Artificial intelligence (AI) is becoming as fundamental to the customer experience (CX) as CX has become to the business.
According to IDC, the global AI market is poised to cross the $ 500 billion mark by 2024. AI grows as the size and diversity of data continues to grow and the cloud becomes an option feasible to rapidly and economically scale up computing power and data storage.
Artificial intelligence and its subcomponents (machine learning, computer vision, natural language processing, and even forecasting) are integrated into the analytical arsenal of marketing departments in organizations across all industries. Marketers today use AI at different levels: AI-enhanced campaigns to create brand preference; AI-enabled intelligent agents to continuously engage consumers; and AI-based marketing technologies to increase efficiency.
Prioritize high friction areas for early returns
“Start by doing what is necessary; then do what is possible; and suddenly you do the impossible. This inspiring maxim is also an effective principle for marketing and customer experience professionals to help develop AI capabilities.
To explore how AI can be used to improve marketing, help marketers better understand their customers, and deliver a great customer experience, start with high friction areas:
- High volume touch points – Identifying higher volume touchpoints and channels means that even minor improvements in interactions with individual customers will be quickly scaled up. For example, artificial intelligence and machine learning techniques can improve over time with access to larger data sets.
- High-value contact points – Transactions or interactions with high financial value or margin can benefit from AI. Marketers can guarantee top quality differentiated treatments with individualized content and the following best actions.
- Contact points with a high complaint rate – Many brands have known issues that are often mentioned anecdotally in customer service and complaints teams. AI can assess the impact of customer journey behavior against experience KPIs, allowing marketers to understand weak spots, entry points, steps skipped, periods of inaction, time past and drop-off points.
Related article: Use AI Thinking to Improve Customer Experience
Make AI practical and profitable
The three high-friction zones – on their own – are great starting points for improving the customer experience. Prioritize use cases that verify two or three of these areas to further increase the benefits.
But how do you tell the difference between gadgets and real transformative use cases that deliver both customer and business value?
AI marketing initiatives can be broken down into three interrelated layers:
AI-enhanced campaign tactics to increase brand visibility
Use video or image analysis to make product recommendations based on facial recognition. Or activate the redemption of loyalty points based on speech recognition and natural language processing (NLP).
For example, Louis Vuitton uses facial recognition within the Baidu e-commerce platform to associate consumers with perfumes.
Discount supermarket chain Lidl uses NLP in its conversational chatbot Margot on Facebook Messenger. Margot helps buyers get the most out of her wine selection.
Related article: The New Wave of Web Chat: Here’s What Has Changed
AI-enabled smart agents to continuously engage consumers
Conversational AI can provide shortcuts to content (eg, how-to tutorials) and status updates for consumer accounts (eg, point balance or orders). Pre-trained vertical AI agents can help with product research (e.g. comparison tools for financial investments, clothing, etc.).
For example, Bank of America’s Erica chatbot has served over 10 million users and is capable of understanding nearly 500,000 question variations.
1-800 Flowers has an AI-powered concierge named Gwyn (Gifts When You Need). Gwyn can successfully answer customer questions, help customers find the best gifts, and walk them through the shopping experience for personalized offers.
AI-based marketing technologies to increase efficiency
Use the optimization capabilities of AI to improve marketing effectiveness and continually increase marketing performance over the long term.
Machine learning and optimization models can automate audience targeting and personalized product recommendations across multiple media channels. Forecasting and optimization techniques can customize campaigns on the fly and even discover new segments.
Coca-Cola has installed AI-powered vending machines that use the Coca-Cola mobile app – in tandem with facial recognition in some countries – to deliver personalized experiences. These new vending machines increased channel revenue by 6%, with 15% fewer replenishment trips thanks to personalization and better inventory management and inventory optimization.
Related article: Personalization at Scale: Is AI the Most Realistic Way Forward?
What this means
Amplify and complement AI with human marketing skills
AI will be an essential part of modern marketing. Marketers need to increase their AIQ (artificial intelligence quotient) to learn, adapt, collaborate with and drive business results from AI. AI will continue to replace mundane and repetitive marketing tasks. Human skills such as creativity, communication, collaboration, empathy and judgment will become increasingly important. Already, new roles such as data artists and data storytellers are emerging, signaling the start of this transformation.
Start small and speed up with a test and learn approach
Marketers are under pressure to generate ROI and often struggle to justify large investments in AI. Take an experimental approach and test and increase variables to simultaneously optimize: web design, incentives, messages, timing, etc. To effectively demonstrate ROI, start with a small campaign or project with clear indicators of success. First, focus on two areas that have clear goals, for example, increasing customer service response rates by X% (a specified percentage).
Use value-based metrics to measure AI. Measure AI in 2 ways
First, use existing value-based metrics to see if AI improves marketing performance and drives business results. Common metrics today include cost per acquisition, sales conversion rates, customer lifetime value, and marketing ROI. Second, determine if AI is increasing the effectiveness of the marketing metric. Don’t measure the success of AI. Measure the success of your marketing initiatives.
AI promises to improve every aspect of the customer experience. To avoid disillusionment, marketers must pursue AI in the context of brand differentiation, profitable growth and efficiency gains.
Wilson Raj is the global director of customer intelligence at SAS, responsible for the commercialization of SAS’s artificial intelligence-based marketing solutions. Inspired by data and driven by creativity, Raj has built brand value, engagement and loyalty through his expertise in strategy and analytical marketing.