Why Companies Should Invest in Chatbots in 2020

‍Many brands are already utilizing chatbot to experience advancement and benefits out of it. Failure to adopt this technology in your business can lead you to lag behind the competitive world. Hence, it’s time to catch up advanced technology as soon as possible.

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Chatbot Trends You Need to Know Before Investing

These statistics confirm that chatbots represent the new generation in tech. No more waiting for the right time to incorporate them into your business. The time has come.

1. According to Business Insider 80% of businesses want chatbots by 2020.  Business Insider
2. 71% millennials are hoping to interact with their favorite brands through chatbots. chatbotslife.com
3. Chatbots will power 85% of all customer service interactions by the year 2020. Gartner
4. 47% of respondents are open to buying items using a chatbot.  HubSpot
5. 57% of respondents are interested in having real-time answers from bots on a company website. HubSpot
6. Facebook has more than 100,000 bots on the messenger platform, which was 33,000 in September,2017.  David Marcus, VP, Messaging Products, Facebook
7. The new research, Chatbots: Retail, eCommerce, Banking & Healthcare 2017-2022, forecasts that chatbots will be responsible for cost savings of over $8 billion/annum by 2022, up from $20 million this year. Juniper Research
8. $4.5 Billion is expected to be invested in enterprise intelligent assistants by 2021. Opus Research
9. By 2020, the revenue by Amazon’s Echo and Alexa is expected to be beyond $11 billion. Mizuho International
10. Artificial Intelligence is expected to power 95% of the customer interactions By 2025. AI Business
11. Chatbots could save $174 Billion across Insurance, Financial Services, Sales, and Customer Service- BI Intelligence
12. ‍By 2021, Research reports believes that chatbots can save companies human wages cost of $262.7 billion. Handgards University

What Type of a ChatBot Would You Need? 

Broadly speaking, there are just two kinds of ChatBots – one that utilizes Machine learning and one that doesn’t. Clearly, the Chatbots powered by machine learning are the smarter ones. The ones that have the ability to learn from human conversations and get better each time. 

However, not all businesses would need a Chatbot that can make a 20 minute conversation or make a restaurant reservation for their customers. Different business operations and processes call for different levels of AI to be consumed by their ChatBot. 

Basic Level ChatBot 

The ability to interact with your customers directly without even hiring a sales-person is priceless! Even in the real world, when customers receive personal attention and feel cared-about, they tend to shop more. 

You could provide them with simple information like your store locations, directions, operating hours, product information, order status, etc. These functions are simple and need only a basic ChatBot which can be fed with information about your operations and processes like your products info, contact info, etc. Basic-level ChatBots can answer simple and repetitive enquiries based on the knowledge-base provided while your executives can focus better on other processes. They do not possess any AI or NLP (Natural Language Processing) abilities. 

High-level Transaction ChatBot

In e-commerce stores, you can serve your customers better by providing products based on their preferences, making fashion choices and also allow them to make purchases without leaving the ChatBot messenger. Transaction-level Chatbots can also guide customers based on their past purchases and make transactions automatically. Or book a cab based on their last interaction.

More intelligent of the lot can provide seamless customer experience by analysing previous transactional data and providing useful information like their total travel expenses or shopping expenses. 

Intelligent Transaction ChatBot

These ChatBots perform similar functions as the High-level ChatBots, except that it is more intelligent. Based on data that it accesses from enterprise systems (CRMs), it can provide very specific and personalized assistance to your customers. These bots are highly preferred in sales and marketing as they help in converting a lead into a customer by automating the entire process. It can also carry context from one channel to another.

Case Studies of Chatbot

1. H&M: The Official H&M Chatbot

Company Description: H&M is a global fashion company that promote sustainable materials and human labor

How it’s being used:

The purpose of H&M’s chatbot is to help mobile customers navigate their search through outfit possibilities and guide you to the online store areas that align with your purchase desires.

H&M’s chatbot leverages the following information and responds differently based on provided information:

• Defines your gender and style
• Suggests outfits and the total price for all items
• If you dislike the suggested outfit, the chatbot will select a different outfit
• If you like the outfit, the chat provides some options: shop – direct link to the H&M internet shop; save – archive your outfit; share – via social networks, email, etc.; next outfit –  provides a new outfit suggestion
  

Value proposition:

H&M’s consistent increased sales over the past year and its August announcement to launch an eCommerce presence in Canada and South Korea during the fall of 2016, along with 11 new H&M online markets (for a total of 35 markets by the end of the year), appear to signify positive results for its chatbot implementation (though direct correlations are unavailable on its website).

Key takeaways:

How can our business leverage technology to better and more often engage younger audiences with our products and services? H&M is one of several retailers experimenting with and leveraging chatbots as a  mobile marketing opportunity – according to a report by Accenture, 32 percent of the world (a large portion of the population 29 years old and younger) uses social media daily and 80 percent of that time is via mobile.

‍2. Amtrak: “Julie” 

Company description: The National Railroad Passenger Corporation (Amtrak) provides rail passenger services for customers in the 48 contiguous U.S. states.

How it’s being used:

• Julie, a newer version of Amtrak’s original telephone-based customer service agent, is designed to guide users through Amtrak.com using natural language capabilities and a broad knowledge-base of the site
• In addition to responding through text, Julie can vocalize “her” answer alongside a written response
• Julie can provide or help customers find information on making a reservation, getting more information on Amtrak’s rewards program, finding station and route information, and a variety of other areas

Value proposition:

According to nextIT (Julie’s product platform), implementation of Julie resulted in the following:

• 25 percent more bookings
• $1 million in customer service email costs saved annually
• 50 percent year-over-year growth in users’ engagement with Julie
• 30 percent more revenue (monthly average) generated per booking

Key takeaways:

• Is there a way to evolve or augment existing technology to make it more engaging and useful for customers? Instead of ditching Julie the telephone rep, Amtrak expanded “her” presence by implementing a more responsive chatbot.

• Do the nature of our services and size of our customer base warrant an investment in a more efficient and automated customer service response? How can we offer a more streamlined experience without (necessarily) increasing costly human resources?  Amtrak’s website receives over 375,000 daily visitors, and they wanted a solution that provided users with instant access to online self-service.

Conclusion

With the advancement of technology, everything can now be automated. Even communicating with people. ChatBots are getting more sophisticated over time, making them more popular with large enterprises and SMBs. They’re definitely here to stay as they provide excellent customer engagement, save time and money and improve operations efficiency. Nevertheless, you cannot ignore the fact that there needs to be a balance between fully-automated personalization and human touch. Plain and simply put, a bot can never outsmart a human nor can be compared to one. A well-struck balance between the two can work wonders for your brand.