While artificial intelligence (AI) has been around for a while, scientists have recently made massive breakthroughs in the field of machine learning, a subfield of AI, as noted by NPR. Machine learning is a form of “deep learning,” where machines possess the ability to enhance their performance and decision making skills without human intervention.
They can collect and analyze information to come to relevant conclusions and learn from examples and experience, instead of programming rules. ML functions on a neural network, a group of software and hardware that imitate the biological neural network. Thus, they can accumulate experience and similarly recognize patterns as human brains.
AI and machine learning are gaining increasing popularity because of their innovative functionalities such as predictive analysis, spam detection and prevention, and voice and facial recognition. In 2017, 38% of the enterprises were using AI, and according to Narrative Science’s survey, the number is set to rise to 62% in 2018.
Moreover, they are also gaining traction in the field of software development. By the end of 2018, 75% of the developers will incorporate AI functionalities in their applications and services to gain a competitive edge in this cluttered field.
How can AI and machine learning assist in app development and marketing?
The digital era has led to inundation of data generated from social channels, devices, sensors, apps, etc. JumpFactor MSP Marketing uses this data to power their strategies. However, the sheer size of the data can make it impossible to mine to acquire relevant knowledge.
AI and machine learning can be utilized to analyze the massive amounts of data. It can enable the developers to gain real-time insights to develop better apps, market them successfully and enhance customer experience.
Machine learning is routinely used to provide users with relevant information about their pursuits in e-commerce apps, video streaming channels, social media platforms, and so on. The intelligent agents analyze a plethora of information pertinent to user’s purchase pattern, history of buying and personal preferences to recommend the most relevant products.
The e-commerce giant, Amazon, uses AI and machine learning to evaluate a customer’s entire buying journey, website navigational trails and product click-through rates. The comprehensive log analysis allows the app to suggest additional products to the consumer based on its algorithmic findings.
Similarly, Netflix analyzes data generated by three primary sources; your preference list, what you watch over time, and the most trending videos. The recommendation engine then predicts what you are most likely to view and prompts you about it.
Increased internet accessibility and ease of posting and sharing have resulted in a barrage of content on social media platforms. Machine learning and social media algorithms analyze each user’s engagement and general sentiments towards every post to determine what is most attractive to them. Social apps filter and optimize the user news feeds with contents most likely to evoke a reaction and generate a response.
Leading social media channels, like Facebook and Twitter, use a combination of AI and linguistic rule creations to deliver the most meaningful content to their users.
AI and machine learning can also predict upcoming trends before they are apparent. The intelligent system can aggregate sales information and latest trends from various digital channels including blogs, social media, and online communications. It uses the collected data to give predictions in real-time.
The system analyzes the customer churn pattern and preferences to notify the marketers about the imminent customer defections.
In addition, it enables the marketers to evaluate their current offers, determine an optimal price for better conversion and manage their costs according to the forecasts. The preliminary price management allows the companies to retain and even expand their consumer base in the event of a trend change.
Read more:App development and design trends 2018
Airbnb travel service uses a dynamic pricing model to determine the charges of every site based on a variety of factors. The model integrates the locality, nearby amenities, time of the year, previous seasonal demand, and expected customer turnover to offer the best prices.
Machine learning can assess data and gather additional insights on the users’ behavior and engagement preferences.
For a small company with a handful of customers, it is relatively easy to generate tailored messages for individual clients. However, for large enterprises with millions of followers, it was almost impossible to interact with each new customer based on their profiles and buying context.
Marketers can use machine learning for better customer categorization. It can divide the customers into smaller segments according to their similar preferences and behaviors. Subsequently, the companies can address each group with a personalized message enhancing customer acquisition and outreach.
Furthermore, machine learning can deliver predictive campaigns most likely to gather positive responses. It can anticipate a customer’s reaction to a marketing tactic with precision and accuracy, augmenting the efficiency of every marketing strategy. Innovative and targeted ad campaigns, like airG advertising solutions, using AI can also generate better ROI.
Due to this, 80% of marketing executives believe that AI will have a revolutionary impact on marketing by 2020. In addition, a survey revealed that 55% of the CMOs expect AI to have a more significant influence on marketing as compared to what social media ever had.
AI and machine learning are creating better opportunities for apps to gain a competitive edge, capture more customers, and personalize the communications. Moreover, it allows the e-commerce apps to offer the best customer service and nominal prices to maintain an affluent online presence. As a result, The Boston Consulting Group found that 85% of the executives believe AI will leverage their companies against the competition.
The potent combination of human-centered engagements and machine learning’s data analysis is set to transform app development and marketing. Have you integrated the intelligent platform in your software development endeavors?