CMOs and marketing directors are embracing artificial intelligence to boost their personalized marketing efforts to deliver a highly targeted message to a highly specific audience.
Companies that grow faster drive 40 percent more of their revenue from personalization marketing efforts compared with their slower-growing counterparts, a McKinsey report found. In fact, 71 percent of consumers are now expecting personalized interactions from companies, and 76 percent get frustrated when this does not happen, the report states. Personalization “drives performance and better customer outcomes,” enhancing both revenue and customer loyalty, McKinsey added.
Moreover, in addition to slashing the costs of customer acquisition by 50 percent and lifting revenues by 5 to 15 percent, personalized marketing also increases marketing ROI by 10 to 20 percent, the report stated.
With these levels of returns, It is no surprise more marketing departments are looking to AI to make their campaigns more personal and targeted. AI provides insights into what individual customers are interested in buying and what messages they are likely to respond to. Marketers are using it to tailor their offers and messages, making them resonate with each customer and therefore improving the chances of converting a sale.
Generative AI drives personalization by:
- Culling through loads of data very quickly and therefore driving efficiency
- Enabling marketers to rapidly create highly personalized messaging and customer experiences across multiple channels in the customer journey
Beauty retail giant Sephora is an example of personalized experiences, finding success through an omnichannel approach that keeps customers engaged. Through its mobile app, Sephora invites shoppers to book in-store makeovers and consultations. It also maintains a sharp focus on its loyal customers, implementing a loyalty program that offers highest-level members anything from exclusive event invites to early access to sales and new products.
Specific ways that AI drives personalized marketing include:
- Product recommendations – A growing number of e-commerce and digital platforms use algorithmic recommendations to match customers with products and reduce long, wasteful searches. AI algorithms analyze a person’s purchase history, search queries, and browsing habits and behaviors to create personalized recommendations. Not only does this enhance the customer experience – 49 percent of customers who left a brand they had been loyal to in the last 12 months say they did so because of poor CX – but it also increases the likelihood of a sale.
- Personalized email marketing – By analyzing customer data, AI can assist marketers in creating highly targeted email campaigns, making them more likely to be opened, read, and favorably acted upon. Aside from empowering marketers to send highly targeted messages efficiently, AI also automates tasks such as segmentation and optimization. The large language model (LLM) AI tool ChatGPT can recommend email subject lines or headlines, letting marketing leaders and teams focus on high-value decision-making and strategy development.
- Customer analysis – AI identifies trends and patterns in customer behavior, including the kinds of products they like, the times of day they are likely to buy, and the communication channels they use and prefer. Analyzing past behavior alongside other data, AI algorithms can predict future customer behavior, such as the inclination to make another purchase.
- Customer profile building – Amassing data from various sources, including demographic information, buying history, and social media activities, AI algorithms can also create detailed customer profiles, from age and gender to location and personal interests.
- Intelligent automation – Intelligent automation combines AI and automation to handle more complex tasks requiring decision-making as well as forecasting. Think of it as a sophisticated work that performs repetitive tasks and makes decisions as needed. A fine example is Google’s Smart Compose, a predictive writing suggestion that appears as users compose their emails. Smart Compose relies on deep-learning algorithms along with data and significant computing power.
AI can also help marketers overcome risks and roadblocks to taking a personalized approach.
Gartner eyes several challenges in fulfilling personalization in marketing, such as:
- Scaling personalization initiatives cost-effectively without overburdening complementary tasks, such as content development
- The risk of getting defunded due to personalization programs falling short of expected ROI
- Connecting personalization efforts and KPIs to show improvements
- Mounting pressures to deliver incremental financial outcomes, leading to high expectations and underestimating what it takes to plan, organize, as well as execute personalized marketing campaigns
- Proof of personalization’s value to customers and the organization itself
Mitigating these challenges, according to Gartner, includes aligning personalized marketing with business objectives as well as careful prioritization, such as making sure that personalization efforts align with customer needs.
McKinsey also urges CMOs and marketing directors to create “an integrated decision-making engine” that uses AI models to:
- Segment customers, identify value triggers, and then score customers accordingly
- Create a playbook or library of content that they can match with customers
- Create a multichannel decision-making engine to produce consistent messages and drive maximum value for every touchpoint
Related Readings:
How AI Will Impact B2B Marketing
10 Reasons Why Humans Still Matter in AI B2B Marketing
The AI Revolution in B2B Marketing: 10 Transforming Strategies for Results
