Quantifying the Value of Contact Center AI
Artificial intelligence (AI) is considered one of the most important technologies for 2019, particularly within the contact center. New research from Vanson Bourne shows that 99% of organizations are using some form of AI in this area of their organization with the belief that it can transform overall business performance.
But how are companies measuring the effectiveness of their AI rollout? This is where AI becomes a source of both excitement and concern, with many executives scratching their heads as to what the real value of contact center AI is and how they can capitalize on it.
According to Vanson Bourne, companies are using several strategies for measuring the success of their contact center AI:
- Customer satisfaction (CSAT) scores (62%)
- Efficiency/productivity gains (60%)
- Financial results (50%)
- Employee satisfaction scores (46%)
- Fewer operational errors (46%)
Here’s how you can measure the effectiveness of contact center AI using the top three strategies above:
CSAT scores: Real-time transcription services can elevate language and voice inflections to quantify next-level drivers of customer satisfaction like attitudes, opinions and emotions. In this way, you can interject during interactions rather than wait for survey results to create better immediate outcomes and drive higher CSAT scores. We’re even seeing companies experimenting with using conversational intelligence (i.e. transcription services, sentiment analysis) to replace their existing Net Promoter Score (NPS) program, with tremendous success. As opposed to surveying small samples of customers after the fact (an approach that can skew towards angry customers and render inaccurate results), brands can analyze customer conversations in real-time to monitor sentiment for 100% of interactions.
Efficiency/productivity gains: Take transcribed text files of customer conversations and auto-populate them into the notes section of your CRM to reduce after-call work and drive measurable efficiency gains. This can also improve productivity across other key departments like sales, marketing, and finance. Marketing, for example, could use these transcribed files to more quickly analyze consumer trends for campaigning. Sales could use them for more quickly creating targeted follow-ups that increase open rates. Your billing department could leverage them for more efficiently tracking missed or late payments. These files can also be used to help agents more effectively meet SLAs with a better understanding of what needs to be done and when.
Financial results: Applying machine learning around conversations helps improve personalization with a better understanding of key issues customers are communicating about. These interaction insights, combined with intelligent inbound and outbound capabilities, can help contact center organizations capitalize on unique new revenue streams through targeted cross-selling, up-selling, and other special promotions or discounts. For example, you could more quickly identify customers at risk of churn to proactively provide personalized retention offers. Or, those customers who are ready to make a purchase or looking for a new product, service or add-on. Be sure to also track the financial performance of AI-enhanced marketing and sales initiatives. For example, profit increases driven by AI-enhanced marketing based on customer data collected in the contact center.
Measuring the success of contact center AI is crucial to determining whether your strategy is working or not, especially with an advanced technology that not many organizations feel completely confident with. Learn more with Vanson Bourne’s new research report, “AI: The De Facto for Contact Center Experience.” Or, schedule a Discovery Workshop with Avaya Professional Services. We’ll help you identify the key areas of your organization where AI can have the greatest impact upfront.