Gartner defines digital commerce as the technology that enables customers to purchase goods and services through an interactive and self-service or assisted experience. The platform provides necessary information for customers to make their buying decisions and uses rules and data to present fully priced orders for payment. The commerce product must support interoperability with customer data, product content (e.g., price, availability) and order functionality and data via APIs. Digital commerce is commonly delivered as single or multitenant SaaS, or as single-tenant hosted or managed hosted (PaaS) applications. It could be offered for on-premises implementations in some circumstances. Digital commerce enables customers to purchase goods and services through an interactive and self-service or assisted experience, providing the necessary information for customers to make buying decisions.
Gartner defines multichannel marketing hubs (MMHs) as software applications, primarily delivered as SaaS, that orchestrate personalized campaigns and event-driven customer journeys across marketing channels. These applications leverage customer data, predictive models and real-time insights to optimize the timing, channel and content of interactions. MMHs apply advanced analytics, AI and prescriptive intelligence to help marketing and technical teams manage the end-to-end life cycle of customer journeys. Although MMHs overlap with customer data platforms (CDPs) and personalization engines, their primary focus is enabling marketing users to manage large-scale consumer interactions, particularly in owned media channels such as email and app push. Multichannel marketing hubs empower marketers to deliver personalized media and orchestrate customer journeys, thus driving revenue, engagement and loyalty. These SaaS applications unify customer data, predictive insights and real-time decision making to optimize interactions across digital channels. MMHs enable multidisciplinary teams to manage campaigns and event-driven journeys via advanced analytics, artificial intelligence/machine learning (AI/ML) and prescriptive intelligence.
Personalization engines use knowledge about customers to create and deliver an optimum experience for them and measure the impact on customer experience. These engines apply AI, advanced analytics and business rules to create meaningful experiences across channels that facilitate customer engagement and drive revenue. Personalization engines create a relevant, individualized interaction between two parties designed to enhance the recipient’s experience. A recipient can be a prospect, customer (known or anonymous) or employee (engaging with a customer or prospect). In commercial settings, the engines apply advanced analytics to interpret customer data — whether known or anonymous, behavioral or contextual — and adjust engagement based on where the customer is in their journey and how they’re interacting. The engines adapt content, offers and interactions in real time that facilitate the customer’s journey.