By Pauline Trotter 20 Jan 2020
Ovum's 5G Business Mobile Subscription Forecast: 2019–24 provides forecasts through 2024 for 5G business mobile subscriptions worldwide, split by region and country.
Today's customers demand seamless and contextually relevant experiences, and they expect the brands they interact with to respond at the right time, at the right place, and with the right messaging. That requires intelligent personalization. Orchestrating intelligent, personalized omnichannel customer interactions requires three key actions: the ability to extract data from customer and third-party databases, the ability to analyze and proactively predict outcomes, and the ability to respond with the right interaction at the right moment with context.
Until recently, enterprises for the most part treated customer engagement as a channel, with individual business units interacting with customers through separate streams via email, the web, social media, or the contact center. This siloed method of engagement only perpetuates the ubiquitous enterprise challenge of fragmented customer experiences. To succeed, enterprises must be supported by complete information on the customer so employees can recognize and respond to individual customers and collaborate in real time across enterprise functions.
This necessitates placing customers at the center of the enterprise and creating a holistic view of them as they move along their journeys. A critical aspect of responding to customers at key moments in the customer journey is having the ability to integrate customer data with existing systems data and then unifying the key customer-facing stakeholders along the enterprise. It also requires merging existing transactional and third-party data sources, including big data and emerging data sources, such as the Internet of Things. This process of unified customer data management monitors and synthesizes the data to create unified customer profiles.
After enterprises integrate the data, they must apply predictive analytics and machine learning to determine next best actions. This often requires predictive and behavioral analytics, machine learning, natural language processing, and robotics process automation to analyze, predict, and contextualize the data and infer customer intent.
Finally, having all this data and intent is inconsequential without the ability to act on it. Having the ability to intelligently orchestrate when and where to deliver a personalized offer or communication and deciding who should deliver it is the pièce de résistance of omnichannel personalization. This requires enterprises to connect all interactions and data and deliver personalized content or next best action through every touchpoint and throughout individual buying cycles.
Once enterprises are successfully enabling these three elements, they'll be well on their way to orchestrating personalized customer engagement activities across a company's value chain in a way that delivers a mutually beneficial set of outcomes – superior customer experience and more profitable revenue.
Mila D'Antonio, Principal Analyst, Customer Engagement
Service Provider Technology, Enterprise Decision Maker
By Kedar Mohite 20 Jan 2020
This case study outlines how Amazon Web Services (AWS) and Amagi are enabling premium content owners to reduce complexity and improve the scalability of their broadcast TV and video distribution workflows.
Enterprise Technology IT, Enterprise Services, Enterprise De...
By Mila D Antonio 17 Jan 2020
Ovum's ICT Enterprise Insights program, based on interviews with around 7,000 senior IT executives, answers the key questions. This brief focuses on the top findings from this program for the Customer Engagement category.
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