“The global market is composed of many submarkets [aka therapeutic categories] (TCs), whose number is given and equal to nTC. Each TC has a different number of patients (PatTC) in need of treatment for a specific disease, which determines the potential demand for drugs in each submarket. This number is set at the beginning of each simulation by drawing from a normal distribution [PatTC~N(μp,σp)] truncated in 0 to avoid negative values, and it is known by firms. Patients of each TC are grouped according to their willingness to buy drugs characterised by different qualities.”*
Yet capturing market share in today’s competitive environment is anything but easy. In the recent past, an army of sales reps would market directly to doctors, their efforts loosely coupled with consumer advertising placed across demographically compatible digital and traditional media.
This “spray and pray” approach with promotional spending, while extremely common, made it difficult to pinpoint specific tactics that drove individual product revenues. Projections and sales data factored heavily into the campaign planning stage, and in reports that summarized weekly, monthly, and quarterly results, but the insights gleaned were nearly always backward-looking and without a predictive element.
A pharmaceutical company pinpoints opportunity
Today, sophisticated pharmaceutical marketers have a much firmer grasp of how to use data to drive sales in a predictive manner – by deploying resources with pinpoint precision. A case in point: To maximize the market share of a prescription asthma medication, a leading pharmaceutical company uses SnapLogic and Amazon Redshift to analyze and correlate enormous volumes of data on a daily basis, capitalizing on even the smallest market and environmental fluctuations.
- Each night, the marketing team takes in pharmacy data from around the US to monitor sales in each region, to learn how many units of the asthma medication sold the previous day. These numbers are processed, analyzed, and reported back to the sales team the following morning, allowing reps to closely monitor progress against their sales objectives.
- With this data, the pharmaceutical marketing team can monitor, at aggregate and territory levels, the gross impact of many variables including:
- Consumer advertising campaigns
- Rep incentive programs
- News coverage of air quality and asthma
- However, the pharmaceutical marketing team takes its exploration much deeper. Layered on top of the core sales data, the marketing team correlates weather data from the National Weather Service (NWS) and multiple data sets from the US Environmental Protection Agency (EPA), such as current air quality, historic air quality, and air quality over time. Like the sales data, the weather and EPA data cover the entire US.
By correlating these multiple data sets, the marketing team can extract extraordinary insights that improve tactical decisions and inform longer-term strategy. At a very granular, local level, the team can see:
- How optimal timing and placement of advertising across digital and traditional media drives demand
- Which regional weather conditions stimulate the most sales in specific locales
- The impact of rep incentive programs on sales
- How news coverage of air quality and asthma influences demand
Ultimately, the pharmaceutical marketing team can identify, with uncanny precision, markets to concentrate spending on local and regional media, which can change on a constant basis. In this way, prospective consumers are targeted with laser-like accuracy, raising their awareness of the pharmaceutical company’s asthma medication at the time they need it most.
The results of the targeted marketing strategy are clear: The pharmaceutical company has enjoyed significant market share growth with its asthma relief medication, while reducing advertising costs due to more effective targeting.
Tools to empower business users
The pharmaceutical industry example exemplifies perhaps the biggest data analytics trend in recent business history: massive demand for massive amounts of data, to provide insight and drive informed decision-making. But five years after data scientist was named “the sexiest job of the 21st century,” it’s not data scientists who are gathering, correlating, and analyzing all this data; at the most advanced companies, it’s business users. At the pharmaceutical company and countless others like it, the analytics explosion is ignited by “citizen data scientists” using SnapLogic and Redshift.
In my next blog post, the second of this two-part series, I’ll talk about how SnapLogic turned around a failing initial integration effort at the pharmaceutical company, replacing Informatica PowerCenter and Informatica Cloud.
To find out more on how to use SnapLogic with Amazon Redshift to ignite discovery within your organization, register for the webcast “Supercharge your Cloud Data Warehouse: 7 ways to achieve 10x improvement in speed and ease of Redshift integration.”
Craig Stewart is Vice President, Product Management at SnapLogic.