Conversational AI Playbook

Roadmap for Conversational AI

Owing to the technological development and changes in the customer

behaviour, e-commerce has developed over different levels of maturity in

recent years. The challenge for companies is to recognise relevant technological and market trends and assess them accordingly.

Companies are currently facing the challenge of achieving the next level

of maturity—so-called Conversational Commerce. This level of maturity

seems desirable at present because current trends could revolutionise the

sales sector. This means that those who proceed slowly with the implementation of Conversational Commerce could lose customers to competitors. On

the other hand, companies could, for example, benefit from public attention

by incorporating bots at an early stage (Fig. 4.8).

Thereby, the leap to Conversational Commerce does not represent a gradual, but a fundamental advancement of e-commerce. This is not only about

another voice-controlled touch point. It is much rather about a new ecosystem which automatically initiates and coordinates ordering processes driven

by customers and situations. Intelligent assistants either follow the instructions of consumers or recognise the need to take action by themselves, e.g.

reordering of detergents or travel booking according to the appointments

diary.

However, it is also decisive that the transition to Conversational

Commerce is well thought out and planned. One possibility to do this sys-

tematically is the DM3 model presented in Part II AI Business: Framework

and Maturity Model

Comments

How to Earn money with Email List Building

Collecting emails is one of the number one ways to have staying power and build a business online for life. Your email list becomes your B USINESS ASSET . As you build rapport with your list and they get to know, like and trust you... sales become effortless! You can earn daily by sending one simple email with an offer to a product (affiliate link). First and foremost where do you get the emails? You must build opt in pages and give people a reason to fill in their email. Give them something in exchange. Free giveaway, compelling headline that makes them curious and want more info. via Blog and/or C apture Page Take a look at my blog as an example:

Why Use Machine Learning?

Consider how you would write a spam filter using traditional programming techniques (Figure 1-1): 1. First you would look at what spam typically looks like. You might notice that some words or phrases (such as “4U,” “credit card,” “free,” and “amazing”) tend to come up a lot in the subject. Perhaps you would also notice a few other patterns in the sender’s name, the email’s body, and so on. 2. You would write a detection algorithm for each of the patterns that you noticed, and your program would flag emails as spam if a number of these patterns are detected. 3. You would test your program, and repeat steps 1 and 2 until it is good enough. Since the problem is not trivial, your program will likely become a long list of com‐ plex rules—pretty hard to maintain. In contrast, a spam filter based on Machine Learning techniques automatically learns which words and phrases are good predictors of spam by detecting unusually fre‐ quent patterns of words in the spam examples compared to the ham e...

What is Machine Learning

The term machine learning (ML) as a part of artificial intelligence is ubiq- uitous nowadays. The term is used for a wide number of various appli- cations and methods that deal with the “generation of knowledge from experience”. The well-known US computer scientist Tom Mitchell defines machine learning as follows: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E (Mitchell 1997). An illustrative example of this would be a chess computer program that improves its performance (P) in playing chess (the task T) by experience (E), by playing as many games as possible (even against itself ) and analysing them (Mitchell 1997). Machine learning is not a fundamentally new approach for machines to generate “knowledge” from experience. Machine learning technology was used to filter out junk e-mails a long time ago. Whilst spam filters that tack- ...

Tech & Fun

Search This Blog