It’s time for a change. Now. Essentially, CRM hasn’t changed since it was introduced to the market in the late nineties, in such that it requires the end user to log into whatever application they’re working with, create and manage tasks in the software (log calls, emails, create reminders, move leads down the pipeline, etc.) manually. This is cumbersome, time-consuming and redundant. It’s time the software (machine) does its job.
Millions in R&D – are we there yet?
Companies spend millions of dollars trying to make CRM work, and it just doesn’t. Well not completely anyway. We do know that there have been improvements to CRM in general with the use of AI (artificial intelligence) or machine learning. Using statistical methods, mathematics and probability in these algorithms, the technology in these CRMs helps with many tasks. Some examples of AI-infused software in CRM:
- Improved sales team productivity: Leveraging machines to gain new insights from past sales data, reducing manual analysis and saving valuable time
- Exceptional support for marketing: AI can assess previous price information, like discounts, promotions and sales history, to calculate price elasticity of the products so price can be optimized.
- Customer retention: AI can analyze previous sales data that shown when and why a customer left. Now you can see the early warning signs of potential customer churn.
- Pattern recognition: AI software can now analyze your most valuable customers based on certain data points and then compare this to potential new customers and predict the value of the new customer. This also allows sales managers to reallocate resources more appropriately based on the customer value.
AI and machine learning are readily available now, and we know it can be used to automate much of the repetitive tasks and processes that humans do now. However, AI can’t handle complex decision-making tasks. To complete the full sales process, it requires the full machine-human team working together. For example, the human element is a critical component of the sales process and is something that can never be programmed. Solving business challenges for a customer is intrinsically far more fulfilling than inputting data into a CRM system.
Additionally, AI alone will not be enough to make or close sales. It will certainly be a machine-human collaborative effort to complete the entire process successfully. However, with the assistance of AI, sales managers now can work efficiently and focus on the bottom line to produce better results. Continual use of this AI-infused software and the help of analytics will optimize both the platform and the process.
AI is no longer science fiction – put it work today
Many companies are trying to fix CRM by adding AI-driven “sales assistants,” which are tools that sit on top of CRM and are designed to help sales reps be more effective. However, this approach is flawed because the assistants are using AI to make decisions on poor and incomplete data in the CRM.
Nevertheless, giving credit where credit is due, there are companies rethinking the approach entirely, building an AI-driven platform from the ground up. One example of this type of platform is Spiro, which has pioneered a new approach called Proactive Relationship Management. Proactive relationship management is built on an AI engine, and consolidates CRM, sales enablement, reporting and calls/texts into a single platform.
The value of this single, AI-driven sales platform, is that it:
- Automatically collects data from emails, texts and calls which, gives sales teams more time to sell
- Helps salespeople reach more prospects with automatic reminders on follow up activities that can be completed within the platform.
- Gives leadership better visibility into the pipeline, which drives better management and revenue performance.
Simply put, proactive relationship management technology works by connecting to your email, comes with a built-in phone system and connects to any corporate data source or directory that you have. Using AI, the platform can collect and learn from all this information. As mentioned previously, it uses natural language processing to read and analyze the text of emails to understand what’s going on in the sales process. Calls made through this this platform are automatically logged, and then transcribe and pull the intent from those calls and ensures these important tasks are captured. This allows for the platform to pull information from any ERP, marketing software, or other data source, to make sure you have a well-rounded picture of what’s going on with your prospects.
Based on that information, this technology uses machine learning to predict what your sales team should be doing, and when they should be doing it, relative to these records. Consequently, it proactively reminds them, without any manual data entry. The result of this is sales leaders get an incredible amount of data about what’s going on for their sales team – what their activity is, the calls they’re making and the success of those calls, where the pipeline is strong or where the pipeline is weak. This information is all pulled together with the built-in analytical tool in the proactive relationship management platform, enabling sales managers to make real-time adjustments.
Knowing what we know about the available technology leveraging machines, this a major gamechanger in the sales industry. Essentially, companies can leverage a machine assistant to accurately and effortlessly handle mundane and routine tasks, from scheduling a meeting, a follow up task, answering customer inquiries via a chatbot, etc. All this while providing real-time predictive modeling to make decisions and adjustments on the fly.
Over time, these tools and processes will only improve. When the software has access to more data, it will be able to provide further insights for decisions. Eventually, the software would be able to make some of those decisions based on a threshold or score level without human intervention. Using technology at this level allows for faster transactions and the best customer experience. Giving extra time back to staff would allow them to work on more advanced or important tasks, improving productivity drastically.