Chatbots work phenomenally great as support in call centers. Call center requests involving straightforward tasks that don’t require critical thinking and are handled by chatbots, reducing the load on actual humans working there. An NLP chatbot can handle requests such as fetching utility bills, changing passwords, or lodging a complaint (utility services, entertainment, etc.).
Back-end process support can easily handled by chatbots. Things such as inventory management or handling CRM come within the scope of a chatbot’s capabilities(real estate industry).
The best of all, chatbots work as digital personal assistants that help customers speedily complete purchases (groceries, apparel apps)
How to create an NLP chatbot
Today, almost everything can be bought off the shelf. If you think that this isn’t possible for chatbots, you are wrong. Kompose offers ready code packages that you can employ to create chatbots in a simple, 1-2-3 step methodology. If you know how to use programming, you can create a chatbot from scratch. If not, you can use templates to start usa student data as a base and build from there.
Top 10 NLP chatbot platforms
Here is a comprehensive list of the top 10 NLP chatbot platforms:
Platform Features Price
Lobster Advanced conversational AI 1500 pounds per month
Drift AI, revenue acceleration Free
Kommunicate Custom bots, codeless bot integration $40+p/m
Messenger People Engage community, manage digital assets $25-$250+ p/m
Agentbot TPI, Multi-language predictive analytics $240
Botscrew Code-free development, omnichannel $600+
Rapiwha Scheduling, Instagram analytics $0-$49+ p/m
Botsociety Reporting, publishing, engagement $99-$249 p/m
Chatfuel NLP, designed for Facebook $300p/m
MobileMonkey Lead generation, drip campaigns $49-$149p/m
Aivo Scalable, real-time, text and voice $240 p/m
Challenges of NLP
Every innovation faces hiccups as it is developing. NLP merging with chatbots is a very lucrative and business-friendly idea, but it does carry some inherent problems that should address to perfect the technology. The first challenge that NLP faces is the problem of homonyms. Inaccuracies in the end result due to homonyms, accented speech, colloquial, vernacular, and slang terms are nearly impossible for a computer to decipher.
Additionally, while all the sentimental analytics are in place, NLP cannot deal with sarcasm, humour, or irony. Jargon also poses a big problem to NLP – seeing how people from different industries tend to use very different vocabulary.
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