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The customer experience (CX) stakes are high. CX influences more than two-thirds of customer loyalty, surpassing the combined impact of brand and price. Nearly 74% of customers will switch brands after a single poor contact center experience. Every year, American businesses suffer a $35.3 Bn loss due to customer attrition stemming from preventable CX issues.
How can brands elevate the customer journey to create competitive differentiation?
Enter: advanced analytics
Delivering experiences that build loyalty and improve the bottom line requires customer service leaders to answer critical questions such as:
- When are my customers most likely to move to my competition?
- Why are my order backlogs reaching record levels?
- Where is the next best opportunity for expansion?
- What is causing my rising costs?
Advanced analytics is the key to answering these questions, enabling brands to identify unsatisfied customers, personalize interactions, and reduce churn. It, therefore, comes as no surprise that customer data and analytics is increasingly becoming a top priority for customer service and support leaders, with 84% citing analytics as ‘very’ or ‘extremely’ important for achieving their organizational goals.
Analytical solutions can aid customer service leaders power customer-centric strategies – from decoding customer sentiment to identifying purchase and fraud patterns to optimizing supply chain and inventory management.
- Interaction analytics: Interaction analytics uses AI, machine learning, and natural language processing (NLP) to analyze call volumes, call types, word groupings, and voice inflection. It provides actionable insights to create a deeper understanding of customers behavior and sentiment. By combining conversational elements – words like ‘disappointed’ or ‘awesome’ with voice pitch, volume, and length of pauses – interaction analytics paints a clearer picture of how customers feel.
Use cases
- Analyze calls in four major areas – Returns/Exchanges, Promotion/Rewards, WISMO (where is my order), and Website – to identify why customers are calling. Root causes could highlight internal process issues, problems with the tools or technology used, and even products and services available or offered. For instance, word groupings like ‘Where is my order’, ‘I didn’t order this’ or ‘Why is this showing up in my statement’ can indicate fulfilment issues or logistical delays.
- Review returns, analyze refund patterns, and blend the insights with interaction data to identify fraud.
Performance analytics: Performance analytics involves the reporting and analysis of customer service performance and effectiveness. It establishes a baseline performance using Key Performance Indicators (KPIs), such as Average Handle Time (AHT), and continuously monitors to assess how it’s changing. By corelating performance to success markers, performance analytics highlights improvement opportunities, in turn reducing costs and elevating the CX.
Use cases
- Determine factors affecting agents’ performance by understanding the amount of time required to understand and analyze issues customers face, and how it impacts AHT targets.
- Develop a strategy around improving agent productivity and efficiency, increasing customer satisfaction, and reducing costs. For example, enable near real-time script updates to cater to changing customer needs; quickly align with changes in sales processes; reinforce standard call procedures; and identify cross sell/upsell opportunities.
Process analytics: Process analytics helps identify, analyze, and improve upon existing business processes within an organization to meet quality standards. It aims to add value to the product or service and create an improved customer experience. For instance, a significant percentage of silence often indicates a process related issue or agent training needs.
Use cases
- Use the insights to develop robust transfer reporting for greater transfer accuracy, increase EQ training for enhanced customer satisfaction, or provide agents with access to relevant knowledge to better serve customers.
- Analyze data pertaining to foot traffic, instore purchase patterns, turn rate of stocked items to optimize shelf inventory.
Get analytics execution right
Roadblocks to execution success include both strategic and tactical pain points. From shortage of skill sets and poorly defined data strategy to ensuring data quality, security, and governance and difficulty in demonstrating ROI. Partnering with a seasoned expert can make it easy to overcome these issues.
At ResultsCX, we take a three-step approach to fulfilling specific or dynamic revenue, cost, and KPI objectives for our clients. Step #1 – Transform raw data (like interaction dialog) into structured data. Step #2 – Focus on further refining data by categorizing potential drivers (such as agent behavior) and applying filters. Step #3 – Uncover hidden patterns and drivers behind success markers (like customer satisfaction and sales conversions). And we do all this without losing sight of security.
Our proven approach helped a retail client uncover the reasons behind high call volumes and low customer sentiment. By initiating real time agent behavior coaching and training to reduce AHT, and devising a service excellence rubric linked to CSAT, we helped the client reduce AHT by 19.6% within the first two months and increase Call Quality scores by 19.6%. We also recommended other process improvements outside the call center, such as tuning up their warehouse QA process and adding size chart to their key webpages, to drive greater impact.
Turn every interaction into opportunity
From figuring out buying patterns to enhancing stock accuracy to enabling personalization, you can provide a better experience by leveraging CX analytics. With nearly 80% of brands believing that CX is a key competitive differentiator, now’s the time to get ahead of the game.