The institutional and wholesale sides of asset management firms face similar challenges, but the technology to support them often falls short. If you are using a “horizontal” CRM solution like Salesforce or a point solution like SalesPage, you are probably missing opportunities. The right CRM software solution offers much more than what you have become accustomed to and plays a large role in a bigger story that includes technology like predictive analytics and machine learning.
This 3-part series talks about how CRM as an operational platform can fill that technology gap. Part 1, Does Your System Measure Up? What Asset Management Firms Need in CRM Software, outlines the components a CRM software solution for Asset Management should provide. Part 2 discusses how machine learning and predictive analytics help give you a competitive advantage. Part 3 will discuss tools you probably already have that extend CRM to help drive productivity and higher performance.
Part 1 discussed that, while CRM systems help to a certain extent, they are not equipped to determine which relationships and opportunities have the most potential. So, how do you determine if your CRM system has the ability to fill this vital role?
The primary goal is to help you organize your data and day to get the most value, and that means ensuring your CRM software not only talks to, but collaborates with other business-critical applications. More than just an application, the right CRM software can be a platform that supports and works in conjunction with all of your applications.
Don't stop reading! This is not a "what if"...this is happening right now, and it's not for technologists; it's for any asset manager who wants to capitalize on every relationship and opportunity because, in short, machine learning can take copious amounts of data and process it in ways humans cannot. As such, it should be something offered by your asset management software solution. Here are five reasons you should make machine learning a part of your daily routine:
Asset management firms are constantly responding to RFPs (Request for Proposal). These can be quite time-consuming to complete, and there is no assurance that they will lead to a successful client relationship. Rather than responding to every request from every broker or client, machine learning allows you to enter specific data about the client, the characteristics of the proposal, the consultant that submitted the RFP and the typical strategy associated with that type of RFP. Machine learning algorithms apply your data to past success rates and identify factors that played a positive role. By supplying your system with the necessary data, you can then use machine learning to determine the likelihood of a successful proposal. You’ll know which RFPs to focus on and you’ll be able to make the best use of your efforts and time.
Who to call first? Do you decide based on relationships or market events? Is there a better way to prioritize your call list? With machine learning, you enter every factor you are aware of. That data combines with the system’s information about past performance, market events, etc., to generate a list with results ranked for the highest likely success rate. Your system can take volumes of information and boil it down to a dependable recommendation.
If you travel for business, you have the same decisions to make: Who do you focus on? Where do you go first? Prioritizing who you’re going to see first and making sure you’re seeing the right people in a particular location can mean the difference between a successful day and one filled with frustration. If machine learning interacts with your CRM solution, route planning will be easier and more productive.
For an asset manager, intuition is one of your key tools. But it shouldn’t be your only one. Without the proper data, your intuition could even lead you astray. You need good data intelligently applied, and you need the most data you can find that applies to your decisions. By increasing the quantitative amount of data with multivariate statistics such as you get with machine learning, your intuition and relationships become more fine-tuned, and your rate of success will continue to grow.
Machine learning works with your intuition and experience. It’s that little voice that says, “Are you sure you want to do that? Because the statistics say you should do this instead.” The decisions are still yours, but now you’ll have all the facts.
You might wonder: if machine learning can do all that, is it complicated and expensive?
You already use technology every day, and you already have a lot of the data you need stored in your CRM. Your experience has helped you identify the characteristics and attributes that make for successful RFPs. Machine learning takes that data and experience and processes it for you, saving both time and money.
Based on your experimental model that has been “trained” based on history and the data you entered, the system can provide you with the percentage of likelihood you will win the business. It’s that simple, and it’s something you’re already doing—entering data. There is no learning a new application and no complex manipulation of data. The system does the heavy lifting, you get the benefits, and every time you use the system, it continues to learn. As the data sets grow, your system gets smarter and smarter, narrowing the results and increasing the likelihood of a win.
The purpose of technology is to make what you already do easier and better. Machine learning integrated with the right CRM, like Microsoft Dynamics 365, can help you work smarter and accomplish more.
Of the many assets of Microsoft Dynamics 365, one of the most powerful is machine learning. If you’d like a demo of machine learning in action, contact our experts at AKA Enterprise Solutions.
The post 5 Reasons Why Asset Managers Should Make Machine Learning Part of Their Daily Routine…and How the Right CRM Can Help appeared first on CRM Software Blog | Dynamics 365.
No related posts.