Customer Service
Leveraging predictive analytics in BPM to anticipate and resolve customer issues
There was a time when customers tended to stick with a business for years no matter what, and exceptional customer service was just an additional perk. However, in today’s hyper-competitive business environment, outstanding customer service with every interaction across the customer lifecycle has become the norm. Customers today expect personalised service from businesses and want them to be proactive and offer solutions before a problem turns into a big issue. This is where predictive analytics in BPM and customer feedback analysis come into play. Businesses can leverage the available customer data to understand customer behaviour, identify potential issues and offer resolution, thus reducing customer churn.
Understanding predictive analytics in BPM
At its core, predictive analytics in BPM or predictive customer analytics involves collecting customer data, analysing their past behaviours, and predicting future events and trends. Businesses can use advanced analytics techniques, statistical algorithms, and artificial intelligence or machine learning tools to achieve this.
Tools like feature tagging to track interactions and micro-surveys can help businesses collect and uncover customer insights, helping them offer quick customer issue resolution. Although predictive customer analytics has limitations and may not be 100% accurate, it still offers great insights into potential event trends and can facilitate data-driven decision management.
How can you use predictive analytics for customer issue resolution?
There are many ways you can leverage predictive customer analytics and customer feedback analysis to elevate your support operations, increase customer confidence, deliver exceptional experience, and achieve customer retention. Here are some ways you can use predictive analytics to deliver superior customer support and resolve customer issues before they become significant:
Identify potential issues
Predictive analytics tools in BPM can help you look at current and historical product data and usage patterns to identify potential issues that can cause significant service disruptions. These insights can help you offer proactive customer issue resolution and build customer trust and loyalty.
Predict customer behaviour and take proactive steps to avoid churn
An extension of the previous point, analysing customer feedback and gauging their satisfaction levels in past interactions can help you predict customer behaviour and focus on at-risk customers. This way, you can take proactive steps to understand their pain points and offer personalised solutions to avoid churn.
Personalise customer experiences at scale
Modern analytics tools can also help you achieve personalisation at scale, from offering product recommendations to adapting your marketing communications. Analysing different customer interactions, you can set dynamic pricing strategies and cross-sell or upsell relevant services to customers based on their interests and preferences.
Optimise resource allocation
Predictive analytics in BPM can also help companies anticipate upcoming support needs and service capacity to optimise resource allocation to the customer service teams. This can help them minimise wait times, offer quick resolution, and meet customer expectations.
Benefits and drawbacks of predictive customer analytics
Predictive analytics can offer you a range of benefits when it comes to providing customer issue resolution. Here are some benefits of predictive analytics that can help you stay ahead of the curve:
- Understand customer behaviour and identify trends to predict potential future events and strategise accordingly.
- Identify high-risk customers who are either stuck in their customer journey or are unsatisfied with the support they have received.
- Foster personalised interactions and quick resolutions for each customer to boost satisfaction and loyalty.
- Reduce customer churn to create lasting relationships and build a loyal customer base.
However, predictive analytics in BPM is not 100% and has limitations, such as:
- The data analytics tools and algorithms cannot always account for and predict all the nuances of complex human behaviour.
- Predictive analytics tools need consistent data updates for accurate insights.
- Customers may not be inclined to complete surveys or offer 100%-honest answers, which can result in incomplete or inaccurate data.
All these can contribute to skewed or inaccurate results that can potentially impact customer issue resolution. However, leveraging next-gen tools and focusing on data hygiene strategies can help companies overcome these limitations.
How can Infosys BPM help you offer quick customer issue resolution?
Customer feedback analysis is a foundational element of offering superior customer support as the company proactively anticipates and resolves customer issues. Infosys BPM customer care services offer you a suite of next-gen tools to manage support needs across the customer lifecycle, from sales and technical support to customer retention, cross-selling, and up-selling. Leveraging predictive analytics in BPM, you can establish an omnichannel presence, manage support quality, and offer self-service options for enhanced support and quick customer issue resolution.
