AI & Machine Learning

Artificial intelligence (AI) technologies have the capacity to take huge amounts of data, process them, and then take action. They can enable you to reach customers with personalized accuracy across a variety of platforms, processes, and channels. Predictive analytics, in particular, allows for reams of data to create reasonable predictions about customer interest– and then act on them in real time.

AI has pervaded our daily lives, from transportation to banking to shopping, with the digital marketing space being no exception. Evolving marketing technology increasingly uses AI, which means companies have the task of mastering and implementing it into their own processes. AI solutions are creating new possibilities for building revenue through a dramatically improved customer experience.

The emergence of Big Data and AI (Artificial Intelligence) in conjunction with machine learning has paved a way that is helping bring operational and business transformations, thereby leading to an increased level of accuracy in decision-making and improved performance. Machine Learning or ML is an application of AI where machines are given certain data and they learn for themselves. ML is actually a subset of AI. There has been a huge advance in machine learning algorithms due to deep learning. Deep Learning (DL) is the process by which we implement ML. It is with the help of Deep Learning that many of the activities in AI happen without setbacks. DL helps break down tasks in manageable chunks. The software in DL learns and then it starts mimicking the activities in the neuron layers of our brain.

Machine Learning for Automotive Dealerships

The process of buying (or selling) and servicing a car has become more data-driven than ever, and digital media platforms are disrupting traditional dealer-customer relationships. Given this landscape, AI and ML algorithms have found an increasing level of applicability in the automotive industry.

The collaboration of big data analytics and ML has boosted the capacity to process large volumes of data, thereby accelerating growth of AI systems. Because of the remarkable ability of ML to bring out relationships among data sets, make predictions, and present actionable insight, it is poised to significantly impact the marketing function in the automotive industry — from how marketers in the automotive sector establish goals and measure returns on their investments, to how they connect with their customers. ML is quickly becoming as much an organizing principle as an analytic ingredient for sophisticated marketing campaigns across this industry.

ML algorithms can also help in real-time customer issue management and reputation monitoring because it accurately incorporates analysis results of customer feedback in social media, for example, text and tweet analytics. Thus ML holds the potential for increasing dealership efficiency and productivity so staff can focus on high-value activities instead of getting bogged down in time-consuming manual monitoring of brand sentiment or 1:1 customer engagement.

ML algorithms can also aid in effective planning and execution of predictive maintenance. Predictive maintenance employs monitoring and prediction modeling for determining the condition of the machine and for predicting what is likely to fail and when it is going to happen. ML systems can help in adjusting maintenance interval, where the same maintenance is conducted but shifted backwards or forward in time or mileage.

With AI, the tedium of segmenting customers into appropriate follow-up can be largely mitigated: predictive analytics can perform these tasks automatically and provide constant contact with customers throughout the buying cycle. AI allows to automatically adapt the next steps for each customer based on information updates, cutting a lot of manual planning and tweaking during the day.

As AI develops, it promises to streamline ROs, and provide customized assistance to help customers along the buying process, and to stay in touch with previous buyers with vehicle updates, continuous education, and service reminders. Again, the advantage of machine learning is the ability to provide these services with much greater accuracy and scalability automatically, and in real time.