Marketing teams are increasingly turning to artificial intelligence (AI) to improve results, with marketers investing over $227 million in AI-based technologies in 2018 alone, according to Statista.  

Yet many companies have yet to use AI or have just started exploring it recently. So what are the best entry-level AI projects in the CX/martech stack to establish success? Below are five recommendations.

1. Better Use of IVAs

“With the rise of low-code technologies, we’re witnessing a Cambrian explosion of AI projects that are considered low-hanging fruit for marketers and customer experience professionals,” said Jen Snell, vice president, product strategy and marketing, Intelligent Self-Service, Verint. “We’re now seeing immense success with automated interactions at the start of customer journeys, which is an area where most marketers are focused.”

IVAs and chatbots were once major AI projects, but low-code solutions make it possible to quickly deploy and still return value to the business, Snell added.

“Above all else, AI is about data,” Snell said. “Data is the frontier of modern marketing, yet most marketers don’t have access to the type of data generated by interactions with an IVA. The depth of insights drawn from multi-dimensional interactions with IVAs is unprecedented and incredibly valuable to marketers. With this data marketers can continuously enhance and personalize experiences, identify gaps in the customer journey, and truly understand what drives their customers.”

Related Article: The Future Is Multimodal: Why Voice Alone Will Never Be the Answer

2. Improved Decision-Making

AI offers a way to help serve the right type of experience to the right type of prospects, according to Adam Smartschan, chief strategy officer for Altitude Marketing. He offered three examples:

Learning Opportunities

  • Google Search Ads — "Google search ads are probably the simplest way to roll out AI/machine learning in marketing. You provide up to 15 headlines and four pieces of descriptive text, and the algorithm does the rest," Smartschan said. “Google tests hundreds or thousands of combinations, then starts serving up high-performing variants on a query-by-query basis. It's marketing AI at its simplest — letting the robots decide.”
  • Testing Landing Pages — Landing page split testing is another great example. Rather than A/B/n tests sending traffic to different variants at random, now platforms like Unbounce (which uses "Smart Traffic") can send specific users to specific variants. The algorithm looks at demographic factors, location and more to maximize the chance of a conversion.
  • Email Marketing — Entry-level AI even extends to email. Platforms like ActiveCampaign and MailChimp now allow you to send blasts over rolling 24-hour periods based on past recipient behavior. Essentially, you're sending an individual email to everyone on your list at the time they're most likely to engage.

3. Quality Management

Implementing a quality management solution enhanced by AI allows companies to coach and train contact center agents more efficiently and without bias, according to Fabrice Martin, chief product officer for Clarabridge. Agents and managers can save time from training/coaching so they can focus on higher-value interactions, measure drivers of customer satisfaction and improve the customer experience across all channels.

Related Article: So You've Been Asked to Revolutionize CX as a Team of One

4. Understanding Customer Feedback, at Scale

To successfully harness and leverage customer feedback, companies should implement solutions that incorporate natural language understanding (NLU) and natural language processing (NLP), Martin said. NLU uncovers the meaning behind text, such as tweets, emails, reviews, etc. On the other hand, NLP helps computers quickly read large amounts of text to uncover important business insights.

Both projects help businesses quickly gather insights into what’s working and what’s not. Incorporating AI drives efficiency, provides real-time insights and helps organizations better understand customers, which leads to an improved customer experience.

5. Sentiment Analyzer of Social Media

“This is one of the most impressive and forward-thinking machine learning ventures I've seen. Social media platforms such as Facebook, Twitter and YouTube are overflowing with big data,” said Bram Jansen, chief editor of vpn Alert. “Mining the data will help explain consumer feelings and viewpoints in a variety of ways. This project may also be used for digital marketing and branding to consider a customer's view or response to a product or service.”