SmartAnalytics

Most of the B2B Marketers feel their current online marketing mix is falling short to meet the sales demand, and that they’re under immense pressure to be able to measure the online campaign effectiveness and also being productive. We often hear that the concept of the marketing mix isn’t so useful any longer in this era of customer-first. But I believe it is still highly relevant today as a framework to develop digital marketing strategies.

Managed Markets in Pharma - A Primer

The managed care model has seen huge growth in the pharmaceutical industry in the US. It is estimated that 85% of all prescription drugs are today reimbursed through a managed care plan, and the remaining 15% of cash paid prescriptions is still shrinking. This proportion is even higher for expensive new specialty pharmaceuticals. Managed markets are the dominant channel for pharmaceutical firms and ensuring unfettered access to their drugs is a critical competitive advantage that could spell the difference between commercial success or failure of the drug.

Interested in slicing, dicing, measuring, and analyzing data for customer and business insights? According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.

When I was coming back from an international travel recently and was browsing through the inflight entertainment shows available, "Person of Interest" series on CBS caught my eye. A computer genius develops a machine for the government which is used to detect information leading to acts of terrorism before they can be executed. "The machine" separates the information gathered into two categories: relevant and irrelevant. The scientist, however, discovered that the irrelevant information also often led to the discovery of other acts of violent crimes. With the state-of-the-art surveillance technology, the program uses pattern recognition to identify people who are about to be involved in violent crimes.

A recent study by the Nucleus Research says that Analytics pays back $10.66 for every dollar spent. The study is based on data from 60 case studies and relates to investments in Business Intelligence, Performance Management and Predictive Analytics. Not surprising are the areas where they saw ROI increase - revenue, gross margin and expenses.

Current BI landscape is cluttered with too many tools and technologies. The plain vanilla BI deployments rarely produce desired results. This leaves IT teams to a mere data steward role rather being a potential business partner. Primary inhibiting factors are: exponential data growth, lack of best practices and leveraging prior experience.

A panel of Pharma industry leaders like GlaxoSmithKline, Bristol-Myers-Squibb, AstraZeneca, Sanofi, Eli Lilly, Pfizer and Shire talked about the importance of Clinical Trials Optimization for 2 days at the recently concluded conference in Philadelphia clearly showing the importance of this topic in the present times of economic hardships and FDA’s pressure to lower healthcare costs. Pharma companies spend millions of dollars on research and probably even more on these clinical trials to ensure safety and efficacy of the drugs. Developing the right protocols, selecting the proper sites, setting the right expectations with all stakeholders, developing and tracking the right metrics and effective communication is the key to optimizing the resources and cost of clinical trials.

Companies like GSK are investing close to $95M on running programs like ‘Simplifying Clinical Development’ with its sole intent to optimize the entire clinical development process and make that cost efficient. Such programs need dedicated personnel, time, money and more importantly the right metrics. The GSK panelist admitted that even after knowing which metrics to capture and track and what to ‘do’, the actual ‘doing’ part of the data integration, data quality and analysis is where they fail. Dealing with humongous amount of data coming from multiple sources (Planning and budgeting systems, EDC, CTMS, etc.) leads to paralysis of analysis or as one of the panelists from Astellas said…DRIP (Data Rich and Information Poor).

Several presenters talked about which metrics to capture and track, how they are working with MCC (Metrics Champion Consortium) to determine the right ones, how to benchmark their performance, etc. But when asked basic questions like… what happens if you don’t have the relevant underlying data to support your metrics, how do you plan to integrate data across so many sources and still make perfect sense of the data, what impact does the data quality issues (especially with the manual entry of CRFs and EDC data) have on your metrics? None was able to provide satisfactory answers. The whole point is, while you build your strategy for optimization, do not under-estimate the efforts in data integration and analytics. It was clear from this conference that even biggies in Pharma are struggling with that. Site and study managers are working with their statisticians to better predict the outcomes, but none is tracking to see whether the actual decisions made and their outcomes were true to their predictions. The feedback loop to relook at the hypothesis or statistical model and make necessary corrections was completely missing.

Also, while most put a lot of efforts to know which metrics or KPIs to track, very few understand that these metrics make no sense if one is unable to act upon them to make decisions that will influence the business outcomes. There is a difference between ‘Business Intelligence’ and ‘Business Analytics’ and not many in the industry understand that. Monitoring and tracking metrics/KPIs in the form of reports/dashboards is ‘Business Intelligence’, but making meaningful sense of these metrics, co-relating them with other factors that influence them, understanding the trends and using statistical algorithms to predict outcomes is where the bang for the buck is…and that is ‘Business Analytics’. I have seen many Senior Executives flash numbers/charts in their board meetings, but can they accurately and confidently take very specific actions to make those numbers look better?  Isnt ‘experience’ or ‘gut feel’ play a majority part in the decision making? If so, these numbers/metrics/KPIs will have no meaning until the next time a report is generated….hoping that the numbers look better this time.

One article I read on Forbes.com said that the average cost of bringing a new drug to market is $1.3 billion (at times $4B to $11B for big pharma companies), a price that would buy 371 Super Bowl ads, 16 million official NFL footballs, two pro football stadiums, pay of almost all NFL football players, and every seat in every NFL stadium for six weeks in a row. This is ridiculous. A lot of that is spent on Clinical Development. Obviously the industry is obsessed with optimizing its efforts in that area and cutting costs. But, are they doing it the right way? Time, Quality and Cost are the factors companies look at and try to optimize each process. This is all fine as long as the processes are not interdependent. But are they? Do they understand enough of the external factors influencing each process, that some are beyond their control, that there can be ways to influence these factors? It is important to know all that before an ‘Optimization’ program is undertaken. ‘Business Analytics’ can help and ensure success of these programs.

It will be interesting to know how successful GSK was in their ‘Simplifying Clinical Development’ program. Having invested $95M, they better get it right 

While most of big data is geared towards social media and stream analytics, traditional EDW can also best leverage the power of Big Data. The concept of Big Data is not new, banks have been doing it for a while using mainframe size computers. The reason it’s being talked so much now is that for the first time, cheap and massive computing power and even cheaper memory has put mainframe size power in the hands of every organization, right at the time when organizations have been struggling to justify the ROI in processing such exponential data volume.

It has been a little over a year since the term “Big Data” became a catch phrase, but it still appears to evoke the same kind of response as it did then with an interesting twist. The twist being, it evoked a sense of “awe” to begin with but that is turing into a sense of “confusion”.   There is still no one crisp, common, consistent definition of the term big data, which is one of the reasons for this confusion [reminds me of the .Net days, when it referring to everything from a coding framework, to a database to office products etc confusing the heck out of everyone]

There is no conversation or corporate strategy presentation today without mentioning Big Data. The focus of this write-up is to understand, what Big Data is and How HR will be impacted due to Big Data: