The road ahead for conversational AI in 2021?
Recent data shows that the conversational AI market was worth about $4.8 billion in 2020 and is set to go up to $13.9 billion by the end of 2025. This is a clear indicator of the confidence that brands and customers have in conversational AI as a way to provide quality customer interactions at scale. Particularly in the retail sector, digital shopping assistants are becoming increasingly common and are often the primary point of contact for both first-time and repeat buyers.
As the hype about conversational AI trends continues, however, it will become increasingly tougher for brands to distinguish themselves in this space. Here are some of the key trends that brands should keep in mind for 2021 to stay ahead of the curve amidst tight competition.
No room for basic bots
If consumer expectations were high before, they’re stratospheric after the Covid-19 pandemic. As business goes digital everywhere, customers expect the same kind of personalized treatment online as they would get in a physical store. A digital assistant will thus need to pick up on conversational nuances, understand customer emotions, and respond in a sufficiently empathetic and helpful manner – which is where Natural Language Processing (NLP) comes in.
Interestingly, a 2020 study showed that only about 17% of companies used NLP in their digital assistants. Brands that wish to unlock the true potential of conversational AI, however, will need to move beyond traditional rule-based systems and invest in NLP.
1. Knowledge graphs
Retail brands receive huge volumes of data every day, much of which is scattered. By using knowledge graphs, this data can be structured properly so that insights can be derived from it. A knowledge graph places entities in relation to one another and classifies them based on attributes, thus enabling digital assistants to learn much faster. This translates into an ability to handle more complex customer questions as well as a wider array of data points for digital assistants to refer to.
2. Voice commerce
Voice-based search has been on the uptick over the last decade, and data shows that sales of smart speakers will go up from $4.4 billion in 2017 to $17.4 billion in 2022. Done right, conversational AI with NLP capabilities and text-to-speech integrations will be able to chat verbally with customers across platforms and devices. Considerations that brands will need to focus on in this context include secure payment options and data privacy concerns that consumers might have.
3. Self-learning assistants
With trends and consumer expectations in a constant state of flux, reducing the time-to-market for new digital assistant solutions is critical. A focus on self-learning solutions will be useful in this regard, as they can scan the company’s knowledge databases and pick up the relevant material on their own. Done correctly, this can ready a digital assistant for deployment in a matter of days or even hours – a process that would traditionally take several weeks. Companies can thus get started on their conversational AI journey and see the benefits for themselves right from day one.
What were the popular conversational AI trends in 2020? Read the free e-book.
4. Scalability from the get-go
When the pandemic hit and in-person commerce shut down, virtual assistants found themselves handling an overwhelming number of queries every day. Many of them weren’t equipped for that, which led to extended periods of downtime and customer frustration. It’s evident now that launching a digital assistant with lower capacity and putting scalability on the shelf until later won’t work. Being able to scale up or scale down as needed will play a big role in how the digital assistant can ride out changes in customer behavior.
5. Data-driven interaction
Gartner estimates that by 2022, abou/t 70% of white-collar workers will have interacted with a virtual assistant in some capacity. This means that giving customers the kind of virtual assistant experience they want is critical, which is where using data inputs from interaction history is critical. Even small details, such as the way a virtual assistant greets a customer or its placement on the company homepage, can make a big difference to the way a customer feels about the interaction. How fast a company responds to data inputs about these details can be what makes or breaks their success in conversational AI. In this regard, the focus on easy-to-build and easy-to-maintain digital assistant solutions will also go up – the more a company can solve its problems, the more customer-friendly it will be.
6. Virtual agent networks
What a single digital assistant can do well, a network of digital assistants can do even better. By connecting multiple assistants each with their unique function, companies can give customers more comprehensive service and also break down organizational silos. This will also be a step forward in self-learning, as connected assistants can share data sets and knowledge graphs with each other.
Convinced that your brand needs a digital shopping assistant to drive sales, conversions, and customer experience? – Here are 5 questions you as a retailer should ask before investing in one. Read the blog!
In conclusion, conversational AI has myriad benefits in terms of accuracy and convenience and is all set to be the dominant feature of the post-Covid retail landscape. As consumer expectations continue to soar, brands will need to adapt their conversational AI strategy to stay relevant, and the trends above are a good place to start.
SID is a digital shopping assistant built for retail brands, helping them leverage the power of conversational AI to sell faster and better. Looking to deploy a conversational AI solution for your retail business?