AI, Chatbots

The Fundamentals of a Successful Chatbot Strategy

By 6 Minute Read

Before diving into 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.

Just as we become familiar with what a chatbot is, market reports inform us that some chatbots are not meeting expectations.

This brings me back 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 strategy, no more than a company’s mobile strategy, will help ensure that chatbot deployments align with overall business strategy and governance requirements.

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.

Although chatbots have only really started to be implemented in earnest by businesses, their potential to transform engagement models and drive business value was highly touted in headlines and predictions such as from Oracle’s survey in 2016 that cited “80% of businesses want chatbots by 2020”. There is no doubt about the hype and the promises of chatbot technology. But, as with the early days of mobile app development, how easy is it to adopt chatbots in the enterprise and create superior experiences?

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.

Some Considerations for Your Chatbot Strategy

Here are some perspectives, based on my experience in enterprise mobility and our current experience with ServisBOT, on how you can avoid some of the common pitfalls and take a planned approach to initiating your bot deployments.

  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 bots to market as quickly as possible. However, by picking a manageable AI 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, or for additional features.
  2. Prioritize the Use Cases: Since bots are not created equal, it can be helpful to think of them in terms of different use cases. 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 Insurance AI 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 AI bot use cases that are more generic and applicable across multiple industries, for example, AI customer service bots where the AI use cases can be as simple as FAQ bots or involve more complex customer journeys, like customer onboarding or application processing. 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 success, see how results improve over time and bring learnings for additional bot projects.
  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 and bring them through a memorable brand experience.
  4. Prototype and Iterate: Proof of Concept (PoC) is often a preferred route for organizations to prototype a bot idea and use case before investing in a full-blown project. This allows the business to experiment and agree 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. Gartner predicts a transition period to 2020, during which AI will start to eliminate 1.8million jobs, while creating 2.3 million. 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 APIs, security, governance, and synchronization. As customers chat via messaging or voice, they may not provide, or even have, all the information needed for the bot 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, bots included. This is where choosing the right platform and technology approaches 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. 

Conversational AI: Chatbots All Grown Up!

There’s no doubt that conversational interfaces are heating up and brands are paying close attention. According to a recent Gartner survey, only 4% of enterprises have deployed conversational interfaces but 38% are planning to, or actively experimenting. Meanwhile, voice search is skyrocketing with Comscore predicting that 50% of searches will be voice searches by 2020. 

As this happens, chatbots need to become smarter to truly add value. In ways, this movement is being recognized by the emergence of the term ‘conversational AI’. Conversational AI represents one of the most significant shifts towards using natural language to do things like transact, book things, search items, interact, and access services, without the need for a human agent. It goes beyond just the conversation to orchestrate and automate underlying processes and tasks needed to execute on the customer’s need. As such, deploying chatbots that can deliver really great experiences is not an easy task.

Contact us at ServisBOT to learn more about how we can help you support a successful chatbot strategy

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