Healthcare Economy

Leading pharmaceutical companies are increasingly using analytics to understand their customers better and to drive improved decision making. This is not the basic data-gathering and review that has been prevalent in the industry: the new capability involves sophisticated analytics-making extensive use of data, statistical and quantitative analysis, predictive models, and fact-based management to drive decisions and actions. In industry parlance, this is referred to as Advanced Analytics, which has emerged from the confluence of IT and statistical techniques (like linear regression models, discrete choice models, time series models, Bayesian networks, etc).

Advanced analytics can enable pharmaceutical marketers to maximize the return on marketing investment by providing valuable insights to target the right customers with right brands with the right messages.

Statistical tools and techniques available for decision making

The key to the successful adoption of analytical CRM and advanced analytics programs is the effective use of various statistical tools and techniques that forms its backbone. Let us review some of the techniques that can be used and their relevance from the perspective of the pharmaceutical industry

• Linear Regression Model analyses relationship between dependent and independent variable and provides an equation to predict values for the dependent variable. This technique can help marketers by providing extent of relationship between two parameters e.g. sales and marketing spend, campaign cost and new prescriptions etc.
• Discrete Choice models - If the dependent variable is discrete then superior methods like logistic regression, multinomial logit and probit models are used. Logistic regression and probit models are used when the dependent variable is binary. Segmenting customers based on treatment preference, media consumption, demographics can be an effective use of this technique.
• Time series models are used for predicting or forecasting the future behavior of variables. This can be used to efficiently forecast the revenues and generation of new prescriptions etc.
• A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. E.g. a Bayesian network could represent the probabilistic relationships between diseases and symptoms.
  

Examples of where advanced analytics can support pharmaceutical sales and marketing functions.

• Acquiring the "right" customer - Predictive analytics can help pharmaceutical marketers by identifying segments of customers with high probability of responding to particular promotional campaign. They can now focus on these core customers and significantly improve returns on the marketing spend
• Improving the life time profitability of a customer - For companies with a wide product range at various price points, predictive analytics can help in cross-sell / up-sell and support customer development. Its applicability to pharmaceutical industry may be limited due to the regulated and ethical nature of the industry
• Enhancing customer satisfaction and loyalty - Like any other industry, retaining existing customers is a key objective for pharmaceutical marketers. Predictive analytics can help in identifying dissatisfied customers through changing patterns in product usage, analyzing feedback provided on performance, spending and other behavior patterns. It helps identify such customers at an early stage and provide an opportunity to design a retention strategy

In conclusion, the adoption of advanced analytics is helping pharmaceutical companies identify trends and thus enabling them to take proactive steps to maximize profits and save costs.