4 Ways AI is Transforming the World of CRM for SaaS Companies
Leading CRM platforms like Salesforce and Zoho have integrated AI into their software to provide users with real-time decision-making, predictive analysis, and virtual assistants.
It has empowered brands with the tools to gain a deeper understanding of what their customers want, how they feel, and, crucially, what future decisions they're most likely to make.
By harnessing the full capabilities of AI-powered CRMs, SaaS firms can collect and action the highest quality data across their entire organisations, enabling them to make faster and better decisions that will outsmart their competitors, grow market share, and scale up fast.
Here's a look at how AI-powered CRM can drive positive change and increase operational efficiency in SaaS companies.
And we'll break down some of the biggest challenges and obstacles they have to overcome when adopting this game-changing technology into their CRM systems.
How AI is transforming the world of customer relationship management for SaaS companies
AI-based CRM software and platforms are helping SaaS businesses work smarter and faster by reducing costs, boosting efficiency and outputs, and improving operations via automation and machine learning.
Take data entry, for example. Sales and service professionals can spend up to 17% of their workweek inputting data - that's almost one entire workday.
AI-driven data caption technology eliminates manual data entry in a CRM, pre-populating data fields with customer or client information.
And not only is the data capture faster, it's also more accurate.
CRMs without machine learning and automated features are usually packed full of dead data or contain missing information. According to one study by business analytics firm Dun & Bradstreet, 91% of data in CRM systems is incomplete, 18% is duplicated, and 70% will be completely stale by the end of the year. This kind of dead data can disrupt sales and service pipelines, meaning teams target the wrong customers at the wrong time.
Things like this don't happen when your CRM is AI-led. Instead, this predictive technology is constantly analysing, refining, and cleaning up CRM data. The AI software detects and corrects irregularities, anomalies, duplicates, and other errors that compromise CRM data and, by extension, your customer relationships and service/sales quality.
AI data collection can collate information from disparate databases and data sources. It pulls valuable customer insights from email, phone conversations, and chatbots into a unified customer or sales journey profile, providing sales and service teams with a complete picture of every single lead or existing customer.
Optimise sales and marketing campaigns
Almost 75% of companies say converting leads into customers is their first priority. And those with an AI-powered CRM are far more likely to make that happen.
The manual lead scoring and ranking approach is outdated. Artificial intelligence integration into CRM systems has brought a shift to the predictive lead scoring model. Predictive lead scoring is a dynamic, data-led approach that analyses different historical and real-time attributes to determine a customer's buying readiness with pinpoint accuracy.
With predictive lead scoring CRM features, sales and marketing teams get the most detailed picture of buyer intent, including actionable insights into what offers, deals, approaches, or communication methods have the best chance of closing the deal.
When integrated with CRM systems, artificial intelligence can also analyse won versus lost deals, detecting trends and patterns you can use to refine and optimise campaigns and lead management processes.
And then there are the real-cutting edge features. AI-sentiment analysis software CallMiner Eureka integrates with CRM to capture and transcribe customer interactions, including web chats, emails, and calls. Salespeople can search the transcript metadata for keywords, phrases, or even change in tone of voice that signal excitement or interest around certain products, services, or features. With this kind of data, sales teams can create fully-personalised pitches or presentations for each customer.
Improving customer experience
AI chatbots linked to a CRM can answer all the simple service issues, freeing up the 'real' workers to focus on complex and high-priority cases that can meaningfully impact revenue, retention, and customer experience.
Automating these time-consuming and repetitive responses is a major productivity booster that can increase efficiency across an entire organisation. With AI bots working non-stop, it becomes much easier for teams to achieve agreed service level agreements while remaining fully compliant with relevant regulations, including recording complaints and responding to them in the appropriate manner and timeframe.
AI chatbots can also identify cross-selling or service upgrade opportunities, then send these directly to sales teams via the CRM. With this data informing your processes and decisions, new and existing customers will always receive offers and contacts relevant to them, increasing conversion rates and significantly improving the overall customer experience.
AI-powered CRM features can also track conversations and interactions in real-time, provide feedback to service agents, and use sentiment analysis technology to monitor language, speech patterns, and psychographic profiles for predicting future customer needs or interactions.
Driving positive change and innovation
With AI running your CRM, it becomes possible to create an entire range of custom-made business apps that deliver smarter customer experiences across sales, customer service, and marketing.
Natural Language Processing (NLP) in CRM machine learning features find patterns within large data sets. For example, sentiment analysis algorithms can look for patterns and trends in social media posts to understand how customers feel about a specific brand or product - ideal data for planning or designing future product or service launches.
CRM predictive analytics can predict future events based on patterns in historical data, including where certain products might sell better, as well as the potential outcomes of disrupting existing technology or your biggest competitors.
With innovative AI-powered CRM features, companies can consistently deliver the cohesive, intelligent, and personalised experiences customers expect. Smart CRM software can identify when a delivery order is delayed and then take appropriate action to prevent a call or email from a frustrated customer, such as sending a friendly update email or text message apologising for the small delay. For longer delays or more serious delivery issues, these smart messages can offer credit or free shipping on the next order - and all without a 'real' customer service agent lifting a finger.
How to leverage the benefits of AI in CRM
When planning your AI-CRM integration strategy, start by clearly identifying your short and long-term goals. In other words, what do you want your AI-powered CRM features and software to do? Your options include...
● Better data handling and analysis
● Automating repetitive and time-consuming tasks
● Content creation
● Optimising lead scoring and sales strategies
● Improving customer satisfaction score through better service
● Analyse customer sentiment
Invest in training and upskilling to ensure you get the maximum benefit out of your new CRM platform and AI features. If you don't have the time or funds for additional training, consider investing in one of the more user-friendly AI CRM platforms, like Salesforce Einstein.
Marketed as a personal data science assistant, Salesforce Einstein is a plug-in-and-play AI platform that requires zero coding or analytics knowledge or experience. Instead, admins and users can create detailed reports and run in-depth data analysis projects with just a few clicks.
The challenges of integrating AI into your CRM and organisation
Cost and training are the two biggest challenges SaaS companies face when switching to an AI-powered CRM.
To truly benefit from this next-generation technology, firms must ensure they have the right skills and knowledge to action their AI-generated data sets. Without it, the only thing they'll have invested in is an expensive new data toy that nobody knows how to play with.