Engaging with customers and securing payments is a challenge the collections team face every day using outbound dialling, freephone inbound numbers and e-mail.
Whilst the business outcomes were being achieved that just wasn't good enough as customers expected and demanded more. Customers are immersing themselves in the world of mobile and digital engagement with up to 70% of sales coming from mobile devices. The collections team decided to adapt their engagement strategies to maximise the use of mobile engagement technology.
Deploying Conversational Messaging
Using Webio's conversational messaging platform, the initial focus was on 'Intelligent Digital Conversations with SMS' that blends chatbots and human agents to allow customers engage at their pace, without the pressures of a voice call or the awfully slow pace of e-mail engagement.
Set up by the internal team at Very, SMS chatbots are deployed to initiate millions of digital conversations and manage any cold inbound message seamlessly.
Key to this process was determining whether that next best step is to let the chatbots continue the engagement or identify whether to bring the live agent team into the SMS conversation, only if needed. Within three months of going live 40% of customer conversations were completed with fully automated bots, allowing the team to work smarter and focus live agents on conversations where they can add value.
Guiding Customer Conversations
Using Webio’s Propensity-X machine learning engine conversations outcomes are predicted and the conversation flow designed to move customers to the next best action step that will deliver on customer requirements and guide the conversation closer to desired business outcome.
The Webio platform anonymised all customer conversations and is continually learning. It improves its models to ensure that all future conversations are guided to the best outcome, every time. Also looking for inaccuracies, patterns, words, and phrases that will assist in managing conversations better each time the customer engages, for example, identifying vulnerability or a pattern of not telling the truth.
Using this intelligence enabled these customers to be seamlessly transferred to specialist agents seamlessly and allowing customers to receive the additional support they needed.
"This has been revolutionary for us as it has allowed us to better understand our customers and appreciate the rationale behind why some of them have been unable to contact us the way we’ve been engaging with them in the past"
Senior Head of Credit Risk