Deploying Artificial Intelligence and Machine Learning Solutions to Make Your Marketing a Hit

“Information is the new oil. The statement might look impressive as a read, but in actuality, how we make use of the information sets the pretext for unlocking holistic business marketing opportunities.”

Artificial intelligence and machine learning solutions gain demand as they help machines build the ability to simulate human behavior and deliver superior experiences. With the development of cognitive abilities, the new-age machines significantly help access, gather, store, and process data at unprecedented speed and accuracy. In short, AI and ML Services empower business marketing strategies as they enable marketers: 

• To streamline processes and automate mundane tasks 
• To optimize enterprise-wide resources and drive marketing efficiency  
• To extract meaningful insights concerning both customers and markets
• To quickly create, publish, and share marketing collaterals with target audiences 

Artificial Intelligence and machine learning solutions now give marketers the power to think beyond automation and bring “learning” into perspective.

Decoding the Intelligence Factor Involved in AL and ML Services 

Before discussing what Artificial Intelligence (AI) is, let us understand what intelligence means.

To simply put, intelligence is a niche ability of the human mind to perform reasoning based on perception and perspective for a given situation. 

The breakdown to this meaning is that intelligence is a mental ability that allows humans to absorb cognitive inputs like visuals, emotions, memory, language, and more to translate the information into a solution. 

To explore the dimensions of intelligence, one needs to understand first principle thinking. 

Intelligence is not just to solve a problem. The idea mechanics to identify a goal or an objective that becomes the final solution is the key to breaking down different complexities. 

Take an example of traveling from your home to your office during the COVID-19 pandemic. Rather than focusing on how to reach, first principle thinking will deduce to reach office safely rather than quickly. You might prefer driving your car rather than taking public transport out of fear of transmission. So, when the focus is on the result, the first principle of thinking goes in reverse gear to formulate a solution to the objective.  

Think hard enough, and you might understand that intelligence has a way of connecting the dots to deduce a solution to a given problem sensibly.

Judgment, investigation, assumption, guess; all become part of the journey of deducing a solution. So given the extrinsic and inquisitive nature of humanity where our daily lives revolve across this and that, if or else, might or might not, it would be prudent to say that experiences influence our reasoning that we “learn” and “actions” we perform. 

Associate this with the fact that our decisions are often factored by the behavior of entities resulting in different “sentiments,” we arrive at a junction of human-level interaction where we sound logical, sensible, empathetical, and much more that shows the actual impression of intelligence. 

If we go through this explanation, artificial intelligence remains to be a machine that can mimic these qualities to solve problems more “intuitively.”

Well, had this explanation about AI been easy, trends in the industry would have been haywire. Artificial Intelligence is a vast tree that simulates human rationality and exhibits a learning process without direct intervention. Put it in layperson terms, AI is a branch of technology that deals with the development of machines that can think rationally, learn from experiences, derive solutions, and recommend the best course of action which should match the end user’s objective. 

The Incremental and Significant Role AI Plays in Marketing 

The essence of AI in marketing dates back to when marketing automation tools, CRM, and more were introduced in the market. As the volume of information increased overtime, mining key insights became relevant for marketing departments across businesses to determine what factors influence outreach. 

Business insights were mined with the help of data analytics that eventually helped managers to derive trends. Luckily, most marketing research tools mixed with hybrid market segmentation led to the need for digital marketing. 

As managers become more inclined to use digital marketing tools and SEO metrics, an underlying trend to identify a competitive edge became important. For example, instead of maintaining the company website page rank in a search engine, managers can use dynamic search advertisements based on AI to promote the products and services on each search landing page.

Another excellent example of how AI mixes well with marketing is the use of sentiment analyzers which use social media analytics and intuitive competitive intelligence to capture the strength of your marketing content. Some examples to support the statement:  

• Radarr, which uses a sentiment API, provides organizations with the platform to discover new target audiences and improve marketing strategies using business intelligence and machine learning. 
   
• Google Analytics Intelligence can help managers determine the quality of campaign sites and recommend suggestions to improve customer traffic and content personalization for site visitors. 

With the age of AI, the marketing domain has started capitalizing on the nuances and available technologies under the umbrella of AI. Think like this, that instead of a dedicated FAQ page on websites, marketing tycoons prefer an AI-enabled bot to answer your queries. 

However, one question is that how AI works is more interesting; there are a couple of nuances as AI encompasses various fields dating back from the early 20th century. To completely reverse engineer human behavior, instinct, and judgment into a machine is not easy as it looks. You cannot teach a toddler coding at the age of a year precisely. The quality of an AI system depends on the conceptual model, degree of refined data sources, rigorous training, and validating the outcomes. 

You do not build a system and expect it to mimic from day one. Unearthing this mystery, let us take a brief yet simple dig into one of the most popular sub-domains on the ground, machine learning.

How ML Complements the Efficiency Triggered by AI? 

To walk through the concept of machine learning, we first need to understand the various stages of AI. In theory, AI has three stages of development. 

1. Machine Learning: It uses computer algorithms and historical data to identify patterns and trends for decision-making. ML teaches a machine how a specific problem can be solved. At this stage, the machine has a narrow intelligence as it attains learning with or without human intervention. 
2. Machine Intelligence: The machine — with its reinforced ability of learning — becomes smarter to solve many problems and scenarios. Machine intelligence is the general AI we employ as decision support or expert systems in the IT landscape.
3. Machine Consciousness (considered to be the most futuristic stage): It entails a machine with a more powerful mind than the best brains and geniuses of different fields. If you look at these stages, marketing is currently at stage one of the AI landscapes as per machine learning standards. 

One of the essential traits of machine learning is the ability to learn in a given scenario. Forecasting, classification, clustering, and more are standard machine learning outcomes used in principal to visualize patterns that the human eye may miss. 

From a marketing perspective, identifying a group of customers and bifurcating their interests is a typical classification and clustering analysis, which resembles market segmentation. To execute the outcomes as decisions, machine learning uses the following learning paradigms:

1. Supervised Learning – This form of learning involves the trainer concept by which using historical data in the form of labeled data sets becomes possible. The ML algorithm creates an output that receives human intervention for feedback. 
   
   Take an example of a small child who learns alphabets in pre-school and is corrected if the child makes a mistake. Similarly, from a marketing perspective, a supervised ML algorithm will use marketing content or business insights to learn the nuances and provide reports on improving ad campaigns, customer relationships, digital presence, and many other marketing challenges.
2. Reinforced Learning – This form of learning involves training an ML algorithm using rewards for desired results and punishment for undesired results as per objectives using hit and trial. The ML algorithm is subjugated to situations where the focus is to maximize intended value and minimize the error through self-feedback. 
   
   It means that the ML algorithm understands to highlight correct outcomes to get maximum reward and where the decision is to be taken in sequence. The difference from supervised learning is that the data sets are not predefined to identify trends via attributes. So, the ML algorithm needs to explore the environment to remember cohesions among data points and process out results. 
   
   For example, a marketing manager can use content from different marketing sources to train a chatbot to answer customer queries. The training is via marking correct responses as rewards and incorrect responses as punishments such that the chatbot will itself learn what to respond to as per customer queries. This type of learning is also referred to as experiential learning.
3. Unsupervised Learning – This form of learning involves identifying associations among an unlabelled data set to set up comparisons as an output. Simply put, we use an ML algorithm that explores an unlabelled data set, tries to group the data based on comparable properties among data points, and highlights comparison among a group of data points as an outcome. 
   
   This type of learning involves guesswork, and there is no human intervention. The algorithm must explore the environment, guess the relationship among data points, and project similar groups for insights like reinforcement learning. 
   
   From a marketing perspective, unsupervised learning can identify customer relationship pain points by grouping customer feedback on your products or services and using sentiment analysis to seek out hidden wish lists in terms of feature sets to improve your products or services.

AI and ML Services Can Significantly Help Create and Execute Winning Marketing Strategies 

Machine Learning is revolutionizing the marketing domain with unique use cases that often have raised the possibility of autonomous transformation opportunities. CRM modules supported with analytics serve as the right platform for machine learning possibilities for marketing teams. Who knows, there are chances that marketing teams can use AI extensively as a part of the digital upgrade to existing practices. See the endless possibilities of how AI can influence your marketing tactics, policies, and objectives. Connect with Kellton Tech Solutions Ltd and drive a journey of futuristic marketing experience for your customers.