For ages, businesses have relied upon the use of consumer data to forecast trends. From determining what to expect in terms of sales and subscriptions to understanding where new demand may appear in emerging markets, data has been a valuable commodity for companies.
However, most data collected by businesses and brands has historically revolved around assessing consumer behavior after each brand interaction: the technology simply didn’t exist to determine what consumers might do beforehand.
Because of this, companies and brands that can use data to peek into the future have an inherent advantage over their competitors. This has wide-reaching consequences for the world of marketing: let’s examine the role that predictive data analytics is having on modern marketing and how it’s changed the game.
What Is Predictive Data Analytics?
As mentioned, predictive data analytics helps businesses determine what people actually want before they engage, purchase, subscribe or interact with a company. Through a combination of machine learning, previously-collected data and analytics, and carefully-devised algorithms, predictive data analytics becomes a science.
As companies strive to be one step ahead of both their competitors and their consumers, predictive data analytics is an enticing concept for many. By knowing what an audience wants beforehand, marketing strategies become exponentially more efficient in most situations.
Allowing Companies to Predict Consumer Behavior
The biggest and most obvious advantage in utilizing predictive data analytics is predicting consumer behavior on a larger scale. In the past, companies had to rely upon the use of data as it was obtained to discover if new trends or consumer behaviors were emerging. With predictive data analytics, companies can now utilize that same data – along with algorithms and machine learning – to project future behaviors.
The impact on marketing effort is obvious. Businesses and brands can employ more sophisticated marketing campaigns across a variety of channels, including:
- Social media
- Email marketing campaigns
- Search engine ads
- Video-based ads on platforms such as YouTube
Thanks to predictive data analytics, companies no longer have to wait for major trends to unfold before reacting to them. This leads to better performance in marketing and for the company at-large. To learn more about how performance can be impacted by predictive data analytics in marketing, click here.
Avoiding Unwanted Goods, Services and Pitches
Success for businesses isn’t always about embracing positives; sometimes it is also about avoiding the negatives. Every business must experiment and develop new ideas and plans, but understanding product fit within a market is essential. Through the use of consumer data and market research, success is generally a possibility.
However, predictive data analytics is changing the marketing game by also providing companies with insight into what audiences want and don’t want. Some of the world’s biggest product flops had major backing, yet failed to connect with the audiences they were geared towards.
Predictive data analytics also offers marketers the opportunity to understand the viability of their own potential campaigns. From lead scoring and lifetime value prediction to upselling potential, this form of analytics guides brands toward better campaigns.
Anticipating Peak Times for Making Pitches
There’s a time and a place for everything. Businesses must be cognizant of when, where and how to reach their audiences via marketing. Historically, this meant assessing previous attempts and guessing what applies for the future. The only problem: consumer behaviors can change over time.
Marketing already revolves around the use of many analytics-based observations. For example, and with regard to email marketing, Omnisend discovered that:
“the best day to send promotional emails, such as weekly newsletters, is Thursday, followed closely by Tuesday. Omnisend also found that Monday and Saturday are the worst days to send promotional emails.”
Predictive data analytics can not only help with discovering optimal times for email marketing, but can also predict when consumers will be most likely to respond to ads, make purchases and even be receptive to upselling. This has changed marketing behavior in many ways, transforming the act into a more refined art.
Calculating Opportunities for Risk
Businesses that naturally attract potentially risky consumers have good reason to embrace predictive data analytics. For example, companies that serve the public by offering lines of credit for their products or services experience a certain amount of liability. Common examples include:
- Real estate agencies
- Car dealerships
- Financial services and lenders
Predictive data analytics (combined with customer risk assessments) helps businesses better understand where inherent risks lie, allowing them to more effectively address – or outright avoid – these issues in their marketing efforts. Some companies may use this data to avoid marketing to high-risk groups altogether, while others take a more aggressive marketing approach to grow revenues and offset potential risk.
Through machine learning, copious amounts of data and refined algorithms, any business can benefit from predictive data analytics. While the possibility for improvement across every aspect of business exists, major effects have already transformed the marketing industry in major ways that show no signs of stopping.
