AI-Infused Decision Making

Have you ever contemplated in the number of times you make decisions in a given day? It’s time consuming as well as irritating to be reluctant to purchase an item or make a request. What if you have been given a “magical” tool that could help narrow down your choices? What if we create simple tools that can adopt to your style, situation and the current context to give you more precise and accurate recommendations. If this resonates with you then continue reading this article, as I will share some thoughts around how new type of digital systems can help you and other organisations make better decisions.

What is AI-Infused Decision Making

Perhaps I’m endeavouring to coin a new term in the world of the business decision making and artificial intelligence. Working for so many years in the IT field and observing numerous successful and indeed as well as failed projects, one prevalent mistake I see is when organisations think the use of AI can solve any problem and will wondrously help them sell more and market better! well, the conspicuous answer is in the negative! I shall explain.

But first let me give a definition for what do I mean by “AI-Infused Decision Making”. For so many years, people have talked about Expert Systems and how they help make better decisions. According to wikipedia, in artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. The expert system is fed a set of knowledge base with the aim to solve complex problems by applying rule based systems. AI Decision Support systems on the other hand are systems that aid in making decisions. Normally, they provide means for users to interact and use the system to reason and finally conclude.

So what is AI-Infused Decision Making? it’s simply put a combination of both Expert Systems and the use of advanced sophisticated methods in decision support systems to help guide users reach possible decision choices. Now that we have defined the term, let us dive into why I believe it provides distinct view and perspective of how new Decision Support Platforms are made and how it contributes into transforming how organisations make strategic and operational decisions. Not just that, even on the personal level, many of us make decisions with the help of mobile or wearable devices. Hence, how significant making decision is!

Successful Transformation = Taking the Right Decisions on the Right Time

I would presume many would to concur that making a successful transformation is profoundly correlated with taking the right decisions on the right time. Numerous groundbreaking transformative (ideas, projects, products, etc.) were not completely novel or original but rather composed and packaged entirely in a manner that is truly exhibit solving real and challenging business problems.

So take a moment and think for a second. How many of us when he/she sees a successful business; say I thought about this many years ago? is not that right? well, what went wrong? why didn’t he or she started that business? Was it a lack of will? Perhaps, but I would reckon it’s to do with “Uncertainty“. Our fears of uncertainty heavily influence our decisions. You, for example, made a decision to read so far! if you were uncertain and skeptical about the value of reading this article you would not have spent time thus far reading.

Dealing with Uncertainty?

We live in a world that is for the most part uncertain, nevertheless we thrive to seek certainty, because certainty gives us confidence in the validity and effectiveness of our decisions. Consequently, most of the organisations look for visionary and talent people who can see through future and anticipate opportunities and make “The Right Decision On the Right Time”.

For so many years, IT companies have sought to create tools to help professionals better make decisions. From automations to the most advanced technologies and use of Machine Learning and to help solve complex problems and provide better recommendations.

A Proposed Model to Solve Uncertainty

Let us take a logical example of how most people would go about making a decision. Let’s say you want to buy a car. First you will go to the web and search for the car spec, features, reviews, pros and cons, …etc. Then you may contact a subject matter expert for consultation or a friend who happens to own one. Then finally, you look at the available data like cars sales, parts costs ..etc. Isn’t that a very logical approach?

This is precisely what I’m proposing here is to build an AI-Infused system that takes into consideration the previously described approach. The proposed platform will utilise the leading-data found on the Internet and source all reported news and social media feeds, including customer reviews and ratings. This measure represents the uncertainly observed on the social media platform likely to represents people’s views. Incorporate that with Subject Matter Experts Opinion using methods such as Delphi method to source feedback. After that, combine it with lagging-data such as company’s sales, past deals ..etc. The common methods of forecasting depend on choosing internal factors that are often available to the companies or service providers, such as prices, daily sales, etc. However, this method relies on combining the previously mentioned method with inputs from open data such as people’s sentiments about a product or the popularity of a product or company. As well as integrating economic indicators in the forecasting process and enhancing it with the participation of experts such as SMEs. Integrating all of these inputs into a deep learning-based system in an effort to give a more accurate prediction and forecasting than the currently available techniques.

To read and know more about this approach click here.

* The images in this article are licensed under Envato Elements

How Artificial Intelligence is Transforming Modern Marketing

Are you struggling to choose the best marketing strategy or measure the effectiveness and adequacy of your marketing campaign? You are not alone I’m too.

I’m no expert in marketing strategies so to set this straight before you go ahead and read the entire article, but I’m an expert in digital transformation and building intelligent systems that can advance your marketing strategy.

Today, most organizations follow a conventional and traditional approach to develop their marketing strategies. It involves a great deal of effort and requires good study of the market and alignment with the cooperate strategy. However, I would argue that these strategies are predominantly based on past experience and little to do with “your data”. It is rare to see organizations employ advanced analytics to build their strategies. Mostly, due to technical complexities or inability to harvest the data.

Data-Driven Organization

You must have seen this title before. Numerous organizations like to put this title in their strategies to indicate the organization puts data first. Although, this is a great direction to take, however, few organizations do manage to perfectly implement. Only those who really understand how to put “Data-First” manage to succeed in building a data-driven organization.

Building a “Data-Driven Organization” is a rather extremely challenging task. It would take the entire organization to achieve it. Many processes need to be redesigned, rules need to be rewritten and business logic needs to be rethought. Equally, the IT infrastructure needs to be ready to help achieve that from building systems to storing and manipulating data.

Data and Marketing

No matter how good and robust your strategy is, it will be extremely fragile if not based on facts and data. Strategy after all is a process; a thoughtful process; you need to collect data about your organization, products, customers, partners in order to tailor the strategy to work best for you.

The data is available in two places. One within your organization’s systems and the other outside your perimeters. The latter is mostly found in open data. Nowadays, social media and global news on the internet represent a big portion of that data. That is why organizations these days use social media monitoring tools to monitor and observe what people are exchanging about them and their brands.

Social Media and Marketing

Companies today are in a race to attract more customers and promote their products to consumers online and most specifically over social media platforms. It has become a practice to analyze what people say over social media platforms to measure the performance of the marketing and communication department. It’s really such a powerful tool and we have seen the impact they present on the social, economic and political life we have today.

Artificial Intelligence and Marketing

Artificial Intelligence was introduced to solve the inability to process a massive amount of data and spot important things like when people are happy or angry about a service or a product we have. Many tools today offer basic to advanced Natural Language Processing to read the unstructured data make sense of it and present insight that could help organizations improve their services.

AI can be used in various marketing scenarios and I will give a shortlist of potential scenarios where AI can be of help

• Personalized Recommendations: AI can be used to help deliver personalized content and therefore improve the chance customer click or choose a product or service. With proper data planning, you can collect information about your customer preferences (with consent) and display the relevant products and services.

• Customer Care: Customer care is a big umbrella that covers interacting with customers, receives feedback and process customers’ requests. AI can be used in various touchpoints within the customer journey.

• Conversational Agents (Basic & Advanced Chatbots): Chatbots and conversational agents are becoming more and more widely accepted due to the high adoption by many organizations.

• Content & Website Design: Today, there exist many tools that help in content generation and website designs recommendations. Organizations can easily leverage these tools to easily create and publish compelling contents.

• Advertisement Bidding: AI is used in all advertisement platforms and organizations can use these available features. For example, you can let google ads decide what best work for you! And without the need to understand how bidding strategies work.

• Understand Buyer Persona: Understand the buyer persona is key. You can use AI to determine the “intent” of the prospect request and then deliver the request to the right team.

• Audience Targeting: You can use analytics and advanced analytics to determine the right target audience. You can also use AI tools to screen the public data and generate insights that can help you define your target audience.

• Topic & Title Generations: Perhaps this is one of the most challenging tasks in AI and today we see quite good advancements in this field. You can generate titles and topics that attract more customers.

• Customer Churns: Identifying the customers churn is important. You can direct certain marketing campaigns or offer discounts for customers likely to churn.

• Lead Scoring and Health: You can use AI to assign a scoring for each lead to help sellers quality the lead. This helps optimize the quality of leads and the sales team’s ability to utilize the marketing efforts.

Marketing Recommendation Platform

Realizing the importance of digital marketing and the current gab in finding the right tools to help markers achieve better decisions. We at Brightaira decided to build a platform that helps people working in marketing and companies make decisions with regard to the services and products they provide.

Brightaira, is an advanced artificial intelligence global media platform that assists organizations in making decisions in the area of Marketing and Customer Success. Brightaira collects Millions of NEWS and SOCIAL MEDIA feeds, analyzes them and provides organizations with the insights and decision choices to help optimize customer experience and improve business outcomes.

Unlike other tools, Brightaira provides “recommendations”, we call them “Parameterized Recommendations”. Where the AI engine determines the best recommendations and then decide the values within these recommendations that fit your organization. For example, available tools identify your negative sentiment, Brightaira’s AI engine on the other hand tells you what improves your sentiment and by which percentage you will probably improve when following the recommendations!

Try it for Free!

We believe AI should be available to all. Most of the available tools are very expensive and few organizations can afford to bear the cost.

We provide a ton of features with very affordable subscriptions fee that is suitable for many. You can also try the tool before you commit to any payment. TRY NOW.

Recommendation Systems. Simple concept - Powerful applications!

What is Recommendation System?

It’s one of the most popular data since applications. It’s a system that predicts the likelihood that a user would prefer an item, based on his past behaviors. That can be done by employing a machine learning algorithm, which can predict user preferences for a particular entity. There are a wide variety of applications for the recommendation systems, and it is used by many of the big technology companies, in order to recommend products to their customers. For instance, Amazon used the recommendation systems for product recommendations, YouTube for video recommendations, Netflix and IMDB for movie recommendations, and Facebook and Twitter for friend recommendations.

The diagram below demonstrates the recommender systems method.

Recommendation System Mechanism:

The engine of the recommendation system filters the data via different machine learning algorithms, and based on that filtering, it can predict the most relevant entities to be recommended. After studying the previous behaviors of the users, it recommends products/services that the user may be interested in.

The engine’s working of a recommendation is classified in these 3 steps:

1- Data Collection: The techniques that can be used to collect data are:

• Explicit, where data are provided intentionally as information (e.g. user’s input such as movies rating)

• Implicit, where data are provided intentionally but gathered from the available data stream (e.g. search history, clicks, order history, etc…)

2- Data Storage: It can be stored in a cloud storage such as SQL database, NoSQL database, or some other kind of object storage. However, it depends on the data type and amount as well. The more data that the storage can have for the model, the better the recommendation system can be.

3- Recommendation System Methods:

There are several methods in recommendation systems, but there are two major approaches to filter data on the system:

1. Collaborative Filtering It is making recommendations according to the combination of your experience and the experiences of other people.

2. Content-Based Filtering (The one that I used in implementing my movie recommendation system)It is based on product attributes, which is the item description and the preferences of users’ profile. It calculates the similarity between different products on the basis of their attributes. It treats recommendation as a user-specific classification problem and learns a classifier for the user’s likes and dislikes based on product features.

The diagram below demonstrates content-based filtering recommender systems.

Recommendation System Applications:

There is a wide and variety of applications for recommendation systems, especially in the data science field. For example, music and video companies like Netflix, YouTube, and Spotify use them to generate music and video recommendations. Amazon uses it for product recommendations. Social media platforms such as Facebook and Twitter use them for friends and content recommendations. Restaurants and hotels use it to generate food-related recommendations. As well as in the research articles, financial services, and life insurance.

Implementing Movie Recommendation System in Python

One simple and direct way to develop a movie recommender system is to use the correlation between the attributes of the movie. Thus, it will find the similarities between the movies to make a suitable recommendation for the user. I used here MovieLense data from Kaggle, and I employed a Machine Learning algorithm to filter data using the content-based filtering method, in the purpose of making those evaluations and predictions. I also used the K-nearest neighbor classifier model, which finds the k most similar items to a particular instance based on a given distance metric.

The diagram below demonstrates the K-nearest neighbor classifier model.

After doing some Exploratory Data Analysis (EDA), I found out that there are only 6 features in the 2 datasets (merged). Thus, I decided to extract new features from the given ones as much as possible. Also, here are some noticed things from exploring the dataset.

About the dataset:

• Number of Movies in the Dataset: 10325 movies

• Number of Users in the Dataset: 668 users

Plot 1:

• Most of the rated movies are having a rate of 4.0

• Only 1198 Movies have a rate of 0.5 (lowest rate)

Plot 2:

• It shows the count of the top 10 genres that the movies in this dataset are categorized.

• The genre that represents the higher number of movies is Drama.

Experiments Results:

After using the K-nearest neighbor classifier as a model to predict the model, its accuracy score was 48.5% and it had beat the baseline’s, by 48.2%.

I tried to implement the model by optimizing it with the GridSearchCV best parameters, but the accuracy did not increase.

Further Recommendations:

Although that I extracted more than 20 features from the 6 ones, there was a shortage of information about the movies and their details! So, I believe that the accuracy score could be better if I had more details related to the movies. (e.g. actors & director)