E-commerce has evolved over various maturity levels in recent years due to technological developments and changes in customer behavior. The challenge for companies is to identify the relevant technology and market trends and evaluate them accordingly.
Companies are currently facing the challenge of climbing to the next level of maturity – so-called conversational commerce. This level of maturity currently appears desirable, as current developments could revolutionize the sales industry. This means that those who are slow to implement conversational commerce may lose customers to the competition. On the other hand, companies could also benefit from public attention by engaging bots early on, for example.
The leap to conversational commerce is not a gradual but a fundamental development of e-commerce. It’s not just about another touchpoint that can be voice-enabled. Rather, it is about a new eco-system that automatically triggers and coordinates order processes driven by customers and situations. Intelligent assistants either carry out instructions from consumers or independently recognize needs for action such as reordering detergents or booking travel according to the schedule.
However, it is also crucial that the transition to conversational commerce is well thought out and planned. One way to do this systematically is to use the following DM3 model.
THE DM3 MODEL AS A SYSTEMATIC APPROACH MODEL FOR CONVERSATIONAL COMMERCE
In order to determine the optimal conversational commerce strategy and roadmap, the digital media maturity model (DM3) is used as the basis for determining the next steps in the transformation. The current customer touchpoints are recorded and evaluated with regard to their automation and technology support.
For this purpose, customer journey tracking and analytics tools are used to measure and analyze consumers across different touchpoints such as websites, display, email and social media. This also makes it possible to analyze which touchpoints have a direct conversion function and which have more of an assistance function.
Likewise, conclusions about the temporal cause-effect chains are possible. The multitude of digital touchpoints and end devices as well as their extremely variable use by customers can no longer be optimized by experience and gut feeling alone.
Fig. 4: Digitale Transformation im E-Commerce: Reifungsweg zum Conversational Commerce
Each touchpoint must be analyzed both individually and in interaction with other touchpoints in terms of costs, benefits and risks. This is the only way to derive the current and future optimal conversational commerce strategy. This usually involves evaluating the trade-off between costs, benefits and risk. For example, a high level of automation of a touchpoint can bring efficiency benefits, but on the other hand it can also result in high costs and possibly a suboptimal customer experience. A systematic comparison of costs, benefits and risks is therefore essential.
This is not about 0/1 decisions. Instead, it must be decided which degree of automation makes sense at which touchpoint and when
Platforms and checklist
One step further operationally is the question of the platform for conversational commerce.
Thus, companies should first opt for the platform on which their customers are already located. Facebook Messenger can be a good choice in many European countries and the US, where the number of users is very high. If the customer base consists primarily of Millennials (the generation born roughly between 1980 and 1999), Snapchat might be a better fit. WhatsApp, Viber or Line also dominate in many countries. If the target audience is mainly in China, WeChat is the most suitable platform. The next step should be to consider whether there are enough resources to not only create a bot, but also maintain it. This applies both in terms of expertise and personnel. If the expertise is not available within the company, it is advisable to engage a partner for the technical implementation.
But also the time and cost of maintaining the bot in the long run should not be underestimated. Because even though the bot is automated, it takes time to a) promote the bot, b) to check the cases where the bot could not help, c) measure customer satisfaction; and d) to work constantly on the improvement of the bot.
Another important point to consider well is how to maintain and promote the company’s brand personality via conversational commerce. The fact that the brand’s values are conveyed in online chat is particularly important, as these conversations have a very human touch. This presupposes that a consistent brand personality exists; if in doubt, it should be created as quickly as possible before conversational commerce is deployed.
Fig. 5: Derivation of individual recommendations for action based on conversational commerce
It is also central that there is a clear, meaningful and well-studied use case for the deployment of chatbots. What goal is to be achieved with the bot, and is this realizable – even in the initial stages? Does the use of bots improve the service for the customer? A negative example is the countless apps that have no advantage for the user compared to the website. Each interface to the brand will be used by the customer in a different way, so it is necessary to explore how the interaction with the customer changes in detail when a new interface is inaugurated. By analyzing the current communication with customers, topics can be found for which the use of a bot is suitable. In general, it is worthwhile for companies if the bots are implemented gradually and in clearly definable areas. In other words, the use of chatbots should be limited to those areas where it works particularly well. The rest should be left to humans until the technology matures. This also increases customer acceptance. If, for example, the entire booking system of an airline is changed over from the very beginning, this can be very risky, because the probability that it will not work smoothly right away is very high. Chris Messina emphasizes that you should never use a bot for spam. In conversational commerce, frustrated customers can have a major impact on a company’s success because they interact with the brand in the same way they interact with a human. On the other hand, if a company succeeds in offering customers a convenient, personalized, and meaningful service, it can benefit significantly from conversational commerce.
CHECKLIST FOR COMPANIES
- What messaging platform are my customers on?
- Are sufficient resources in terms of expertise and personnel available for long-term maintenance of the bot?
- Does my company have a brand personality, and does a strategy exist to convey it in online conversations?
- Is the area in which bots are to be used clearly defined, and can the bots achieve the planned goal without disappointing customers?
A study conducted by the Cologne-based Institut für Handelsforschung (IFH) found that 57% of consumers surveyed on the Internet have already used conversational commerce. With one in two of this group over the age of 50, it seems that the implementation of conversational commerce is not just appealing to younger people. The IFH recommends the use of conversational commerce above all for sectors with an increased need for customer advice. According to the study, the consumer electronics, tourism, and banking and insurance industries appear to be particularly well suited for the use of conversational commerce. The majority of respondents also said they could imagine purchasing sports and leisure items as well as clothing and accessories via conversational commerce.
KONVERSATIONELLE KI & BIG DATA
Against the backdrop of the current discussion about customer centricity and artificial intelligence, the sometimes shockingly poor performance of automated customer communication and interaction in corporate practice is surprising.
In this context, conversational AI technologies such as NLP, NLU and Deep Learning, which have improved massively in recent years, enable high economic benefits and thus competitive advantages through their performance and scalability.
Recent success stories of Google’s Meena, Facebook’s Blender or OpenAI’s GPT-3 proclaim the next big AI milestone after the hype around Deep Learning. In fact, the AI-based dialogs are fascinatingly human-like and entertaining. This development refers both to the improved understanding of the language in terms of recognition of intents and contexts, and to the increasing response quality. All three AI systems are based on huge training sets on the Internet. This opens up vast spaces of language and knowledge that make those inspiring dialogues and responses possible. However, there is no supervised quality control here; the bias present on the Internet is also learned along with it. The question is what benefits, besides the entertainment effect, this new stage of Conversational AI means for companies. For general dialogs and larger industry issues, companies could definitely benefit. For company and product specific answers, the training volume is not comparable. The loss of quality could be compared to the Google solution in the intranet, analogously to Google in the open Internet. Likewise, customer- and company-specific contexts can only be reliably guaranteed with NLU (Natural Language Understanding) approaches, which require appropriate lexicons as well as extensive context modeling.
Fig. 6: Converational AI – Hybrid AI as the silver bullet
The royal road in the future is therefore, in my opinion, a hybrid approach that combines knowledge concepts and control logics with the generative approach. This allows the power of implicit learning through Deep Learning to be used in a quality-assured manner, while at the same time incorporating company- and product-specific knowledge.
