Predictive Analytics: Shifting Power from Consumers back to Marketers

A power button with a green light turned on next to it.
Power has shifted from consumers back to marketers through predictive analytics. Source: Forbes magazine.

While surfing on the web have you ever noticed how advertisements appear suggesting that you purchase a particular product or utilize a service that aligns with your personal tastes, such as a pair of running shoes, which are not only your favorite color but also your favorite brand? Through your previous purchases, browsing history, and social media, technology was able to create a personalized offering.

Although consumers once had the purchasing power, the pendulum is beginning to shift back to marketers through the use of technology. Often, the wide array of data available to consumers confuses rather than assists them in their decision-making.  Recently, as noted in a Forbes article, technology is making decisions and taking action for customers instead of empowering them.

As dependence on technology for daily tasks grows, consumers have higher expectations about what technology can provide them with. The more time spent online increases the personalization of advertisements and offerings that you see on your screen. Although it may be somewhat unnerving that your computer knows more about your preferences than you do, marketers are using this personalization to their benefit. By better understanding consumers’ preferences through analytic technology, marketers are becoming more effective targeting consumer needs. Instead of predicting what consumers want, marketers can now anticipate what consumers desire and adapt their marketing messages and offerings on an individual basis.

From a marketing perspective, predictive analytics can provide many advantages to an organization. First, predictive analytics can increase sales conversions. According to Marketing Land, by looking at a buyer’s research patterns, predictive analytic tools can forecast what customers are going to buy and when customers are going to buy with an 85% accuracy rating. Second, through predictive analytics, data can be more efficiently segmented resulting in a greater focus towards the target audience. By focusing on individuals, campaigns are more successful and budgeting and resources are reduced. Lattice Engines, a technology provider that delivers predictive marketing and sales cloud applications to business-to-business companies, has enabled organizations to reduce marketing costs by 20%. Lastly, predictive analytics can be used by marketers to understand industry trends and whether new products need to be created or if existing products can be modified to better meet consumer demands.

Predictive analytic tools have helped to directly increase sales. Amazon, for example, notes that its recommendations engine roughly accounts for 30% of the company’s sales. Amazon tracks customer’s last purchases, items that customers have rated and liked, customers purchases compared to similar purchases by other customers, and items in customers wish or shopping lists, to produce personalized recommendations. Additionally, Netflix has used predictive analytic tools to suggest movies for viewers to watch next by using past viewed movies and through the correlation of preferences of other viewers with similar tastes.

A comparison of how Amazon and Netflix track customers to create future and more personalized recommendations.
How Amazon and Netflix use predictive analytics to make future product recommendations. Source: Google Images.

As the amount of data continually increases, using predictive analytics to sift through this data is increasingly essential.  Through interpretation of all the data, businesses can better prepare for trends and not only anticipate but also respond to consumer spending habits more quickly. Brands can adapt their marketing tactics on a situational basis. Technology, yet again, has provided businesses with another tool to improve their bottom lines.

From a marketing management perspective, here are some questions to consider:

  • Do you agree that technology has caused a power shift from consumers back to marketers?
  • How should predictive analytics be used? What type of predictive analytics do you think are most important for a business?
  • Research other companies that have used predictive analytic tools to increase company sales.