![\"Why](\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2022\/11\/Why-Chatbots-Fail-And-How-to-Ensure-Yours-Doesnt.jpg\")
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As part of Solutions Review\u2019s\u00a0Contributed Content Series<\/a>\u2014a collection of articles written by industry thought leaders in maturing software categories\u2014<\/span><\/span>Diego Bartolome, the<\/span><\/span> Chief Innovation Officer for <\/span><\/span>Language I\/O<\/span><\/span><\/a>, outlines how companies can ensure their chatbots don’t fail.<\/span><\/span><\/strong><\/em><\/p>\n Nearly 40 percent of internet users worldwide <\/span>prefer interacting with a chatbot over a live agent<\/span><\/a>. This milestone shows that consumers are willing and happy to resolve issues with a bot. For enterprise leaders, the question is no longer “<\/span>Should I implement a chatbot?<\/span><\/i>” but rather, “<\/span>How can I successfully implement a chatbot?<\/span><\/i>“<\/span>\u00a0<\/span><\/p>\n Many factors can impact a chatbot’s effectiveness, from the associated metrics and Key Performance Indicators (KPIs) being measured to the level of personalized service it offers consumers. Just as an interaction with a testy live agent can leave a sour taste in a consumer’s mouth, a faulty or frustrating chatbot session can ruin the customer care experience. As such, enterprise leaders must focus on a failproof chatbot strategy that prioritizes the customer experience upon implementation and throughout its lifecycle.<\/span>\u00a0<\/span><\/p>\n To understand what makes a chatbot truly successful, let’s first explore four chatbot attributes that often lead to failure.<\/span>\u00a0<\/span><\/p>\n Most chatbots won’t account for all written languages, but successful bots will at least prepare for the internet’s most common languages, <\/span>including Chinese, Spanish, and Arabic<\/span><\/a>. Doing so improves an organization’s relationship with its consumers, as <\/span>more than three-fourths of customers prefer<\/span><\/a> purchasing from sites with services provided in their native language. Organizations with English-only or English-first chatbot strategies will inevitably fall behind in customer loyalty.<\/span>\u00a0<\/span><\/p>\n But AI, the science upon which chatbots are built, is complicated. A recent Gartner survey found that <\/span>81 percent of respondents from large businesses<\/span><\/a> find the process of training AI with data to be more complex than they expected. Now, multiply those complications by the number of languages a chatbot should ideally provide, and you’re left with a convoluted and seemingly never-ending training cycle.<\/span>\u00a0<\/span><\/p>\n In most cases, establishing a multilingual chatbot engine requires ground-up setup and maintenance for each new language. Part of that process involves accounting for “internet speak”\u2014that is, culturally specific shorthand, slang, and typos, which are common across all languages. Various other considerations, <\/span>including message consistency<\/span><\/a>, further complicate establishing a chatbot versed in multiple languages. The trick to making progress is finding a centralized AI that intelligently translates across a wide range of languages and ensures that the key terms are consistently translated.<\/span>\u00a0<\/span><\/p>\n Just because chatbots are predicated on AI doesn’t mean their customer care services should lack the human touch. <\/span>Consumers overwhelmingly crave personalized experiences<\/span><\/a>; this is especially true as e-commerce engines become increasingly popular and more brands enter the saturated B2C and B2B retail space. Brands that use current user information to personalize marketing and sales outreach will ultimately win the customer service game.<\/span>\u00a0<\/span><\/p>\n Most consumers who navigate a chatbot have already interacted with a brand’s website to provide essential information like name and product of interest. Successful chatbots will use the information stored in their Customer Relationship Management (CRM)<\/a><\/strong> platform to personalize the first point of contact with a consumer. Failure to do so may lead to customer frustration or the perception that a brand doesn’t value their loyalty. Ultimately, that perception may translate to a lost sale or customer churn. Worse, it can lead consumers to find alternative brands, which are far more costly in the long run.<\/span>\u00a0<\/span><\/p>\n When deploying a chatbot, enterprise leaders must be on the same page regarding what the chatbot is and isn’t capable of accomplishing.<\/span>\u00a0<\/span><\/p>\n Chatbots come with many benefits, including <\/span>improved customer satisfaction and 24\/7 availability<\/span>. Not only that but having a chatbot in place can redirect inbound customer inquiries away from live agents and resolve those questions or issues without ever needing to involve a brand representative. With this being the case, it’s no surprise IBM reports that <\/span>chatbots can reduce customer service labor costs by up to 30 percent<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n However, brand leaders must understand that chatbots as a solution do not make live agents redundant. While chatbots can predictably curb costs and reduce the volume of inquiries that live agents must address, there will always be complicated or nuanced customer queries that require a delicate human touch to resolve effectively. In cases where a chatbot cannot fully address a problem, handoff to a human representative is required.<\/span>\u00a0<\/span><\/p>\n Good marketers know that no campaign should be launched without a proper measurement strategy to evaluate performance. The same is true for chatbots. Though they may not be human, chatbots are still a living and breathing part of your customer service machine\u2014and, as a result, will require ongoing adjustments and consistent monitoring to be successful. Launching a chatbot without knowing what metrics to base decisions on is a losing strategy.<\/span>\u00a0<\/span><\/p>\n Generally, brands should track chat volume, average conversation duration, and self-service resolution rate. These metrics determine the usefulness and strength of existing chatbots. Other metrics of interest include chat abandonment rates, which might reveal whether a chatbot is truly resolving issues or simply frustrating consumers, and ticket volume, which should decrease over time if chatbots successfully solve common customer problems. <\/span>\u00a0<\/span><\/p>\n Chatbot success metrics should align with broader objectives, such as agent satisfaction. As chatbot success increases, agents should have more time to tackle fulfilling mission-critical tasks, like improving the employee experience. This is another proof-positive for chatbots, which provide an incredibly rewarding ROI for customers and employees\u2014if, and only if, they’re set up for success.<\/span>\u00a0<\/span><\/p>\n Savvy enterprise leaders should take note of these common chatbot pain points. By understanding what makes a chatbot fail, leaders can navigate how to create a more successful chatbot\u2014that is, one that places customer experience at the heart of its tech stack. First, leaders should tap into evolving tech like advanced CRMs and chatbot language engines to facilitate a healthier consumer-bot relationship. Just as important, they must prioritize a successful chatbot campaign by establishing expectations early and tracking success with proven metrics.<\/span>\u00a0<\/span><\/p>\n And time is of the essence. According to Juniper Research, consumer retail spending conducted via chatbots <\/span>will reach $142 billion by 2024<\/span><\/a>. That’s compared to just $2.8 billion in 2019. During this exponential growth in chatbot-led revenue, it’s critical that enterprise leaders focus not only on deploying a chatbot but doing so successfully\u2014and soon.<\/span><\/p>\nMistake 1: English is the only language with proper support<\/span><\/b>\u00a0<\/span><\/h3>\n
Mistake 2: The consumer experience isn’t personalized<\/span><\/b>\u00a0<\/span><\/h3>\n
Mistake 3: Administrators have unrealistic expectations<\/span><\/b>\u00a0<\/span><\/h3>\n
Mistake 4: Correct performance metrics haven’t been established and monitored<\/span><\/b>\u00a0<\/span><\/h3>\n
What Makes a Successful Chatbot?<\/span><\/b>\u00a0<\/span><\/h3>\n
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![](\"https:\/\/solutionsreview.com\/marketing-automation\/files\/2019\/02\/marketing-automation-bg-speedbump.jpg\" "\\"Marketing")
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