Budget Allocations – Are You Using a ‘Stone Age’ or ‘Modern Age’ Process?

Dr Andree Bates explains while detailed analytics are imperative for sales and marketing.

Different companies and countries use different approaches for budget allocation. These range from the very basic (i.e. crossing fingers and hoping, a judgment call to response tracking), to better than nothing - which have been found to be unreliable and ineffective in dynamic markets like Pharma (i.e. response curve analysis and econometrics), to the most sophisticated that have proven results in Pharma, which take the current market into account and perform complex algorithms on that data to determine results (reality-based analytics).

Interestingly, in China and a few other emerging markets, we have noticed that until we get involved with analytics, many companies are in the ‘cross fingers and hope’ category with their budget allocations. In fact, what is most shocking to me is that a lot of companies do not even know where or how most of their money is being spent as they are simply releasing money and allowing departments to spend it as they see fit. The companies themselves have no way of knowing or tracking what was spent, nor what results were gained! For companies relying on growing their sales and profits in China and other emerging markets, simply adding rigor and analytics to the process will result in significantly improved results for the company.

The good news is that in the last two decades, we have had a revolution in measurement, allowing companies to understand in much more detail what their customers perceive and believe regarding their brands and company, what their sales and marketing activities are doing, and what their company strategies are achieving. This improvement in measurement is creating new opportunities to manage things differently. Finally we are seeing a shift from using intuition and gut feel toward using data and analytics in making decisions. This shift has gone hand-in-hand with measurable improvement in productivity and other results measures.

A study conducted in 2011 demonstrated that simply by increasing use of data and analytics, one could create a significant improvement in productivity and an even larger increase in profitability. The implications for Pharmaceutical executives are enormous - by changing the way they make decisions, they’re far better equipped to be able to outperform their previous results, and their competitors.

Needed to achieve this, of course, is both the ability to measure and the ability to take those measurements and turn them into something meaningful that will impact results, and also to get the team to use the results. Sometimes, a client who is doing very well (i.e. have a top brand in a specific therapy area) will tend to use the data to support and back up what they are doing; however, what they really should be doing is taking the few things that the data is showing them they should change and making the changes.

There needs to be a cultural change in some companies so that data is created to be used as an experiment --- ‘here is our problem, let’s use analytics to solve it’. The data being measured needs to consist of both non-financial and financial aspects. The financial phase is the result of what you are doing in the non-financial phase, and all sides must be measured. 

It can sometimes be frustrating when you come across managers who say, “I have the experience and I manage from the gut”, and who do not wish to examine the opportunity for improved results big data can provide. All management should leave their egos at the door and have the confidence to let the data speak.

One client CEO pushed his belief on all his reluctant sales and marketing managers and fought hard for the changes, which was not easy. However, the results were worth it. All brands grew dramatically and even the marketing managers, who were against it from the beginning, became raving fans after the first results were seen. Nonetheless, it is not always an easy course to take.

So, what is the process?

  1. Define the objectives and end results required
  2. Design what data is needed to solve this
  3. Collect the data
  4. Analyze the data and model everything, including optimal allocations
  5. Review the results and decide on key actions and timelines

A thorough analytics budget allocation Budget Allocation model will take all the data into account. It will also consider strategic importance, stage of lifecycle, whether brands are supporting other brands or are ‘follow on’ brands, competitor brands and performance, and much more.


There is no doubt about the importance of strong, reality-based analytics to show you the key factors to be focused on to maximize success and, thereafter, how to optimally allocate budget. We have now moved beyond the response curve/econometrics style approaches, which clearly lack the depth needed for dynamic markets such as biotech and Pharma, which the more modern analytics provides. Now is the time to take advantage of analytics to really start growing your profits and results!

Questions or comments? Share your thoughts with our audience in the comments section below, alternatively you can email the author directly at abates@eularis.com.

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