When you think of a chatbot you probably think of an experience you might have had in your consumer life, be that talking to your bank, ordering an item or conversing with a company's customer services team. Up until now, in a B2B setting, these same low-level use cases existed. However, this is starting to change as opportunities emerge across enterprise businesses to provide new ways to offer products and services through chatbots to new and existing customers.

But why now? The pandemic created a paradigm shift in the usage of chat in the workplace, whether that was Teams, Symphony or Slack, all experienced huge growth. As a result of this ubiquity, there is a demand to move beyond standard 'chat' and utilise bots for more and more everyday tasks. Through the rest of this blog, we will explore how they're being used and why they matter for helping acquire customers and bringing new products to market faster.

What is a data on-demand chatbot?

In its simplest terms, it's a way for a person to talk to a machine in a human-friendly way. It provides a more intuitive interface, most people have encountered chat apps and know how to interact with them. However, chatbots need to support everyday tasks and workflows to add value in a business setting.

At ipushpull, we enable 'smart' chatbots by connecting your data to a bot and allowing the user the ability to investigate the data using simple chat queries. We refer to these as data on-demand chatbots, as the name suggests, users can access your data as and when they need it. In financial markets there are many examples where accessing data on-demand is essential, be that reference data or corporate actions, OTC market data (fixed income, credit or derivatives), ETF, index or crypto data. 

Why use data on-demand chatbots?

Data on-demand chatbots provide a way to increase the distribution of your data by making it more accessible to end users while unlocking new ways to monetise your data through ipushpull's granular access controls and permissioning. 

Providing your data on-demand increases the distribution and accessibility of data. The benefit to your customers and clients is that your data can be integrated into their workflow without requiring any development, integration or onboarding.

We recently caught up with Rob Friend, Product Manager at Symphony, who explains the use cases they're seeing:

In addition, there are more use cases emerging for external chatbots, for things like sending research to a client, issue resolution or decision trees for customer services. 

The added benefit of using a bot platform such as Symphony or Teams is that the authentication and encryption already exist, users don't have to go to a website or log in to another application, and chat remains ever present on the desktop or mobile device. This massively reduces friction to user adoption. This is also important when screen real estate is at a premium.

Scale services and monetise more of your data 

Regardless of your use case, ipushpull offers a way to scale your services and enable you to monetise more of your data. For example, historically, reference data and corporate actions data were delivered as a flat file once a day via SFTP. This delivery method has not changed in decades, however, the world has moved on and the demand for timely intraday updates is now the norm. Using ipushpull it's now possible to offer your data on-demand direct to end users. 

Simon Coughlan, Technology Director at FOW, shared that introducing their chatbot has meant they can now serve up all of their market data (ISINs and other regulatory data as well as pricing information) in near real-time. This has meant they can now support new use cases such as trade breaks.

Providing your data in new ways to more users across more channels grows usage and therefore demand for your data, allowing you to realise new revenue streams impossible previously. Watch the demo of FOW's reference data bot on Symphony.

Where is it heading next?

There's an interesting convergence happening between traditional channels such as voice and email and new ways of working with bots (chat) and increasingly AI and NLP. These technologies will mesh and work together more seamlessly allowing for more intelligent bots that can reference data wherever it's held. The ultimate goal is to reduce friction and re-keying for users and make workflows more automated.

If you're interested in learning more in this space, why not listen to the full podcast with Simon and Rob or head over to our chatbot reference data solutions page.