Both companies have a focus on in-app support, a solution category that basically got introduced by Helpshift, after Abinash identified a lack of good options or delivering support directly to and via mobile phones. One of the premises is that a lot of the technically necessary and relevant information can get collected directly and sent to the service back end transparently. They have some big customers, including Microsoft and a raft of gaming companies, including Zynga and Supercell. He, of course, has an opinion on bots in support, which he recently also expressed on Venturebeat.
Hotline.io has a customer base that is mainly made of transactional companies, which, too, leads to a high message load but also leads to different approaches, as the user context is often about past transactions. This means that regularly not that much information gets sent together with the support request. Sri, too, has a vision on how to incorporate AIs and bots into support.
Hotline.io is offering a browsing style of offering help using a shallow tree with icon-supported categories on top of a search interface as it is also offered by Helpshift. Of course both systems offer direct in-app chat to support, too; here again hotline.io offers context via the categories (called channels), which can be used for entering the chat session. Helpshift is more relying on system context here. What both companies are doing with this is to establish a focus and to initiate meaningful first reactions.
Why do I talk about this here and now? Because both companies, as well as others, are looking into adding bots into their infrastructures.
AIs and Chatbots have a Problem
While my criticism to quite an extent was around the poor user interface that a chat application offers, as compared to richer environments, I acknowledge that many people are texting and messaging. In fact, the number is only increasing. This means that there is a viable user interface.
However, everybody has their own dialect, choice of words and, worse, abbreviations. Sometimes people even go to the stretch of asking their questions rap style or a veritable rhyming competition about a dead worm evolves. A lot of important context that is not immediately visible to a machine is needed by this type of communication. Add potentially overlapping messages between the communication partners to this.
All this makes it hard for machines to ‘understand’ the nature of a request and to answer correctly. It is already hard for humans.
It seems to be general consensus that the accuracy of natural language recognition is by far not yet where it needs to be in order to provide useful support; support being delivered in text based environments or, even more difficult, in speech. As good as a 90 per cent plus recognition rate sounds, this is still far too low to be really accepted and the remaining about 10 per cent will antagonize a lot of customers.
A lot of them!
On top of this, although specialized AIs often work surprisingly well, more generalized tasks still are difficult to cover by them. Yet, chatbot platforms are focusing in on helping customers who are already in distress – or are doing funny stuff like selling flowers, for which one wouldn’t really need an artificial intelligence … but then this is likely also the easier part, as the process is much more guided.
I think this phenomenon of applying AI everywhere is largely fueled by a technological hype that lets us forget that not everything that is possible needs to be done, let alone should be done.
A hype that seems to put the cart in front of the horse, as the outcome could potentially be disastrous for a company’s image.
After all bad news travels fast and far – faster and farther than good news.
On the other hand, if AI’s are not working well enough yet, they need to get trained. This works best by, well, using them.
A Way Ahead
Of course this causes a classic chicken vs. egg problem, which could become a real problem for companies that need to keep their investments in check.
There seem to be three ways out of this dilemma:
- Follow the KISS principle and increment the usage of AI’s and/or bots from a domain of structured data into unstructured data, essentially starting from the simple problems (although these do not need an AI nor machine learning)
- Train the AIs in parallel to support sessions done by customer service agents or self-service sessions
- Combining the above approaches
The first approach is pursued by both Helpshift and hotline.io, again using different approaches. An additional precondition to AIs successfully delivering support is that chat via mobiles will be recognized as an important channel, if not the primary channel for the delivery of customer service; this not only by customers and businesses, but also by software vendors. According to Abinash, e.g. Microsoft and Salesforce are ahead of SAP and Oracle with this understanding.
This way the bots can provide some value early, which then gradually and constantly can increase by supporting more difficult requests. How could this look like in real life?
- Use a kind of first response bot that takes up essential missing user data, routes the request into the proper queue and sends an acknowledgement, thus buying some time for the support agents
- Improve the quality of the retrieved knowledge base articles – learn using the time that a user spends reading an article and the users’ rating on helpfulness in correlation to the question asked as well as from the suggestions of the human operators
- Forecast wait times and provide intelligent notifications, so that customers are not bound to ‘places’ when in chat based support
More sophisticated approaches include
- Have a bot ask relevant questions about signs and symptoms that the user did observe or could have observed before calling support, while the human operators are busy. This helps in shortening the wait times for the customers, who already are in distress.
- Narrow down the range of possible hits in the knowledge base and/or suggest next best steps; this then gets evaluated/used by the agent. The human operator takes over equipped with relevant information.
- Have the bot in addition suggest solutions or next best steps for simpler problems directly to the customer. In essence this would model a tiered support system. The bot in the first level catches as much information as possible and also attempts at solutions, if the problem appears simple enough. Else the incident is handed over to a human agent with deeper knowledge.
- Analysis of usage patterns for potential improvements of the application, to better help the service agent, or (if not too annoying) suggestions on how to do things more efficiently
Most of these approaches require a seamless handover to the human service agent. And, using these approaches, the covered scenarios can become increasingly complex, thus becoming more valuable for both, customers and service providers.
Additionally, communities can be used as a helpful vehicle, too. Not only are they possible training grounds for AI’s but they also serve as a valuable source of results. Further, it is possible to have artificially intelligent community managers or even –members that have the ability to provide other members with helpful answers.
In a highly advanced future state these AIs could then have the expertise to answer and solve problems on their own (thanks to Esteban Kolsky for planting this train of thought).
But that might be part of another post.