By Erin Healy

In todays world, data is everything. When it comes to gaining business intelligence and insights, companies immediately turn to data analytics and similar tools to help gain a competitive advantage. Between the rise of AI and ever-expanding use of technology, it makes sense to turn to data when looking for answers. 

One particularly interesting connection is between marketing and data analysis. We have access to more information than humans ever had before, which is bound to provide surprising insights for businesses. Over the years, marketing research has evolved to be able to do things we never could have imagined. For example, Amazon uses data analysis to understand customer price sensitivity, and change the price based on the level of sensitivity the customer has, resulting in greater profitability.  

Traditionally, marketing has relied on very generalized data, creativity, and hope that the campaign would be a success. Despite these strategies having worked for decades, they were either tremendously successful or detrimental to the companies image. Today, we have technologies that can accurately tell us if something will be successful. There are a variety of methods to predict this, including historical data analysis. Another name for this is predictive analytics. Predictive analytics uses past data, statistical analysis methods, and machine learning to yield accurate and valuable information that we have not had access to in the past.  

Companies use this information in a variety of ways. For example, Netflix provides users with recommendations based on the analysis of shows they have watched in the past. They generate a “Match Score” and display it to the user. The Match Score is a percentage that helps the user indicate if they would like to watch based on their historical watch data. 

Another interesting way that data is used is by Google Ads. Google Ads provides personalized advertising based on segmentation. Segmentation is essentially grouping people together based on a similar characteristic, interest, personality, etc. Google Ads both increases revenue and saves money on advertising to the wrong segments through data modeling. They gain these insights through models that analyze things such as conversion tracking, performance forecasting, budget forecasting, and more.  

Data can be used for more than just prescriptive analysis. Let’s go over some of the many possibilities below. 

Strategic decision making: In the past, marketers relied on intuition and general analysis of previous campaigns. Today, marketers have millions of data points that can understand things about customers that we didn’t think was possible. For example, machine learning systems can understand peoples unique personality traits, family life, career, purchasing habits, risk aversion, and beyond. When all of these traits are combined, it essentially provides machine learning systems with a copy of your thought process. This machine can then cater marketing initiatives to the customers unique profile. These insights help executives make informed decisions and allowing them to no longer rely on intuition, leading to better business results. 

Increased advertising campaign effectiveness: As stated above, data can create extremely detailed profiles of customers that aid in marketing efforts like decision making. Another one of those marketing efforts is advertising campaigns. Companies are able to gauge customer opinions such as the interest that the target audience had in the advertisement, what audience the advertisement resonated with most, and if it was successful. To measure this, businesses usually take a look at engagement and other KPI’s.

Data is a fascinating tool that is invaluable in the business world. The integration of analytics in marketing efforts has transformed the marketing landscape and opened new doors for understanding and connecting with customers. Companies are able to generate accurately targeted campaigns, optimize their marketing budget, and make overall better business decisions. Not only are they able to improve decision-making processes, but they are able to see the live reactions of their customers to the decisions that they made themselves. The ability to gauge customer reactions through tactics like social listening continue to aid major companies in understanding the strengths and weaknesses of their choices and the direct effects that they have on people. Overall, as today’s technology continues to evolve we can expect to see even more exciting advancements in data analysis and machine learning.