Why you may need an external chatbot provider even with an engineering team

If you have an internal engineering team, why do you need an external chatbot provider? Here are some things to help you make a decision

Many of the businesses that we work with on chatbots already had internal hackathon versions of a chatbot. While it’s easy to get a basic chatbot up, it can require lots of expertise to be able to scale and improve the chatbot to the level where it can be seriously useful, rather than gimmicky.

Your initial chatbot concept

  • Usually, your team has a good idea what kind of purpose your chatbot can serve. As the people who work with your technology and your product day-to-day, they can have some creative ideas around what to build. (See 7 great ways to add a chatbot to your business if you’re looking for more information on use cases.)
  • A quick internal mockup or build of what the chatbot could look like for your business will help a lot in visualizing and understanding the boundaries of its functions.
  • A lightweight build can be tested with a few internal or external users to show interest and viability, without worrying too much about how to manage large numbers of users pinging the chatbot’s servers.

Where an experienced chatbot provider can add real value to you (and your bot)

Since you have a good idea of what you want to build, do you still need an external chatbot provider? These are some of the ways that a specialized chatbot provider can help you:

  • Strategic overview: Your chatbot provider should put a clear emphasis on your strategy. Mercenaries who are just building a chatbot to make some money, and without the expertise listed above, will not ask many questions about what your business is and how, or even if at all, a chatbot can help you reach the objectives that you want to hit for the year. A good chatbot provider will also advise you on the best way your chatbot can serve these objectives, based on their knowledge of chatbots’ advantages and limitations.
  • Dialog writing: Writing chat dialogs can be challenging, because it needs to sound conversational while being informative. Experienced providers should have a repertoire of examples that help them to craft the right tone and message for your intended target audience, backed by previous experiences.
  • Machine learning: Since a chatbot can be one of the mainstays in your artificial intelligence strategy, machine learning needs to be incorporated by your chatbot provider in order to build a self-learning, self-improving chatbot over time. This is important because you want your investment to go a longer way, and so that it can provide for your customers even better in the future.
  • Natural language processing: Robust NLP is essential to a good chatbot. Since understanding users, answering questions, sensing frustration, and other vital functions are all underpinned by NLP capabilities, your chatbot provider should have some experience with natural language processing in order for you to be confident in your handing over your customer experience to them.
  • Detailed chat analytics: When you implement your chatbot, you also want to keep a close eye on its performance. Very likely, a lot of these KPIs set for your chatbot may not necessarily be captured by structured data like clicks and views like a website would, but rather the quality of the conversations and whether there are knowledge gaps in the chatbot. Hence, your chatbot provider should use some of their expertise in ML/NLP to analyze topics, performance and future optimizations for you to be able to improve the chatbot.
  • Ongoing scaling and maintenance: As your chatbot scales up to handle more users, it will require some time to manage, maintain and scale the infrastructure and modules running the chatbot. This will take some engineering time, and the time taken may not be something that your business can support if your engineers are tied up with building your core products or new features. For some businesses, your developers may also not focus on the scaling or infrastructure that the chatbot will require, while the chatbot provider will likely have enough scale and experience to make sure everything runs well.
  • Managed support: Related to the point above, sometimes you want to add or change features to the chatbot, but may not want to spend the time doing it — especially for complex features that will take more resources to do. In this case, support from your chatbot provider as part of your managed service can help you as your business needs evolve.

Finally, you may have engineering resources on hand, but may want to save them to work on the core product functions that require more of their expertise. In this way, a chatbot provider with the right expertise combined with a good handle on your engineering practices and business strategy, will well-placed to ease the load off your team while still creating a best-in-class chatbot experience.

When deciding, always think about the tradeoffs in the “Make vs. Buy” decision — Making may save some upfront costs, but will put a strain on your core product team; Buying may bring in specialized expertise, but might not fit into your business lifecycle or budget. Whatever you choose, always emphasize quality, and be sure to ask as many questions as you need to to be sure of your decision.

Artificial Intelligence Bots Chatbots Outsourcing Strategy

Keyreply

KeyReply Blog 🤖

Stories about chatbots, digests and other superpowers for your team