Maintaining valuable and profitable client relationships has come a long way since the days of the Rolodex and the leather-bound address book. Since the 1990s, companies have started implementing customer relationship management (CRM) software into their business processes. These are specialized systems — often built as bespoke solutions to fit the needs of individual businesses — that collect and manage data about all client interactions.
For more than a decade already, businesses have been integrating artificial intelligence and machine learning into their CRMs. This is not only to organize this data, but also to gain valuable client insights, promote products, generate leads, and even to close deals. In this short article, we would like to describe just a few of these AI tools and strategies.
Data Capture
A major area of lost value for many companies is the data intake process. Customers provide a wealth of useful information about themselves during interactions with sales staff. These interactions may involve a wide range of communication methods, including email, phone and video calls, messaging apps, and social media.
In most cases, while this data could be analyzed to provide precious business insights, it ends up being lost. It’s just not feasible for employees to physically type out a transcript of every interaction. AI technology can be used to automatically record these communications, then compile them in a single place.
A number of natural language processing solutions are already frequently used by enterprises to transcribe calls en masse. These texts are then stored together awaiting the application of analytical algorithms. The same technology can be applied across communication platforms when they are joined together as part of a common CRM system.
So, if a sales manager and their lead initiate their dialogue on LinkedIn, continue the conversation in an email thread, then agree to meet up on WhatsApp, the whole thing can easily be downloaded as a single file. Even offline, in-person, discussions can be captured and stored for future analysis with the sales manager’s voice recorder.
Lead Scoring
One of the most exciting ways that companies now use the data they store in their CRMs is lead scoring. Not only does this help in the budgeting process — businesses need not waste resources on hopeless leads — but it also helps to make better decisions about how to approach the client.
Traditionally, sales professionals have taken a rules-based approach, making decisions as to which clients to focus on based on a set of parameters fixed within the business development team. Predictive algorithms, on the other hand, provide a much more sophisticated alternative, analyzing large quantities of data, including a synthesis of metrics relating to the client (i.e. age, location, online behavior) and information gathered and managed by the CRM.
Now, sales managers can be presented with a dashboard that provides a clear rating of each lead with whom they are working. They will be able to see right away which leads should be ignored, and receive suggestions as to how to approach more viable opportunities. For example, it could be suggested to communicate with the client via one social media channel rather than another. Another possibility is for managers to receive reminders to get into contact with leads that may be going cold.
Sentiment Analysis
Good sales professionals have always been able to set themselves apart with excellent interpersonal skills. Successful business negotiation is highly dependent on the ability to listen, and more broadly, to be empathetic. AI can actually help with this.
In a fast-paced business setting, humans are not always best-suited to human interactions. A sales manager may be juggling more than a dozen leads at any given moment, not to mention things that might be happening in their own personal lives. Machine learning algorithms can be implemented as an add-on to CRMs that can analyze data collected during communications for client sentiment.
From the sales manager’s perspective, this kind of solution is really quite intuitive. While the manager communicates with clients, text is analyzed on the back-end, in real-time. On the front-end, managers can see in the dashboard a color-coded representation of client sentiment and indicators that show the level to which the lead is interested in the product or service being sold.
If in recent communications the potential client seemed approachable, their name on the sales dashboard will appear in green and the salesperson may even receive a notification to their mobile device to that effect. If the client seems to be uninterested, their name will be in red. Or, if they expressed what the AI deems to be frustration in a recent email, their name would appear in yellow and the software could even provide suggestions as to how to remedy the situation.
Drive Sales Success With Data
CRMs have, over the last thirty or so years, proven themselves to be fantastic tools for gathering and organizing data. Today, AI is making all the difference by processing and analyzing that information in order to drive sales even more effectively. The opportunities, without a doubt, go beyond the three discussed above. If you would like to discuss these implementations — or any other questions that you might have — feel free to contact a member of the Daiger AI/ML consulting team. We would be happy to have a chat.
