3 challenges in getting value from analytics investments

There are a lot of success stories of analytics applications. Organizations across industries from banks to sports have used analytics to create competitive advantages or finding winning ideas.

Tesco– one of the biggest retailer, Capital One – a leading credit card provider, Netflix – a movie rental organization, and Marriott International – a hotelier are some of the organizations which have employed analytics for sustainable competitive advantage.

Some of the common challenges or difficulties with analytics application for the business decisions are

    • Poor quality of data
    • Limited data or poorly structured data sample
    • Poor design of analytics deployment and over fitting the analytics

Above 3 hindrances limit the value addition of analytics deployment for improved business decisions

Poor quality of data

Data analytics and insights are based on input data and if the data has an issue the insights will be inaccurate. It is garbage in garbage out. So, the recommendation in such a scenario is not to use analytics or insights.  But organizations should focus on to improve quality of the data.

For one of the clients, at the end customer calls the customer service representatives enter the comments to capture the important points.  When we started looking at the data – unstructured data, we realized that comments are not really making sense from the business perspective or just illustrative general category of the call, which is already available as a structured column. This is not an isolated example.

One of the other issues with the data is a lot of missing values. But this is lesser of the devil. There are multiple approaches available for missing value treatment and analysis. One of the important points to keep in mind is to review the variables and find out the rationale around missing values. There may a business reason for missing value and the reason could be helpful e.g. in understanding customer behavior.  A few years back we were building a customer churn model for a telecom client and found that a variable had around 80% missing values. Typically analyst would exclude the variables with over 30-40% missing.  When we look at the variables, we found that one of the variables was “Value of international calls”. Of course, it is not expected that all the customers would be international callers. We have treated the variable and used in the model.

Limited data or poorly structured data samples

In the age of big data, you might be wondering why I am bringing this point. There is a difference between volume of data and diversification data. We may have huge volume of customer transactions for the recent period. We may have all customer interaction data but the not the calls or web interaction data.

 For developing good statistical model, we may not require high volume of data. The volume of data may necessarily improve model effectiveness or quality. But we have to be very careful in creating data sample for the statistical modeling and analysis.

Example:  If one wants to develop a mortgage customer attrition model, the sample data points used to build the model play an important role. The customer behavior in terms of attrition is influenced by economic condition – whether interest rate increasing or decreasing scenario.  So, relevant sample of data points be available and used in an appropriate way to bring out the right insights and patterns.

Poor design of analytics deployment and over fitting the analytics

One of the crucial aspects of Capital One’s analytics success story is running thousands of business experiments and learning from them.  The successful experiments are deployed on a larger scale. If analytics are not deployed properly, a limited learning and performance can be derived.

A lot of organizations use analytics in an ad-hoc way and any un-successful result is taken as an excuse of not using analytics in future.  But the main result is a poor design of analytics deployment.

What is measured can be managed but not improved upon. And for improving decisions, one has to synthesize and learn from the historical decisions-results, what works or do not work and why. A proper design of implementation plan before deployment will ensure that the insights can be generated on what works and why it works. Analytics deployment and learning is a systematic adaptive improvement mechanism, which is key in getting value from analytics investment and creating competitive advantage.

 Reference

Thomas H. Davenport, and Jeanne G. Harris , Competing on Analytics: The New Science of Winning

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