2 minute read
By Mila D'Antonio
Posted in Customer Engagement
Mila D’Antonio, Principal Analyst, Customer Engagement, Omdia (formerly Ovum)
In the evolving Experience Economy, 2020 is shaping up to become the year enterprises apply the building blocks to enable proactive, personalized experiences at every step of the customer journey. In this dynamic environment, organizations must continuously re-calculate the next-best action for each customer, and at each moment.
This requires a shift from traditional customer journey mapping tools to a more holistic and actionable approach of customer journey management powered by AI and reliant on a foundation of data as an enterprise asset. With this shift, enterprises can track, map, analyze, visualize, and personalize customer journeys in real time.
Journey mapping coupled with natural language processing (NLP), predictive analytics, and machine learning will automate systems that can evaluate the behavioral and transactional factors in customers’ decision-making. These factors contribute to whether people are getting through the process outlined for them, determine the reasons why people falter on some journeys, and orchestrate real-time next-best actions to engage with them at critical moments mapped out in the journey.
Recent Ovum survey data points to continued adoption of journey mapping, AI, and predictive analytics to ensure more actionable customer journeys. According to ICT Enterprise Insights 2019/20 – Global: ICT Drivers and Technology Priorities, 24% of respondents have strategic investments planned for customer journey mapping and 37% have minor investment planned.
Additionally, 26% have strategic investments planned for predictive analytics in the area of customer engagement, and 37% are planning minor investments. In terms of AI adoption, 19% of respondents reported having fully deployed packaged AI for the enterprise, 32% are trialing, and 27% were planning to deploy packaged AI.
As these functions inevitably converge, the utilization of AI and machine learning will give context to customer journeys, helping enterprises understand the significance of the events that shape a customer’s behavior. Proactively responding to such key moments will enable true personalization to take hold throughout the digital customer journey. That requires an awareness of both the customer journey touchpoints and their corresponding customer behaviors.
The power of AI-enabled customer journey orchestration is that it can sift through a larger and more complex data space and thereby uncover many more business opportunities and prioritize the insights. It finds every single relationship in the data and predicts the likelihood of future behaviors with high accuracy, while simultaneously identifying the drivers and inhibitors of customer performance.
It also provides important quantitative data to determine the impact of any obstacles along the customer journeys on business objectives, as well as the effectiveness of any remediations. Armed with that information, enterprises can do more than find the “next best action” or the optimal audience. They can take action on data regarding each portion of the journey, measure its impact to advance prediction models, use the results of that analysis to drive better and more predictive models, and even serve as customers’ personal “concierge” for interactions with their brands.
As the quest for connected, intelligent customer experiences accelerates, AI-powered journeys will naturally take the spotlight. But enterprises must first build upon core data integration use cases and start investing in the systems and integrated technologies that will enable them to create precise audiences for activation.
Making the right investments in AI and embedding it across applications will build a foundation to optimize customer experiences that will set enterprises apart from their competition and drive future growth.
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