Machine learning is not a sci-fi concept anymore. Over the past few years, it has improved business operations in multiple ways. One of the areas that benefit from ML technologies the most is business communications. So, how does machine learning impact your internal and external communications? Let’s find out!

AI Chatbots Deliver Personalization

AI chatbots are the backbone of any tech-savvy business. They easily integrate with your website and social channels and significantly improve user experiences. Apart from being available 24/7 and providing customers with immediate and relevant support, chatbots also offer personalization opportunities.

For example, brands like Sephora use a Messenger bot to deliver individualized customer experiences and boost sales. With the help of augmented reality and artificial intelligence, users can virtually try on different products. Spotify offers similar personalization opportunities. Its bot analyzes users’ emotions and content preferences and, based on them, recommends relevant music.

Chatbots also make the conversation more natural. They use natural language processing to understand customer queries and provide relevant answers. Stats say that over 60% of users do not even know that they are chatting with a bot.

They can tap into your CRM system to determine a user’s previous touchpoints with your brand. For example, if a customer has already interacted with your brand, a bot could greet them with the “Hey, welcome back message.”

Finally, bots automate many dull aspects of your customer service agents’ jobs. They respond to repetitive questions, letting customer service agents focus on more challenging inquiries.

Advanced Phone Services Offer Faster Data Collection and Analysis

Call centers are still an essential customer care channel. According to a research study by BrightLocal, 60% of customers would call your business on the phone after finding it online. Moreover, 16% of them would send you an email, while only 3% would contact your brand via social networks.

Precisely because of that, many companies are striving to improve the performance of their call cents. They replace analog call centers with VoIP phone vendors, such as Nextiva, OnSip, and Dialpad. The major benefit of VoIP business services is that they integrate with your CRM system. They make customer data collection, analysis, and management faster.

As such, online phone services can be augmented by artificial intelligence and machine learning. There are multiple benefits of ML algorithms for modern call centers, including:

  • Automated data capturing: Machine learning interprets customer interactions and lets customer service agents understand customer engagement and sentiment.
  • Delivering personalized customer service: Based on customers’ previous touchpoints with your brand and the complexity of their support tickets, artificial intelligence can automatically connect a customer with a customer representative that can help them faster.
  • Analyzing and predicting trends in customer service: By continuously analyzing vast customer data, you can identify their behavior patterns, needs, and expectations. That way, you will create personalized offers and content and upsell and cross-sell faster.

With the help of ML and automation, you can streamline many repetitive aspects of your business communication and help your marketing and customer service teams focus on more complicated and creative business operations.

Email Filtering Changes your Approach to Marketing

According to Statista, spam emails accounted for 53.5% of all email traffic in 2018. That is where machine learning helps. Advanced email platforms use ML algorithms to improve user experiences and reduce spam messages. ML tools detect spam email and promotional content. For instance, Gmail separates emails before they reach users’ inboxes. It uses a wide range of machine learning features, such as text filtering, engagement, and client filtering, to identify spam.

Text filtering is based on natural language processing. Its goal is to identify the keywords and phrases that are commonly used in spammy content. Client filtering, on the other hand, focuses on the credibility of a sender. It analyzes the attributes of their email address, such as their domain, to filter out content from untrustworthy sources.

So, what does that mean for your business? To reach target customers’ inboxes and avoid ending up in spam, companies need to be careful in creating marketing campaigns. If mail servers return your email, your subscribers will not be able to see your message. To reduce email bounce rates or avoid ending up in spam, you should:

  • Update the email list regularly. Use email validation tools to identify and remove outdated accounts.
  • Create top-quality content. Keep it authentic, personal, and relevant.
  • Do not use spammy and promotional words.
  • Send emails only to recipients that have signed up for your newsletter list.
  • Avoid using multiple CTAs. Stick to the one that aligns with the rest of the content and drives conversions.
  • Never use sending-free domains, such as Gmail or Yahoo. Sending emails from your company’s domain increases your authority.

Enhancing the Workplace Communication

Using machine learning in internal communication can significantly improve employee morale, performance, and engagement. You can insert ML into almost any aspect of your business operations, from human resources to marketing.

For example, it streamlines the process of candidate screening, recruiting, and onboarding. Machine learning allows you to analyze the mountains of resumes and candidate profiles and compiles a list of relevant candidates for you. Once you hire a candidate, a chatbot automatically performs the onboarding process, helping new employees understand internal processes and company culture.

Machine learning and natural language processing also analyze employee communications and help you assess their sentiment and engagement rates. They identify employees that may decide to switch jobs and help take steps to boost their satisfaction.

When combined with ML technologies, workplace communication tools, such as VoIP tools, also enhance employee performance. They allow employees to communicate via multiple IP addresses, access the internal knowledge base, and analyze coworkers’ sentiment.

Over to You

Those are just some of the numerous uses of machine learning in business communication. Apart from enhancing internal communications and enhancing the productivity of your staff, ML technologies also augment customer experience. They allow you to deliver personalized and user-centric experiences that cater to your target audience at every touchpoint.

Machine learning gives your business a competitive edge. Do you use these technologies to boost business communications and how? We are listening!

About the Author

Emma Miller is a marketer and a writer from Sydney, working with Australian startups on business and marketing development. Emma writes for many relevant, industry related online publications and does a job of an Executive Editor at Bizzmark blog and a guest lecturer at Melbourne University.

Featured image via Unsplash.