Article | June 21, 2021
The marketing industry keeps changing every year. Businesses and enterprises have the task of keeping up with the changes in marketing trends as they evolve. As consumer demands and behavior changed, brands had to move from traditional marketing channels like print and electronic to digital channels like social media, Google Ads, YouTube, and more. Businesses have begun to consider marketing analytics a crucial component of marketing as they are the primary reason for success.
In uncertain times, marketing analytics tools calculate and evaluate the market status and enhances better planning for enterprises.
As Covid-19 hit the world, organizations that used traditional marketing analytics tools and relied on historical data realized that many of these models became irrelevant. The pandemic rendered a lot of data useless.
With machine learning (ML) and artificial intelligence (AI) in marketers’ arsenal, marketing analytics is turning virtual with a shift in the marketing landscape in 2021. They are also pivoting from relying on just AI technologies but rather combining big data with it.
AI and machine learning help advertisers and marketers to improve their target audience and re-strategize their campaigns through advanced marketing attributes, which in turn increases customer retention and customer loyalty.
While technology is making targeting and measuring possible, marketers have had to reassure their commitment to consumer privacy and data regulations and governance in their initiatives. They are also relying on third-party data.
These data and analytics trends will help organizations deal with radical changes and uncertainties, with opportunities they bring with them over the next few years.
To know why businesses are gravitating towards these trends in marketing analytics, let us look at why it is so important.
Importance of Marketing Analytics
As businesses extended into new marketing categories, new technologies were implemented to support them. This new technology was usually deployed in isolation, which resulted in assorted and disconnected data sets.
Usually, marketers based their decisions on data from individual channels like website metrics, not considering other marketers channels. Website and social media metrics alone are not enough. In contrast, marketing analytics tools look at all marketing done across channels over a period of time that is vital for sound decision-making and effective program execution.
Marketing analytics helps understand how well a campaign is working to achieve business goals or key performance indicators.
Marketing analytics allows you to answer questions like:
• How are your marketing initiatives/ campaigns working? What can be done to improve them?
• How do your marketing campaigns compare with others? What are they spending their time and money on? What marketing analytics software are they using that helps them?
• What should be your next step? How should you allocate the marketing budget according to your current spending?
Now that the advantages of marketing analytics are clear, let us get into the details of the trends in marketing analytics of 2021:
Rise of real-time marketing data analytics
Reciprocation to any action is the biggest trend right now in digital marketing, especially post Covid. Brands and businesses strive to respond to customer queries and provide them with solutions. Running queries in a low-latency customer data platform have allowed marketers to filter the view by the audience and identify underachieving sectors. Once this data is collected, businesses and brands can then readjust their customer targeting and messaging to optimize their performance.
To achieve this on a larger scale, organizations need to invest in marketing analytics software and platforms to balance data loads with processing for business intelligence and analytics. The platform needs to allow different types of jobs to run parallel by adding resources to groups as required. This gives data scientists more flexibility and access to response data at any given time.
Real-time analytics will also aid marketers in identifying underlying threats and problems in their strategies. Marketers will have to conduct a SWOT analysis and continuously optimize their campaigns to suit them better.
Data security, regulatory compliance, and protecting consumer privacy
Protecting market data from a rise in cybercrimes and breaches are crucial problems to be addressed in 2021. This year has seen a surge in data breaches that have damaged businesses and their infrastructures to different levels. As a result, marketers have increased their investments in encryption, access control, network monitoring, and other security measures.
To help comply with the General Data Protection Regulation (GDPR) of the European Union, the California Consumer Privacy Act (CCPA), and other regulatory bodies, organizations have made the shift to platforms where all consumer data is in one place. Advanced encryptions and stateless computing have made it possible to securely store and share governed data that can be kept in a single location. Interacting with a single copy of the same data will help compliance officers tasked with identifying and deleting every piece of information related to a particular customer much easier and the possibility of overseeing something gets canceled.
Protecting consumer privacy is imperative for marketers. They offer consumers the control to opt out, eradicate their data once they have left the platform, and remove information like location, access control to personally identifiable information like email addresses and billing details separated from other marketing data.
Predictive analytics’ analyzes collected data and predicts future outcomes through ML and AI. It maps out a lookalike audience and identifies which strata are most likely to become a high-value customer and which customer strata has the highest likelihood of churn. It also gauges people’s interests based on their browsing history. With better ML models, predictions have become better overtime, leading to increased customer retention and a drop in churn.
According to the research by Zion Market Research, by 2022, the global market for predictive analytics is set to hit $11 billion.
Investment in first-party data
Cookies-enabled website tracking led marketers to know who was visiting their website and re-calibrate their ads to these people throughout the web.
However, in 2020, Google announced cookies would be phased out of Chrome within two years while they had already removed them from Safari and Firefox.
Now that adding low-friction tracking to web pages will be tough, marketers will have to gather more limited data. This will then be then integrated with first-party data sets to get a rounded view of the customer. Although a big win for consumer privacy activists, it is difficult for advertisers and agencies to find it more difficult to retarget ads and build audiences in their data management platforms.
In a digital world without cookies, marketers now understand how customer data is collected, introspect on their marketing models, and evaluate their marketing strategy.
Emergence of contextual customer experience
These trends in marketing analytics have become more contextually conscious since the denunciation of cookies. Since marketers are losing their data sets and behavioral data, they have an added motivation to invest in insights.
This means that marketers have to target messaging based on known and inferred customer characteristics like their age, location, income, brand affinity, and where these customers are in their buying journey. For example, marketers should tailor messaging in ads to make up consumers based on the frequency of their visits to the store.
Effective contextual targeting hinges upon marketers using a single platform for their data and creates a holistic customer profile.
Reliance on third-party data
Even though there has been a drop in third-party data collection, marketers will continue to invest in third-party data which have a complete understanding of their customers that augments the first-party data they have.
Historically, third-party data has been difficult to source and maintain for marketers. There are new platforms that counter improvement of data like long time to value, cost of maintaining third-party data pipelines, and data governance problems.
U.S. marketers have spent upwards of $11.9 billion on third-party audience data in 2019, up 6.1% from 2018, and this reported growth curve is going to be even steeper in 2021, according to a study by Interactive Advertising Bureau and Winterberry Group.
Marketing analytics enables more successful marketing as it shows off direct results of the marketing efforts and investments.
These new marketing data analytics trends have made their definite mark and are set to make this year interesting with data and AI-based applications mixed with the changing landscape of marketing channels. Digital marketing will be in demand more than ever as people are purchasing more online.
Frequently Asked Questions
Why is marketing analytics so important?
Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity.
What is the use of marketing analytics?
Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods.
Which industries use marketing analytics?
Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically.
What are the types of marketing analytics tools?
Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc.
"name": "Why is marketing analytics so important?",
"text": "Marketing analytics has two main purposes; to gauge how well your marketing efforts perform and measure the effectiveness of marketing activity."
"name": "What is the use of marketing analytics?",
"text": "Marketing analytics help us understand how everything plays off of each other and decide how to invest, whether to re-prioritize or keep going with the current methods."
"name": "Which industries use marketing analytics?",
"text": "Commercial organizations use it to analyze data from different sources, use analytics to determine the success of a marketing campaign, and target customers specifically."
"name": "What are the types of marketing analytics tools?",
"text": "Some marketing analytics’ tools are Google Analytics, HubSpot Marketing Hub, Semrush, Looker, Optimizely, etc."
Article | January 28, 2021
Since the internet became popular, the way we purchase things has evolved from a simple process to a more complicated process. Unlike traditional shopping, it is not possible to experience the products first-hand when purchasing online. Not only this, but there are more options or variants in a single product than ever before, which makes it more challenging to decide.
To not make a bad investment, the consumer has to rely heavily on the customer reviews posted by people who are using the product. However, sorting through relevant reviews at multiple eCommerce platforms of different products and then comparing them to choose can work too much. To provide a solution to this problem, Amazon has come up with sentiment analysis using product review data. Amazon performs sentiment analysis on product review data with Artificial Intelligence technology to develop the best suitable products for the customer. This technology enables Amazon to create products that are most likely to be ideal for the customer.
A consumer wants to search for only relevant and useful reviews when deciding on a product. A rating system is an excellent way to determine the quality and efficiency of a product. However, it still cannot provide complete information about the product as ratings can be biased. Textual detailed reviews are necessary to improve the consumer experience and in helping them make informed choices. Consumer experience is a vital tool to understand the customer's behavior and increase sales.
Amazon has come up with a unique way to make things easier for their customers. They do not promote products that look similar to the other customer's search history. Instead, they recommend products that are similar to the product a user is searching for. This way, they guide the customer using the correlation between the products.
To understand this concept better, we must understand how Amazon's recommendation algorithm has upgraded with time.
The history of Amazon's recommendation algorithm
Before Amazon started a sentiment analysis of customer product reviews using machine learning, they used the same collaborative filtering to make recommendations. Collaborative filtering is the most used way to recommend products online. Earlier, people used user-based collaborative filtering, which was not suitable as there were many uncounted factors.
Researchers at Amazon came up with a better way to recommend products that depend on the correlation between products instead of similarities between customers. In user-based collaborative filtering, a customer would be shown recommendations based on people's purchase history with similar search history. In item-to-item collaborative filtering, people are shown recommendations of similar products to their recent purchase history. For example, if a person bought a mobile phone, he will be shown hints of that phone's accessories.
Amazon's Personalization team found that using purchase history at a product level can provide better recommendations. This way of filtering also offered a better computational advantage. User-based collaborative filtering requires analyzing several users that have similar shopping history. This process is time-consuming as there are several demographic factors to consider, such as location, gender, age, etc. Also, a customer's shopping history can change in a day. To keep the data relevant, you would have to update the index storing the shopping history daily.
However, item-to-item collaborative filtering is easy to maintain as only a tiny subset of the website's customers purchase a specific product. Computing a list of individuals who bought a particular item is much easier than analyzing all the site's customers for similar shopping history. However, there is a proper science between calculating the relatedness of a product. You cannot merely count the number of times a person bought two items together, as that would not make accurate recommendations.
Amazon research uses a relatedness metric to come up with recommendations. If a person purchased an item X, then the item Y will only be related to the person if purchasers of item X are more likely to buy item Y. If users who purchased the item X are more likely to purchase the item Y, then only it is considered to be an accurate recommendation.
In order to provide a good recommendation to a customer, you must show products that have a higher chance of being relevant. There are countless products on Amazon's marketplace, and the customer will not go through several of them to figure out the best one. Eventually, the customer will become frustrated with thousands of options and choose to try a different platform. So Amazon has to develop a unique and efficient way to recommend the products that work better than its competition.
User-based collaborative filtering was working fine until the competition increased. As the product listing has increased in the marketplace, you cannot merely rely on previous working algorithms. There are more filters and factors to consider than there were before. Item-to-item collaborative filtering is much more efficient as it automatically filters out products that are likely to be purchased. This limits the factors that require analysis to provide useful recommendations.
Amazon has grown into the biggest marketplace in the industry as customers trust and rely on its service. They frequently make changes to fit the recent trends and provide the best customer experience possible.
Article | June 18, 2021
Data is an important asset. Data leads to innovation and organizations tend to compete for leading these innovations on a global scale. Today, every business requires data and insights to stay relevant in the market. Big Data has a huge impact on the way organizations conduct their businesses. Big Data is used in different enterprises like travel, healthcare, manufacturing, governments, and more. If they need to determine their audience, understand what clients want, forecast the needs of the customers and the clients, AI and big data analysis is vital to every decision-making scenario. When companies process the collected data accurately, they get the desired results, which leads them to their desired goals.
The term Big Data has been around since the 1990s. By the time we could fully comprehend it, Big Data had already amassed a huge amount of stored data. If this data is analyzed properly, it would reveal valuable industry insights into the industry to which the data belonged.
IT professionals and computer scientists realized that going through all of the data and analyzing it for the purpose was too big of a task for humans to undertake. When artificial intelligence (AI) algorithm came into the picture, it accomplished analyzing the accumulated data and deriving insights. The use of AI in Big Data is fundamental to get desired results for organizations.
According to Northeastern University, the amount of data in the world was 4.4 zettabytes in 2013. By of 2020, the data rose to 44 zettabytes.
When there is this amount of data produced globally, this information is invaluable to the enterprises and now can leverage AI algorithms to process it. Because of this, the companies can understand and influence customer behavior. By 2018, over 50% of countries had adopted Big Data.
Let us understand what Big Data, convergence of big data and AI, and impact of AI on big data analytics.
Understanding Big Data
In simple words, Big Data is a term that comprises every tool and process that helps people use and manage vast sets of data. According to Gartner, Big Data is a “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing to enable enhanced insight, decision-making, and process automation.”
The concept of Big Data was created to capture trends, preferences, and user behavior in one place called the data lake. Big Data in enterprises can help them analyze and configure their customers’ motivations and come up with new ideas for the creation of new offerings. Big Data studies different methods of extracting, analyzing, or dealing with data sets that are too complicated for traditional data processing systems. To analyze a large amount of data requires a system designed to stretch its extraction and analysis capability.
Data is everywhere. This stockpile of data can give us insights and business analytics to the industry belonging to the data set. Therefore, the AI algorithms are written to benefit from large and complex data.
Importance of Big Data
Data is an integral part of understanding customer demographics and their motivations.
When customers interact with technology in active or passive manner, these actions create a new set of data. What contributes to this data creation is what they carry with them every day - their smartphones. Their cameras, credit cards, purchased products all contribute to their growing data profile. A correctly done analysis can tell a lot about their behavior patterns, personality, and events in the customer’s life. Companies can use this information to rethink their strategies, improve on their product, and create targeted marketing campaigns, which would ultimately lead them to their target customer.
Industry experts, for years and years, have discussed Big Data and its impact on businesses. Only in recent years, however, has it become possible to calculate that impact. Algorithms and software can now analyze large datasets quickly and efficiently.The forty-four zettabyte of data will only quadruple in the coming years. This collection and analysis of the data will help companies get the AI insights that will aid them in generating profits and be future-ready.
Organizations have been using Big Data for a long time. Here’s how those organizations are using Big Data to drive success:
Answering customer questions
Using big data and analytics, companies can learn the following things:
• What do customers want?
• Where are they missing out on?
• Who are their best and loyal customers?
• Why people choose different products?
Every day, as organizations gather more information, they can get more insights into sales and marketing. Once they get this data, they can optimize their campaigns to suit the customer’s needs. Learning from their online habits and with correct analysis, companies can send personalized promotional emails. These emails may prompt this target audience to convert into full-time customers.
Making confident decisions
As companies grow, they all need to make complex decisions. With in-depth analysis of marketplace knowledge, industry, and customers, Big Data can help you make confident choices. Big Data gives you a complete overview of everything you need to know. With the help of this, you can launch your marketing campaign or launch a new product in the market, or make a focused decision to generate the highest ROI. Once you add machine learning and AI to the mix, your Big Data collections can form a neural network to help your AI suggest useful company changes.
Optimizing and Understanding Business Processes
Cloud computing and machine learning help you to stay ahead by identifying opportunities in your company’s practices. Big Data analytics can tell you if your email strategy is working even when your social media marketing isn’t gaining you any following. You can also check which parts of your company culture have the right impact and result in the desired turnover. The existing evidence can help you make quick decisions and ensure you spend more of your budget on things that help your business grow.
Convergence of Big Data and AI
Big Data and Artificial Intelligence have a synergistic relationship. Data powers AI. The constantly evolving data sets or Big Data makes it possible for machine learning applications to learn and acquire new skills. This is what they were built to do. Big Data’s role in AI is supplying algorithms with all the essential information for developing and improving features, pattern recognition capabilities.
AI and machine learning use data that has been cleansed of duplicate and unnecessary data. This clean and high-quality big data is then utilized to create and train intelligent AI algorithms, neural networks, and predictive models.
AI applications rarely stop working and learning. Once the “initial training” is done (initial training is preparing already collected data), they adjust their work as and when the data changes. This makes it necessary for data to be constantly collected.
When it comes to businesses using this technology, AI helps them use Big Data for analytics by making advanced tools accessible and obtainable to help users gain insights that would otherwise have been hidden in the huge amount of data. Once firms and businesses gain a hold on using AI and Big Data, they can provide decision-makers with a clear understanding of factors that affect their businesses.
Impact of AI on Big Data Analytics
AI supports users in the Big Data cycle, including aggregation, storage, and retrieval of diverse data types from different data sources. This includes data management, context management, decision management, action management, and risk management.
Big Data can help alert problems and help find new solutions and get ideas about any new prospects. With the amount of information stream that comes in, it can be difficult to determine what is important and what isn’t. This is where AI and machine learning come in. It can help identify unusual patterns in the processes, help in the analysis, and suggest further steps to be taken.
It can also learn how users interact with analytics and learn subtle differences in meanings or context-specific nuances to understand numeric data sources. AI can also caution users about anomalies, unforeseen data patterns, monitoring events, and threats from system logs or social networking data.
Application of Big Data and Artificial Intelligence
After establishing how AI and Big Data work together, let us look at how some applications are benefitting from their synergy:
Banking and financial sectors
The banking and financial sectors apply these to monitor financial marketing activities. These institutions also use AI to keep an eye on any illegal trading activities. Trading data analytics are obtained for high-frequency trading, and decision making based on trading, risk analysis, and predictive analysis. It is also used for fraud warning and detection, archival and analysis of audit trails, reporting enterprise credit, customer data transformation, etc.
AI has simplified health data prescriptions and health analysis, thus benefitting healthcare providers from the large data pool. Hospitals are using millions of collected data that allow doctors to use evidence-based medicine. Chronic diseases can be tracked faster by AI.
Manufacturing and supply chain
AI and Big Data in manufacturing, production management, supply chain management and analysis, and customer satisfaction techniques are flawless. The quality of products is thus much better with higher energy efficiency, reliable increase in levels, and profit increase.
Governments worldwide use AI applications like facial recognition, vehicle recognition for traffic management, population demographics, financial classifications, energy explorations, environmental conservation, criminal investigations, and more.
Other sectors that use AI are mainly retail, entertainment, education, and more.
According to Gartner’s predictions, artificial intelligence will replace one in five workers by 2022. Firms and businesses can no longer afford to avoid using artificial intelligence and Big Data in their day-to-day. Investments in AI and Big Data analysis will be beneficial for everyone. Data sets will increase in the future, and with it, its application and investment will grow over time. Human relevance will continue to decrease as time goes by.
AI enables machine learning to be the future of the development of business technologies. It will automate data analysis and find new insights that were previously impossible to imagine by processing data manually. With machine learning, AI, and Big Data, we can redraw the way we approach everything else.
Frequently Asked Questions
Why does big data affect artificial intelligence?
Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents.
Is AI or big data better?
AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale.
Is AI used in big data?
When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI.
What is the future of AI in big data?
AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics.
"name": "Why does big data affect artificial intelligence?",
"text": "Big Data and AI customize business processes and make better-suited decisions for individual needs and expectations. This improves its efficiency of processes and decisions. Data has the potential to give insights into a variety of predicted behaviors and incidents."
"name": "Is AI or big data better?",
"text": "AI becomes better as it is fed more and more information. This information is gathered from Big Data which helps companies understand their customers better. On the other hand, Big Data is useless if there is no AI to analyze it. Humans are not capable of analyzing the data on a large scale."
"name": "Is AI used in big data?",
"text": "When the gathered Big Data is to be analyzed, AI steps in to do the job. Big Data makes use of AI."
"name": "What is the future of AI in big data?",
"text": "AI’s ability to work so well with data analytics is the primary reason why AI and Big Data now seem inseparable. AI machine learning and deep learning are learning from every data input and using those inputs to generate new rules for future business analytics."
Article | May 17, 2021
One approach for better data utilization is the data fabric, a data management approach that arranges data in a single "fabric" that spans multiple systems and endpoints. The goal of the fabric is to link all data so it can easily be accessed.
"DataOps and data fabric are two different but related things," said Ed Thompson, CTO at Matillion, which provides a cloud data integration platform. "DataOps is about taking practices which are common in modern software development and applying them to data projects. Data fabric is about the type of data landscape that you create and how the tools that you use work together."