Six Areas Where AI Is Improving Customer Experiences
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 contextualize 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 summarizes 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-center an organization 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 capitalize 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 e-mails 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 personalized 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 customize 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 personalization, 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.
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