Artificial intelligence (AI) is fast becoming as fundamental to customer experience (CX) as CX has become to the business.

According to IDC, the global AI market is poised to break the $500 billion mark by 2024. AI is surging as data size and diversity continue to grow and the cloud becomes a feasible option for quickly and economically scaling compute power and data storage.

AI and its subcomponents (machine learning, computer vision, natural language processing and even forecasting) are being woven into the analytics arsenal of marketing departments at organizations across industries. Marketers today use AI at different levels: AI-enhanced campaigns to build brand preference; AI-enabled smart agents to continuously engage consumers; and AI-powered marketing technologies to drive efficiency.

Prioritize High-Friction Areas for Early Returns

"Start by doing what's necessary; then do what's possible; and suddenly you are doing the impossible." This inspirational maxim is also an effective principle for marketing and CX pros to help build out AI capabilities.

To explore how AI can be used to enhance marketing, help marketers better understand their customers and deliver a great customer experience, start with high-friction areas:

  • High-volume touchpoints — Identifying higher volume touchpoints and channels means that even minor improvements to individual customer interactions will quickly scale up. For instance, AI and machine learning techniques can improve over time with access to larger datasets.
  • High-value touchpoints — Transactions or interactions with high financial value or margin can benefit from AI. Marketers can ensure premium, differentiated treatments with individualized content and next-best-actions.
  • High-complaint touchpoints — Many brands have known pain points that are often thrown around anecdotally in customer service and complaint-handling teams. AI can assess the impact of customer journey behavior against experience KPIs, enabling marketers to understand pain points, entry points, skipped steps, periods of inaction, time spent and drop off points.

Related Article: Use AI Thinking to Improve Customer Experience

Make AI Practical and Profitable

The three high-friction areas — on their own — are solid starting points for improving customer experience. Prioritize use cases that check two or three of these areas to compound the benefits even more.

But how you can differentiate between gimmicks and actual transformative use cases that deliver both customer and business value?

AI marketing initiatives can fall into three interrelated layers:

AI-Enhanced Campaign Tactics to Gain Brand Visibility

Use video or image analytics to make product recommendations based on facial recognition. Or enable redemption of loyalty points based on voice recognition and natural language processing (NLP).

For example, Louis Vuitton uses facial recognition within the Baidu ecommerce platform to match consumers with fragrances.

Discount supermarket chain, Lidl, uses NLP in its conversational chatbot Margot on Facebook Messenger. Margot helps shoppers get the best out of its 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 (e.g., how-to tutorials) and status updates to consumer accounts (e.g., points balance or orders). Pre-trained vertical AI agents can assist with product research (e.g., comparison tools for financial investments, apparel, etc.).

Learning Opportunities

For example, the Bank of America chatbot Erica has served more than 10 million users and is able to understand close to 500,000 question variations.

1-800 Flowers has an AI-powered concierge named Gwyn (Gifts When You Need). Gwyn can successfully reply to customer questions, help customers find the best gifts and assist them through the entire shopping experience for individually tailored offers.

AI-Powered Marketing Technologies to Drive Efficiency

Use AI’s optimization capabilities to improve marketing efficiency and continuously lift marketing performance over the long term.

Machine learning and optimization models can automate audience targeting and personalized product recommendations over multiple media channels. Forecasting and optimization techniques can tailor campaigns on-the-fly, and even discover new segments.

Coca-Cola installed AI-powered vending machines that use the Coca-Cola mobile app — in tandem with facial recognition in some countries — to deliver customized experiences. These new vending machines increased channel revenue by 6%, with 15% fewer restocking trips owing to personalization and better stock management and inventory optimization.

Related Article: Personalization at Scale: Is AI the Most Realistic Way Forward?

What It Means

Amplify and Complement AI With Human Marketing Skills

AI will be an essential part of modern marketing. Marketers must ramp up their AIQ (artificial intelligence quotient) to learn from, adapt to, collaborate with and generate business results from AI. AI will continue to replace mundane, repetitive marketing tasks. Human skills like creativity, communication, collaboration, empathy and judgement will become increasingly important. Already, new roles such as data artists and data storytellers are emerging, signaling the beginning of this transformation.

Start Small and Accelerate With a Test-and-Learn Approach

Marketers are under pressure to deliver ROI and often find it difficult to justify big AI investments. Take an experimental, testing approach and increase the variables to optimize simultaneously: web design, incentives, messages, timing, etc. To effectively demonstrate ROI, start with a small campaign or project with clear success metrics. Focus first on two areas that have clear goals, for example, increase customer service response rates by X% (a specified percent).

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 delivers business outcomes. Common metrics today include cost per acquisition, sales conversion rates, customer lifetime value and return on marketing investment. Second, determine whether AI increases the efficiency of marketing measurement. Don’t measure the success of AI. Measure the success of your marketing initiatives.

AI promises to enhance every aspect of customer experience. To prevent disillusionment, marketing leaders must pursue AI in the context of brand differentiation, profitable growth and efficiency gains.

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