How to Make Dollars Out Of Your Data?

March 31, 20168:57 am570 views

Productivity is one of the key concerns, if not the most pressing issue, faced by top business executives today. How do you make the most of what resources you have whilst making profit?

Especially in Singapore in the recent times, focus has been on how to integrate new technology, business processes and training to increase performance. But how about making your customers more productive?

After decades of applying traditional propensity algorithms, the ability to improve responses or forecasts is declining. These algorithms view data from an individual standpoint and do not take actual behaviour into account that consumers can be influenced by others.

The prevalence of social behaviour amongst consumers and the creation of social networks driven by technology have led to growing interest in social scoring amongst data heavy organisations that collect billions of bytes of customer data daily.

Social scoring is a form of data analytics, which considers the connections between customers in their social group and predicts that they will have similar needs.

Making use of the social nature of individuals makes most sense for organisations like banks and telcos, since they already have the necessary data points on hand and a real business need to increase profitability for the existing clients.

Analytics-driven companies such as Facebook and Google already use social connections to make recommendations of ads and products to individuals.

Using the same concepts and applying them to existing and proprietary data sets can lead to better understanding of consumer behaviour and higher and stronger predictions.

See: Predictive HR Metrics: Smart Way to Use Data

Make Data More Productive By Understanding Connections

The premise of social scoring is based on the belief that people make connections for a reason, and by analysing connections between individuals, we are able to predict certain behaviours.

When dealing with volumes of data it is easy to think of customers as simply numbers on a spreadsheet, however in actuality the numbers are the result of personal choice – choice driven by ideals and preferences that are often influenced by one’s social environment.

Concepts such as homophily (the tendency of individuals to associate and bond with similar others), social influence (when the action and behaviour of a person is activated by recent actions of friends and contacts) and confounding (when behaviour drives to share similar activity or place) – concepts more often applied to the scientific study of social networks – can be effectively applied to data to create a more holistic view of the customer.

In the banking sector, for example, social scoring can help predict the probability of use of a certain credit card, how likely a person is to repay his debts, likelihood of fraud, and even the types of complaints people are most likely to have (and address those issues before complaints are made).

In a test case scenario, a global bank based in Europe, SunCaged was able to increase credit portfolio profitability by 36 per cent and decrease fraud by 10 per cent.

The data points that were used in this trial were already sitting on the bank’s servers, albeit in various departments, which demonstrates that with the right application, existing data resource can be made to work harder to provide a competitive edge that affects the bottom line.

Getting on the Data Analytics Brand Wagon without Big Problems

A common concern we hear from CIOs is that investing in data analytics puts additional stress and demands on the existing resource.

While there are many black box solutions available from big data companies, they are generally expensive and create dependency on the vendors for support and expertise.

However, some forward-looking businesses are changing their starting point from buying big infrastructure and software investments into staff training.

Investments should always start with a ‘knowledge first’ approach and end with software and hardware. Before embarking on the data analytics journey, organisations should think about:

  1. Making employees data scientists

Invest in training. Have people in your organisation who are familiar with your own processes and making them aware of the possibilities, is the first and most important step to start this journey.

  1. Getting the right data for your needs

As mentioned earlier, many organisations already collect large volumes of data that can be used in data analytics and social scoring. Look at existing data and determine what you need to arrive at and the insights you can derive.

  1. Develop software that fits your specific needs

It is much better to create bespoke solutions that meet specific business needs. Every organisation has its own unique requirements, and having a software system that is customised to your needs will increase the productivity of your data.

  1. Ensure that organisational processes support the new model

Data analytics is more than a solution to a business problem. It is really about how to think differently. More importantly, to ensure that this new way of thinking continues to reap rewards. This will make it a cost effective exercise without big problems.

Author Credits: Aydin Ilhan, Founder and CEO, SunCaged Analytics

Aydin’s 15-year career in the banking industry has spanned stints in Corporate and Retail Banking, Portfolio Management, Risk Management and Business Consulting. An economist by training, he is a specialist in applying analytics and market intelligence to business. Since 2012, he has focused on developing business in Southeast Asia.

Singapore’s aspirations to become a global hub for big data, business analytics and solid business infrastructure led Aydin to start SunCaged Analytics in May 2013.

A strong advocate of using data-driven insights to drive business decisions, he currently consults with local businesses and MNCs based on data analytics techniques. Aydin is also the lead consultant in Asia for Bayes Forecast, a global consulting firm.

Also read: What It Takes to be an HR Data Analyst

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