Having the right set of processes ensures consistency in delivering promised business outcomes through intelligent systems powered by the right technology and right capability. Businesses will have to invest in the right technology across the data value chain from data engineering to data consumption. Right capabilities with focus on business outcomes and in building innovative solutions at scale will prove to be a differentiator for businesses.
The focus of this paper is on the right processes that will ensure organizations obtain maximum value out of their AI initiatives for building great customer experience. Enterprises should adopt a Sense, Think, Respond and Learn (STRL) Framework to tackle business challenges through AI.
On identifying the customer experience-related business problem that the enterprise would like to address through advanced analytics and AI use cases, it should
Sense: As a ﬁrst step, it is imperative for organizations to take stock of the current situation by acquiring and assimilating relevant data and by preparing accurate and efﬁcient datasets that could feed predictive models. This step generally leverages techniques for data preparation, descriptive analytics, text and speech analytics, video and image analytics to provide a view into what is happening currently.
For instance, in the CPG or Retail industry, organizations could gather complete customer information by analyzing web metrics, social media proﬁle updates, customer purchase patterns, cart abandonment and category spend analysis.
Think: As a next step, organizations should identify the appropriate analytical models and the statistical/machine learning algorithms that drive these models to facilitate insight generation and cognitive intelligence. This step involves making use of advanced analytics and AI techniques to explain and predict customer behavior, and provide recommendations for next-best-action scenarios. For example, organizations need to apply AI/ML techniques to analyze search patterns and customer behavior to predict when a customer is going to churn out; how much a customer is likely to spend and which product is a customer most likely to buy etc. AI techniques like image recognition and computer vision help in revolutionizing product discovery through AR-enabled visual product searches. This enhances the experience of an online shopper and provides an ofﬂine experience online.
Respond: Based on the actionable intelligence obtained, businesses should strategize their plan of action to deliver business outcomes. This step involves delivering personalized customer
engagement leveraging new digital capabilities. For example, organizations need to design personalized campaigns based on the insights to engage with customers and enhance customer experience and loyalty. Organizations must leverage AI techniques like Natural Language Processing, and Generation, Speech and Text Analytics to build intelligent chat-bots and virtual assistants to deliver high impact business outcomes in customer service through voice-based shopping and product discovery.
Learn: The key to making this framework actually work is ensuring the system continually learns through feedback systems during the Sense, Think and Respond stages. The network effect applies perfectly here as with more and more usage of intelligent systems, the systems become more intelligent.
AI challenges and the way around
Fundamental: Given the possibilities of leveraging AI to deliver excellent CX, organizations are contemplating ways to tackle one major barrier – Gaining user trust on technologies like AI. AI gone wrong can have huge repercussions. Design plays an important role here.
Organizations must have a design-thinking led approach, which places the user at the center of it all, while engineering the CX. Key principles that enable trust in the end-user should be incorporated. These include transparency and explainability aspects of the recommendations thrown by the AI system, testing of extreme cases, and availability of holistic training data sets to avoid ingrained biases.
Organizational: With data-driven customer experience taking the driver’s seat and being seen as a critical differentiator in gaining online market share; the CMO and CIO organizations need to work hand-in-hand to sync market requirements with organizational technology maturity.
Technology: Legacy and latency issues could impede new rollouts. Back-ofﬁce systems need to be upgraded before charting a roadmap for the future. Stakeholders need to be careful of leaving latency loopholes within existing systems.
Today, CIOs are being measured on the impact they bring to the business. CX improvements across the ecosystem (supply chain partners, marketing value chain and back ofﬁce systems) will now be more closely measured on improvements to benchmarked bottom-line levels. The times ahead are exciting for enterprises that leverage AI to create meaningful experiences for the customer. The coming together of data, algorithms and design will catapult CX, which is geared for disruption.