Helm, Africa’s leading customer experience innovation experts, has done it again with DStv Assist, a bot-based solution that has changed the self-service game.
Since its launch, the chatbot has sent and received over 1.4 billion messages, and currently helps around 1.2 million DStv customers solve their own account queries every month.
While some businesses are happy with call centres, service centres or auto-response e-mail addresses, DStv wanted to give its customers a top-tier self-service option to go with its top-tier service offering. So, it approached Helm (previously known as Praekelt Consulting) to help it radically simplify its self-service game.
MultiChoice Group, one of Africa’s most valuable brands, launched its WhatsApp assistant in 2019, allowing customers to perform everyday functions on South Africa’s most popular social app. The intelligent assistant can clear decoder errors, manage accounts and solve problems that previously required a phone call or physical presence in-store. Given the Covid-19 pandemic and geographical location challenges, the latter is not an option for most customers.
“A queue is a reality we’ve all had to face from time to time,” says Patel. “While some companies remain focused on making queues shorter, our aim is to eliminate them altogether.
“With DStv’s understanding of their customers and our understanding of user behaviour, we’ve used our technology to radically simplify self-service for over a million people every month.”
DStv’s WhatsApp Self-Service Chatbot was built using Helm Engine, an AI-powered conversational platform that is smart enough to understand and reply to users thanks to natural language understanding. “Having gathered data and learnt language nuances from millions of unique users, DStv Assist can now complete tasks in a matter of seconds,” says Patel.
Natural language understanding is a form of artificial intelligence that uses software to understand the intent behind what users are saying and allows bots, for example, to respond to them in the right way.
It’s this kind of technology that has enabled DStv customers to upgrade, pay accounts, check balances, manage holiday viewing, rent BoxOffice movies and reconnect their services via WhatsApp – without the need for human intervention. By making WhatsApp its platform of choice, they have shown an inherent understanding of their users by meeting them where they already are.
Saret Marais, executive head of group digital enablement at MultiChoice, is excited about how the bot has evolved and been adopted by customers. “It’s amazing to see more and more of our customers using DStv Assist as their preferred channel. What we have achieved here builds on DStv’s already-strong self-service offering, and this is only the beginning.”
In the not-too-distant future, the bot will be able to proactively learn the behaviour of each unique user, such as the times of the year they tend to change packages.
How has the DStv WhatsApp bot performed?
- 1.2 million users per month (average)
- 8.4 million unique users since launch
- 480 million messages received since launch, 937 million sent
- DStv’s best-performing self-service channel
Not only has the tool strengthened DStv’s overall customer experience, customer relations and customer retention, it has also kept the company at the forefront of digital innovation in the global entertainment market.
For more information on Helm and the services it offers, please visit www.helm.africa.
Formerly Praekelt Consulting, Helm has spent the last 20 years creating best-across-class products and services that have solved complex customer experience challenges. Helm designs intelligent solutions that have helped Africa’s biggest brands turn messy customer realities into simple experiences they can’t live without. Its tiered product offering combines the tools, tech and expertise to meet business needs of any size. To date, Helm has helped its clients connect, converse with and convert over 500 million users across multiple markets, channels and languages. Clients include DStv, MTN, Absa and Makro.
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