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The role of AI in the future of technology cannot be understated. It is considered to be one of the fastest growing industries while ChatGPT alone has over 60 million daily website visits. The talk of AI is ubiquitous. However, what does the future hold for the relationship between CRM and AI?
Natural Language Processing
Natural Language Processing (NLP) is a field of artificial intelligence that focuses on a computer’s ability to understand the human language in the same way people do. Think about how we use ChatGPT (if you haven’t, you should check it out here); you type a request in your native language, and the AI does its best to understand the context of your request and outputs a text based answer as best as possible.
While there are various sub-fields of AI (e.g. Machine Learning, Deep Learning, etc.), our primary focus is on the transformative potential of NLP, where we offer a glimpse into a future where we effortlessly interact with our CRM and AI. Let’s go through how features exist today vs. how they could be done with NLP.
Table of Contents
1. Filter Data
How It’s Done Today
Whether we want to filter company, contact, or deal data, the process is the same regardless of system; we bring up a filter, select fields, and choose corresponding data that we want to segment.
With NLP
With Natural Language Processing, we could type in the data we want to see.
Example:
How It’s Done Today
Filter deals:
- “Close Date” is between January 1, 2024 and December 31, 2024
- “Country” is equal to United States
- “Deal Stage” does not equal Closed/Won, Closed/Lost, Closed/Qualified Out
With NLP
“Show me open deals in the US that are set to close in 2024.”
Comparison
Today, we need to know the names of the fields and the exact values.
For NLP, we ask with natural language whereby it does not matter if we use the proper field names or values. The AI is able to understand contextual clues to ascertain what we seek.
2. Reports
How It’s Done Today
Most CRMs run reports in a way that is slightly unique. However, the overall gist is the same; we must select the data we want to see, add filters, and choose the visual of how we want to see our data.
With NLP
Much like Filter Data above, with NLP, we could type in the data we want to see.
Example:
How It’s Done Today
- Set “Deal Name” along the Y axis
- Set “Close Month” along the X axis
- Set the output values as “Annual Recurring Revenue”
- Filter the “Close Date” for between January 1, 2024 and December 31, 2024
- Filter the “Deal Stage” to not equal Closed/Won, Closed/Lost, Closed/Qualified Out
With NLP
“Create an ARR report that shows me open deals, which are set to close in 2024 by month and by name.”
Comparison
Today, we need to know the names of the fields, the exact values, how to use reports in a CRM, and perhaps even know Boolean logic for some systems.
For NLP, we type and ask the system to show us the data that we want to see and how we want to see it visually.
3. Upcoming Renewals
How It’s Done Today
Many CRMs today don’t handle renewals all that well. We create deals for new opportunities, which is standard. However, when it comes time for a renewal, we must input that deal again manually, with a workflow, or with an integration.
With NLP
We could ask the system to create new deals for all upcoming renewals as long as we capture the end date of the contracts for won deals in our CRM.
Example:
How It’s Done Today
- Filter “Contract End Date” is known
- Filter “Deal Stage” to Closed/Won
- Filter the “Close Date” for between January 1, 2024 and December 31, 2024
- Filter the “Deal Type” to not equal Renewals
- Look at the deals to see when the contracts end
- Create new renewal deals based on the aforementioned
- Check that no duplicate renewal deals exist
With NLP
“Look at the contract end date for existing deals and create renewals if they don’t already exist.”
Comparison
It can be a complicated and time consuming process to create a renewal as there are plenty of chances to make errors, omit key data, and create duplicate renewal deals.
For NLP, ask the CRM and AI to create new renewal deals.
4. Help & Customer Support
How It’s Done Today
When we need help with an issue, we look at our CRM’s documentation, ask our co-workers, and look at online community forums. When we need to escalate our issue, we reach out to Customer Support via email, text chat, or phone.
With an email, there can be a back and forth that takes time.
If we chat online, it may take a bit of time and effort to explain our issue, have support fully understand it, have support solve it, and get them to relay that information back to you in an actionable manner.
When we talk on the phone, we may have to wait a bit to get a hold of someone. Even then, there is no guarantee that they can resolve our issue immediately.
With NLP
We would not need support documentation because we could query the system, which could then answer us directly. Just as we discussed above with Filters, Reports, and Renewals, we could tell our CRM and AI to perform actions instead of our need to research and ask questions to support. While AI in our CRM is not likely to completely replace Customer Support, it could drastically reduce our dependence on them.
Example:
How It’s Done Today
- Search in documentation: How do I create a new deal field?
- Search for the right article
- Follow the steps, which don’t work
- Reach out to your administrator about why this does not work
- They do not know the answer
- They ask Customer Support
- Customer Support explains answer
- Your administrator changes permissions
- Your administrator shows you how to make changes
- You make the changes
With NLP
You: “Create a new single line text field called, “Company Tagline.”
AI Response: “You don’t have permissions to create a new field. I can reach out to your CRM administrator to ask for permission updates if you’d like.”
You: “Yes, please.”
AI Response: “Permissions granted. Do you still want me to create a new single line text field called, ‘Company Tagline’?”
You: “Yes, please.”
Comparison
The quest to find an answer to the unknown can be a frustrating and arduous one. It may take hours of research and back & forth emails among parties to get to the bottom of an inquiry.
With NLP, we can ask for what we want and then system can then either perform the action or directly take steps to help you get to your answer as with the example above.
Wrap Up – CRM and AI
The main benefit of NLP as related to CRM and AI is ease of use. The time and effort it takes to become a power user will be much less than today. When we don’t have to know every bit of functionality, and we can simply type our requests, we can onboard team members quicker, get data faster, and need less support help. This saves your time and your company’s money. Users become far more independent and confident in their usage of the CRM compared to today.