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How To Create A Guided Conversation Chatbot Framework

Online and traditional retail are similar in practice, but they can often yield very different results. The in-store customer service that defines traditional retail is often lacking online as shoppers roam idly from page to page in search of information to help them make informed decisions and purchase the right products.

Published by James Rice Mcaulay23.04.2020

This often causes a lag on online store conversion rates. If the sites are not optimized effectively, people may not find the information they need and may opt out of making a purchase. You try to reskin the site, but the results are still unsatisfactory. What do you do?

Brick and mortar stores improve their conversion rates with the help of in-store shopping assistants. Online retailers can take a step in this direction using conversion rate optimization technology that recreates the interactive in-store experience and can boost conversion rates up to 12%.

Chatbot conversational agent and dialogue systems

Many websites are incorporating conversational commerce into their business models in order to boost conversions. By leveraging machine learning technology, businesses provide the types of interactions that customers require and also create strategic business opportunities that increase potential for growth.

Examples of effective conversational commerce technology include virtual shopping assistants and chatbots. Customers benefit from virtual shopping assistants by receiving the same types of product recommendations that in-store reps provide, enabling them to make more informed decisions and create their own paths to the point of conversion.

Similarly, chatbots allow companies to automate customer support. They can use the chatbot to manage frequently asked questions from new shoppers and provide insightful responses that are learned by the chatbot through real interactions with similar shoppers. This allows the company to streamline customer service and allocate more human resources to manage the types of services that are most likely to generate high growth for the brand.

How to build a conversational chatbot

Building a conversational chatbot typically involves these steps:

  1. Collect data on customer shopping habits (via a survey or through analytics)
  2. Analyze the most recurring questions asked by shoppers
  3. Conduct an internal survey to gain more insights from your customer support team
  4. Create a template of responses to the most common questions
  5. Program those responses into an AI-powered chatbot
  6. Deploy a test run of the chatbot across select pages of the website
  7. Analyze the engagement with the chatbot from a sample of participants
  8. Optimize the chatbot based on those test results and deploy an updated version
  9. Monitor the performance of the chatbot and analyze the impact on conversions
  10. Repeat any optimization and analysis as needed to build a scalable program

Chatbots and conversational artificial intelligence aren’t the future of online shopping - they’re already here

Implementing machine learning chatbots and conversational AI will help drive more opportunities to generate conversions and increase your total sales. They’ve been used in practice to enhance the scope and scale of food delivery service or even manage customer inquiries or concerns for airlines.

Using chatbots and conversational AI platforms puts you ahead of your competitors and allows you to build or expand upon customer loyalty. Leveraging relatively new technologies like chatbots and conversational AI helps you grow your business faster by increasing your customers’ lifetime value and streamlining your business processes, giving you more space for scale and sustainability.

Boost sales and improve service with conversational commerce

Our user-friendly conversational commerce platform and powerful conversion tools transform any encounter, using any channel and at any stage of the buying journey into a first-class experience to enhance value and increase customer loyalty and brand affinity.