21 Jun
5 Reasons For Enterprise Chatbot Failure and How to avoid them
A Forrester Report declared that “the majority of chatbots were poorly- implemented, systematically ruining customer experiences and – in the case of the worst incarnations – nothing more than “virtual idiots.”
Cut to today – and chatbots have become a key element of the digital transformation initiatives that businesses have undertaken globally. While the pandemic has undoubtedly had a disruptive impact on many social, economic, and financial aspects, it has been a significant catalyst in the chatbot’s “return to life.”
As consumers were forced to move to online modes of shopping, their digital footprint increased exponentially, with a huge rise in the use of digital channels, including social media messaging, email, and chatbots. Chatbots help customers solve queries faster, at scale, anytime, and anywhere.
Moreover, remote and distributed workforces are here to stay, and by implementing chatbots, organizations can simplify knowledge management, automate repetitive tasks, and free employees to focus on strategic, revenue-generating activities or on customers that demand more attention.
However, even now, achieving full-scale implementation and adoption is difficult, with many chatbot projects stuck in the pilot mode or hesitant to move to large-scale deployment.
So, why do chatbots fail? Most importantly, what can enterprises do to avoid such failures?
5 Reasons for Enterprise Chatbot Failure
1. Trying to achieve too much at the onset
Organizations often try to do much with chatbots right at the beginning, which leads to failure at the pilot stage. At the end of the day, it’s all about the value that the chatbot provides. Therefore, it’s best to limit the bot to a narrow set of use cases that serve customers and employees well on the get-go, and then gain momentum, as needed.
2. Lack of alignment on success metrics
There could also be a misalignment on the success metrics for a chatbot. That is, organizations may not be able to define the right set of KPIs that decide how effective the bot is. For example, an organization builds a chatbot mainly for the purpose of answering customers’ FAQs.
In such a scenario, measuring the success of the pilot run by “how funny and natural sounding” the bot is doesn’t serve the purpose of the business.
Moreover, organizations must approach chatbots as a long-term investment that requires a dedicated team to continuously monitor trial results and improve performance over time.
3. Lack of involvement from business users
Failure to manage change effectively is another reason why chatbots don’t move past the pilot stage. As humans, it’s harder to alter habits and adapt to new tools.
While the end-users may be drawn in by the hype of a chatbot release, they may soon revert to their old methods of communication. Moreover, technology teams in-charge of the chatbot may move on to another project without investing the time and effort required in improving the bot’s content.
4. Lack of specificity
Customers and employees are looking for quick and specific answers when they get in touch with a chatbot. Sometimes, chatbots bombard the user with web pages and FAQ documents, instead of providing the exact resolution.
When chatbots aren’t designed well to solve the user’s queries, it lowers their success rate in the pilot stage, deterring organizations from implementing them on a large-scale in the future.
4. Lack of specificity
Based on customer mood, transaction value, or its inability to solve the user’s issues, the chatbot must be capable of transferring the conversation to a human agent with the full contextual elements.
Moreover, queries requiring multi-step resolutions must be resolved seamlessly without the customer having to repeat information or steps already completed.
However, chatbots that fail to do so provide little utility to the user and may be shelved in the pilot stage.
Tips to avoid chatbot failure
Scaling a chatbot is less about AI or conversational design than it is about information architecture, knowledge management (KM), and organizational alignment. Firms must radically simplify how they deploy and support bots instead of overengineering them.
So, here are a few tips to avoid enterprise chatbot failure –
- Think about the goal of the chatbot from the user’s perspective and the objectives of the business. For example, do you only want your bot to respond quickly? Or, is the quality of response and user satisfaction more important?
- Put the right people in charge of deployment, with the right budget, and the right chatbot KPIs in mind.
- Ensure the quantity and quality of data that feeds the bot is optimum. Keep collecting more data, but don’t over-complicate the process with an overabundance of data from siloed systems.
- Pick the right vendors to ensure the chatbots are well-designed to understand user intent and the context of the conversation, respond accurately, and escalate the chat to human agents when needed.
- Demonstrate the benefits of the chatbot to help employees embrace the change. For example, let them know that by automating a certain percentage of customer interactions, they can spend “X” amount of time on the more challenging tasks.
How can Acuvate help?
At Acuvate, we help clients build and deploy AI-enabled chatbots with our enterprise bot-building platform called BotCore.
With its low-code, graphical interface and visual tools, organizations can deploy omnichannel, multilingual chatbots within a few weeks.
As Microsoft Gold Partners, we use the best of Microsoft Technologies, including artificial intelligence, machine learning, and natural language processing (for example, Microsoft Bot Framework, LUIS, Azure Cognitive Services, etc.). We also help clients implement chatbots using the Microsoft Power Virtual Agents platform.
To know more about BotCore and other services, please feel free to schedule a personalized consultation with our chatbot experts.
Abhishek is the AI & Automation Practice Head at Acuvate and brings with him 17+ years of strong expertise across the Microsoft stack. He has consulted with clients globally to provide solutions on technologies such as Cognitive Services, Azure, RPA, SharePoint & Office 365. He has worked with clients across multiple industry domains including Retail & FMCG, Government, BFSI, Manufacturing and Telecom.
Abhishek Shanbhag