Predicting Consumer Behavior with Data Analytics
It’s no surprise that businesses spend millions of dollars in carrying out market research before coming up with a new service or product. Despite that, it is important to recognize that the final product doesn’t sell itself and actually requires the right marketing tools to make itself visible to the potential consumers. Doing this the conventional way is equivalent to taking the right amount of risk — but unfortunately, businesses can’t afford to run on a trial and error method approach. This has always been a challenging task, but today, the stakes are even higher, as consumers are continuously being exposed to new technologies, products, and ever-changing ‘must-haves.’ With millions of buying options at our disposal, the behavior of this era’s consumers keeps flickering! Thanks to m-commerce, purchasing a simple service or product isn’t a simple task anymore; the consumer has a lot to weigh in before he or she finally makes the purchase.
However, that’s not all. Even if a product does happen to be the best, it is still often abandoned in the shopping cart because the buyer has most likely found a better deal or a greater value alternative somewhere else. While it is discouraging for the businesses to lose potential customers, NOT all hope is lost. And, this is precisely where data analytics comes into play.
Digital and business savvy marketers of today prefer to put their money and trust in data analytics to get a better understanding of their customers and their behavior. In an era of digital overexposure, shaping a potential buyer into a loyal customer requires a more in-depth study of users’ preferences, motivations, behavior and purchasing habits. Hence, a smart marketing strategy revolves around tracing the digital footprints of prospective customers with the help of intelligent tools, using data as their fuel.
Today, we’ll take a quick peek into the world of analytics and why we need to be pro-active rather than re-active and how predictive analytics can help us predict consumer behavior in this age.
What Do You Mean by ‘Analytics’?
Analytics is the systematic computational analysis of data or statistics to obtain meaningful patterns and the process of applying those patterns to make effective decisions.
There are three types of analytics:
However, the focus of this discussion will be predictive analytics. So, as the name implies, predictive analytics is the process of using data or statistics to obtain meaningful patterns that can be used to predict the future. While descriptive analytics describes the past and prescriptive analytics aid in planning the best course of action.
Companies are still struggling to interpret and implement this new form of analytics as the skills to analyze data are limited to a small number of data scientists who often don’t have any idea about the conceptual background of what the company does when it comes down to predicting their consumer’s behavior. However, one shouldn’t feel intimidated by that. A survey conducted by Forbes claims:
A vast majority of executives who have been overseeing predictive marketing efforts for at least two years (86%) report increased return on investment (ROI) as a result of their predictive marketing.
So, even if using this new form of analytics sounds a little scary, one can trust Forbes on this one and consider plunging right into it.
Reasons to Analyze Consumer Behavior
Imagine if you knew what your customer wants the moment he set foot in your store and — you don’t have that particular item in stock. Well, you might end up upsetting and losing a potential customer forever. Wouldn’t it just be easier if you already knew what your consumers wanted and when? For that to happen, it is crucial to analyze consumer behavior to
- Gain Insight: By segmenting customer database to pinpoint consumer segments.
- Attract and Engage Potential Customers: Targeting the segment of customers with relevant offers by analyzing past purchases and profile.
- Improve Customer Retention: It allows businesses to evaluate customer value and use proactive retention approach to retain customers.
Applying Predictive Analytics to Marketing
Although predictive analytics can support a bunch of other business activities, marketing is one of the best applications of predictive analytics these days. So here are a few ways in which predictive analytics can be used in marketing:
Segmentation revolves around the process of splitting up a market into various subgroups with similar features like demographics, geographic, behaviors, or attitudes. This way the business owner can target each group individually and cater to their needs more adequately.
Here, data helps in developing your target segments and deciding the most effective positioning for each one. With the help of predictive analytics, you can even identify the most ‘money-making’ segments and target them accordingly based on historical consumer behavior within these segments.
This data is used by marketing managers to allot resources to where they are truly needed, that is, to reach the most profitable segments.
The primary use of predictive analytics is in developing demand models that predict sales and revenue which is crucial for making a budget.
3. Demand Pricing
Also known as yield management demand pricing, is the process of pricing products and services based on differences in elasticity of demand among consumer segments. For example, services like Uber charge more during rush hour, but it doesn’t cost everyone the same. Office going people are willing to pay more for a comfortable ride than the casual passengers, so those rides can be charged more, and the price can be reduced for the other group of passengers to meet ROI (Return On Investment) goals without becoming unpopular among the consumers.
Using predictive analytics, one can design experiments that can help a business figure out the factors affecting the influence of price on demand which helps to develop most favorable pricing strategies that maximize the company’s financial growth.
4. Improve Customer Satisfaction
If you’re selling to your customer than you’re doing it wrong! It’s 2019, and your business should be more about serving the customers rather than pulling money out of their pockets.
Customer satisfaction has a significant impact on retention and loyalty. A happy customer equals an excellent business deal. Statistics suggest that losing a customer can be five times more expensive than retaining one. Predictive analytics can play an essential part in customer retention; tools like conjoint analysis enable companies to pinpoint which product/service enhancements produce a more considerable improvement in customer satisfaction.
Predictive analytics is a challenging-to-adapt, but powerful technique which if embedded flawlessly with the right marketing strategies can efficiently predict consumer behavior and help companies maximize ROI. Checkout Infor OS to find more about Predictive Analytics