It comes as no surprise that CRM systems are fantastic platforms for organizing and storing customer information. But these days, they need to be so much more. Machine learning and AI applications are the new must-have CRM features. Many of the top CRM providers are offering these capabilities pre-baked into their solutions.

For example, SugarCRM’s Hint data enrichment tool scans the internet to fill in the blanks on customer information, schedule real-time alerts for emails and push notifications, and deliver actionable intelligence for recognizing upsell and cross-sell opportunities. With just a name and email entered in the CRM, Hint can find the contact’s company, social media pages, annual revenue, recent news articles, and more.

In a similar thread, Creatio (formerly bpm’online) touts its intelligent data enrichment features as an important way to accelerate productivity and minimize manual data analysis. Built-in machine learning and AI processes use data enrichment to fill in contact information, recognize patterns/trends, predict customer needs, and automate predictive learning models.

The good news is that these two CRMs are not alone in their machine learning focus. Solutions like Zendesk, Salesforce, Microsoft Dynamics, Zoho, and Marketo all offer some sort of machine learning capabilities.

But, what if your CRM doesn’t have those tools? Can you still capitalize on machine learning for your business?

Yep. You may need to build out and integrate some functions to enhance the CRM, but it’s possible. If you’re looking to do a very in-depth machine learning strategy without an intelligent CRM though, you may need to segue to a different conversation first – is it time to switch CRMs?

In the end, you want a system that can grow and evolve with your business. If the CRM technology is stagnant, your investment will have been for nothing. Keep that in mind as you start using your existing CRM beyond its standard definition.

Why Machine Learning?

A CRM is great at answering the “what” about your data. Machine learning takes that “what” and elaborates on it by defining the “why”. Why are your website visitors taking XYZ actions? Why are you gaining or losing sales on XYZ product? Why does your service team have a bad rapport with customers?

Machine learning couples historical data with emerging trends to recognize the patterns in your information and reveal how the data points relate. This is an invaluable insight when you’re trying to build proactive sales strategies, reduce customer churn, and recognize marketing opportunities.  

Machine Learning Algorithms

Putting machine learning into practice takes some careful planning. First, you need to know what you want to accomplish with your machine learning initiative. The second piece to implementing those practices, though, is knowing where to begin. You must understand the machine learning algorithms that are out there so you can choose the right algorithm for your goals. There are four major machine learning algorithms to choose from, each with various methods attached to them. Here are the basic, high-level definitions:

  1. Supervised Learning: This algorithm uses a set of examples to make predictions. It draws on known (aka labeled) input and output variables to learn how to map the function from the input to the output. Through testing, the algorithm learns where the desired outputs are misaligned with the actual results, and then it adjusts. As the algorithm continues to learn, it’s able to accurately anticipate the outcomes of future scenarios based on the examples. This model would be best if you’re planning to use historical CRM data to make predictions about future events.

  2. Unsupervised Learning: In supervised learning, the algorithm is told what conclusions it should come to, but in unsupervised learning, the machine learning is discovering its own conclusions. The variables are considered “unlabeled”, since we have no historical context for them. The algorithm must discover inherent patterns and underlying data structures to learn what the results should be. This method is useful for segmenting datasets into groups and uncovering data relationships that were otherwise unknown.

  3. Semisupervised Learning: This approach is a combination of supervised and unsupervised machine learning. In supervised learning, the algorithm has labeled input and output variables. In unsupervised learning, the algorithm has only labeled input variables. So, if you don’t have enough known data to build out a fully supervised model, or the ability to gather more example data, you’d use semisupervised learning to fill in the gaps. For example, if you were trying to detect bank fraud but only had proof of a few instances (labeled data), you could combine those known examples of fraud with the larger set of unlabeled data to help the system improve learning accuracy.

  4. Reinforcement Learning: This algorithm uses trial and error to make discoveries. It learns which actions yield the greatest rewards and then optimizes behaviors to maximize those rewards. This method could be useful for analyzing email data from the CRM to define the most important messages for sales team responses.

The type of algorithm that’s right for your goals depends on a lot of factors. It can get complicated, but SAS put together a very nice article and cheat sheet to help you break it down.

Their idea is to use an "if-then" analysis to determine your best approach. For example, if you answer "yes" to the question "Is the speed of my numeric prediction of regression most important to me?", then you would use either decision trees or linear regression algorithms to perform that analysis. Of course, you then have to understand what those terms mean and how those models work, but SAS's article does a nice job of explaining that, too. 

<a href=""><em>Image Credit: SAS Blog</em></a>

Machine Learning & CRM Data

With most companies counting CRM as their biggest data hub, it makes sense to try and utilize those insights for machine learning. Here are a few ways you can capitalized on machine learning using your CRM data:

  1. Making sales predictions. – Here’s where that supervised learning algorithm comes in handy. There should be plenty of historical sales data in your CRM to uncover accurate predictions. You can break out predictions based on the salesperson, the product, the region, or whichever factors are most important to you. Accurate sales forecasts result in a cost savings through pinpoint budgeting and fiscal planning.
  2. Drawing conclusions from free text fields. – Free text fields in a CRM can be both a blessing a curse. Notes and comments may be helpful on an individual account, but it’s difficult to draw any meaningful analysis of the bigger trends. Machine learning algorithms can connect the dots by searching for terms that imply certain behaviors. For example, the algorithm could be set to seek out language related to product inquiries, complaints, additional product purchases, or even references to specific salespeople. The patterns will reveal valuable new information.
  3. Improving customer lifetime value. – Knowing how to best support your customers helps you extend their lifetime value with your company. Machine learning algorithms can make predictions about support needs, predict when a customer may buy again, and even notice patterns/user habits that may imply customer churn.
  4. Optimizing prospect scoring. – By reviewing historical data, the machine learning algorithm can start to uncover prospects who share similar traits to existing customers. Prospect scoring can help sales people prioritize their actions towards the most likely sales.

These are just a few of the many ways you can use your CRM data to enact machine learning algorithms. If you are interested in machine learning but unsure of your CRM's capabilities, contact us. We can help you understand what's possible with your current system, or define a CRM that's better suited to your business goals. 

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