The Future Of Shoppable Video Ads In Performance Marketing
The Future Of Shoppable Video Ads In Performance Marketing
Blog Article
AI-Powered Chatbots for Performance Marketing Campaigns
Personalized, dynamic messaging via chatbots is an essential part of modern digital marketing strategies. Whether e-commerce or service based, AI-driven chatbots are available 24/7 and offer real-time support based on customer trends and preferences.
This level of responsiveness is particularly important for performance marketing campaigns. According to Sprout Social, over three-quarters of consumers expect brands to respond within 24 hours to questions on social media.
1. Automated Resolution of L1 and L2 Support Queries
Unlike rule-based chatbots that only recognize keywords to deliver tightly structured responses, AI bots use natural language processing to understand context and generate contextually relevant replies. This allows them to solve complex queries without human intervention.
Robust knowledge management boosts self-service to help employees proactively resolve issues and reduce reliance on L1 support. This helps you manage workforce shortages and save costs.
2. Automated Ticket Creation
Automated ticketing rules can automatically sort and assign tickets based on predefined criteria. For example, a software company could create custom workflows that route feature requests to product teams and bug reports to engineering, making sure issues are handled quickly and accurately.
Advanced AI capabilities can take automated ticketing even further by interpreting ticket content to work on quick replies, suggest solutions from your knowledge base, or categorize and prioritize tickets. This streamlines ticketing and frees up your team to focus on more challenging tasks.
3. Automated Resolution of L1 and L2 Support Queries
L1 support automation reduces costs and optimizes resource allocation. Automating routine tasks also frees up time for support agents to focus on resolving complex or unique issues.
GenAI chatbots backed by large language models can help improve self-service through contextual responses. They can also ease issue escalation by connecting with other teams to deliver the right information. This increases employee satisfaction and ensures that issues are resolved effectively.
4. Automated Resolution of L1 and L2 Support Queries
AI chatbots can efficiently handle frequently asked questions, freeing up human support agents to concentrate on complex issues. These bots use NLP to understand the context of queries and generate contextually relevant responses.
GenAI chatbots can also provide more personalized and natural language responses compared to rule-based chatbots that follow fixed scripts. This significantly improves the self-service experience for employees. They can even detect that a query cannot be solved under L1 support and escalate it to the next level of support.
5. Automated Resolution of L1 and L2 Support Queries
AI chatbots use Natural Language Processing (NLP) to analyze user input and determine their intent. They also factor in context to deliver personalized, relevant responses.
Using NLP, AI-based bots can access internal wikis and documentation, as well as search public internet resources to answer questions. They can also offer personalized sales assistance to help reduce return rates.
The real-time nature of Workativ Knowledge AI chatbots ensures employees have access to the latest information and prevents costly delays in productivity. They can even automate alerts to run software updates and reset passwords.
6. Automated Resolution of L1 and L2 Support Queries
Often, stale or irrelevant knowledge leads employees to escalate their queries to L1 support. GenAI chatbots enable self-service with contextual knowledge access that helps overcome L1 support agent shortages and drives employee engagement.
AI bots handle common requests and queries, freeing up live agents to help with conversion funnel optimization the more complex problems. They also help you manage seasonal peaks and unexpected drop-offs in availability, improving business continuity.
7. Automated Resolution of L1 and L2 Support Queries
AI chatbots understand natural language and can provide a personalized response to customer queries. They also provide contextual information to help customers find the answer they need.
AI bots also handle L1 support queries automatically to reduce ticket resolution times. They comb through knowledge sources to identify answers and return them. Knowledge AI uses large language models like ChatGPT to retrieve accurate and relevant responses for employees.
8. Automated Resolution of L1 and L2 Support Queries
AI-powered chatbots deliver a valuable advantage to digital marketing teams. They automate tedious tasks, reduce response times, and improve customer service.
L1 support chatbots quickly answer questions, freeing up human resources for more complex issues. This helps improve customer satisfaction and fosters loyalty.
Using GenAI, Workativ provides self-service chatbots that tap knowledge trapped in various formats, such as PDFs, Excel sheets, text documents, and other data. This empowers employees to discover contextual answers and avoid escalating issues to L1.
9. Automated Resolution of L1 and L2 Support Queries
L1 support is a crucial triage layer, and automation of simple queries and tasks allows higher-tiered engineers to focus on more complex issues. This improves support quality, customer satisfaction, and retention.
When integrated with GenAI or large language models, chatbots also unleash predictive capabilities that prevent outages and help employees self-resolve problems. This reduces escalations to the help desk and prevents downtime.
10. Automated Resolution of L1 and L2 Support Queries
AI chatbots can handle routine queries and escalations, eliminating call center wait times and streamlining workflows. They can also be integrated into employee portals to improve self-service.
GenAI chatbots can ingest large open source or company-owned datasets such as knowledge bases to automate monitoring and alerts. This enables them to prevent outages and reduce L1 support costs. Then, they can collaborate with higher levels of support to resolve complex issues.