10 Ways AI Can Improve Digital Transformation’s Success Rate
Bottom Line: Customers’ preferences, purchasing power, and loyalty need to be the catalyst that drives any digital transformation strategy with AI providing meaningful insights that motivate companies to change.
Digitally transforming or improving customers’ experiences needs to be the cornerstone of any investment in new technologies or business processes. It’s important to get beyond digital transformation as purely a technical, abstract construct and see it as a must-have strategy to retain customers and attract new ones. The most successful digital transformation projects are known for the hundreds, if not thousands, of successful customer stories they generate.
The Better the Data, the More Powerful the Customer Results
Why are customer stories and the data supporting them so essential for digital transformation strategies to succeed? Because everyone loves a good customer story that shows how digital transformation can make you a more empathetic business that understands your customers’ biggest problems.
COVID-19 is creating entirely new paradoxes for organizations in the middle of digital transformation projects. For example, designed-in human contact now needs to be replaced with personalized self-service that can respond 24/7 with perfect accuracy. The pandemic is creating entirely new customer problems that digital transformation needs to be unleashed to solve. Solving them needs to be based on solid data and AI-driven insights. The better the data and insights, the more powerful the customer results. The following are ten ways AI can improve digital transformation’s success rate:
- AI is helping to more precisely define customers’ preferences and needs, leading to more accurate personas that guide digital transformation projects from the very beginning. Organizations that are the most successful with digital transformation initiatives can see improvements in customer loyalty rates and customer satisfaction based on a more well-defined persona quickly. Using AI to better understand customer, personas needs to be the foundation of any digital transformation initiative. The most advanced uses of AI for persona development combine brand, event and product preferences, location data, content viewed, transaction histories, and, most of all, channel and communication preferences.
- AI-based algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition, propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. The following is a dashboard that shows how propensity models work. Source: The customer propensities dashboard is from TIBCO.
- Digital transformation frameworks that are customer-centric and rely on AI are essential for a business to reinvent themselves today. The need for more agile, customer-centric approaches to digital transformation has never been greater. BMC’s Autonomous Digital Enterprise (ADE), characterized by the three traits of agility, customer centricity, and actionable insights driven by AI and Machine Learning (ML), is prescient in how it integrates every aspect of an organization around the customer while providing AI-driven insights.
- Capitalizing on insights gained from AI, organizations are redesigning IT infrastructure and integration so they can better scale customer experiences. IT infrastructure needs to flex quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer, or manufacturer’s supply chain from their supplier onboarding, quality management, and strategic sourcing to manufacturing and fulfillment need to be orchestrated around customers. Frameworks like BMC’s Autonomous Digital Enterprise is an example of a framework that is enabling this level of IT infrastructure change.
- Digital transformation initiatives often include digitizing supply chains, enabling on-time performance based on insights gained from AI. For any digital transformation strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 45% of organizations say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
- AI is revolutionizing how organizations digitally transform their security strategies as threats to customers’ identities, and personal data continue to proliferate. It’s rare to hear any digital transformation strategy prioritize security. BMC’s ADE framework is an exception as it recognizes how integral securing customers’ identities is a core part of delivering positive customer experience. Organizations are turning to the Zero Trust Security (ZTS) framework to secure every network, cloud, and on-premise platform, operating system, and application across their supply chain and production networks. Chase Cunningham of Forrester, Principal Analyst, is the leading authority on Zero Trust Security, and his recent video, Zero Trust In Action, is worth watching to learn more about how manufacturers can secure their IT infrastructures. You can find his blog here. There are several fascinating companies to watch in this area, including MobileIron, which has created a mobile-centric, zero-trust enterprise security framework manufacturers are relying on today.
- Gartner predicts that by 2025, customer service organizations that embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where digital transformation strategies fail due to a lack of real-time contextual data and insight. Use cases are abundant in customer service, where AI can improve customer experiences and channel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot, and Natural Language (NLP) performance. There’s also the opportunity to improve knowledge management, content discovery, and improve field service routing and support.
- AI is improving digital transformation’s success rate in the areas of marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI, marketers will, for the first time, be able to isolate why and where their marketing and selling strategies are succeeding or failing. By using AI to qualify the further customer and prospect lists using relevant data from CRM systems, predictive models, including AI, can better predict ideal customer profiles.
- Track-and-traceability enhanced with AI-based predictive algorithms is now a must-have in a post-COVID-19 world where excellent customer experience is increasingly defined by the transparency it provides. Order tracking across each supplier, distribution outlet, and e-commerce channel combined with predictions of allocation and out-of-stock conditions using AI is reducing out-of-stock and shortage positions today in the wake of the COVID-19 initial run on stores. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer, all leading to excellent customer experiences.
- AI is also making an impact on digital transformation efforts to improve manufacturing by reducing producer’s conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity. BCG found that producers will be able to generate additional sales by using AI to develop and produce innovative products tailored to specific customers and to deliver them in a much shorter lead-time. The following graphic illustrates how AI will bring increased flexibility and scale to production processes based on BCG’s analysis. Source: Boston Consulting Group, AI in the Factory of the Future, April 18, 2018.
Keeping customers, employees, suppliers, and partners informed about digital transformation initiatives’ progress is a good way to earn and keep their trust. In these uncertain times, the more transparent any organization is, the better. A good place to start is by creating a communication framework. How certain organizations responded to the COVID-19 pandemic, provides a useful framework for creating a communications plan that fits equally well with digital transformation initiatives. Lessons learned from speaking with manufacturers and services organizations is provided in the recent article, What Needs To Be In A CIO’s Communication Framework For COVID-19. The communication plan is predicated on the belief that the most challenging area of any digital transformation strategy is convincing people that changing how they work will help them.
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