A revolutionary model to optimize promotion pricing – Georgia Perakis

MIT Sloan Prof. Georgia Perakis

MIT Sloan Prof. Georgia Perakis

From The Huffington Post.

Grocery stores run price promotions all the time. You see them when a particular brand of spaghetti sauce is $1 off or your favorite coffee is buy one get one free. Promotions are used for a variety of reasons from increasing traffic in stores to boosting sales of a particular brand. They are responsible for a lot of revenue, as a 2009 A.C. Nielsen study found that 42.8% of grocery store sales in the U.S. are made during promotions. This raises an important question: How much money does a retailer leave on the table by using current pricing practices as opposed to a more scientific, data-driven approach in order to determine optimal promotional prices?

The promotion planning tools currently available in the industry are mostly manual and based on “what-if” scenarios. In other words, supermarkets tend to use intuition and habit to decide when, how deep, and how often to promote products. Yet promotion pricing is very complicated. Product managers have to solve problems like whether or not to promote an item in a particular week, whether or not to promote two items together, and how to order upcoming discounts ― not to mention incorporating seasonality issues in their decision-making process.

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The philanthropy data gap: measuring what matters – Tavneet Suri

MIT Sloan Associate Prof. Tavneet Suri

MIT Sloan Associate Prof. Tavneet Suri

From Financial Times

As philanthropy becomes a common source of finance for poverty-fighting programmes, it is natural for donors to want data about their impact on the people they want to help.

Yet measuring the benefits of philanthropy is surprisingly hard. How can we define and measure “income” in a village of subsistence farmers? Can we ask a street kid enrolled in a violence-prevention programme about his illegal activities? How do we know if a change in nutritional outcomes was the result of a social programme and not some other variable, like a change in food prices? How can we measure non-quantitative or non-monetary outcomes, like women’s empowerment or entrepreneurial motivation?

For many years, aid impact studies were based on anecdotal evidence or fragments of data. Over the past decade, searching for a more rigorous approach, development researchers have applied the “gold standard” of medical research: randomised controlled trials. In an RCT, researchers allocate an intervention, such as a microfinance loan, to a randomly selected test group of people and compare their outcomes with a control group. Read More »

Contemplating a career in data science/business analytics? – Dimitris Bertsimas

MIT Sloan Prof. Dimitris Bertsimas

MIT Sloan Prof. Dimitris Bertsimas

From Accepted

Since we recorded this interview, the Wall Street Journal published a short article discussing the strong demand for tech skills around the world. Apparently the area with the greatest gap between supply and demand is Big data/analytics, where 39% of IT leaders feel there is a shortage of people skilled in this area, the highest of any tech field in the survey.

The shortage makes this podcast interview particularly timely because you’ll hear from Dr. Dimitris Bertsimas, Co-Director of MIT Sloan’s Master in Business Analytics, and we discuss this brand new program in depth.

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Using statistics can can improve clinical trials and outcomes – Dimitris Bertsimas

MIT Sloan Professor Dimitris Bertsimas

MIT Sloan Professor Dimitris Bertsimas

From Times Higher Education 

Sometimes science can be personal. When my father, who was living in Greece at the time, was diagnosed with stage IV gastric cancer in 2007, I set out to find the best possible care for him. As is the case with many patients with advanced disease, drug therapy was his best course. So, after unsuccessful surgery in Greece, he came to the US for treatment.

I contacted the most prestigious cancer hospitals in the country and found that they all used different drugs in different treatment regimens to treat advanced gastric cancer. As both a son and a scientist, I was surprised to discover that there was no standard treatment – something I would later realise was true of many different kinds of late-stage cancers.

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The rise of data-driven decision making is real but uneven — Kristina McElheran and Erik Brynjolfsson

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar

Kristina McElheran, MIT Initiative on the Digital Economy Visiting Scholar

 

 Professor of Information Technology, Director, The MIT Initiative on the Digital Economy


Professor of Information Technology,
Director, The MIT Initiative on the Digital Economy

From Harvard Business Review

Growing opportunities to collect and leverage digital information have led many managers to change how they make decisions – relying less on intuition and more on data. As Jim Barksdale, the former CEO of Netscape quipped, “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” Following pathbreakers such as Caesar’s CEO Gary Loveman – who attributes his firm’s success to the use of databases and cutting-edge analytical tools – managers at many levels are now consuming data and analytical output in unprecedented ways.

This should come as no surprise. At their most fundamental level, all organizations can be thought of as “information processors” that rely on the technologies of hierarchy, specialization, and human perception to collect, disseminate, and act on insights. Therefore, it’s only natural that technologies delivering faster, cheaper, more accurate information create opportunities to re-invent the managerial machinery.

At the same time, large corporations are not always nimble creatures. How quickly are managers actually making the investments and process changes required to embrace decision-making practices rooted in objective data? And should all firms jump on this latest managerial bandwagon?

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