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AI-Based Customer Data Platform Supports ABM Operations Customer Data


Lattice Engines, a provider of account-based marketing tools driven by artificial intelligence, on Thursday announced the launch of Lattice Atlas, positioning it as the first customer data platform for ABM. The new platform synchronizes all customer data across a single, integrated view, the company said, making it easier for customers to see their data, no matter the source. Customers often have to view data across multiple tools and workflows -- for example, in order to compare data with existing versus new customers, noted Nipul Chokshi, vice president of product marketing at Lattice. "We realized that our customers needed a customer data platform that unifies all data," he said, one that "enables AI-driven audience creation as well as omnichannel activation and personalization all in one centralized place, and provides enterprise grade marketing governance." The new platform employs the ABM identity graph, using patent-pending adaptive match technology to resolve identities by matching first-party to third-party data in Lattice Data Cloud, which has more than 20,000 curated insights, Chokshi added.

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.

What marketers should know about Artificial Intelligence and Customer Experience - BehaviourExchange


What do the two buzzwords actually mean in concrete terms? What unites both is a foundation of data. But how can artificial intelligence (AI) and customer experience (CX) really be meaningfully and profitably integrated into marketing? When talking specifically about AI in marketing, most of the discussions focus on automation (such as targeting, media buying, or reporting), analysis (pattern recognition in user behavior to find behavioral intersections or anomalies), and assistance (batch processing of assets or custom interfaces based on user needs). Customer experience, on the other hand, is the ambition of a brand to implement content for its customers, both on and off-line, as context-sensitive, consistent and as relevant as possible.



Enterprises across all industries are looking for innovative ways to engage their customers and grow beyond traditional business models. For many, this means exploring new digital channels and ecosystems and building a digital platform for their business. As leaders embark on their ecosystem strategy, they often find that APIs are critical building blocks for developers that are creating new connected experiences for customers. In this research note, Gartner explains the role of APIs and shares best practices for managing APIs as you create your digital platform.

Marketing platform Kahuna applies its machine learning across customer journeys


Since last fall, marketing platform Kahuna has been expanding its targeted channels beyond mobile, to include pop-up messaging on web sites and greater capabilities for email and in-app messaging. Now, the Palo Alto, California-based company is expanding again to focus on customer journeys instead of individual messaging campaigns, with the recent launch of Experiences for optimizing messages across a journey. This newest incarnation of its marketing platform, senior vice president for product Mihir Nanavati told me, allows the firm's RevIQ machine learning engine to be applied across all the paths taken by a customer toward the marketer's goal. Previously, he said, the platform focused on a marketer's ability to, say, send a email to a mobile user encouraging them to install a new travel app. Once installed, there might be an in-app message suggesting that the user search for airfare deals.