Dezember 6, 2019


Consider the rapid advances in artificial intelligence in the past years. AI improvements have enabled chatbots and conversational bots to move from a limited set of pre-programmed responses to a learning platform. As the data from customer interactions is collected, an AI-enabled customer service system builds a repository of data that fuels the AI engine. Over time, and with the collection of more and more data, the AI engine systematically improves the chatbot performance and expands its capabilities. 

Chatbots are now frontline customer service tools for many consumer-facing brands. A brief search yields numerous stories about how consumer-facing companies are using chatbots as a primary engagement mechanism for customer service. As chatbot technology has improved, a large percentage of customer service inquiries can be either resolved or the issue defined well-enough that company customer service people can act on the actual problem instead of the routine data gathering. Most companies with deployed chatbots have a solution that comprises of both chatbots and human customer service representatives, with the chatbots ceding the conversation to a human once it recognizes the request is beyond its capabilities. 

Many B2B companies have customer service challenges as well that can be well-served with AI augmented systems. A SaaS company serving business customers has a significant customer service component to their operations. As sales occur, the need to deploy the solution and get users familiar and competent with the software can be an adoption and usage constraint that can slow the deployment of the software within the customer’s operations. For many B2B companies that now provide service around product, getting users to understand and use software is a critical step in assuring the solution is adopted, accepted, and exploited so that it delivers the value promised by the SaaS company.


For consumer-facing companies with a high volume of sales and customer interactions, the data generated in a few months is likely a strong platform for AI to get to work and deliver meaningful and actionable learning that customer service chatbots can employ. Consider though, B2B companies with neither the customer volume or automation to systematically capture customer service data. These companies typically have a systematic way to capture customer issues and mechanisms to act upon and track these issues and decent knowledge of each customer operations and challenges, but likely don’t have the volume of data to optimise AI enabled solutions such as chatbots. With the emergence of useful AI-enabled customer facing solutions, how does a B2B company maintain closeness to its customers while using automated customer service to both improve service quality and lower costs?  

B2B companies provide expertise embedded within product and service. This expertise spans customers and perhaps industries, and grows with each use case, each customer deployment, each industrial sector served. Their customers have deep experience in their particular industry or sector, but often lack the perspective in the technologies and practices that their supplier has gained through its years of encounters and service to other companies. Solving problems of the day through discussion and working together has happened for decades, but this work does not systematically escalate to systems level solutions that can advance solutions for a customer and for an industry. This is where co-creation as a systematic mechanism can be powerful and impactful.


Think about the principles of co-creation; agile processes, quick test-and-learn cycles, and a deep understanding of customers. Collaboration with the mindset of working together to solve problems with the collective expertise of both companies can yield insights and advances not possible if the companies tried to solve the problems on their own. Co-creation not only unlocks value rapidly by delivering high-quality products and service innovation but also sustains that impact over time—all with little additional R&D overhead.

Co-Creation methods applied to customer service problems and process improvement can be a highly effective means to systematically address problems and constraints in the service delivery between two companies. Asking your customer what they need from you elicits one set of responses. Framing the problem and working together towards a solution typically unearths a much deeper set of needs and constraints that gets to the root of the problem and enables much more thoughtful and productive solutions and innovations. 


I illustrate the process with an example: Often, customers complain about a singular thing when they actually have a different overall need. To solve problems in the long run, uncovering root causes that often are not expressed and not obvious requires systematically walking through processes and tasks from multiple perspectives. Starting with the premise that customers’ complaints are the purest form of product development research. These problems, when probed and understood in the co-creation process, can extract the root causes, and be that start of the generation of potential solutions that are the path to iterative improvement and ultimate innovation.

Co-creation engagement with supplier and customer working together to extract and understand root causes is a highly effective approach. Getting expert co-creation help speeds the process, greatly increases the potential for success, and helps build long term competence around sound practice and process. Be it Consulteer or another expert resource, we urge our clients not to go it alone.

As an outside catalyst, we can help companies with the co-creation process, string together a series of successful projects, and develop a long-term capability. Consulteer can help you develop a systematic capability to innovate while delivering outcomes that drive revenue growth going forward. We work at the intersection of business strategy and software development.