Explore the Strategy of Scientific Marketing
In 2004, Boston Scientific, a medical device manufacturer, created a new technology that executives thought might be of particular interest to universities and other academic institutions. In order to discover if they were right, the company purchased MarketSight, a marketing data analysis software system.
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The MarketSight software pulled data from numerous research studies and online information, highlighting statistically significant results that were relevant to the new technology.
Surprisingly, the research results showed Boston Scientific executives that their initial target was off mark. The primary audience for their new technology was actually community hospitals, not academic institutions.
Switching gears, Boston Scientific implemented targeted marketing campaigns for the new technology to community hospitals throughout the country, resulting in significant sales numbers and new long-term contracts.
Scientific marketing uses data mining to gather information. This information might include where target consumers live, how much they earn, how much time they spend online, what websites they visit, and what they purchase online (See also Analytical Marketing). The data is analyzed to create specific reports, such as who a company’s primary audience might be for a specific product. Marketing campaigns can then be tailored to focus on a specific audience that is statistically more likely to be interested in the product, thereby increasing the likelihood of successful advertising efforts.
For example, the grocery industry encourages customers to sign up for loyalty programs at stores such as Albertson’s or Safeway. When signing up, customers provide information about themselves. Every time they swipe their card when making a purchase, the store receives information about what they purchased and how much they spent. In return, the customer gets targeted coupons and discounts on items they buy frequently.
Grocery stores are not the only industry using scientific marketing, however – most online companies, from retailers to major social media sites all use scientific marketing.
For example, Facebook gathers data about user age, location, preferences, and even topics used in personal posts or conversations. This data is then analyzed and used to plan marketing strategies, from the targeted advertisements that appear on the side of your Facebook profile, to websites that Facebook thinks you will “like” (Facebook's data use policy).
Brick and mortar stores use scientific marketing to gather information about what people purchase, as well as their income levels, names, and addresses. For example, CVS Pharmacies offers a loyalty program that gives customers special deals and discounts. In return, customers provide personal information about who they are and what they buy. This information is then used to tailor marketing campaigns for each customer. (See also Loyalty Marketing)
Scientific marketing is changing how we see marketing messages in everyday life. One example of successful scientific marketing is Facebook, which collects extensive data on users that ranges from where they are to what they talk about to others. This information is sold to advertisers who then sponsor targeted advertisements on Facebook that match an individual’s location, spending habits, and personal preferences. As a result of scientific marketing, advertisers experience higher traffic and revenues since their ads are specifically matched to an individual’s likes and dislikes.
A scientific marketing plan starts with an observation, such as what has happened in the past and what is happening now. For example, analyst company Intellidyn recently gathered data about thousands of Americans. From this data, the company realized that people over the age of 64, married couples, ex-military individuals, and farmers are highly likely to want to travel to Asia. Because of this information, a vacation package company marketed their Asian vacation packages to those who fit this profile, and saw an increase in their sales as a result.
Once an observation is made, the hypothesis can be created from this information. For example, online companies such as Amazon.com collect data about what individuals look at online and what they purchase. They then make a hypothesis about what that person is likely to purchase in the future. If a customer searches a book by a certain author, Amazon.com considers it a strong possibility that they might be interested in buying another book by the same author and suggest more books by that author to that person. (See also Personalized Marketing)
Companies then experiment based on their observation and hypothesis. For example, a phone company might analyze user records and see that households with heavy phone use between the hours of 3:00 p.m. and 6:00 p.m. are more likely to have teenagers. The company can then launch a marketing campaign to those homes, advertising a special deal on additional phones and lines for the home.
Once just a search engine, Google is now the current king of scientific marketing. There are an average of 4,717,000,000 searches performed on Google.com every day, up from 9,800 during 1998, Google’s official first year.
Now much more than just a search engine, Google offers a variety of analytical tools for companies to see, understand and act upon data about their website, online advertising, mobile applications, websites, and social media networks.
Google data analysis tools includes tracking and reporting anything from how many people download their mobile app to how many of their online customers make purchases. These reports also incorporate information about social media usage and online keyword searches.
All of this data mining and reporting allows companies to gather valuable information about their audience and make targeted business decisions based on this information. See www.StatisticBrain.com and Google Analytics.
A statistical modeling analyst finds, gathers, and analyzes data to identify current market trends. This position involves the ability to build statistical models using computer technology as well as scientific and mathematical methods in order to predict individual behavior and overall trends. The statistical modeling analyst also understands marketing theories and techniques in order to apply the data to marketing campaigns.
Source: Bureau of Labor Statistics
A statistical modeling analyst needs to have a bachelor’s degree in an analytical or statistical discipline, such as economics, finance, statistics, or mathematics. Some companies also prefer analysts with a master’s degree. A bachelor’s or master’s degree in marketing can provide potential job candidates an extra edge in this competitive field. In addition, an analyst will need to have experience with SAS/SQL, Design of Experiment, CHAID, and professional experience with data mining and analysis.
An analytics engineer creates, develops, and analyzes data, then presents the information to a marketing team. They use a variety of technology sources to find, process, and analyze data on a real-time basis. It is important to have an understanding of how this data translates into real-world information about marketing and consumer behavior.
An analytics engineer needs a bachelor’s degree in math, computer science, physics, or other technical fields. A bachelor’s or master’s degree in marketing is also important in this field.
In addition, professional experience performing quantitative analysis for a marketing department is highly preferable, as is fluency in SQL and other programming and/or scripting languages.
A quantitative media analyst needs to be able to perform quantitative analysis and algorithm development. They need a thorough understanding of various marketing methodologies in order to provide rapid information about customers, website visitors, and clients. Within this position, a quantitative media analyst should be able to use new technologies to develop data gathering and be able to comprehend and present the information in a clear, comprehensive manner to others.
A quantitative media analyst needs a bachelor’s degree in statistics, math, engineering, or physics with a complimentary degree or experience in marketing. Many companies prefer a master’s degree in either marketing or statistics or a related field as well. Experience with programming, data modeling, marketing, and written and verbal reporting is essential in this position.
Individuals interested in scientific marketing need a degree in marketing as well as a data-related field such as statistics. Potential job candidates need to have a thorough understanding of marketing terms and techniques, data collection, and analysis.
Students in a marketing program will learn about data mining techniques and how to analyze the gathered information. In addition, marketing students learn how to improve profit and grow sales using effective marketing techniques such as market research and various marketing campaigns.
This information is useful for a career in scientific marketing because it allows students to expand the tools needed for data mining and analysis. It is important to learn not only how this data is tracked and analyzed, but how to use this data to create and implement powerful marketing campaigns.