It’s been here all along

Finance companies, particularly those with quant teams and a heavy investment focus, have long been data pioneers whether they realise it or not. The analytics involved in day-to-day assessment and strategy already drive content efforts. Companies now increasingly employ data scientists to take that raw traditional data, discern patterns and draw further insights to predict what content will work best in a particular scenario.

Heads or tails?

The best definition of data science is from GE Digital: “Data science is the art of looking at data and applying scientific principles to figure out how to make heads or tails of that data.” And data is in overabundance. Geolocation technology tells you where customers live and purchase history can predict future shopping. A data scientist will take this information and tease out insights to engage and entice customers.

Brands use data science to develop new products and services. Consider LinkedIn’s ‘People You May Know’ feature, iTunes’ suggestions that predict what films or songs you’d like based on your buying history. Facebook is brilliant at analysing what you’ve liked, shared, and responded to – and giving you more of it (at the possible peril of users living an online echo chamber). Of course, Wall Street, and political prognosticators, through advanced algorithmic trading and polling, rely on data science for their very existence.

Companies now increasingly employ data scientists to take that raw traditional data, discern patterns and draw further insights to predict what content will work best in a particular scenario

Data science in finance

The onslaught of big data in the financial sector promises consistent new avenues to break down and benefit from. New insights and corresponding application can revolutionise user experience: from mobile app interactions to social media shares, market feeds, mock-ups, livestream simulations, and much more.

Streaming analytics is a key development the financial industry should leverage. This lets you manage and monitor performance and customer experience inside of streaming data by way of real-time query alerts. For example, setting an alert each time users spend more than two minutes on a transaction or receiving an alert if an IP address tries to sign in incorrectly more than five times. In this way, financial institutions are able to track and exploit opportunities, while targeting trust concerns such as possible fraud or malfeasance. Streaming analytics is the future of data science.

The crystal ball

By cannily ascertaining what content will work for (and expand) their client base, strategies become more targeted. Predictive analytics combine content data with powerful statistical models. Finance companies can work out the factors that best help sales or engagement. For example, what age demographic most responds to a marketing post; what is the most used (or most avoided) tool on an app; what exactly has people clicking to sign up most of the time? Data science streamlines the experience – and can make assumptions and adjustments without the need for a costly, old school focus group or customer questionnaire.

Watch this space.

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There are even a host of expert surveys that have tried to pinpoint exactly what Big Data is. Big Data is technically defined as: “extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.” However, the reality of Big Data in the day-to-day workplace can seem far removed from its technical definition.

So why is Big Data important?

Every action and interaction every person, business and brand takes with any other entity creates data, which contributes to an endlessly growing stream of data. Some might even call it Big Data. According to IBM, we create an estimated 2.5 billion quintillion bytes of data each day. There’s so much data out there that it is inevitably overwhelming. So while it could help us in myriad ways, it is more likely to simply confuse us. Many companies are now collecting their data from a wide variety of sources – analytics, social media, CRM, PoS etc. – but still too few know what to do with it.

According to stats shared by Forbes, for a typical Fortune 1000 company, just a 10% increase in data accessibility could result in more than $65 million additional net income.

And what can it do?

Knowing more about your customers has obvious benefits: better targeting, more relevant information and higher returns, to name a few. However, the impact of Big Data does not end there. The implications of data science and analytics are much broader with APIs, algorithms, AI, predictive analytics and machine learning all rapidly becoming a crucial part of any brand’s toolset.

Take the banking sector as an example

Over the years, there have been increasing numbers of reasons for customers to lose an element of trust in their banks, particularly digital natives who prefer to have more direct control over their banking – outdated websites, poor customer service, lack of mobile app, poor UX. All of these elements can be improved through the use of big data – it’s all about better understanding your customers and knowing where to invest first. According to stats shared by Forbes, for a typical Fortune 1000 company, just a 10% increase in data accessibility could result in more than $65 million additional net income.

What is the future of Big Data?

Unlike some areas of marketing, the use of big data does require fairly significant investment. Being able to effectively join up datasets in order to answer key business questions is not a simple task and requires both the tools and the people to manage it. The shortage in data scientists remains a key issue as companies become increasingly aware of the critical need for data-driven decision making. The potential to turn all data surrounding our businesses into meaningful insights, actionable updates and ROI is very much out there, but the first step must be the implementation of a clear strategy with solid foundations. It’s easy to get side-tracked by meaningless numbers; just because you can measure something doesn’t mean you should. The key to harnessing the value of big data for your business is ensuring you are using it to answer your key questions, not accidentally asking new ones.

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The post What is Big Data and what does it mean for your business? appeared first on Financial Marketer.

Dealing with competing interests and stakeholders can be a real headache. Historical budgets will give you an idea of where the business has been, and hopefully you’ve had some indication of Return On Investment (ROI) on activity, they can however also be a distraction. Often individuals and their pet projects may have made prominence or a key influencer has determined what is in the budget. These activities may or may not add value.

What is going to generate value in the future

Strategy is your first step. By the time you are developing your budget you should have a clear view on where you’re headed and why. Research has been done and business KPIs are in place. If your strategy is not in place and agreed with stakeholders the marketing budget becomes the place where people’s pet projects end up and you’ll be pushed and pulled by stakeholders.

In the end you’re accountable so being able to lead the discussion on how money is spent is critical. The strategy needs to be supported by facts based on data analytics. So give yourself, and your team, plenty of time to get the information. The theory is great but in practice often these things aren’t there when you’re asked for your budget. So taking a bottom-up and top-down approach to budgeting is often happening real time. There is nothing wrong with this…except if you want to increase or maintain your total marketing budget.

If finance have already told you what your budget is, and you haven’t had a say, you’ll need to start looking a bit closer to home as to why you’re not ready, and negotiate deadlines on when submissions need to be made.

In the end you’re accountable so being able to lead the discussion on how money is spent is critical

Build your budget from the bottom-up

Get your team involved with campaign ideas complete with approximate costs and ROI to the business. This will help you prioritise and cross-reference whether you’re achieving business KPIs. It will also give you visibility on communications channels and whether you are over communicating to some customers. Think iterative process and don’t be surprised if you’re asked to change things multiple times. Be ready for it with flexible approaches to reporting and changing assumptions. You don’t need to have new state-of-the-art technology to do this – make Excel your friend!

Life made easy is having one template for the whole team. Make the time otherwise it’ll lead to hours of consolidation. If not yours, it will be someone else. Gaining the insight into what is happening within your budget is well worth the time and effort. Apart from understanding what the team is doing it helps with relationships and coaching to get the best business outcomes. Transparency in your marketing budget will deliver trust with others and improved business outcomes.

Read the other two articles in this marketing budget series:
MARKETING BUDGETS PART 2: MANAGING A CAMPAIGN BUDGET
MARKETING BUDGETS PART 3: HOW DO YOU BEST SPEND THE MONEY?

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