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6 Ways Machine Learning Can Generate More CRM Value
Making sense of data, becoming more efficient, and, most important, pleasing customers can now be done on a large scale.
Posted Oct 12, 2015
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CRM is a big expense. Implementation, updates, and training all add up. But does the value you're getting match your investment?

With an ocean of data from sales and marketing, customer support, and product development, your CRM has the potential to deliver more value to the organization. But only if you can make sense of all that data. After all, more data doesn't necessarily mean better decision making.

One way to capitalize on your CRM investment, to improve ROI and drive better outcomes, is through machine learning, which teaches computers to think and act like people in order to augment human decision making. An intelligent layer on top of existing CRM systems, machine learning teases out insight from all of your data to tell the full customer story. It is composed of three stages:

  • analyzing the past to understand what actions and data led to great outcomes, such as high customer satisfaction;
  • interpreting each new customer interaction and making recommendations on the next best action to influence a successful outcome; and
  • continually updating its "learning" based on the most recent set of outcomes to remain relevant without the need for manual changes and inputs.

Machine learning can optimize how you manage, understand, and serve customers—both at the individual customer level and across your entire customer base. Creating highly personalized customer experiences can now be achieved on a much larger scale.

Here are six areas where machine learning can help you extend the value of your CRM investment, driving efficiencies without losing the personal touch.  

1. Gaining insight into the future. CRM systems are focused on aggregating historical data. Machine learning's greatest strength lies in the other direction: providing a future-facing, predictive view. Machine learning looks at every interaction and makes recommendations on how to next engage with that customer to get a better outcome.

2. Continually updating predictions. The world doesn't stand still. As all of your data and interactions shift—because of new product releases, staff turnover, and customer life cycle changes—machine learning evolves with them. By automatically interpreting past actions, machine learning eliminates the need to manually set up and maintain rules, continually learning and making recommendations beyond the static analysis typical of a CRM. 

3. Discovering the "why." CRM can help you put all your data in one spot for a unified view, but it lacks the insight into why interactions happen. Even if the CRM flags an at-risk customer, you still need to spend time researching the reasons behind it. While traditional machine learning has been a black box (spitting out a prediction without the reason behind it) recent advances in machine learning allow the self-learning systems to uncover the reason behind recommended actions and demystify the prediction. By understanding why a particular prediction is made, customer-facing individuals are more likely to use the information to take the right action to drive better outcomes.

For example, machine learning may see that a customer has filed 10 support tickets in the last two weeks; plus, their average usage has dropped by 50 percent; plus, their head of customer success just changed. A single data point, such as the change of leadership, doesn't necessarily mean churn. But by analyzing all the data in context, machine learning intuits the best course of action, augmenting the account manager's ability to have an impactful conversation with that customer. With machine learning, efficiency and personalized customer service are no longer mutually exclusive.

4. Making predictions at the individual customer level. CRM is beneficial at reporting on the general health of all customers, as well as at the individual account or customer level. However, it starts to fall apart at the individual person level (where there may be multiple people associated with the customer) and at the individual interaction level—unless you have the right insight into data.

Machine learning treats each component of any interaction as a separate data point, and that's where the magic happens. This helps to draw out much richer customer engagement patterns, which machine learning can then use to recommend the right message to the right person at the right time for extreme personalization.

For example, machine learning can recommend the best template for a customer service agent to use for a specific issue, which the agent can then modify and personalize. Instead of the "peanut butter" approach of applying the same template to every customer, machine learning guides the best course of action—for that individual customer and at scale, across the entire customer base. 

5. Analyzing unstructured data. CRM excels at handling structured data like revenue or customer category, but that's only one piece of the customer story. Understanding the nuances of unstructured, qualitative data—like email text, response templates, or meeting notes—can be the key to competitive advantage. Machine learning treats unstructured text as data, giving new value to an otherwise elusive email conversation between a customer and the support agent, for instance. Together with the structured data already captured in the CRM, the additional unstructured data becomes a powerful data element to inform and drive better outcomes.

6. Encouraging more consistent CRM use. We know that sales reps don't want to enter meeting notes, that marketing folks may not enter all the campaign ROI data, and that support agents forget to share with the rest of the team templates they've personalized. Your CRM is only as good as the data within it, and machine learning is the best antidote to this persistent garbage-in, garbage-out dilemma. On one hand, machine learning can automate manual, tedious processes—like classifying and tagging support tickets—to streamline agents' work and drive consistency. It can also uncover individual templates that are working well, for example, and make them more widely available by recommending them for certain customer issues.

With machine learning, you have an opportunity to transform your CRM into a predictive system of intelligence that improves productivity and helps you create happy, loyal customers, all the while driving more ROI from a system you already have in place.

 Jeff Erhardt is CEO of Wise.io,  a provider of predictive intelligence solutions.

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