Bottom Line: This year’s hard reset is amplifying how vital customer relationships are and how much potential AI has to find new ways to improve them.
- 30% of customers will leave a brand and never come back because of a bad experience.
- 27% of companies say improving their customer intelligence and data efforts are their highest priority when it comes to customer experience (CX).
- By 2023, 30% of customer service organizations will deliver proactive customer services by using AI-enabled process orchestration and continuous intelligence, according to Gartner.
- $13.9B was invested in CX-focused AI and $42.7B in CX-focused Big Data and analytics in 2019, with both expected to grow to $90B in 2022, according to IDC.
The hard reset every company is going through today is making senior management teams re-evaluate every line item and expense, especially in marketing. Spending on Customer Experience is getting re-evaluated as are supporting AI, analytics, business intelligence (BI), and machine learning projects and spending. Marketers able to quantify their contributions to revenue gains are succeeding the most at defending their budgets.
Fundamentals of CX Economics
Knowing if and by how much CX initiatives and strategies are paying off has been elusive. Fortunately, there are a variety of benchmarks and supporting methodologies being developed that contextualise the contribution of CX. KPMG’s recent study, How Much Is Customer Experience Worth? provides guidance in the areas of CX and its supporting economics. The following table provides an overview of key financial measures’ interrelationships with CX. The table below summarises their findings:
The KPMG study also found that failing to meet customer expectations is two times more destructive than exceeding them. That’s a powerful argument for having AI and machine learning ingrained into CX company-wide. The following graphic quantifies the economic value of improving CX:
Where AI is improving CX
For AI projects to make it through the budgeting crucible that the COVID-19 pandemic has created, they’re going to have to show a contribution to revenue, cost reduction, and improved customer experiences in a contactless world. Add in the need for any CX strategy to be on a resilient, proven platform and the future of marketing comes into focus. Examples of platforms and customer-centric digital transformation networks that can help re-centre an organisation on data- and AI-driven customer insights include BMC’s Autonomous Digital Enterprise (ADE) and others. The framework is differentiated from many others in how it is designed to capitalise on AI and Machine Learning’s core strengths to improve every aspect of the customer (CX) and employee experience (EX). BMC believes that providing employees with the digital resources they need to excel at their jobs also delivers excellent customer experiences.
Having worked my way through college in customer service roles, I can attest to how valuable having the right digital resources are for serving customers.
What I like about their framework is how they’re trying to go beyond just satisfying customers, they’re wanting to delight them. BMC calls this delivering a transcendent customer experience. From my collegiate career doing customer service, I recall the emails delighted customers sent to my bosses that would be posted along a wall in our offices. In customer service and customer experience, you get what you give. Having customer service reps like my younger self on the front line able to get resources and support they need to deliver more authentic and responsive support is key. I see BMC’s ADE doing the same by ensuring a scalable CX strategy that retains its authenticity even as response times shrink and customer volume increases.
The following are six ways AI can improve customer experiences:
Improving contactless personalised customer care is considered one of the most valuable areas where AI is improving customer experiences
These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity, according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source: Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. (PDF, 28 pp).
Anticipating and predicting how each customers’ preferences of where, when, and what they will buy will change and removing roadblocks well ahead of time for them
Reducing the friction customers face when they’re attempting to buy within a channel they’ve never purchased through before can’t be left to chance. Using augmented, predictive analytics to generate insights in real-time to customise the marketing mix for every individual Customer improves sales funnels, preserves margins, and can increase sales velocity.
Knowing which customer touchpoints are the most and least effective in improving CX and driving repurchase rates
Successfully using AI to improve CX needs to be based on data from all trackable channels that prospects and customers interact with. Digital touchpoints, including mobile app usage, social media, and website visits, all need to be aggregated into data sets ML algorithms to use to learn more about every Customer continually and anticipate which touchpoint is the most valuable to them and why. Knowing how touchpoints stack up from a customer’s point of view immediately says which channels are doing well and which need improvement.
Recruiting new customer segments by using CX improvements to gain them as prospects and then convert them to customers
AI and ML have been used for customer segmentation for years. Online retailers are using AI to identify which CX enhancements on their mobile apps, websites, and customer care systems are the most likely to attract new customers.
Retailers are combining personalisation, AI-based pattern matching, and product-based recommendation engines in their mobile apps enabling shoppers to try on garments they’re interested in buying virtually
Machine learning excels at pattern recognition, and AI is well-suited for fine-tuning recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.
Relying on AI to best understand customers and redefine IT and operations management infrastructure to support them is a true test of how customer-centric a business is
Digital transformation networks need to support every touchpoint of the customer experience. They must have AI and ML designed to anticipate customer needs and deliver the goods and services required at the right time, via the Customer’s preferred channel. BMC’s Autonomous Digital Enterprise Framework is a case in point. Source: Cognizant, The 2020 Customer Experience.
- 4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017
- 84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
- AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn. Feb. 8, 2018.
- AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.
- Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017
- Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)
- Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.
- AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights
- B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
- Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y. J., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227-243.
- David Simchi-Levi
- Earley, S. (2017). The Problem of Personalization: AI-Driven Analytics at Scale. IT Professional, 19(6), 74-80.
- Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
- Gacanin, H., & Wagner, M. (2019). Artificial intelligence paradigm for customer experience management in next-generation networks: Challenges and perspectives. IEEE Network, 33(2), 188-194.
- Hildebrand, C., & Bergner, A. (2019). AI-Driven Sales Automation: Using Chatbots to Boost Sales. NIM Marketing Intelligence Review, 11(2), 36-41.
- How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant
- Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017
- Kaczmarek, J., & Ryżko, D. (2009). Quantifying and optimising user experience: Adapting AI methodologies for Customer Experience Management.
- KPMG, Customer first. Customer obsessed. Global Customer Experience Excellence report, 2019 (92 pp., PDF)
- Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank
- Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence.
- Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.
- OpenText, AI in customer experience improves loyalty and retention (11 pp., PDF)
- Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) Pw
- The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.
- The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.
- Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting
- Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog,
- What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.
Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, Cyber Security & Cloud Expo and 5G Expo World Series with upcoming events in Silicon Valley, London and Amsterdam and explore the future of enterprise technology.