Decision Modeling at the C-Suite Level



Harnham Decision Modelling

New York has always been a leader on the world stage in everything from fashion to finance. It’s focus always on the newest trends, cutting edge technology, and business acumen. As Frank Sinatra sang, “If you can make it here, you can make it anywhere”; many have and many will put their stamp on the world.

Following in the footsteps of their business forefathers, executives are turning to decision modeling in record numbers. The number of channels from which businesses can collect customer data to offer better products and improve services has grown at a staggering rate opening new pathways for more accurate predictions and problem solving capabilities.

Where Model Applications Dazzle


Executive decision making requires bold action and the ability to execute ideas. Where big data and models dazzle is in their application to retail. The customer insights offered through real-time data can both evolve the customer experience and stop fraudulent activities in their tracks.

Successful models are most often thought of within the retail industry. Customer Insights are gained by analyzing customer behavior using real-time data to monitor preferences and spending patterns. This information gives businesses the tools they need to assess best pricing or packaging structures to improve product or service and boost sales.

Yet, anything with a product or service can be considered retail, even banks and financial institutions. They, too, must focus on the customer experience to provide the best possible service and best product to the market. Banks and financial institutions must stay ahead of the current and often utilize their decision models not only to improve customer insights, but can and have used that information to detect fraudulent activities in regard to credit card usage.

Though data analysis, predictive modeling, and decision modeling have their place in objectively deciding outcomes, there is still a human element which must also prevail; human intuition, interaction, and emotional intelligence. Statistical and decision models are objective. One answer fits all. Yet, in life’s variations, one action does not necessarily precipitate the next. The future can be changed.

Balancing Act - Outcomes


Overwhelmed by the vast amount of information, volume, and complexity, many executives may overuse decision modeling in their practices to ease some of their load. This would be unwise.

A better bet is to take a step back and consider how their models might influence outcome. Tasks requiring impartial analysis of large data sets are becoming increasingly powerful. Estimating consumer reaction to a promotion may be more suited to decision models, while motivating a team or individual to achieve high performance is not. To get the most out of decision modeling and improve business performance and best practices, a combination of skills is the answer.

Improving Models Over Time


Feedback is a powerful technique and is most often applied in a retail setting. Dynamic improvement depends on both the observation and cycle of adjustment; a place where customer behavior can be measured. As customer insight turns to real-life application, prices and other features can change in real-time adding to the improved customer experience.

Ultimately, decision analysis and predictive models underscore a powerful main point; there is a fine line between an appreciation of decision analytics and an understanding of when techniques are most useful and when they are not.

If you’re at the top of your game and interested in applied decision modeling, we may have a role for you. We specialize in Data and Analytics recruitment and always have a wide range of vacancies at both junior and senior level. Take a look at our current vacancies or contact us to find out more.

For the East Coast and Mid-West teams please call 212-796-6070, or email newyorkinfo@harnham.com.

For the West Coast team call 415-614-4999 or email sanfraninfo@harnham.com.

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Death of the DMP, Rise of the CDP

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Putting The Pieces Together – Setting Up Your Credit Risk Team

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