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<p>In today&#39;s digital world,&nbsp;<a href="https://gecdesigns.com/blog/impact-of-artificial-intelligence-on-digital-marketing" target="_blank">artificial intelligence</a>&nbsp;(AI) is revolutionizing industries by enabling them to extract insights from raw vast amounts of available data. With AI, businesses can uncover hidden patterns, and market trends, and make more accurate predictions, leading to better decision-making processes. It&#39;s no wonder that AI is leading every industry it is adopted. According to a recent report, 61.4% of marketers have used AI in their marketing activities.</p><p>Let&rsquo;s explore, how today&#39;s marketers are utilizing the potential of AI to enhance the efficiency of their marketing strategies.</p><h2>AI in marketing analytics</h2><p>With AI analytics, users can comprehend not only what happened, but also why it happened, what&#39;s expected to happen next, and the potential outcomes if a specific action is taken. By doing so, AI analytics empowers companies to efficiently manage every aspect of their business, including predicting customer behavior and identifying patterns in user behavior, optimizing strategies to enhance performance, or leveraging opportunities.</p><p>Related post:&nbsp;<a href="https://gecdesigns.com/blog/role-of-big-data-in-digital-marketing" target="_blank">Role of big data in digital marketing</a></p><h3>How do AI analytics transform the marketing world?</h3><p>Here we have listed some types of AI analytics that you can use to enhance business performance,</p><h4>Predictive analytics</h4><p>Predictive analytics is used to analyze data and make predictions.</p><p>Predictive analytics uses machine learning algorithms and historical data to identify the probability of future trends, customer behavior, and outcomes. This technology can help businesses predict customer needs, optimize marketing campaigns, and identify potential churn.</p><p>For instance, by using predictive analytics, businesses can determine which customers are likely to make purchases in the near future and target them more effectively with their marketing strategies. It also helps to determine which products are likely to be popular in the future. This valuable information can be used to create effective marketing strategies and improve product development.</p><p>With the help of AI, predictive analytics has been improved greatly by enabling the creation of more advanced models and algorithms. Regression models, decision trees, and neural networks are the three main types of predictive models.</p><h4>Prescriptive analytics</h4><p>Prescriptive analytics is used to analyze data and make recommendations.</p><p>Prescriptive analytics refers to the utilization of mathematical models, algorithms, and data to prescribe the next course of action. It surpasses predictive and descriptive analytics by suggesting recommendations on what actions to take to achieve a desired goal based on the available data.</p><p>The integration of AI has made prescriptive analytics more precise and influential. AI-based prescriptive analytics involves analyzing data using machine learning and deep learning algorithms to make recommendations based on data patterns.</p><p>Businesses use prescriptive analytics to solve all their problems and marketers use it to improve their advertising for a certain demographic. Prescriptive analytics also help corporations to identify how to engage different customers and how to effectively price and discount their products and services.</p>

<h4>Sentiment analysis</h4><p>The process of sentiment analysis involves the use of AI to analyze text data, such as customer reviews and social media comments, to determine the sentiment expressed by users, whether it is positive, negative, or neutral. Businesses use sentiment analysis to understand public opinion about their brand, products, or industry. This feedback can then be used to refine marketing strategies, respond to customer concerns, and identify emerging trends.</p><h3>Benefits of AI in analytics</h3><h4>Identify new opportunities</h4><p>AI data analytics offer the crucial benefit of precise audience identification and targeting. By analyzing vast datasets, AI algorithms can locate potential customers who have a higher likelihood of converting.</p><p>By studying the demographics, online behavior, and buying patterns of each individual, businesses can create campaigns that resonate with each segment. This targeted approach not only enhances the chances of successful conversions but also helps to avoid wasting resources on audiences that are less likely to engage.</p><h4>Better decision making</h4><p>With the help of AI-driven analytics, users can speed up their decision-making process. Instead of creating insights or complex dashboards and drilling down into them, they can simply type in their demands using natural language and get immediate answers.</p><p>The responses generated by AI provide hidden insights, such as discovering data relationships and patterns and detecting anomalies. This can be very useful for businesses, as they can use these insights to improve their operations, optimize supply chains, enhance stakeholder relationships, elevate customer experiences, and more.</p><h4>Enhanced efficiency and productivity</h4><p>AI in analytics helps businesses to improve their operations with great efficiency and productivity. AI can streamline processes, and it also helps businesses to save time and resources. Marketing teams spend considerable time fine-tuning reports and dashboards. But with the help of AI, analytics will be too easy for marketing teams.</p><p>Apart from this, AI analytics can help businesses to identify pinpoint areas to optimize their workflows.</p><h4>Risk management</h4><p>In the ever-evolving business landscape, it is important to have the ability to anticipate potential risks and vulnerabilities. AI analytics is highly effective in recognizing emerging threats, which empowers organizations to put into place risk management strategies.</p><h4>Personalized marketing</h4><p>By analyzing customer data, marketers can understand customers&#39; unique preferences and behaviors. So that they can deliver personalized marketing campaigns.&nbsp;<strong>The right data can help marketers reach the right people at the right time with the right marketing messages and offers. It helps to improve targeting, higher engagement rates, and happier customers.</strong></p><h4>Successful marketing campaigns</h4><p>AI algorithms can estimate the potential success of a campaign by analyzing historical data and considering various factors. This enables businesses to make informed decisions about resource allocation, minimizing the risk of investing heavily in campaigns that may not yield the desired results. As a result, a valuable marketing budget can be saved.</p><h3>Conclusion</h3><p>The concept of AI in marketing is no longer just a futuristic idea, it has become a reality that is revolutionizing the way businesses interact with and understand their customers. AI is becoming more advanced, which means it can offer greater potential in marketing automation. Companies that integrate this technology will gain a competitive edge in the marketplace and deliver more value to their customers.</p>

AI-driven marketing analytics: Understand past events, predict future outcomes, & optimize strategies. Empower business decisions through predictive insights, enhanced performance.

AI in marketing analytics - how it transforms the marketing world?

 An enthusiastic SEO expert, passion for digital marketing with two years of expertise in writing Digital Marketing and SEO content. She is a Master of Business Administration graduate from a reputed university in south India. Her passion for SEO and online marketing helps her to stay up to date with the trends and strategies. Follow her on social media sites, to stay up to date with SEO, and Digital Marketing, Updates. To contact Raji, visit the contact page.

Content writer and Marketer

<p>Market basket analysis is the most important topic for every online or offline retail business. Let us begin by understanding the basics of market basket analysis.</p>

<h2>What is market basket analysis?</h2>

<p>Market basket analysis is a modeling method used to identify the products purchased together. In other words, if a customer buys a product, what is the probability that he/she will buy another product along. Market Basket analysis also called Affinity Analysis.</p>

<p>The objective of market basket analysis is to increase sales by identifying the products bought together by customers. Based on this data or prediction a recommendation can be displayed on the e-commerce website.</p>

<p>Before we move on to the association rules and measures of market basket analysis, let us understand the recommender system. It will give you a complete picture of the market basket analysis concept and objective.</p>

<h4>Recommender System</h4>

<p>The recommender system primarily collects the data of customers purchasing behavior and predicts the possibility of products bought together. Based on the prediction of a buyer&#39;s preferences, it recommends a list of products that the customer is more likely to buy. Amazon, Netflix are great examples of recommender system usage.</p>

<h3>Types of Recommender System</h3>

<h4>1. Knowledge-based Recommendations</h4>

<p>The knowledge base system primarily predicts based on the products bought together. Examples, retail stores, or supermarkets.</p>

<h4>2. Collaborative Filtering Models</h4>

<p>Collaborative Filtering uses the data collected based on the buyer&#39;s preferences and recommends products to a similar type of buyer. For example, Online stores</p>

<h4>3. Content-based Recommendations</h4>

<p>The content-based support system predicts based on the historical purchase data of customers. For example, Movies, News, etc.</p>

<h4>Association Rules</h4>

<p>There are multiple methods to analyze the relationships between products. Association rules are the techniques that help to study the connection between products in common. The result of these methods is the set of rules to describe if this, then that.</p>

<h5>Type of Association</h5>

<p>1. Useful &ndash; Example, On Saturday, Hyper city customers purchase diapers and beer together.</p>

<p>2. Trivial &ndash; Example, Croma customers who purchase maintenance contracts also purchase large appliances.</p>

<p>3. Inexplicable &ndash; Example, when a new home depot store opens in an area, the most commonly sold items are toilet rings.</p>

<h4>Three Important Measures</h4>

<p>To understand the Market basket analysis measures better we have used the below case.</p>

<p>Assume 100 Customers visit a store</p>

<p>10 of them bought bread</p>

<p>8 of them bought butter, and</p>

<p>6 bought both bread and butter</p>

<p>Bought bread=&gt; bought butter</p>

<p>Support&nbsp;= P(Buy both Bread &amp; Butter) = 6/100 = 0.06</p>

<p>Confidence&nbsp;= P(Buy both Bread &amp; Butter)/P(Buy Bread) = 0.06/0.08 = 0.75</p>

<ul>
	<li><em>This is a conditional probability, what is the probability that the customer who has bought bread will also buy Butter</em>

	<p>Lift&nbsp;= Confidence/P(Buy Bread) = 0.75/0.1 = 7.5</p>
	<em>The customers who bought bread are 7.5% likely to buy butter as well. Lift is the measure that helps to improve the performance</em></li>
</ul>

<h3>Why is Market Basket Analysis Important for e-commerce Business?</h3>

<p>Market Basket Analysis helps to increase sales by recommending appropriate products to the customers who are likely to buy. It improves the store credibility and user experience by providing specific recommendations. Also, reduces the time spent by the customers on product search.</p>

<h3>Market Basket Analysis with R</h3>

<p><a download="" href="https://gecdesigns.com/doc/groceries-market-basket-analysis.csv">Groceries DataSet&nbsp;</a></p>

<p><a download="" href="https://gecdesigns.com/doc/marketbasketanalysis.R">Click here to Download Market Basket Analysis R code (R Document)&nbsp;</a></p>

<p>In the given Groceries DataSet, we have 9835 transactions. Each line contains a list of products bought together by customers. Our objective is to do the market basket analysis to find actionable and meaningful patterns or associations with the purchasing of products by consumers. This knowledge could be leveraged in-store layout and boosting sales and online (e-commerce store) product recommendations. To do the market basket analysis for the given data set we are following the below steps,</p>

<p>1. Exploratory data analysis</p>

<p>2. Market basket analysis using R</p>

<p>3. Displaying the result to Analyse the insight</p>

<p>4. Conclusion</p>

<h4>Exploratory data analysis</h4>

<p>There are 9835 transaction line items with 169 columns. Transactions as item Matrix in sparse format with 9835 rows (elements/item sets/transactions) and 169 columns (items) and a density of 0.02609146.</p>

<p>Most frequently purchased products list</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-01.jpg" target="_blank"><img alt="Market Basket Analysis Image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-01.jpg" /></a></p>

<h4>Summary of Groceries data set</h4>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-02.jpg" target="_blank"><img alt="Market Basket Analysis Image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-02.jpg" /></a></p>

<p>Density is the total number of none empty cells in the product data, in other words, the total number of products bought divided by the total number of possible products in the provided data.</p>

<p>In the most frequently purchased items, we can see that the whole milk is the most purchased in 2513 transactions. The second most purchased item is Vegetables 1903. If you add all these items, we get the total number of purchased items in the given dataset, which is 43,367 total items.</p>

<p>Elements section represents the size of the transactions, there are 2159 transactions that have only one item and 1643 transactions have only two items so on. There is only one transaction with 32 items and it occurred only once.</p>

<p>From the five-number summary stats, we can understand that the minimum transaction product is one and max is 32 with a median of 3 items per transaction. Also, it is a right-skewed distribution data. In the 2nd and 3rd quartile, we can interpret that 50% of the transactions have 2 to 6 products, only 25% of transactions have over 6 products purchased.</p>

<h4>Displaying frequently purchased products graph</h4>

<p>Analyzing the items that are having a purchase frequency of 10%</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-03-a.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-03-a.jpg" /></a></p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-03.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-03.jpg" /></a></p>

<p>From the above graph, we can understand that these are the products having purchase frequency of 10% and above.</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-04.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-04.jpg" /></a></p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-05.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-05.jpg" /></a></p>

<p>The above graph represents the top 20 frequently purchased products. This matrix will help in terms of product focus and layout. The top 5 products are Whole milk, Vegetables, Rolls/buns, Soda, and Yogurt.</p>

<h3>Market basket analysis using R</h3>

<p>In the rule setting section, we are determining the level of support and confidence we want for this basket analysis. So here, we are creating the basket with the Support level of 0.001, and the confidence level of 0.8. Here we are trying to build the baskets, which can significantly occur. This helps us to analyze the product&#39;s associate better.</p>

<p>Now let us interpret the rules output we have got for the given data set. Creating the rules to basket the products</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-06.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-06.jpg" /></a></p>

<p>Displaying the result to analyze the insight</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-07.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-07.jpg" /></a></p>

<p>With the given support and confidence parameter we have 410 rules in order to create the baskets. From the summary, we can understand that there are 29 rules for 3 products, 229 rules for 4 products, 140 rules for 5 products and 12 rules for 6 products.</p>

<p>Now let us inspect the first few rules to study them. Below listed are 20 rules for references.</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-08.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-08.jpg" /></a></p>

<ul>
	<li>The rule 1 reads, the consumer who have bought rice and sugar more likely to purchase whole milk, with the support level of 0.0012 and the confidence level of 1. This occurred 12 products in this data set. Lift is simply the confidence over the support of whole milk. So Higher lift is always better for product association, the higher lift is what needs more focus and action on.</li>
	<li>The better understanding of lift, in the first line the lift score indicates that whole milk is likely to be purchased almost 4 times higher along with rice &amp; sugar compared with general purchase.</li>
</ul>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-09.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-09.jpg" /></a></p>

<ul>
	<li>Likewise, in the rule, 13 Brown bread, and pip fruit and whipped or sour cream purchased consumer will 5 times likely to by Vegetables with the confidence level of 1.</li>
	<li>Rule 13 to 16 are the 4 rules with the highest lift.</li>
</ul>

<p>Now let us plot the 20 rules in a graph for better representation,</p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-10.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-10.jpg" /></a></p>

<p><a href="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-11.jpg" target="_blank"><img alt="Market basket analysis image" src="https://gecdesigns.com/img/blog/market-basket/market-basket-analysis-11.jpg" /></a></p>

<ul>
	<li>This 20 rules products association graph helps to identify the associated products visually.</li>
</ul>

<h3>Conclusion</h3>

<ul>
	<li>We have identified the product association using market basket analysis. The top 20 rules will help the seller to determine their marketing strategy to promote sales.</li>
	<li>What we have identified by market basket analysis can be used to planning store layouts.</li>
	<li>&ldquo;The concept of loss leader&rdquo; are the products most frequently purchased and relatively low-value items which will attract the consumer to walk into the store. Once the consumers are in, the market basket analysis will help to promote and boost sales.</li>
</ul>

<h3>Benefits of Market Basket Analysis</h3>

<ul>
	<li>Increase sales by providing best recommendations</li>
	<li>Most appropriate when several different products are being recommended to the same Buyer</li>
	<li>Improves user experience</li>
	<li>Bundles products that go together</li>
	<li>Helps to gain the efficiency of overall e-commerce store performance.</li>
	<li>Also useful in placing related products close-by in physical stores and displaying in the online store</li>
</ul>

What is Market Basket Analysis? How can market basket analysis help e-commerce business?

Market basket analysis in e-commerce business explained (A Case study)

An energetic entrepreneur with 12 years of corporate experience in the field of brokerage operations and functions. A Data Science Aspirant - Business Analytics and Business Intelligence postgraduate professional from a reputed B-School in India. His passion for Data Visualization, Web and social media Analytics helped him to become a learner, speaker, and writer in the space of SEO, Digital Marketing, and UI, UX designs.

 Business Owner

<h2>Introduction to Web and Social Media Analytics (WSMA)</h2>

<h3>Web Analytics</h3>

<p>First, Let us understand Web Analytics, by definition &ldquo;Web analytics is the measurement of data, the collection of information, analysis, and reporting of Internet data for the purposes of optimizing and understanding Web usage. Here the web usage refers to the respective business website usage data.</p>

<p>What are all the information can be gathered from web usage data? And, how could it be leveraged for business benefits? Let us discuss these questions later in this article.</p>

<h3>Social Media Analytics</h3>

<p>Now Let us understand social media analytics. Social media analytics is the practice of gathering data from social media websites or networks such as Facebook, Twitter, Google plus, etc., and analyzing those metrics to understand insights to make business decisions.</p>

<p>Most often people confuse web analytics and Social media Analytics, let us have a clear demarcation between these two. Web analytics uses the data collected directly from a particular business website and Social media analytics uses the data collected from social media networks.</p>

<h3>What information can be gathered from Web Analytics?</h3>

<p>Web analytics Gathers information from the Business website only. In general, Web Analytics tells you about your traffic levels, referral sources, bounce rate, and user behavior on your website.</p>

<p>Web Analytics mainly used to improve the user experience and conversion rate. Below are the few important analysis can be done through web analytics,</p>

<ul>
	<li>Where are your website visitors coming from?</li>
	<li>On which page of your website they are spending more time?</li>
	<li>How are they connecting to your website?</li>
	<li>Which part of the day is your website getting more traffic?</li>
	<li>How well do you retain users?</li>
</ul>

<p>In other words, the four important key metrics can be analyzed from web analytics,</p>

<ul>
	<li>1. Total Traffic</li>
	<li>2. Traffic Source</li>
	<li>3. Bounce Rate</li>
	<li>4. Conversion rate</li>
</ul>

<h4>Total Traffic</h4>

<p>Total Traffic to your website gives insights about where are you getting more traffic, which helps you to understand your target market. In addition, you can analyze which hours of the day and days of the week, you are getting more visits to your websites. Based on this information you can run a campaign to optimize more conversions.</p>

<h4>Traffic Source</h4>

<p>Traffic source is about how you are getting most visitors to your website. Is it through social media, search engines or Referral Sites? Based on that information you can strategize your marketing campaign or write a blog or focusing on a particular social media network. For example, if most of your visitors are coming from social media networks, use that information to brand your business more on Facebook, Twitter or any other social media platforms to boost your website traffic.</p>

<h4>Bounce Rate</h4>

<p>Bounce rate is the percentage of visitors to a particular website who are leaving the site after viewing only one page without navigating other pages on the website.</p>

<p>This could be higher for any number of reasons may be,</p>

<ul>
	<li>Irrelevant content</li>
	<li>Inappropriate designs</li>
	<li>Confusing navigation</li>
	<li>Frequent pop-ups</li>
	<li>Unnecessary ads</li>
	<li>Or, Annoying sounds</li>
</ul>

<p>However, this metric helps you to improve your web design overall.</p>

<h4>Conversion Rate</h4>

<p>A conversion rate is the percentage of visitors who have taken some action on your website or complete the desired goal; it could be purchasing a product, Sign up for news letters, etc.</p>

<p>There are many tools available in the market to track and analyze web data, however, Google Analytics is one of the vital tools for any websites, as it offers the chance to obtain and analyze in-depth statistics about who your customers are, what they are interested in, and how they interact with your website or online store. When you take advantage of the information, which Google Analytics offers, you can make data-informed decisions about the best ways to improve business.</p>

<p>Some important insights that google analytics offers are,</p>

<h4>Audience Overview Website Traffic trend</h4>

<p><a href="https://gecdesigns.com/img/blog/web-analytics/wsma_02.jpg" target="_blank"><img alt="Web and Social Media analytics image" src="https://gecdesigns.com/img/blog/web-analytics/wsma_02.jpg" /></a></p>

<h4>Users Behaviour and Bounce rate metrics</h4>

<p><a href="https://gecdesigns.com/img/blog/web-analytics/wsma_03.jpg" target="_blank"><img alt="Web and Social Media analytics image" src="https://gecdesigns.com/img/blog/web-analytics/wsma_03.jpg" /></a></p>

<h4>Demographics: Overview</h4>

<p><a href="https://gecdesigns.com/img/blog/web-analytics/wsma_04.jpg" target="_blank"><img alt="Web and Social Media analytics image" src="https://gecdesigns.com/img/blog/web-analytics/wsma_04.jpg" /></a></p>

<p>There are many more questions that can be answered through google analytics metrics such as,</p>

<ul>
	<li>How do you acquire users?</li>
	<li>How are your active users trending over time?</li>
	<li>How well do you retain users?</li>
	<li>When do your users visit?</li>
	<li>Where are your users?</li>
	<li>What are your top devices?</li>
	<li>What pages do your users visit?</li>
	<li>How are you performing against goals?</li>
	<li>What are your top-selling products?</li>
	<li>How are your AdWords campaigns performing?</li>
</ul>

<p>In a separate article, we will discuss how does google analytics help to master web analytics to maximize website conversion rate.</p>

<p>Let us now move on to Social media analytics.</p>

<h3>What information can be gathered from Social media Analytics?</h3>

<p>Social media analytics gathers information from social media networking sites and helps businesses better understand customer sentiment, users&rsquo; attitudes, build rich consumer profiles, and, most importantly, build effective business strategies.</p>

<p>There are many tools available in the market to track and analyze social media data.</p>

<p>The most common use of social media analytics is to discover</p>

<ul>
	<li>Sentiments</li>
	<li>Opinions</li>
	<li>Emotions</li>
	<li>Topics</li>
</ul>

<p>Put together known as Sentiment Analysis.</p>

<h3>Sentiment Analysis</h3>

<p>It is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer&#39;s attitude towards a particular topic, product, etc. is positive, negative, or neutral. Opinion mining or sentiment analysis refers to the use of natural language processing.</p>

<p>Importantly, the first step is to define which business goal we are trying to address through social media analytics.</p>

<p>In general, the business objectives include increasing revenues, reducing customer service costs, getting feedback on products and services, and improving public opinion of a particular product or business division.</p>

<h4>A quick recap of information gathering</h4>

<p><a href="https://gecdesigns.com/img/blog/web-analytics/wsma_info.jpg" target="_blank"><img alt="Web and Social Media analytics infographic" src="https://gecdesigns.com/img/blog/web-analytics/wsma_info.jpg" /></a></p>

<h4>Importance of social media analytics</h4>

<p>There is a tremendous amount of information in social media data. In decades past, enterprises paid market research companies to poll consumers and conduct focus groups to get the kind of information that consumers now willingly post to public social media platforms.</p>

<p>In the past few years, businesses have rushed to use Web and Social Media widely. Almost 94% of all businesses now use some form of social media to promote their brand and engage with customers. While companies sprint to master web and social media marketing, the analysis of data available in web and social media more of a struggle.</p>

<p>The problem is this information is in the form of free text and natural language, the kind of unstructured data that analytics algorithms have traditionally. However, as machine learning and artificial intelligence have advanced, it has become easier for businesses to quantify in a scalable way the information in social media posts.</p>

<p>This allows enterprises to extract information about how the public perceives their brand, what kind of products consumers like and dislike and generally, where markets are going. Social media analytics makes it possible for businesses to quantify all this without using less reliable polling and focus groups.</p>

<p><iframe frameborder="0" height="480" src="https://www.youtube.com/embed/frC74NvVSVw" width="100%"></iframe></p>

<p>&quot;Web and Social media analytics is the backbone for every business moving forward.</p>

An introduction to Web and Social Media Analytics for beginners. Overview of How these data-driven techniques can transform your online business results.

AI in marketing analytics - how it transforms the marketing world?

Market basket analysis in e-commerce business explained (A Case study)

Web and social media analytics: A data-driven Optimization techniques

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