The sheer volume of data in recent years has overwhelmed humans in their ability to understand and managerial decisions about it. While large datasets are becoming increasingly prevalent due to firms computerizing their activities, both Big Data and Small Data are increasing and astonishingly varied. But generating real value can be a daunting task. Event-level data from a single digital campaign can contain hundreds of millions of data points – valueless alone, but invaluable together. Operationalizing big data is a major challenge to overcome for the marketing industry. While machine learning helps you solve one piece of this puzzle, data visualization is another step towards ‘making more sense’ of the information at your disposal. Unfortunately, too many marketers are behind the curve when it comes to using customer data and predictive analytics. Just 24% of marketers use data for actionable marketing insight; meanwhile, 45% of the marketers surveyed say they lack the capacity to analyze Big Data.
Traditional business intelligence tools usually fail when managing large quantities of data. Thus, data management vendors cannot turn insights into action. The right analytical tools make a huge difference for CMOs, including giving them access to real-time data streams and predictive analytics capabilities that strengthen decision making by enabling marketing leaders to quickly grasp emerging customer or marketing performance trends. Power Bi provides powerful visualization capabilities to help marketers at all levels discover actionable customer insights.
What is Predictive Analytics?
A large variety of predictive analytic models can be used in this application, including affinity analysis, response modeling, and churn analysis, all of which can, for example, tell you whether it’s a good idea to combine digital and print subscriptions or keep them separate, or help you determine content that should be charged a subscription fee versus content that should be given a one-time sales price or other structure.
Salesforce explains the marketing connection: “Predictive marketing uses data science to accurately predict which marketing actions and strategies are the most likely to succeed. In short, predictive intelligence drives marketing decisions.”
Complex questions that Predictive analytics can answer:
Who is a target customer? What is the right time and right content to target the right customer?
How can I predict customer behavior?
Why did customer respond to the offer?
How powerful can predictive analytics be for marketing purposes?
Very powerful. While direct marketing activity is retrospective; it’s about trying to replicate the past. With predictive analytics, you’re being proactive.
What are the use case scenarios for Predictive analytics and why do CMOs need it?
When marketers utilize predictive analytics, they are immensely effective at identifying potential customers. Once customers are identified and successfully closed, a host of other products can be marketed to them based on their purchasing patterns. Again, in concert with big data, predictive analytics can indicate which products to cross-sell to which consumers.
Whether you are looking for customer acquisition, developing relationships or customer retention, predictive analytics can help. Here are a few use case scenarios:
1. Customer Acquisition
For effective customer acquisition and retention campaigns, organizations need to first analyze their prospects as well as their existing customer base. They need to identify the needs and preferences of their most profitable customers. Similarly, they need to prioritize their commercial initiatives. Predictive analytics can help organizations easily get over these challenges by providing insightful predictions and insights. With predictive analytics, organizations can acquire the desired customer intelligence they seek.
High-volume, variety, and veracity of information assets fuel the colossal size of big data. Data may be structured or unstructured and include both traditional and non-traditional data sets, such as transactional, web-based, historic, demographic, economic, audio, video, sensor and social media. Predictive analytics can help organizations make sense of big data for their short-term, as well as long-term business objectives.
To realize optimal outcomes, it is required that relevant and quality data is collected from diverse data sources and properly analyzed. When combined with big data, predictive analytics can empower organizations foresee their potential prospects, understand their needs and wants, and discover the most effective ways to reach out to them.
2. Customer Engagement
Years ago, mass marketers discovered they could narrow their focus and create products by targeting customer segments. Organizations now segment to some extent—targeting different customers with a unique styles of marketing—but many create only big, macro-level segments. Today, thanks to advanced analytics, organizations can trim a segment down to an individual. This single-centric marketing approach enables organizations to track and understand individual customer behavior, which helps them convert visitors into long-term, high-value customers at more profitable rates.
a) Customer Lifetime Value
Customer lifetime value (CLV) models are fashioned from a variety of behavioral, demographic, and psychographic variables to help us predict someone’s propensity to be a high-value customer. Essentially, this model infers a customer’s future value from their current level of engagement and defining characteristics. It then evaluates how much revenue they are likely to contribute long-term. CLV models can become more complex than just measuring purchase over time. Variables can also be drawn from certain behaviors like social shares and referrals, where the value of the customer extends to their ability to convert others.
b) Next Best Action
Next-best action models help us understand what a given customer is likely to do next. They help encourage certain actions and can be programmed to be ready with a relevant offer. Next-best action is determined by evaluating a customer’s expectations, needs, and interests and our objective for that customer.
c) Up-sell and Cross-sell
Cross-sell and upsell models focus on what’s in a customer’s shopping cart. Amazon’s “you might also be interested in…” and “often bought together…” are good examples of using insight gained from cross-sell and upsell analytics to inform a bundled pricing strategy (selling multiple goods as a single unit so that the sale of one item buoys the sale of another).
d) Customer Retention
Customer retention is the process of maintaining your customer base and keeping your customers stimulated after the conversion. Regardless of what business you are in, a happy and a satisfied customer is a loyal customer who will stay with you for a long time.
Using data collected from your customer’s journey will give you a complete view of the purchasing patterns behind each customer account you have. Utilizing a two-dimensional machine learning algorithm that combines customer intent signals and pre-purchase research patterns with past practices and behaviors using CRM, POS and first party databases, BrickRed Systems offers you a complete view of your long-term customers intent. Not only will this assist your retention strategy, you’ll be able to see what worked, what didn’t, and what needs improvement. Your strategy gets smarter with every sales cycle.
Churn prevention models encourage better customer engagement and loyalty because they identify churn warning signs as they arise. These models are created from abandonment traits — a predetermined set of variables that (historically) indicate that a customer is about to disengage or from our brand. By creating a churn model, we can easily anticipate which customers are at risk of leaving and stage an appropriate intervention to keep them both satisfied and engaged. Request for FREE Proof of Concept