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

However, many companies have not yet used AI or have only recently started researching it. What are the best AI entry-level projects in the CX / Martech stack to be successful? Below are five recommendations.

1. Better use of IVAs

“With the advent of low-code technologies, we are seeing a Cambrian explosion of AI projects that are considered low-hanging fruit for marketers and customer experience professionals,” said Jen Snell, vice president of product strategy and marketing for Intelligent Self-Service, Verint. “We’re now seeing tremendous success with automated interactions early on in customer journeys. This is an area where most marketers focus.”

IVAs and chatbots were once big AI projects, but low-code solutions enable fast deployment and still add value to the business, added Snell.

“AI is all about data,” said Snell. “Data is the limit of modern day marketing, but most marketers don’t have access to the type of data that is generated through interactions with an IVA. The depth of insights from multidimensional interactions with IVAs is unparalleled and incredibly valuable to marketers. With this data, marketers can continuously improve and personalize their experience, identify gaps in the customer journey, and really understand what drives their customers. “

Related article: The Future is Multimodal: Why Voice Alone Will Never Be the Answer

2. Improved decision making

According to Adam Smartschan, chief strategy officer for Altitude Marketing, AI offers a way to deliver the right kind of experience to the right kind of prospect. He offered three examples:

• Google Search Ads – “Google search ads are probably the easiest way to introduce AI / machine learning into marketing. They provide up to 15 headings and four descriptive texts and the algorithm does the rest,” said Smartschan. “Google tests hundreds or thousands of combinations and then offers high-performance variants on a query-by-query basis. It’s marketing AI in its simplest form – letting the robots decide. “
• Test landing pages – Landing page split tests are another great example. Instead of sending A / B / n tests randomly to send traffic to different variants, platforms like Unbounce (which use “Smart Traffic”) can now send certain users to specific variants. The algorithm takes into account demographic factors, location and much more to maximize the likelihood of a conversion.
• Email Marketing – AI for beginners even extends to email. With platforms like ActiveCampaign and MailChimp, you can now send blasts over a 24-hour period based on the behavior of previous recipients. Essentially, you’re sending out a customized email to everyone on your list at the time they’re most likely to get involved.

3. Quality management

Fabrice Martin, Clarabridge’s chief product officer, said that by implementing an AI-enhanced quality management solution, companies can coach and train contact center agents more efficiently and without prejudice. Agents and managers can save time on training / coaching to focus on higher quality interactions, measure the 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. Understand customer feedback on a scale

To successfully leverage and leverage customer feedback, companies should implement solutions that include natural language understanding (NLU) and natural language processing (NLP), Martin said. NLU reveals the meaning of text like tweets, emails, reviews, etc. On the other hand, NLP helps computers read large amounts of text quickly to gain critical business insights.

Both projects help companies quickly gain insights into what is working and what is not. The integration of AI increases efficiency, provides real-time insights and helps companies better understand customers, which leads to an improved customer experience.

5. Social Media Sentiment Analyzer

“This is one of the most impressive and forward-looking machine learning projects I’ve seen. Social media platforms such as Facebook, Twitter and YouTube are overflowing with big data, ”said Bram Jansen, editor-in-chief of vpn Alert. “By mining the data, consumers’ feelings and viewpoints can be explained in a number of ways. This project can also be used for digital marketing and branding to take into account a customer’s point of view or reaction to a product or service. “