Cutting edge companies increasingly use sophisticated CRM platforms to aggregate customer facing data. These platforms help support teams manage customers, and when machine learning is applied, they also create a new opportunity for support to re-engineer their processes to increase value and customer success.
With machine learning, seemingly one-off actions from support or sales or marketing are assembled into recognizable patterns to help predict customer behavior on a much larger scale. It creates a treasure trove of knowledge for support teams to tap into by finding patterns in disparate data aggregated by CRMs from sales, product usage and customer journeys.
Using all of this customer data together, innovative support teams can predict and optimize outcomes to move beyond a traditional reactive relationship with customers to become the sought-out voice of the customer in the organization.
When a support team is equipped with insight from machine learning, it can proactively adapt processes to meet the expected actions of the customer to influence future actions and customer success. Even before customer support reps take a call or answer a ticket, they can be equipped with knowledge such as whether the customer:
- Has a high probability of churning
- Is more likely to accept an offer
- Would be best served by a particular agent
According to Jim Sinur, research vice president at Gartner, “The impact of integrating real-time analytics with business operations is immediately apparent to business people because it changes the way they do their jobs. The most dramatic change is the increased visibility in how the company is running and what is happening in its external environment.”
With predictive analytics working to discover patterns in conversations with existing customers, the voice of the external environment is accelerated back into the organization to help the company turn insights into tangible assets. Support becomes a live feed actively shaping the entire organization rather than being a reactive sounding board.
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