The Imitation Game

 

I propose to consider the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.” The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.

 

The new form of the problem can be described in terms of a game which we call the ‘imitation game.” It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either “X is A and Y is B” or “X is B and Y is A.” The interrogator is allowed to put questions to A and B thus:

 

C: Will X please tell me the length of his or her hair?

 

Now suppose X is actually A, then A must answer. It is A’s object in the game to try and cause C to make the wrong identification. His answer might therefore be:

 

“My hair is shingled, and the longest strands are about nine inches long.”

 

In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers. She can add such things as “I am the woman, don’t listen to him!” to her answers, but it will avail nothing as the man can make similar remarks.

 

We now ask the question, “What will happen when a machine takes the part of A in this game?” Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, “Can machines think?”

[Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.]

 

60+ years ago this brilliant British mathematician gave the theoretical foundation for today’s development and usage of Machine Learning and AI mechanisms. Of course, he hadn’t dreamt of what we take for granted these days, e.g. extremely efficient computers that operate on enormous amounts of data. He did, however, said that by 2000 approximately 30% of judges wouldn’t be able to tell the machine from a real person after five minutes of conversation.

Many tried to predict how the future with intelligent robots and androids would look like. They were depicting it mostly in dark colors. Great dreamers, like Phillip K. Dick, or Frank Herbert warned us that giving too much free will, combined with superintelligence and lack of human moral system, might not end up well. We saw just a teaser in the recent TV series blockbuster – Westworld.

 

Fortunately, we don’t have to face Skynet, fight in the Butlerian Jihad, or hire Rick Deckard to hunt down Roy Batty… yet.

Today we can enjoy all perks of using learning machines and AI – also in marketing.

The merge between intelligent software and big data seemed inevitable. Who else would patiently and precisely process humongous streams of data from various sources?

 

 SALESmanago Copernicus – Machine Learning & AI

Traditional Marketing Automation cliche “Right offer to the right person at the right time” with  SALESmanago Copernicus – Machine Learning & AI sounds much more intelligent, and it goes “Right offer to the right person at the right time in the right channel.”

 

SALESmanago Copernicus – Machine Learning & AI is an advanced self-learning algorithm that analyzes the behavior of individual customers, predicts future purchases. Then it sends personalized product recommendations according to what the algorithm deems most likely to be bought.

 

It provides insight into customer purchase history, buyer’s journey, and analyzes the way products correlate in categories, allowing for highly appealing and eye-catching offers to be delivered to individual shoppers.

 

Next generation predictive marketing based on self-learning algorithms

The technology of SALESmanago Copernicus – Machine Learning & AI rests on two recommendation models. Each is optimized to support a particular marketing approach. For inbound marketing – affinity analysis (or the so-called Inbound Predictive Marketing). For outbound – behavioral analysis (the so-called Predictive Outbound Channel). Used in tandem, the models enhance both inbound and outbound marketing activities.

 

The mechanism of affinity analysis relies on sophisticated algorithms used in association analysis. By thoroughly analyzing transaction data and correlations between specific products and in categories, they calculate the optimal combination of items in each offer. After the resulting data is parsed and modeled, a frame with product recommendations can be shown to each customer. Also, the use of metadata makes it possible to react to changes in consumer preferences instantly.

 

The system can employ machine learning to compare predictions from product association analysis for end customers on an ongoing basis. Then it assigns scoring to each given recommendation to indicate how likely that product is to be bought by individual clients. Moreover, by updating product exclusion grids, the algorithm ensures that products are not recommended to customers who already bought them.

 

Predicting Customer Journey models and AI-driven recommendations

SALESmanago Copernicus – Machine Learning & AI module can automatically select appropriate products for each client and recommend the best possible way of communication with them based on their Customer Journey. Moreover, the system stores information on the behavior of anonymous website visitors, allowing the content to be personalized even for unidentified contacts. It means that all customers are subject to analysis of their Behavior Mechanisms.

 

New Outbound and Inbound predictive Marketing – Predicting channel and time of personalized offer delivery

 

Inbound and outbound marketing activities greatly benefit from the additional information gained in the process. The knowledge on which products or categories are currently sought after the most, as well as which communication channels result in highest conversion rates, it is possible to focus on efficient solutions and optimize marketing expenditures. What is more, knowing exactly when and how customers act, helps to choose the perfect time to deliver a sales pitch and strengthens relationships between you and the customer. This entire process is what we call “Predicting Customer Journey & Behavior Mechanisms.”

 

Business benefits

  • Impress your customers with personalized product offers
  • Access detailed transaction data analytics
  • Get real results and grow your sales by adjusting your marketing to what consumers want to see
  • Achieve the maximum customer lifetime value in each case
  • Streamline your marketing budget
  • Find out which products and categories are the most successful
  • Learn about your clients’ individual preferences and predict their next purchase