The practice of collecting basic demographic information from customers to create a successful business marketing strategy is one of the past. In recent times, there has been a major shift in the way that businesses interact with their customers. The digital space has spread so far and wide that it has had a lasting influence on virtually everything we do. As a result, the conventional approaches to marketing that were prevalent even as early as a few years ago are considered severely ineffective today. The rapidly growing popularity of Big Data means that marketers need to embrace sophisticated approaches to processes and perform in-depth analysis of customer data, preferably in real-time.
AI and machine learning are making the customer experience more personalized and contextual than ever before. Banks and credit unions are using advanced technology to make websites, emails, digital advertising, social media and other content more efficient and effective. This is increasing marketing ROI as well as customer satisfaction. Subscribe to The Financial Brand via email for FREE!There is a great deal of discussion of the potential value of artificial intelligence, machine learning and robotics in banking. Unfortunately, much of the implementation of these technologies lags the potential by a significant margin.
Every second of every day, companies are inundated by massive volumes of data from diverse sources: sensor data, clickstream data, location data, social data, video data, and so forth. But the pace at which enterprises can leverage this data to sense and respond intelligently to customers lags well behind the pace at which data is exploding. Fortunately, advances in marketing automation and artificial intelligence (AI) are enabling enterprises to respond to the data challenge. We are at the threshold of a new era in marketing that I call sentient marketing - a vision for customer engagement that is powered by data, scaled with automation, and personalized through AI. Sentient marketing is a set of capabilities and processes that enable enterprises to create personalized customer engagement at scale and in real-time.
This blog describes a five-step process to improve your customer retention analytics efforts. This process leverages existing data to draw useful insights to show you how you can improve retention rates, increase purchasing behavior and maximize the lifetime value of your customers. Customer retention is a key driver of business success. One approach to keep your customers from leaving is to optimize customer retention. Customer retention is defined as the activities a business participates in to create repeat customers and boost the profitability of future purchases.
The expression, "Marketers are data rich and insight poor" is more true today than ever. Marketers all over the world are working to optimize marketing operations and effectiveness using their abundance of data. Many are turning to tools and platforms powered by artificial intelligence and machine learning. AI promises to make sense of all the dark data companies are sitting on as well as structured and unstructured data online to surface insights about customer behaviors, opportunistic content and emotional triggers to inspire conversions. In an age of too many choices, increased competition for customer attention requires every advantage to optimize for reach, engagement and conversion.