It’s no surprise that companies spend millions of dollars performing market research before creating and launching a product. Understanding what consumers want and how they will act — ideally, before they do — is an invaluable strategy for marketers to take on. With the emergence of big data, artificial intelligence makes that job easier. 

Not to mention, marketers have heaps of historical and transactional data to pull from, which helps them process information about future risks and opportunities. But with companies managing three times as much data as they did five years ago, it’s become increasingly difficult to determine which metrics matter.

In this article, we’re examining the world of analytics and why it’s important to be proactive rather than reactive in understanding consumers’ needs.

Applying Predictive Analysis to Marketing

Predictive analysis can inform which products to cross-sell to certain consumers. For example, if a man were to purchase a $4,000 suit, he would be a more likely prospect for a BMW than for a Honda. Smart marketers have begun focusing their budgets on big data — including geospatial data and other forms of real-time data — to inform their decisions.

Here are some of the top ways marketers apply predictive analysis to marketing:

1 - Segmentation

Customer segmentation allows marketers to create highly personalized, targeted messages for more effective acquisition and retention. Rather than simply grouping customers by age, gender, geography, or other demographic traits, marketers can use their habits and behaviors to group them more strategically.

Behavioral segmentation informs you about how your target audience behaves: Are they discount addicts? Do they frequently make purchases in stores or online? How much do they spend, and how much time until they make another purchase? Knowing these answers helps guide your messaging. For instance, discount buyers would react more favorably to a message advertising 60% off your entire website.

Another type of segmentation is product-based segmentation. Essentially, this involves clustering people based on the product groups they buy from. This information comes in handy when deciding which content or email offers to target people with.

2 - Forecasting

Arguably one of the best benefits in using data for predicting consumer behavior is the ability to predict sales and ROI.

A big part of accurately forecasting sales and analyzing trends and seasonality. Many retailers consult knowledgeable organizations, like the National Retail Federation. The NRF publishes outlooks on consumer’s total spend (e.g., Christmas spending increasing) as well as category spend (e.g., video games are expected to be up x% and board games are expected to be down y%). 

Seasonality affects many other consumer behaviors as well. When do certain people invest? What quarter of the year yields the highest savings versus the highest spending? With predictive analysis, marketers can make demand forecasts and predict market changes, allowing them to execute strategies before their competitors. 

3 - Social Listening 

For years, marketers have been using social media listening to understand and anticipate customer needs. 

Deloitte’s Blab tool, for instance, mines over 50,000 sources — including news and social channels — to predict themes that will shape consumers’ conversations up to 72 hours in advance. Equipped with this information, CMOs and marketers can more accurately purchase programmatic media. Tools like Blab offer marketing teams predictive analytics like where to locate influencers or how to recognize potential threats of viral conversations.

4 - Price Optimization

Properly optimizing your prices is essential for businesses, especially in the e-commerce landscape. Data-optimized pricing can make retailers a great deal of money. Many online retailers, including Walmart, Target, Home Depot, and Staples, vary pricing based on intricate formulas. Cost analysis, market analysis, and competitor analysis are all crucial components when it comes to pricing.

5 - Cross-Selling

A longstanding marketing strategy is leveraging an existing customer base into new sales. Perhaps the most well-known examples of cross-selling are Netflix and Amazon, who suggest TV shows, products, and services best suited for a particular person. With efficient use of cross-selling, you can keep consumers coming back and

6 - Improving Customer Satisfaction 

Sure, big data can help you predict how your consumers act. But it can also influence how they act, improving overall satisfaction in the process.

Let’s take Domino’s Pizza for example. The pizza chain has relied on technology as its competitive differentiator, which helped it achieve more than 50% of all global retail sales in 2017 from digital channels. It’s big data platform, Domino’s Anyware, uses location-based services to activate customer loyalty offers based on each consumer’s habits. Domino’s relies on AI to analyze alternative outlets people may order pizza from — like through a smartwatch or smart TV. By using data to meet consumers on the platforms they used most, Domino’s was able to make huge strides in its business.

Conclusion

Predicting ever-evolving customer behaviors is one of the biggest challenges faced by marketers. But the bottom line? Brands need to have the right product for the right consumers at the right time. Luckily, advances in AI, machine learning, and analytics has made it possible for marketers to make better, more detailed forecasts with more accuracy.