Artificial Intelligence (AI) and Machine Learning (ML) can empower businesses to build personalized customer experiences and target specific customers at scale. AI and ML tools help marketing teams iteratively test their marketing and sales strategy to ensure the best results for both customers and businesses. The use of data-driven insights allows marketing and sales teams to optimize their campaigns, improve customer segmentation, boost customer satisfaction and engagement, increase the likelihood of sales wins, and much more.

Businesses are competing to reach customers across a multitude of social and online platforms. Online advertising space can be expensive and the cost of running campaigns across multiple online channels can quickly accumulate. It is common practice for businesses to try and ensure returns on their marketing investments. AI can optimize the media buying process by intelligently purchasing ad space on the right marketing channels for the right cost and right time.

Marketing teams have access to vast amounts of campaign data on customer responses, but these data must be analyzed to make the right choices regarding marketing strategy. By analyzing live campaign data, AI can autonomously adjust the distribution of campaigns across platforms and identify the most effective campaign placement and duration to adapt to changing customer responses. Further, insights from AI can help marketing teams design future campaigns and predict their success.

Machine Learning can detect patterns in customer data and generate insights on customer demand, customer preferences, and market trends. ML can help marketing teams understand what’s most important to different groups of customers and which variables impact their customers’ decisions to purchase products and services. ML can identify patterns in the behaviors of customers and categorize them into segments based on some shared characteristics. The in-depth knowledge of each customer segment from ML enables marketing and sales teams to build a targeted strategy to maximize the impact of marketing activities on customers and the likelihood of successful sales.

Artificial Intelligence allows marketing teams to tailor content for customers. AI can recommend marketing content to a customer based on historical data of campaigns the customers have previously interacted with. AI and ML can use customer data to create profiles for each customer and each customer segment. It can determine which types of messages resonate the most with customers, how often to advertise, which products and services a customer is most likely to buy, and so on. AI can use customer profiles to ensure a personalized customer experience.

Insights from customer segmentation can also enhance how businesses connect and communicate with customers. AI can help businesses improve their customer experience on social platforms and business channels (customer service, technical support, etc.). AI tools can autonomously respond to customer communications, such as emails, comments, support requests, and queries. Further, AI can leverage customer profiles to craft responses and messages with the appropriate mix of meaningful information and engagement.

Artificial Intelligence can be highly influential in marketing and sales across a range of industries, including financial services, enterprise software, healthcare, e-commerce, and much more. Businesses have access to more customer data than ever before. Integrating AI can help businesses optimize their marketing and sales operations with incredible speed and effectiveness, which is vital for them to stay competitive in a data-driven business landscape.

ML enables computer systems engaging in data-related applications to learn from data in real-time by employing statistical tools and techniques. It allows businesses to capitalize on their data in a variety of applications.

ML incorporates machine learning algorithms, which are formal statistical methods and processes, to drive intended outcomes and generate predictions or forecasts. These algorithms are a series of mathematical steps to solve a data-related problem. There are many different ML algorithms, each of them suitable for specific scenarios and applications. They generally fall into the following categories:

• Supervised Learning: Algorithms train themselves using labeled and well-defined data where the expected output is known. The algorithm applies statistical methods and processes to make predictions based on the input data. Conventionally, the algorithm’s results can be compared to the actual known results and adjustments can be made prior to the next iteration of the algorithm. The process can be repeated until the desired results achieve an acceptable level. 
• Unsupervised Learning: Algorithms train themselves using unlabeled data where the expected outcome is not known. The algorithm applies statistical methods and processes to identify patterns, correlations, and relationships that may be hidden or difficult to perceive in a given data set. Typically, unsupervised machine learning algorithms will organize and group data in some meaningful way.
• Semi-supervised Learning: Algorithms train themselves using a combination of labeled and unlabeled data. The algorithms apply a small set of labeled data to identify meaningful information that can be used to understand a larger set of unlabeled data.

A comprehensive understanding of business context and data systems is critical when leveraging machine learning algorithms to solve problems. The algorithm implemented directly impacts the quality of results that can be obtained from your data. Therefore, applying the optimal algorithm is critical when implementing machine learning. AIPI3® offers solutions specifically focused on machine learning algorithms: We design, build, and tailor algorithms to your business need to ensure optimal results.