The Fundamentals of a Successful Chatbot Strategy

By 5 Minute Read

chatbot strategyBefore diving into business chatbot strategy let’s start with a look at the evolution of chatbots.

How AI bots have emerged and are maturing gives me flashbacks to the early days of mobile apps, as businesses started to look to use mobile as a new means of engaging with customers and employees. The hype and promises gave both consumers and businesses reason for excitement and anticipation, opening up a whole new world of opportunity for how businesses would engage and conduct business with their customers.

Remember the early mobile apps that businesses and brands launched? They were pretty basic, more informational than transactional. Fast forward a few years. By integrating with internal business systems and third-party software, mobile apps became much more transactional, enabling users to reserve appointments, book travel, notify customers of events and alerts, and deliver contextual information and experiences that allowed us unprecedented opportunities to transact and interact on the go. But this didn’t happen overnight or by chance.

Emerging technologies, that often seem simple to use and consumer-friendly, such as mobile apps then and chatbots now, can pose many unforeseen challenges as businesses adopt them. A chatbot implementation strategy, no more than a company’s mobile strategy, will help guide your chatbot success.

The Hype and The Reality of Chatbot Implementations

Although the first chatbot, ELIZA, was conceived in 1966, the true power of chatbots has only recently become a reality as advancements and access to natural language processing (NLP) technologies and the ubiquitous nature of mobile devices and messaging apps have opened up a whole slew of business opportunities that put them at the center of digital strategy.

Chatbot deployments have been wildly successful but also have failed miserably for many businesses. Some companies jumped in too quickly without considering some of the longer-term implications of having a poor bot experience. Some under-estimated how easy it actually is to implement bots that can respond successfully to a customer and execute the necessary business tasks, without having to immediately hand over to a human agent. And for others, it’s been a combination of factors that have led to bots not meeting the desired expectations.

When the pandemic hit in 2020, many businesses had to quickly pivot to digital business models and accelerate their digital transformation initiatives. Many organizations had already deployed a chatbot, others were still in the planning stage of the conversational AI journey. The crisis created unprecedented urgency in implementing digital assistants that could fully or partially replace the need for human workers to handle routine and repetitive interactions and tasks, allowing the human role to be focused on resolving more complicated customer or employee issues. The strategic role that chatbots could play in automation and digital engagement placed them firmly in the spotlight. 

Some Considerations for Your Chatbot Strategy

Here are some perspectives on how you can avoid some of the common pitfalls in chatbot implementation strategy and strive for success. Think of your chatbot strategy as a living plan that is not set in stone. It doesn’t have to be a lengthy or tedious exercise but these tips will help pave the way.

  1. Don’t Boil the Ocean: Like all new technologies, starting small is often the best approach. It can be tempting to yield to pressure from business leadership and try to bring multiple bot projects to market as quickly as possible. However, by picking a manageable chatbot use case and rolling it out to a small customer- or user- base before doing a broader rollout, kinks can be ironed out and poor experiences can be averted. The learnings gained in starting small usually open up new ideas for subsequent use cases, for broader reach, a better chatbot architecture, or for additional features.
  2. Prioritize the Use Cases: Since all chatbots are not created equal, it can be helpful to think of them in terms of different skills. Each industry can have unique needs for deploying digital assistants or bots and then each business has its own unique priorities and objectives. An industry-specific example of use cases is that of AI insurance bots that can be deployed at different stages of the customer lifecycle, assisting customers with online quotations, onboarding new policyholders, helping them file a claim, or renewing their coverage. There are also use cases that are more generic and applicable across multiple industries, for example, AI customer service bots that can be as simple as an FAQ bot or involve a more complex customer journey, like customer onboarding. Identifying the use cases across departments and prioritizing these in terms of the business value and complexity of development, helps tie bot investments to business strategy and key performance indicators (KPIs). Your KPIs or bot metrics will help you track chatbot success and set you up for continuous improvements.
  3. Think Transformation: Chatbots have the potential to change the way your business engages with customers so when considering the use cases for bots, don’t just think of a bot as something that replaces a human agent and works 24/7. Conversational AI enables a whole new model for engaging with your customers through fluid and frictionless conversations, in contrast to interacting via guided clicks, swipes, and forms. So, rather than having chatbots emulate your current workflows, consider how the power of conversation can eliminate or automate some process tasks, remove friction, and transform how you meet your customers’ needs.
  4. Prototype and Iterate: Proof of Concept (PoC) is often a preferred route for organizations to prototype a chatbot idea before investing in a full-blown project. This allows the business to experiment and agree on features, design, and technology, gathering feedback from early users and stakeholders to improve the bot experience. Remember that your bots are also your brand ambassadors so careful consideration to how they properly reflect your brand is an important part of early design and prototyping phases.
  5. Communicate Early and Often: As with any new wave of technology that has the power to replace humans and automate processes, the fear factor around AI is palpable. Gaining buy-in and understanding for chatbot projects from your employees, especially those that will be directly affected, is critical to company culture. Communicating the bot strategy and even including frontline employees in decisions around bot deployments, helps avert unrest and assure staff of their continued role. AI in the workplace is already a reality. According to Gartner’s Future of Work, the good news is that “few jobs will be replaced by AI, but almost all jobs will experience some automation or augmentation”. The best business leaders understand that the workforce will continue to be their greatest asset and like any game-changing technology, the manner in which organizations implement AI will set them apart.
  6. Don’t Forget About the Data (And How to Secure it!): One of the many downfalls of early chatbot deployments points to them being too simple to be effective. Unless a chatbot can execute the necessary business tasks, it will quickly fail or need to hand over to a human agent. And executing a business process requires rules and data, necessitating integration, security, and governance. As customers chat via messaging or voice, they may not provide, or even have, all the information needed for a chatbot to fulfill their need, requiring the bot to access data via an appropriate API. For example, if a customer asks via SMS chat to change their flight, the bot can retrieve the data needed to do this, filling in the information gaps such as PNR, payment details, itinerary, flight schedule, etc. Needless to say, security becomes paramount as customer data moves from the business system to the customer so authentication, data isolation, governance, and control become important. This mirrors the path of mobile apps as they evolved in the enterprise and backend-as-a-service (BaaS) technology emerged to enable apps to integrate securely with backend systems to deliver more meaningful experiences.
  7. Choose the Right Technology: When planning chatbot implementations, consider how they may need to scale and extend their reach beyond the initial use case or audience. Underlying infrastructure and architecture decisions, while not the skillset or priority for lines of business, can often make or break the success of any software project, chatbots included. This is where choosing an enterprise conversational AI platform can really matter in terms of speed to market, performance, reusability, scalability, and value.  Read more about how a conversational AI platform works to support successful chatbot implementations. 

Chatbots are here to Stay and Grow!

There’s no doubt that conversational AI technology is a hot topic for today’s enterprise. While the pandemic may have accelerated its adoption, there is no going back. Crafting and implementing an enterprise chatbot strategy doesn’t have to be a long or tedious exercise and ServisBOT is here to help support you.

If you need more clarification about Conversational AI and chatbots, read our Conversational AI vs Chatbot post.

If you’d like to learn more about how to progress your conversational AI journey and maturity please check out our new Guide for a Successful Conversational AI Journey.

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