Big Data and Consumer Behavior

Using Big Data to Predict Consumer Behavior and Improve ROI

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Relying on data to make more informed decisions has become vital for business owners. Being able to more accurately predict results and improve upon current decisions based on past experiences is an invaluable tool.

Marketers today place a stronger reliance on the use of big data to predict consumer behavior trends and to better reach new and existing customers. Eighty-six percent of surveyed executives who were overseeing predictive analytics campaigns for at least two years reported noticing a resulting increase in their return on investment (ROI).

The retail industry is astoundingly competitive in the modern world, where millions of comparable options are presented to customers shopping online. Standing out amongst the competition is extremely important to succeed in online retail. By making accurate customer predictions based on data, retailers can expect to see a result of higher sales numbers and increased customer satisfaction levels.

What is Big Data?

The term “big data” is often overcomplicated and confusing. In the most basic definition of the phrase, it simply refers to large data sets that are analyzed to reveal trends and patterns. In consumer behavior marketing, big data is used to analyze data points of a customer’s journey from exploration to sale, powering marketers with tools and knowledge to make more informed decisions.

There are three primary levels of data that marketers typically engage with:

  • Predictive: Data that is used to make smarter decisions in the future by forecasting what could happen.
  • Descriptive: Data that provides a clearer picture of what happened in a specific scenario.
  • Prescriptive: Data that suggests decision making options based on the results revealed by predictive and descriptive analysis.

The most widely used data set in consumer behavior, and the one we’ll be referring mostly to in this article, is predictive analytics.

 

How is it Used?

Predictive behavior modeling can reveal many insights for modern marketers to use to maximize their strategy efforts. Going further than analyzing the historical data, this regression model uses mathematics and statistical analyses to make the most educated guesses on what will happen in the future. By backing up statistics with fact-driven data, your results will always be better than solely relying on intuition and could significantly help drive a successful strategy.

Here are four strategic focus areas where predictive analytics can help improve a company’s ROI:

 

Personalized Marketing

Delivering a message to your customers with accurate timing is essential for any marketing strategy. With the burst in e-commerce sales over the last decade, it’s more vital than ever before to reach your customers before your competitors.
 

By segmenting the market into specific subgroups based on similarities in behaviors, geographic location, or other demographics, marketers can better target groups with personalized marketing strategies. With this knowledge at hand, focusing customers on the right product and positioning becomes easier. Segmentation can also highlight the most profitable subgroups based on past purchasing behaviors.

Popular examples of personalized marketing include customized recommendations based on a user’s watch history, highlighting products a consumer may be interested in within banner ads, and recommendations of items “you may also like” when shopping on a company’s product page.

 

Demand Pricing

Predictive analytics can even aid in creating better demand pricing. By evaluating the purchasing trends of consumers in each data set, marketers are able to better see what effect pricing decisions have on demand. More competitive pricing may still help achieve ROI objectives if the demand is strong enough.

One example of this is the Disney Theme Parks, which have switched their pricing model over to surge pricing after noticeable changes in demand at various points throughout the year. For instance, it could be noticeably less expensive to visit “the happiest place on earth” during the off-peak times (such as Mondays, or the month of September) than it would be to go during the busiest times (think Spring Break, weekends and Christmas). However, this will still satisfy many consumers as this surging aids with attendance and wait times for popular attractions during the peak seasons.

 

Resource Allocation

Having properly allocated resources in place is vital to achieving your organization’s objectives. With predictive analytics in place, a company can better forecast and segment where resources will need to be allocated to most.

For this process, it can be challenging for an individual to manage large data sets tracked against objectives. Implementing an ERP system into current planning strategies can help to organize these data sets for more straightforward analysis. ERP systems also make it easier to view the progression towards achieving set goals and objectives over time.
 
 

Forecasting

Arguably one of the most significant benefits for using data in consumer behavior analytics is forecasting sales and ROI. These predictions are essential for creating budgets and setting strategies. Using predictive forecasting creates intelligent and evidence-based estimates of sales goals based upon current and past sales performance reports.

This can help reduce or even eliminate the element of human error that can often be attributed to mistakes in manual sales forecasting.
 
The use of predictive analytics in maximizing a company’s ROI can prove to be an invaluable tool for marketers. As you evaluate your strategies in this new year, it may be time to consider implementing predictive analytics into your strategy.

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