The right CRM solution is the backbone for building quality relationships between brands and prospects. From executing marketing campaigns to tracking leads to forecasting sales, it’s no doubt that CRM tools provide a powerful sandbox for growing your pipeline.
Nevertheless, CRMs are rarely used to their full potential. In fact, they often cause more friction than function due to arduous manual reporting and clunky implementations.
Artificial intelligence has the power to enhance many technologies, and CRM is no exception. Here are the ways that AI-based tools can expand CRM solutions — via automation, language translation and data analytics — to fuel customer growth while keeping customer service teams lean.
Automate More, Hire Less, Minimize Costs
CRM tools power interactions between customers and customer service agents via an array of digital channels such as email and live chat. While the ability to organize and facilitate these interactions is helpful, the real value lies in the ability to remediate issues more efficiently.
That’s where AI comes in.
When AI-based tools are integrated into CRM, the AI can execute routine duties with minimal or no human intervention. This includes answering basic customer questions, prioritizing and troubleshooting incoming requests, and checking on the status of existing requests. Chatbot tools within CRMs — like Zendesk’s Answer Bot — are the most widely implemented uses of AI in the customer service space.
Chatbots have the capacity to handle lower level customer inquiries, which frees up agents to focus their efforts on customer issues that require more nuanced assistance, emotional intelligence, and empathy. Chatbots alone, however, are not a complete customer service solution. They may excel at starting customer interactions and handing off the case to a human agent if a solution doesn’t exist within the AI algorithm, but most customers ultimately require human interaction.
With this strategic mix of AI and human agents, brands can scale their customer service functions without having to invest the time it takes to recruit, hire, train, and retain additional team members. It’s a successful mix that has proven to lead to a happier agent workforce, especially during seasonal surges, which can be taxing and lead to burnout on lean customer service teams.
Implement Machine Translation
In addition to taking the burden of routine tasks off of human agents, AI can also work within a CRM system to help human agents provide high-quality customer interactions with a quick turnaround.
For instance, some AI-based machine translations can instantly make any agent multilingual, allowing the agent to respond to and service customers no matter what country they’re from or language they speak.
By removing language barriers, a brand can expand its customer base globally without worrying about hiring native speakers for all markets. Rather than hiring three different people to support three different languages, a company can equip one agent with multilingual superpowers so he or she can accommodate multiple languages at once.
Now, agents can have more engaging and productive conversations with customers. NLP (natural language processing) tools analyze the language and tone of customer service conversations to determine a customer’s satisfaction level with a brand. These tools then provide real-time insights that can help customer service agents through tricky conversations — with prompts to use empathetic language or offer discounts to keep customers satisfied and reduce churn.
Analyze Customer Data
CRMs also house vast amounts of customer data, although data streams aren’t always organized in a way that contextualizes information to create a comprehensive — and actionable — customer profile.
Manually reviewing data to mine for insights is extremely costly and time-consuming. When AI is combined with the data that lives inside a CRM, companies can identify patterns and trends across a variety of analytics areas, including:
- Customer experience: Identifying the “what happened” metrics such as first response time (FRT) and total time to resolution (TTR).
- Customer journey: Focusing on strategies that are succeeding and growing revenue.
- Customer retention: Uncovering points of friction in the customer experience.
- Customer engagement: Analyzing all interactions customers have with a brand on digital channels.
Using AI to mine data across the above categories can help teams predict a customer’s satisfaction level or their risk of churning, which teams can then use to strategically cross-sell and upsell products.
Building a More Useful CRM
CRM alone has its limitations. But brands that fully utilize AI within CRM platforms to automate routine tasks, translate language, and analyze data have a game-changing opportunity to work smarter, not harder, and ultimately turn more prospects into loyal customers.