Art  Lind

Art Lind

1604173860

Explainable AI: Application of shapely values in Marketing Analytics

Recently, I stumbled upon a white paper, which talked about latest in AI applications in Marketing Analytics. It specifically talked about the application of XAI(Explainable AI) in marketing mix modelling [source]. This caught my attention and I started exploring more about three things XAI, the current state of marketing analytics, and XAI’s potential applications in marketing analytics. After going through the available resources, I realized that it has huge potential to reinvent marketing analytics. In this article, firstly we will talk about the specific challenges and their solutions related to current state of marketing analytics. Secondly, we will try to develop an intuition about the XAI and finally, we will implement XAI with some basic marketing dataset. So let’s begin with the challenges and the possible solutions.

Challenges associated with current state in Marketing Analytics and its possible solutions:

There are many challenges but the three significant challenges associated with the current state of Marketing Analytics are related to accuracy of models used(GLMs: Generalized Linear Models), inherent non linearity in market response, and attribution; because of these challenges it becomes very difficult and cumbersome to identify the metrics mentioned below. I am specifically discussing below mentioned metrics because moving forward we will see how a different approach can address these issues.

  1. Channel Attribution

Existing Challenge: This is one of the biggest pain points for marketers. Since there are interactions between channels, so it becomes almost impossible to fairly distribute or assign the payoffs to the different channels.

**Possible Solution: **Shapely values from Cooperative Game Theory comes to rescue here. The Shapley value is a way to fairly distribute the total incremental gains to the collaborating players in the game. In our case the marketing channels are the players cooperating with each other to increase the metrics such as revenue, total conversions etc. Even Google Analytics use shapely values in their Data-Driven Attribution methodology.[source]

2. Interactions of different Marketing Channels

Existing Challenge: There are channels, which as a standalone, are not significant contributors ; however in combination with other channels could play a significant role. Therefore it is important for a marketer to know about the different combinations of the channels, which are interacting with each other. The number of interactions increases significantly as number of channels increases and it becomes very cumbersome to include all such interaction terms in GLMs.

Possible Solution: To address this challenge we will again use the shapely values but in a different way. We will use SHAP algorithm to study the interactions of channels at scale. SHAP algorithm is the implementation of shapely values in machine learning to explain and interpret any black box ML models. Since, we will be replacing GLMs with highly accurate tree based ensemble models in our example, therefore we will be using SHAP to interpret and explain our model.

#marketing #data-science #python #analytics #ai

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Explainable AI: Application of shapely values in Marketing Analytics
Art  Lind

Art Lind

1604173860

Explainable AI: Application of shapely values in Marketing Analytics

Recently, I stumbled upon a white paper, which talked about latest in AI applications in Marketing Analytics. It specifically talked about the application of XAI(Explainable AI) in marketing mix modelling [source]. This caught my attention and I started exploring more about three things XAI, the current state of marketing analytics, and XAI’s potential applications in marketing analytics. After going through the available resources, I realized that it has huge potential to reinvent marketing analytics. In this article, firstly we will talk about the specific challenges and their solutions related to current state of marketing analytics. Secondly, we will try to develop an intuition about the XAI and finally, we will implement XAI with some basic marketing dataset. So let’s begin with the challenges and the possible solutions.

Challenges associated with current state in Marketing Analytics and its possible solutions:

There are many challenges but the three significant challenges associated with the current state of Marketing Analytics are related to accuracy of models used(GLMs: Generalized Linear Models), inherent non linearity in market response, and attribution; because of these challenges it becomes very difficult and cumbersome to identify the metrics mentioned below. I am specifically discussing below mentioned metrics because moving forward we will see how a different approach can address these issues.

  1. Channel Attribution

Existing Challenge: This is one of the biggest pain points for marketers. Since there are interactions between channels, so it becomes almost impossible to fairly distribute or assign the payoffs to the different channels.

**Possible Solution: **Shapely values from Cooperative Game Theory comes to rescue here. The Shapley value is a way to fairly distribute the total incremental gains to the collaborating players in the game. In our case the marketing channels are the players cooperating with each other to increase the metrics such as revenue, total conversions etc. Even Google Analytics use shapely values in their Data-Driven Attribution methodology.[source]

2. Interactions of different Marketing Channels

Existing Challenge: There are channels, which as a standalone, are not significant contributors ; however in combination with other channels could play a significant role. Therefore it is important for a marketer to know about the different combinations of the channels, which are interacting with each other. The number of interactions increases significantly as number of channels increases and it becomes very cumbersome to include all such interaction terms in GLMs.

Possible Solution: To address this challenge we will again use the shapely values but in a different way. We will use SHAP algorithm to study the interactions of channels at scale. SHAP algorithm is the implementation of shapely values in machine learning to explain and interpret any black box ML models. Since, we will be replacing GLMs with highly accurate tree based ensemble models in our example, therefore we will be using SHAP to interpret and explain our model.

#marketing #data-science #python #analytics #ai

Artificial Intelligence & Marketing: Do They Feasibly Match? - TopDevelopers.co

The use of AI in marketing will help to improve user experience and boost ROI with insights on targeted customers using machine learning & deep learning.

With all these examples of how AI technology helps in marketing, the advancing technology is not going to stop making further contributions to transform the world of marketing. The applications of AI with marketing fundamentals that we see today are just some of the initial ones, the Revolution has just begun. The ultra-personalization of services/products is an upward trend and this can only be achieved by knowing your customers closely and predicting the user intent. There are many reasons accountable for why these technologies take on such special importance and will continue to do so in the future with their ever-widening scope of bringing revolutionary changes.

#marketing reshaped by artificial intelligence #ai technology helps in marketing #role of ai in marketing #top digital marketers #applications of ai #ai technology

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Gerhard  Brink

Gerhard Brink

1624006278

The Rising Value of Big Data in Application Monitoring

In an ecosystem that has become increasingly integrated with huge chunks of data and information traveling through the airwaves, Big Data has become irreplaceable for establishments.

From day-to-day business operations to detailed customer interactions, many ventures heavily invest in data sciences and data analysis  to find breakthroughs and marketable insights.

Plus, surviving in the current era, mandates taking informed decisions and surgical precision based on the projected forecast of current trends to retain profitability. Hence these days, data is revered as the most valuable resource.

According to a recent study by Sigma Computing , the world of Big Data is only projected to grow bigger, and by 2025 it is estimated that the global data-sphere will grow to reach 17.5 Zettabytes. FYI one Zettabyte is equal to 1 million Petabytes.

Moreover, the Big Data industry will be worth an estimate of $77 billion by 2023. Furthermore, the Banking sector generates unparalleled quantities of data, with the amount of data generated by the financial industry each second growing by 700% in 2021.

In light of this information, let’s take a quick look at some of the ways application monitoring can use Big Data, along with its growing importance and impact.

#ai in business #ai application #application monitoring #big data #the rising value of big data in application monitoring #application monitoring

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai