In December 2016, a customer of Lemonade Insurance Company hit ‘submit’ on an insurance claim for a $979 Canada Goose coat. Approximately 3 seconds later, Lemonade’s claims bot – AI Jim had reviewed the claim, cross referenced it with the policy, ran 18 anti-fraud algorithms on it, approved the claim, sent wiring instructions to the bank, and informed the customer that the claim was closed.


However, not all chatbot experiences are like the Lemonade experience. Despite the sudden surge in chatbot usage, most of us would have had clunky conversations with chatbots on retail or financial services websites.

These include:

  • A poor user experience e.g. not understanding the dialect
  • Confidence in the product offering dipping
  • Provided us a good reason to go somewhere else that offers a similar product
  • Being redirected ultimately to a call centre, which is not what we wanted to do in the first place
  • So why are chatbots today not so smart?

Many retailers are embracing chatbots for the sake of chatbots – which in most cases is unlikely to yield any meaningful results. Retailers need to integrate chatbots in ways that interest consumers and make their shopping experience more personalized and entertaining to their needs. It’s all about working the goals of a consumer once that interaction happens, which is often not planned effectively with any chatbot integration.

The key issues chatbots battle with today are:

  • They use historical data gathered from your CRM, social feed etc. to attempt personalization
  • They are unable to learn in real-time. This means chatbots cannot follow live requests being made by a customer in a chat
    For most organizations, chatbots are insufficiently trained. They cannot understand things like human expressions of frustration or delight, acronyms etc. and thus end up giving inappropriate reactions to situations
  • Contrary to the perception of an automated service, chatbots need continuous tweaking and monitoring to get the best from business goals, which in most cases change at a rapid pace

Why does this happen?

Often, there is a trade-off between the best technology, speed of integration, & actual consumer POS requirements. Without understanding the core needs of their consumers and what drives their purchasing decisions, many retail and financial services organizations are being shaped ineffectively by the ‘potential’ of messaging apps, nudge technology and chatbots as the future for improved customer retention and churn rate.

A frantic race to full automation and the AI ‘blockchain dream’ is also to blame. Many organizations want to use chatbots to connect with customers in as many ways as possible, without thinking of the financial implications, technological dependencies and consequences. For example – designing sophisticated chatbots that:

  • Give out retail advice and product usage information
  • Award frequent users with discounts and loyalty benefits
  • Assist with shipping and logistics
  • Provide bi-directional marketing, i.e. give out information about the product to the consumer, whilst also simultaneously collecting information for market research through surveys and feedback

Emotions run deep in every human interaction and are unfortunately ignored. Deciphering these human emotions by way of studying voice modulation, facial expressions, text tonality etc. can reveal a wealth of information that can add context to a customer interaction with a chatbot and make the interaction more fulfilling. Only if AI has the capability to empathize with users’ feelings and learn in real-time about what the customer wants can it design the perfect responses, leading to outstanding user experiences.

This means that organizations must-

  • Ensure that chatbots quantify what matters to customers when making a purchase decision, and by how much
  • Ensure that chatbots can determine a customer’s emotional state

How can OSG help?

OSG uses its proprietary trade-off methodology ASEMAP™ to let customers prioritize benefits that are best suited to nudge customer behavior for each customer. By using a combination of cognitive analytics (historical data that looks at the “who” and “what” behind customer decision making) and future looking behavioral analytics (that go beyond traditional analytics to identify the “how” and “why” behind customer decision making), OSG can train your chatbot to learn in real time what matters to your customers. Our big data analytics platform OSG Dynamo™ can use text, tone and voice analysis to understand customer sentiment, helping you to humanize your chatbot during an interaction.

This helps you deliver a superior chatbot experience designed around a customer’s current and future needs. Write to us at to learn more about our products!