically increased the effectiveness of our messages – as it should – because the targeted messages aligned more closely with the customer’s wants and needs.

Reverse engineering

Now we are using the analytical insights to inform our product development decisions. This is critical and has helped us reshape our products over the past two years – offering more Web and mobile applications, and providing loan products that will appeal to a customer base that is recovering from the recent recession. Analytics helped us see that people increasingly want “simple” products – and that’s what we’re offering.

As we’ve increased our use of analytics, we’ve also discovered two key things worth sharing:

One question leads to another. When you start working with analytics, you end up asking more questions than you ever thought possible. Each answer spurs another round of questions. That helps drive modernization and improvement.

Visual analytics tools matter. To articulate the insights from data, you need to present them in a way that doesn’t require an analytics background to understand. Being able to present the data visually is probably as important as trying to get the data.

Although we aren’t there yet, we are working toward making sure that when a customer contacts us we know exactly the right offer to provide them. After all, if they are contacting us, it’s highly probable that they are actively looking for a financial product. We are utilizing our analytics to prompt our staff to offer just the right product.

Catching the analytics fever

In addition, our early successes in marketing caught the attention of other units in the bank. A lot of other internal customers are looking for customer information that we might be able to supply. A good example is in the collections environment: We’ve unearthed some customer insights that allow collections to focus its efforts a little bit more in certain areas.

There is one area that we aren’t that focused on yet – the whole big data area. We’re a midsize financial institution in a country of four million people. Maybe it is because the analytics solution we’ve deployed is taking care of our needs so well that we aren’t looking to adopt a big data solution right now. It is definitely something we’re watching, though.


Click here for the article on the web. 

Related blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out the related posts below.

Mitigating Risk In The Financial Services Sector With Machine Learning

Data & Analytics is an industry that is constantly evolving and is always using the latest technology to innovate its services and capabilities. More recently, these advancements have moved in areas such as Artificial Intelligence (AI) and Machine Learning (ML). Machine Learning is a method of data analysis, under the branch of Artificial Intelligence, that allows systems to learn from data, identify patterns and ultimately make decisions with little to no human intervention. Used across a vast range of sectors, this arm of Data & Analytics has become widely popular, especially within highly-advanced industries such as Finance.  Since the 2008 financial crash, at the top of the agenda for many Financial Institutions (FIs) was, and still is, the need to protect business, increase profitability and, possibly most importantly, address the abundant inadequacies of risk management. This includes risks posed by consumers such as liquidity, insolvency, model and sovereign, as well as any internal process and operational risk the FIs may also be facing through any failures or glitches.   Machine Learning has played a crucial role in improving the quality and precision of FIs risk management abilities. In HPC Wire, it has been reported that the use of AI and ML within the financial sphere to mitigate risk, improve insights and develop new offerings may generate more than $250 billion for the banking industry.  How does ML work? By using incredibly large data sets, drawn (with consent) from consumers, ML can learn, and predict, patterns in consumer behaviour. This can be done in one of two ways: through supervised learning tools, or unsupervised learning tools.  Explained by Aziz and Dowling; “In supervised learning you have input data that you wish to test to determine an output. In unsupervised learning, you only have input data and wish to learn more about the structure of the data.” How do banks use ML to mitigate risk? In FIs, a mix of the two ML tools are used. Most commonly, we can expect to see learning systems such as data mining, neural networks and business rules management systems in play across a lot of banks. These models work in tandem to identify relationships between the data given from the FIs and their consumers – from their profiles to their spending habits, credit card applications to recorded phone calls – which then build ‘character profiles’ of each individual customer. The process can then begin, spotting signs of potential risk factors. This may include debt, fraud and/or money laundering.  Here we break down two key examples.  Fraud Thanks to ML, customers have become accustomed to incredibly quick and effective notification of fraudulent activity from their banks. This ability from FIs comes from large and historical datasets of credit card transactions and machines which have been algorithmically trained to understand and spot problematic activity. As stated by Bart van Liebergen; “The historical transaction datasets showcase a wide variety of pre-determined features of fraud, which distinguish normal card usage from fraudulent card usage, ranging from features from transactions, the card holder, or from transaction history.”  For example, if your usual ‘character profile’ is known by ML tools to spend between £500 - £1000 per month on your credit card, and suddenly this limit is overtly exceeded, fraudulent activity tags will be alerted, and the freezing of your account can be done in real-time.  Credit applications When borrowing from a bank or any other FI, consumers must undertake a credit risk assessment to ensure that they have a record of paying back debt on time, and therefore not adding greater cost, and risk, to the lender.  Traditionally, FIs have approached credit risk with linear, logit and probit regressions but, serious flaws were found in these methods, with many applications being left incomplete. In this space, the evidence for the effectiveness of ML is overwhelming. Khandani et al. found that FIs using ML to analyse and review credit risk can lead to a 25 per cent cost saving for the FI involved.   These ML models come in various shapes and sizes, with the most common being instantaneous apps or websites which allow users and their banks to have access to real-time scoring, data visualisation tools and business intelligence tools.  The risk of risk management with ML Like with any AI or ML application or tool, there will always be cause for concern and real need to always remain vigilant. While ML has shown to be an invaluable tool across lower risk areas, the complexity of more statistical areas of banking, such as loans, has proven to be an Achilles heel for the technology. This usually stems from bias, a perpetual problem for AI and ML across all industries.  Technology Review notes that “There are two main ways that bias shows up in training data: either the data you collect is unrepresentative of reality, or it reflects existing prejudices.”  Data, analytics, AI and ML are notoriously non-diverse working sectors. The person behind the screen creating learning algorithms tends to be white and male, and very unrepresentative of the whole society that the machine learning tool will serve. Over the years, we have heard numerous accounts of unfair and unjust machines, which have learned from a very narrow and unrepresentative dataset, which stems from the lack of diversity amongst the employees within the Data & Analytics industry. For example, Microsoft’s racist bot and Amazon’s sexist recruitment tool, both clear examples that ML and AI are not ready to be used on their own, and humans still need to play an integral part in decision making.  Banks and FIs must be aware of the, potentially lethal, consequences that bias in ML may present. Lenders must be careful to ensure they are working within the guidelines of fair lending laws and that no one group of people are being penalised for no reason other than issues within the technology and its algorithms. It is vital that the humans behind the technology don’t rely on ML to provide them with an answer 100 per cent of the time but, instead, use it to aid them in their decision making when it comes to risk mitigation.  If you’re looking for a role in Data & Analytics or are interested in finance or Risk Analytics, we may have a role for you. Take a look at our latest opportunities or get in touch with one of our expert consultants to learn more.  

National Storytelling Week: Telling A Story Through Data

A story is a lot more than just words on a page. It’s a combination of interesting language, images, colour and, perhaps most importantly, a brilliant narrator.  This is no different in Data Analytics. Like any story, the beginning of any data report starts out as numbers and figures on a page which, let’s face it, isn’t the most interesting read. To ensure the data reaches its full potential and entices an engaged audience, a good Data Analyst will wind and weave them into a compelling story.  So, how might you go about doing this? Know your audience How your story is crafted will be completely dependent on who will be reading it. It’s important to consider your audience’s age, knowledge and expertise. For example, if you were reporting to a junior team, the information given will be simplified, and specific language and jargon should be broken down to include explanations, making the data accessible. The story may also be a lot longer than usual to ensure all areas of information are covered, with room for questions if need be. This is crucial if you want your data, and your story, to benefit the learning and development of the team as well as to encourage their interest and curiosity in the topic.  On the other hand, if you were telling your data story to a group of expert professionals, the explanations will be a lot more top line and the story much pithier and succinct. The depth should instead lie in the narrative of how the data impacts them and their company, providing solutions to problems or providing compelling ideas for innovation and change.  Choose an engaging narrative Undoubtedly, your data will have thrown up all sorts of storylines, from the mundane to the thrilling. When you’re creating your presentation or report, if the data is relevant, opt to design your story around the most exciting dataset. Your aim is to keep your audience engaged and wanting to know more, not to bore them with too many, or figures that are not relevant or provide further guidance.  Be creative No matter how electrifying your data may be, there's only so much information an individual can take in. Your story needs visuals to bring what you are reporting on to life. Typography, font and font size, colour, images, graphs and tables are all valuable assets to include to help stimulate your audience’s imagination.  Of course, in this day and age, these visuals don’t have to be limited to static pictures either. Don’t be afraid to play around with movement and interactivity to get your audience involved and engaged. That being said, it’s important to find a good balance of static and interactive. Be an appealing narrator If you’re having to present your data, you’ve got an extra challenge on your plate. Your story is only as good as you are. No matter how visually fantastic your report is, or how apt it is for your audience, if you are bored, unengaged and uninterested by the information you are presenting, you will pass all these feelings onto your audience.  Not only is it important you know the story you’re telling inside out, but you should be excited by the data you are presenting. Don’t be afraid to inject personality into your data, make it characteristic and make it feel human. If you are passionate about your data and your story, then your audience will be too.  Data doesn’t just have to be statistics on a page. It can be thrilling, it can be colourful, it can be loud, and it can be enticing. You, as a Data Analyst, are that brilliant narrator.  If you're looking to take the next step in your career or build out your Data & Analytics, we may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. 

RELATED Jobs

recently viewed jobs