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Maturity Levels and Examples of Bots and AI Systems

 Maturity Model

The possibilities of implementation of bots are as diversified as the needs of

the business and its customers. For a better overview, for degrees of maturity

of chatbots can be differentiated



The first and lowest level is represented by chatbots without any access

whatsoever to other data. Many bots that have been established in customer

service until now can be categorised on this level. They secure basic communication, pick up the customer for the time being but soon reach their limits 

and pass the customer on to the next touchpoint.

On the second level, context information about the consumers is already

used. For the duration of the interactions, the bot remembers. thcustomer’s location or the products viewed in the shop and can make

recommendations based on this. It is highly situational communication that

offers a lot of potential for the customer journey, yet is not aimed at strong

customer retention and an empathetic appearance of the system.

The next and third level is represented by a bot that has additional access

to historical context information. It is the first level with real communication between the company and the customer. In the bot’s memory, an inter-

nal database, besides previously purchased products there are also all of the

customer’s reviews and problems, which can be used accordingly.

An extensive personalisation is achieved with the fourth level. They are

connected to the company’s CRM system and add to it during customer

interaction in real time. Digital butlers such as Alexa can be categorised

here. They get to know their customers and act on behalf of the customer as

a digital entity to place and order, for example. It is not only a communication system but there is actual interaction with the customer.

With the increasing degree of maturity, not only the complexity and

added value of the bot increases, but also the legal challenges. Data protection implications of the application must be considered and weighed up, as

the collection of customer data can be problematic. It depends on the scaling in this case, as well.

The use of surf context information, with the help of cookies for example, is usually unproblematic, even in Germany. In contrast to that, personal

butler systems such as Amazon’s Alexa, are being criticised by the public for

collecting and analysing too much user information. Data protectionists are

criticising the system on all leading media in this respect, which can turn the

marketing of the product into a challenge. Likewise, the customer’s inclination to use the system is also declining. In the worst case, trust in the brand

can be shattered and a negative downward spiral of the customer review

can be instituted. Enhancement effects and possible consequences must be

weighed up carefully with the benefit.



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