Predictive Analytics in Marketing: Key to Drive Business Profitability in 2019
What is Predictive Analytics? Predictive analytics is an approach that helps in predicting unknown future events. There are many techniques that are used in predictive analytics such as machine learning, data mining, data modeling, and artificial intelligence to examine current data and make future predictions. Also, it helps in finding patterns in both structured and [...]
What is Predictive Analytics?
Predictive analytics is an approach that helps in predicting unknown future events. There are many techniques that are used in predictive analytics such as machine learning, data mining, data modeling, and artificial intelligence to examine current data and make future predictions. Also, it helps in finding patterns in both structured and unstructured data sets, thereby assisting in the identification of future risk and opportunities. Furthermore, predictive analytics has the potential to integrate management and technology together to drive better business outcomes. By leveraging predictive analytics solutions, businesses can become dynamic and can anticipate behaviors and outcomes based on the known facts and data and not merely upon assumptions.
How can predictive analytics solutions help businesses derive successful outcome and boost ROI? Read Quantzig’s recent blog to find out.
Leveraging Predictive analytics in marketing can help businesses refine their marketing strategies and provide personalized services to customers. Want to know how? Get in touch with us right now!
Predictive Analytics in Marketing Realm
How can predictive analytics in marketing drive profitability for business? Are you thinking the same? The answer to it is, any tool, process or technique that can guide marketers to identify the buying habits of consumers is nothing less than a boon to their business. This is because if the past buying habits of a customer are identified and analyzed well, it can help in projecting the future buying habits, thereby helping in future decision-making based on those projections. Predictive analytics in marketing helps to ensure that these predictions are precise and accurate.
Here are a few things that a business can do when the available data is mined and predictive analytics in the marketing realm is applied:
Analyze and predict the seasonal behavior of customers
Today most of the products and services are sold online. Application of predictive analytics in marketing especially helps in this case. It helps in highlighting the products that are on high demand and those that customers prefer to buy at any given time.
Target the most profitable product category
The second benefit that businesses gain by applying predictive analytics in marketing is that they can target the most profitable products and services. By administering the technique of artificial intelligence and machine learning, it is easy to identify affluent customers who prefer high-end products. This is an integral part of effective and predictive marketing strategy too.
By applying predictive analytics in marketing businesses can gain insights into new profits streams, better ways to conduct the business, and ultimately lead the game. Request a free proposal to know more.
Employ the most suitable marketing strategy for winning repeat business
Predictive analytics in marketing can inform businesses about customers who are most likely to be repeat customers. Owing to the high competition, businesses need to allocate resources on targeting such customers that are likely to profit the business the most. And applying predictive analytics in marketing is the best step to achieve this.
How can predictive analytics help in forecasting consumer demand precisely? Read our latest success story here to gain better insight.
Finally, predictive analytics in marketing helps in prioritizing customers. It helps in identifying factors that indicate that a particular customer s most likely to become a repeat customer. It guides to recognize customers who buy the highest-margin products and are most likely to initiate returns.
Use-Cases of Predictive Analytics in Marketing
Use Case #1: Refine segmentation for better campaigns
Applying predictive analytics in marketing helps in refining customer segmentation and creating customized campaigns. It allows to mine behavioral and demographic data to push quality leads further down the sales funnel.
Use Case #2: Improves content distribution strategy
Sometimes even the good content fails to drive business and the reason behind this is an ill-defined content distribution strategy. By applying predictive analytics in developing a marketing strategy, this problem can be tackled head-on. Using predictive analytics in marketing makes it easier to analyze the types of content that resonate most with customers of certain behavioral or demographic backgrounds. Furthermore, this helps in distributing similar content to such customers sharing the same demographic or behavioral habits.
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Use Case #3: Precise prediction of customer lifetime value
Artificial intelligence and machine learning can make predictive analytics in marketing more efficient. It can enable businesses to gauge the historical lifetime value of existing customers that match the backgrounds of new customers. Consequently, this can help in making a fair and precise estimate of the lifetime value of new customers.
Use Case #4: Better insight into the propensity to churn
Protecting your bottom-line becomes much easier by leveraging predictive analytics in marketing. How do you ask? By analyzing and learning from the mistakes committed in the past. By applying predictive analytics in marketing, businesses can analyze the behavioral patterns of previously-churned customers. Furthermore, this can help in identifying the warning signs from current customers. Consequently, businesses can take measures to plug such customers into a churn-prevention nurture campaign.
Use Case #5: Optimization of campaign channels and content
By leveraging predictive analytics in marketing, businesses can optimize their campaign channels as well as the content. With the entry of new customers in the business pipeline, there is an availability of their data which can be utilized for the various purpose. These purposes include identification of most suitable marketing channels, content type and even data and time to target specific and potential customers.
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