As a predictive analytics team, we at CAN take the science behind Big Data very seriously, but that doesn’t mean that our whole process is centered around the software we create. In fact, we prioritize our relationships with our customers on a human level, and do our best to educate them about what we do best: data. The following article is an educational piece for our customers to learn more about CAN and CAN’s process.
With technology developing so quickly, new ways to implement marketing strategies and more effectively reach consumers are popping up all the time. Predictive analysis is one such technique. Praised for its ability to inform companies of future trends and reveal important information, predictive analysis is growing in popularity, with 87 percent of B2B marketing leaders saying they had already implemented or were planning to implement predictive analytics in the coming 12 months. So what is predictive analysis and how can it benefit you? Let’s check out the details of this new process sweeping its way through the business world.
What Is Predictive Analysis?
Before fleshing out its benefits, it’s probably best to first explain what predictive analysis is: through data mining, statistics, modeling, machine learning and artificial intelligence, predictive analysis is a process for collecting and analyzing current data. To learn more about how CAN uses predictive analysis, check out our blog post here.
As a result, brands are able to interpret big data and uncover patterns and relationship regarding consumer behavior. For example, the latest mobile technology, such as the Samsung Galaxy S7, has developed sophisticated and compressive methods to retrieve such data from app behavior and mobile activity. With mobile being such a popular device choice for consumers, this is beneficial for retrieving fast and relevant information.
How Can Predictive Analysis Benefit Marketing and Sales?
- More Efficient Customer Acquisition
By providing your sales team with specific data, predictive analysis can allow them to acquire new customers and keep old ones more efficiently and with less cost. What journey do they take to purchase a product? What advertising do they respond to? What is it about your product/service that they enjoy the most? All these questions can be answered by analyzing previous data and drawing conclusions about future activity. This information can then be used to determine which customers to reach out and how best to appeal to them, saving time and money.
- Determine Up-sell Opportunities
Predictive analysis also assists in drawing conclusions about other aspects of your customers’ buying behavior. Through analysis, brands can better understand what their customers’ needs are and what exactly they’re looking for. This can then be used to tailor the sales and marketing strategy to specific customers.
For example, if you are a fashion brand and have customers who are in need of shoes, it would be inefficient and wasteful to send them an advertisement for a new shoe promotion. Instead, it would be better to send this to customers in need of footwear to maximize on profit.
- Optimize Marketing Strategy
Not only can predictive analysis benefit brands by helping to find information on customers, it can also help in regards to the market environment. You can learn what time of the year spending peaks, how much people are spending and what they’re spending their money on. This information can assist in the successful execution of marketing strategies by ensuring you are targeting the right people at the right time.
Or you can figure out where to score the most candy on Halloween, like CAN did here. See, predictive analysis can be fun too.
Predictive analysis is an increasingly popular method for brands to more effectively initiate sales and marketing strategies. By providing detailed information about market trends and buying behavior, brands can cut costs, boost profit and increase overall efficiency.
Hooked on predictive analysis? We’d love to chat with you! Contact Nate Watson via e-mail at firstname.lastname@example.org.