The goal of the so-called GAFA economy (Google, Amazon, Facebook, Apple) is to know the consumer ecosystem as well as possible and to be able to serve it accordingly. Whoever manages this task best can also place their own products best with the consumer. It is not for nothing that the GAFA world develops systems to monopolize access to the consumer. This new form of market exploitation entails the risk of abuse of market power and can result in heavy penalties, as Google recently experienced.
Those who have the direct interface to the customer in the form of a bots or messaging system, who know consumer preferences and behavior across all areas of life, determine information, advertising and purchases. If the consumer still selects his favorites from the hit lists in a Google search or an Amazon product search, the bot recommendation is usually reduced to one product or one piece of information. Bot sovereignty thus replaces active evaluation by the consumer. That this approach is highly relevant and lucrative is demonstrated, for example, by Amazon’s efforts to gain control of the customer under the convenience guise through its Dash Button and DRS (Dash Replenishment Service) system. This shows how Amazon is trying to penetrate the consumer ecosystem. The still manual automation of ordering a new detergent at the push of a button is just the beginning. In the next step, a voice-controlled Dash Button is available. But the system can do even more: an automatically acting DRS system enables connected devices to automatically order products from Amazon. That is, the system tracks the consumption of the product and thus knows the inventory of, for example, detergent, toothpaste or printer cartridges. When the product is nearing the end, the ordering process is triggered.
One of the biggest strengths, but also the biggest criticism, of the Alexa ecosystem is the integrated and automated AI-based analysis of customer interaction. In this way, the customer’s digital data trail can be used so that his Alexa also gets to know him properly. Not only are the settings of the DASH buttons stored in the cloud, but the customer’s preferences and needs are also derived and stored via purchasing behavior and search queries. With the help of AI, high-quality forecasts for further customer communication can be created from this information so that this knowledge can be incorporated into cross-selling strategies.
Fig. 3 AI, Big Data and Bot-based Platform from Amazon
Likewise, location-based data and services can be collected and offered through location services. The potential number of data points to be recorded that can be correlated with customer behavior appears almost infinite due to the wide variety of uses and broadly spread subject occasions in the Amazon ecosystem.
But it is not only the text- or data-based analysis of customer behavior that is relevant. Due to the massive advances in Natural Language Processing (NLP), i.e. digital language processing, it is possible to analyze the factual level of the customer’s statement as well as to determine the customer’s current mood. This enables emotionalization of the bot-customer relationship through trained empathic behavior of the bot, which is close to interpersonal communication.
For companies, the deep integration into the everyday world of the customer creates unique opportunities for data acquisition and analysis. By centralizing and monopolizing the customer interface, companies can lock consumers into their “commerce bubble” based on comprehensive preference and behavioral profiles.
One consequence of this development could be that emotional brand loyalty loses relevance and marketing becomes more objective. This is because purchasing decision processes are now made more rationally than before. The development of smart homes or smart products is leading to rationally prepared purchasing decisions – bots now increasingly represent humans. The refrigerator “decides” when a milk is replenished. A digital representative of the customer is logically immune to emotional and empathetic advertising, which thus loses its meaning. The ideal value of the brand is irrelevant for the customer bot, which in the optimal case acts objectively as the customer’s representative in e-commerce through the customer’s digital signature. Thus, companies’ and customers’ access to the platform becomes more important than the brand itself. Whether the bot is also immune to the interests of the provider (its lord and master) may and should of course be critically questioned by customers.
Data-based marketing (intent-based marketing) is continuously on the rise. Marketing departments are already collecting masses of behavior-based data. When Alexa, Siri and Google Assistant make their way into living rooms, the comparison with a Trojan horse is not far-fetched. If providers, for example, get to know “up close and personal” that they have been married, it is possible that offspring are planned soon. This information can be worth its weight in gold. It remains to be seen how the benefits from greater convenience can be reconciled with the risk of market abuse by monopoly-like commerce ecosystems. The trend toward voice-based interactions shows that consumers are open to new convenience technologies. This year, one in five queries to Google came via voice. A 70 percent rate is projected for 2022. In ten years, probably over 80% % of Google queries will be made via Voice.
While current communication is still between the consumer and the enterprise bot, in the coming years there will be increased communication between the consumer bot and the enterprise bot. Therefore, marketing activities need to be adapted to bot channels. A rethink will also have to take place in SEO and SEM. The so-called “Bot Engine Optimization”, short BEO, transforms the guiding principle “Rule the first page on google” to “Rule the first bot answer”. The focus is on personalized one-to-one bot-to-customer campaigns. Of course, companies have always analyzed data about consumers as part of database marketing and analytical CRM to target products and communications to be as profitable as possible. The only difference is that companies and consumers increasingly no longer meet in traditional markets; instead, the supplier internalizes the market to a certain extent. Amazon has long since ceased to be a retailer of products, but rather a smart ecosystem that intelligently collects, analyzes and uses data to keep consumers in their own Commerce Bubble.
Areas of application in e-commerce
Chatbots can be used at various points in e-commerce, for example to qualify requests in advance, to provide leads with information (nurturing) or to provide automated information in service. Chatbots are currently used primarily as an inbound touchpoint to answer consumer questions about product, company, and campaigns. Increasingly, outbound scenarios are also emerging in which chatbots actively communicate with customers according to defined rules and events (drip communication through Nurture Bots). Engagement bots, which actively interact with users as market and brand ambassadors, go one step further. The best-known example here is Microsoft’s chatbot Tay. Unfortunately, the community trained him negatively, so that he posted right-wing extremist and sexist posts. Within a day, Microsoft apologetically removed Tay from the network.
Poll bots can also be used to obtain customer insights via surveys as automatically as possible.