Digital insurance company Lemonade developed a chatbot, named Maya, to guide users navigating the insurance-buying process.
Maya can collect information, provide quotes, and handle payments. The bot makes sure the customer gets insurance within 90 seconds and paid within three minutes.
Maya also chats with customers to provide customized answers to difficult questions but helps the company make changes to existing policies. As a machine learning system, the more customer service Maya provides, the smarter it gets as each interaction helps it to learn.
Lemonade reports that Maya now handles a quarter of their inquiries and has sold 1.2M insurance policies in just three years from its launch.
4. Improved Campaign Optimization & Performance Measurement
The ability to optimize a campaign and monitor its performance is crucial for marketers. While this could be a laborious process, AI-driven tools can not only track KPIs but they also provide real-time feedback and actionable insights.
With this level of understanding, marketers can optimize campaigns to discover the top performing channel (e.g. email) and get insights on trends or roadblocks to get the most out of any campaign.
There are also AI tools that can automatically adjust campaigns based on KPIs like engagement, click-through rate, and conversions such as Google Analytics 360 and Zoho Analytics.
Brand example - The North Face
Exploration brand, North Face wanted to understand what consumers looked for in each market to optimize the consumer experience to those preferences. The company often does this by constantly monitoring how consumers search for items on its website.
By using Google Tag Manager 360, in combination with Analytics 360, the brand discovered that their customers were searching for a new term - “midi parka.” To tap into this, the company renamed one of its products and drove a 3X increase in conversions and revenue.
5. Lead Scoring & Enhanced Sales Automation
According to Salesforce’s State of Sales report, 98% of sales teams think automated lead scoring improves lead prioritization.
AI lead scoring uses algorithms to track and assess user or client interactions. This information is then used by the AI scoring model to forecast which leads will result in more profitable sales or clients and can improve handoff to sales teams.
Sales teams can also automate lead nurturing by setting up AI-triggered campaigns that adapt based on a lead’s actions making them more personalized and likely to drive engagement.
AI can also be used to get greater insights into customer or client behavior and automate customer-facing processes such as sending emails or automating sales or customer reports.
Brand example - U.S Bank
U.S. Bank wanted to use predictive lead scoring to help its sales team focus on the most promising leads and opportunities.
To do this they use Salesforce’s Einstein, an integrated set of AI and machine learning technologies.
Using Einstein’s predictive lead scoring helped U.S. Bank see a 25% increase in closed deals, a 260% increase in lead conversion rates, and a 300% increase in marketing qualified leads.
6. Visual Recognition for Social and Ecommerce
AI can now analyze images to identify brand-relevant content, user-generated content, or product matches.
As a result, visual search engines are seeing significant demand due to their applications in the retail and e-commerce sectors, meaning that the global AI-powered ecommerce market is expected to reach $16.8 billion by 2030.
For example, image classification for mobile commerce and social commerce are becoming more popular as people use phones to search for a product or service. And facial recognition could be used to detect the emotion of the person to help enhance sentiment analysis.
Automated image recognition can help marketers analyze their visual content to measure its quality and relevance. It can also be used to optimize your visual content to enhance your images and generate tags or keywords to improve SEO and accessibility.
Brand example - L’Oreal
L’Oreal has developed a generative AI-powered personal beauty assistant, Beauty Genius, to offer personalized diagnostics, beauty routine recommendations, and Q&A sessions.
The tool uses advanced technologies like augmented reality, computer vision, and Gen AI to provide an immersive and secure experience. The aim of the assistant is to provide customers with an experience that “resembles a natural conversation with a beauty expert”.
Customers can also use a virtual try-on feature that uses augmented reality to experiment with new looks in real-time and get suggestions tested by makeup artists. a
7. Ethical Considerations and Transparency
When you use AI in your marketing activities, you allow the technology to access a vast wealth of customer and company data. This means you have a responsibility to protect that data.
To address these and other AI-related ethical concerns, businesses need to be transparent and accountable when they use AI in their automation processes. Be honest with consumers about the fact your company uses AI and outline what you use it for so they have information and can opt-out or consent.
Brand example - O2
In an effort to boost awareness of the U.K mobile network’s scam protection technology and ramp up its customer centricity, O2 created AI grandma.
This campaign created Daisy, an AI ‘Granny’ to answer calls in real-time from fraudsters to keep them on the phone and away from customers for as long as possible.
Daisy has her own phone number, which O2’s anti-fraud team added to contact lists used by scammers. She combines AI models to listen to a caller and transcribe their voice into text and then uses a custom large language model to respond.
It’s a great example of a brand highlighting the dangers of AI and providing a solution along with boosting brand awareness.