Why Companies switch Chatbots and why Migration is so important
Users rely on chatbots primarily for the following reasons: speed, convenience, and 24/7 availability. But what if the requests are answered inadequately or fundamentally misinterpreted? If chatbot functionalities are insufficient to improve the customer experience, there is a risk that support costs will increase and customers will be lost. Typical reasons why companies change their chatbot provider are as follows:
- Insufficient performance: Particularly complex customer inquiries pose challenges for chatbots and the underlying language model. If the bot fails to provide the customer with a contextually appropriate response, frustration ensues and users rate the chatbot's response errors as inadequate service. According to studies, almost half of those surveyed consider switching brands after just one negative customer service experience.
- Poor integration into existing systems: If chatbots are not comprehensively connected, there is no knowledge base that enables complex questions to be answered, and no satisfactory support response can be generated. To avoid this, it is particularly important to integrate CRM, ticketing systems, and knowledge databases.
- Inadequate automation and escalation processes: If the chatbot cannot help the user find a suitable solution, the issue is escalated and forwarded to a support agent. Poorly defined escalation logic leads to endless loops and frustration for customers. This has a direct impact on customer satisfaction and ultimately leads to customer churn.
AI-powered chatbots in particular can significantly increase the level of automation and thus reduce costs. But only if the chosen solution offers the right features and works reliably. If this is not the case, there is an increased risk that customers will abandon the process due to dissatisfaction and increasingly use the expensive support channel. In such cases, the cause is not necessarily incorrect implementation by the company, but rather that the chatbot solution itself is inadequate or does not meet the requirements.
What are the typical Challenges of Chatbot Migration?
Typical challenges of chatbot migration include the complex transfer of existing data and dialogue flows and integration into company systems. It can be difficult to bridge functionality gaps between the old and new solutions and to familiarize the team with the new tools quickly and comprehensively. A targeted chatbot migration to more powerful systems measurably improves customer satisfaction and ROI. However, during the migration, it is essential to ensure that the core functions of the chatbot, customer data, and the existing user base are preserved in order to avoid communication breakdowns. The following points are explained in detail below:
Data Migration (Dialogues, Content)
Data migration is one of the most complex phases of the migration project. Content should not only be copied into a new system from a technical standpoint, but also checked and adjusted or, if necessary, cleaned up so that redundancies are avoided and data quality is improved. For chatbots, this includes the transfer of previously conducted dialogues and content used, including, for example:
- FAQ dialogues and standard responses (e.g., “How can I reset my password?”)
- Service workflows and transaction dialogs (“Please enter your ticket number”)
- Personalized content (“Based on your last order...”)
- Escalation and fallback dialogs
It is crucial to ensure data quality and consistency during migration across both platforms.
Interfaces and Integrations (CRM, Ticket System, Shop Systems)
Seamless integration of existing internal and external knowledge sources, workflows, and company systems plays a key role in chatbot migration. On the one hand, this serves as the basis for training the new chatbot, and on the other hand, it enables functions such as real-time analysis and personalization. If systems such as CRM solutions are successfully integrated, customer interactions and data can be bundled centrally; if these integrations are missing or faulty, there is a risk of data inconsistencies and additional manual effort. It is therefore essential to ensure that the new chatbot provider provides all the necessary APIs.
Data Protection and Governance
Since sensitive customer data is being handled, concerns about data protection and security are not least a factor. Robust security measures must be implemented to protect customer data from unauthorized access and cyber threats and to comply with data protection regulations. We show how data processing practices can comply with legal requirements in the article Chatbots & Data Protection: 7 Tips for a GDPR-Compliant Chatbot.
Change Management: Acceptance within the Team
In addition to technical factors, the success of the transition also depends on acceptance by users and employees. It is crucial to provide comprehensive training to the people who work with the chatbot on a daily basis in order to simplify its use and at the same time demonstrate the added value of the new system. Resistance due to uncertainty or lack of knowledge can be reduced through clear communication and continuous support during the transition. In addition, switching to a provider with a UX-optimized backend makes sense so that employees can quickly understand and directly use the solution without prior technical knowledge.
The most important steps for successful chatbot migration
Successful chatbot migration therefore covers both technical and organizational dimensions:
Current State Analysis
First, the current functionalities and connections of the chatbot must be recorded. The chatbot's performance to date provides information about the quality of the content and the effectiveness of the channels used. The inventory includes, among other things:
- Review of existing FAQs and response logic
- Documenting all interfaces
- Measuring technical performance (loading times, response speed, error rates)
- Analyzing user feedback, e.g., negative reviews or abandonment rates
- Recording the human takeover rate, i.e., how often a human had to intervene because the chatbot was unable to respond or responded incorrectly
Based on this, it is possible to determine which specific functions should be taken over, improved, or even replaced.
Goal Definition
Clear goals are then to be defined. What improvements should be achieved with the new chatbot? These could include optimized response times or more comprehensive automation of routine tasks. The goals should always be oriented toward the customer journey and business processes.
Provider and Technology Selection
Selecting the right provider and the appropriate technology depends on a variety of criteria, including
- Functionality and features (multilingualism, degree of automation)
- Integrations
- Data protection and GDPR compliance
- Scalability
- Implementation support
These must be carefully evaluated to ensure that the new solution meets current and potential future requirements.
Migration Planning
Important steps in the planning phase include creating a project plan and defining responsibilities. Realistic time frames must be chosen and milestones agreed upon. In addition, internal and external communication about progress and changes must be coordinated to keep stakeholders and customers informed about innovations. Risks must be identified from the outset and measures to minimize them must be planned.
Content and Data Migration
A key stumbling block in content and data migration is the diversity of data sources: content is often scattered across FAQ databases, CMS, or support tickets and must first be consolidated. In addition, existing processes and integrations at the old provider often cannot be transferred 1:1, which necessitates changes to the concept. The focus here should be on core processes and knowledge data, which already cover around 80% of the inquiry volume.
A complete 100% migration rarely can be supported, as it is usually very time-consuming and expensive, and systems are not normally completely identical. Moving a chatbot is not just a matter of technical adjustments, but also of conceptual decisions: Which content is still relevant? Which conversational flows need to be rethought? Under no circumstances should the new system be viewed simply as a mirror image of the old one. Changing providers is rather an opportunity to question existing processes and make optimal use of the new provider's functionalities to achieve better results, rather than repeating old patterns. In some cases, it may even make sense to “start from scratch” in order to avoid tedious adjustments and build customer-oriented structures from the outset.
Test Phase
Before the solution can go live, tests must be carried out to ensure quality and verify that the content has been successfully migrated and that the chatbot is functional. Optional tests such as system checks or UATs (user acceptance tests) can provide valuable insights into whether the chatbot offers a good user experience. However, especially for small and medium-sized companies, it is just as effective to optimize directly after going live based on real conversations. This allows customer feedback to flow directly into further development, enabling quick, practical improvements to be made in short iterations. In this way, weaknesses can be identified and remedied at an early stage before the chatbot goes live.
Go-Live and Monitoring
The go-live marks the transition from the test system to productive use. Immediately afterwards, structured monitoring is essential. Technical metrics such as conversion rate, degree of automation, and number of conversations provide valuable insights. Qualitative factors such as user satisfaction or abandonment rate in chat are also important. Here we have summarized the six most important metrics for measuring the success of chatbots. Feedback should be sought at all times, as feedback from customers and employees provides valuable information on potential for optimization.
Best Practices and Tips
Chatbot migrations in companies have shown that reduced development costs and higher automation are particularly beneficial. Carefully planned and implemented migration is therefore crucial to avoid frustration and customer churn. Negative chatbot experiences jeopardize trust and business success.
As an experienced migration partner, moinAI brings the necessary expertise in process flows and automation to the table, ensuring high accuracy and a successful transition. A success story: Thüringer Energie AG (TEAG) implemented the chatbot in just 14 days and now answers 68% of all inquiries automatically.
In general, it is recommended to start with the most common customer inquiries and problems so that the new chatbot provides noticeable relief right from the start. Using moinAI's import options, existing FAQ data can be imported directly, saving valuable time. Early integration of the CRM or ticket system is essential so that customer data and chat histories are available centrally. After go-live, short feedback loops are important in order to collect valuable information, which comes directly from real customer dialogues, and can be implemented quickly.
Conclusion
The most important components for a holistic chatbot migration are listed here:
Strategic planning and a focus on the user experience are essential for a successful migration. To improve user-friendliness and operational efficiency, continuous communication with customers must be maintained with the help of seamless integration of the new solution. Our guide “The 5 most important tips for a successful chatbot” lists further tips for successful implementation. The key is to address potential challenges directly and minimize risks when migrating the chatbot in order to reduce costs in the long term and fully exploit the efficiency of the new chatbot solution.
