Home Virto Commerce blog The Rapid Evolution of Chatbots for B2B

The Rapid Evolution of Chatbots for B2B

Jul 24, 2020 • 5 min

B2B chatbots: the bots that do more than just chat

If you try to find some tangible data on B2B chatbot usage, you'll probably end up with these numbers from two studies done by Relay (1 and 2) and Business Insider (3): 
  1. 58% of companies using chatbots are B2B; 
  2. Only 0.5% of B2B companies are using chatbots; 
  3. 80% of the companies want a chatbot for their business by 2020. 
These numbers look contradictory, and they are. The thing is, B2B chatbots are a new technology; they evolve and mutate so fast, it becomes difficult for researchers to keep track of them. To evaluate the need for a B2B chatbot, a company should look at the current and possible future use-cases where such a chatbot can produce value. 

Lead qualification

Chatbots have long been used in B2C e-commerce for lead generation and conversion. No surprise, the same use-case was the first one to be tried out in the B2B sector. Just a few examples: 

  • Perfecto Mobile (a software testing platform developer) increased the conversion rate of the website by 230% using a chatbot;
  • MongoDB (a database/development platform) got 70% leads increase in three months;
  • ConnectWise (a managed IT services platform) has seen a 500% conversion increase with a bot;
  • RapidMiner (data science software developer) has 25% of sales pipeline influenced by chatbots;
  • Demandbase (an account-based marketing platform) used a chatbot to convert 150% more leads.
There is, however, a certain important difference in the lead-related values that B2C and B2B chatbots are bringing. A single B2B deal is usually bigger than a B2C one and happens less often. The cost of transaction support is higher in B2B, and the losses on nurturing unqualified leads (the ones that either can't or don't intend to buy) are more substantial. Hence, a B2B company needs to qualify leads early and spend valuable resources only when sale is at least possible. Simultaneously, unqualified leads are also useful: maybe they are just investigating the opportunities, determining their future needs, and will qualify later; perhaps they are just interested in the product and will promote brand awareness. The qualification process separates leads into different communication channels.
An American truck manufacturer Mack utilizes an AI bot to converse with anyone interested in their products. The bot will keep up the conversation for weeks and months, all the time evaluating the lead. Once this evaluation hits a specific threshold, the lead is turned over to the closest dealer, where the actual sale starts. By that time, the company knows that the lead is really interested in buying, and the lead is educated enough to know exactly what they need.
Printer manufacturer Epson uses a similar AI bot to converse with prospective B2B customers, increasing the number of qualified leads by 75%.
These two cases bring us to the next topic:

Personalized service

Whereas B2C e-commerce usually separates its customer base into segments and sets up communication channels for each segment, B2B has to deal with each client individually. B2B clients are accustomed to personalized service, and chatbots must meet their expectations. This means a B2B business can not just drop a scripted bot on their client base and hope to improve it over time; such a bot must meet specific intelligence requirements right from the start.
Jeanette Paschen, et al., in their work on AI marketing in B2B distinguish two key AI features:
  • an AI acts intelligently, i.e. it can support a coherent conversation and understand user requests; 
  • an AI acts as a computational agent, i.e. it gathers and processes the relevant data and builds a personalized communication flow upon it.
Epson successfully increased the customer response rate to 35% (240% increase) by utilizing a specialized natural language processing AI to write their automated emails to prospective B2B customers. An AI-driven chatbot uses ML to constantly improve its communication skills and better understand the client's demands.
The data mining aspect is important both during the initial lead acquisition and the ongoing B2B collaboration. Perfecto Mobile uses a chatbot for the first contact. While the bot engages the prospective client in the initial communication, it gathers all the available data. In one case, it managed to identify an anonymous visitor as someone from a major sports brand, which enabled the monitoring SDR to call the sales rep with the relevant knowledge; the appointment was booked on the spot.
An existing client expects the business to know their needs and expectations. An AI stores the history of the relations and is able to process it quickly, allowing both prompt service and pro-active communication in cases when the client forgets something they should be doing or misses out on some profitable opportunities.

Omnichannel communication

The B2B e-commerce trends 2020 study by Magento shows that 75% of buyers purchase from the same supplier again if they had excellent omnichannel capabilities; 45% of customers believe that retailers don't spend enough effort on delivering multichannel experience; and 61% state they are not able to switch channels due to the valuable support services (or the lack thereof on other available channels). 
The clients’ demand for omnichannel capabilities is driven by their wish to use familiar communication tools. If they use a particular messenger in their working environment, they wish to have all their procurement talks in the same messenger. If they deal with multiple suppliers, they want all these suppliers to use similar communication tools. To be close to their client and build strong customer relationships, a B2B enterprise must provide a selection of communication channels to choose from at each phase of the B2B relations. Michelle D. Steward, et al., point in their paper on B2B buying process evolution:
'Understanding when and why potential customers interact with an omnichannel experience better enables the supplier to understand what information might be helpful to a customer at which time during the buying process. Potential customers may enter one channel, for example, before a need and budget for that need is fully determined in an effort to keep up with industry trends. Then later in the buying process, a potential customer may wish to pick up on that exploration in a different channel, without loss of the insights gained in the earlier search.
Regarding B2B chatbots, there are two omnichannel aspects: 
  • A chatbot is a part of the omnichannel ecosystem; 
  • A chatbot itself is an omnichannel communication tool. 
The first aspect is obvious: people want more familiar channels, and a chatbot may serve as one. Businesses cite a lack of resources and technical expertise as the reason for the lack of omnichannel support; well, a chatbot is relatively easy to deploy
The second aspect shows an AI chatbot as a content generation and processing tool, not just a simple messenger text interface. An omnichannel AI chatbot utilizes different communication methods, depending on the message type and urgency. Some content is better suited for an unhurried reading from a computer; an e-mail is an ideal carrier for it. Some messages need to be pushed into the client's phone: for example, they forgot to place an order before the national holidays. In some cases, such a bot may even prepare a customized web page for the client's perusal. In all these scenarios, the bot still works on a deep personalization level, knows the customer's history, needs, habits, speaks their language and predicts their wishes. 
But can such AI contraptions be categorized as 'chatbots'? Will they still remain 'chatbots' once they enter the AR field, for example? Well, who knows? The term 'customer's virtual assistant' is used in some publications. But, as stated earlier, the technology itself is undergoing such a rapid evolution, that coining the right term may be a bit premature, so let's stick with 'chatbots' for the time being.
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