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Finding your best customers with machine learning


Improving the customer experience through advanced analytics is necessary for modern banks to stay competitive. Seacoast excels in this arena due to its proprietary customer analytics platform, which is powered by the SAS Platform and used to unearth customer insight for many purposes. After aggregating and contextualizing its data for analytics, the bank used SAS Enterprise Miner to build a customer lifetime value (CLTV) model, which looks at every customer, measures their value and specifies why they're valuable. Calculating the CLTV is critical for estimating a customer's potential and how much the bank should invest in reaching and serving that customer to receive maximum ROI. "Because we're more aware of which customer groups drive value, we can fine-tune our customer treatment strategies as well as our acquisition efforts to generate very high returns," says Jeff Lee, Chief Marketing Officer for Seacoast.

Artificial Intelligence


It's been reckoned that about one third of large companies globally are using Artificial Intelligence as part of their marketing mechanics. The main goal of AI in this area is one of anticipating what customers will be buying in the short, medium and longer term – allowing special packages and promotions to be tailored to specific trends, on and offline. It also has the obvious advantages of improving media advertising placement, monitoring social media platforms and analysing brand loyalty. In essence, a strategy for informing what the buyer wants and when, which can enable dynamic pricing whilst at the same time vastly improving the automation of in-store or department selling function. AI is allowing clearer visibility into not only the retail sector, but is making its impact felt in the banking and telecom industries.

4 Applications of Machine Learning (ML) in Retail – Karl Utermohlen – Medium


The retail industry has benefited greatly from the advancements of machine learning (ML), which have the ability to improve a company's bottom line. The technology can do so by improving the retail experience for consumers with a better user interface, a personalized recommendation engine, the optimization of stocking and inventory and to more accurately price an object. Many companies have already shifted towards a more digitized platform in order to have a better understanding of when to push products more aggressively and when to use more tact with customers. There's no telling how far ML will go in revolutionizing the retail world, but a recent study by McKinsey suggests that U.S. retailers that have adopted data and analytics into their supply chain have experienced up to a 10% increase in operating margin over the last five year. Attaining data and developing the right smart solutions platform with predictive capabilities have been key to boosting businesses' ROIs.

Mindbody investing in AI and machine learning to 'reimagine platform'


Mindbody is focusing its product development efforts on new technologies, as it looks to "reimagine" its software platform. The cloud-based wellness platform said it is making strategic investments in its core technology, in addition to advancing its product development in what it described as an "increasingly challenging business environment brought on by the coronavirus". Regina Wallace-Jones, senior VP of insight and innovation, said: "Our artificial intelligence and machine learning units are accelerating the build out of sophisticated recommendation engines that will extend Mindbody's reach across the consumer marketplace. "It will ensure that the right inventory, accessed through the Mindbody app or, "The AI/ML teams will also deliver a lead management engine that supports wellness businesses in their challenge of identifying new clients for the purpose of unlocking growth."

Machine Learning Engineer posted by Versive on


Massive data: You will examine terabytes of structured and unstructured data with our platform to create value for customers. Machine learning: You will use machine learning and data science to generate insights and decisions. This process is highly iterative and will entail owning all aspects of the end-to-end machine learning workflow (eg, data ingestion, feature engineering, modeling, predicting, explaining, deploying, diagnosing). Customer facing: You will own all technical aspects of the customer experience and work directly with customers to deliver high quality results within a constrained timeline. Production deployment: You will be responsible for integration and deployment of the machine learning pipelines into production where your ideas can come to life.