It goes without saying that businesses accumulate huge volumes of data from multiple sources. Data is gathered from websites, social media, apps, advertising and marketing channels, physical stores and more. However, a very small percentage of all this data is actually used, as accessing, decoding, breaking it into manageable bits and comprehending it is heavily time and resource consuming. Such a mismanagement of data means that your business is not optimizing a wealth of data for better sales and revenue.
Not being able to use data is not the only weight dragging your business down. Any business has multiple teams that require reports often but are unable to access them easily or effectively for the purpose they have in mind. This cuts out the possibility of exploring various interrelations, analyzing data combinations and determining the efficacy of marketing campaigns. For instance, your clothing business might have data on products sold through different channels, such as Web, mobile app and in stores. While the data is available, a team member wanting to find out how a certain men’s jacket has sold across these channels along with color and size preferences would have to go through a long drawn out search process.
AI, along with Machine Learning (ML), and further yet, Deep Learning can make it easy for businesses to utilize data effectively and for users to process the great volume of information. With a combination of these, you can derive descriptive, diagnostic, predictive and prescriptive analytics, making your business immensely efficient and competent.
ML, a facet of AI, enables systems to learn from experience and adapt without having to be separately programmed. ML develops programs that find data and learn from the information they access. The programs move on to discovering patterns in the data to predict outputs using this analysis. Deep learning is a method in machine learning that trains computer programs to learn by example. With Deep learning, devices are able to respond to voice commands and help businesses classify data and deliver accurate information that surpasses human performance.
With ML and AI, a business depending on its size and functions can optimize any of the following types of analytics to increase revenues and efficiencies or to improve marketing and customer service:
- Descriptive Analytics: This form of analytics is mostly used for reports and to make observations from what has happened. For instance, descriptive analytics are useful in understanding customer behaviors, marketing outcomes and to help assess consumer buying patterns and preferences. The limitation with descriptive analytics, however, is that it focuses only on a limited data set.
- Diagnostic Analysis: Descriptive analytics does not give answers to questions like why an outcome came about or revenues are different at certain times and the causes behind that. With diagnostic analysis a business can compare various data sets and determine answers to such questions. But as data sets and variables grow and differ, the analysis does not go beyond specifications.
- Predictive Analytics: A step ahead of these is predictive analytics that uses ML and other AI systems to analyze patterns, match them to occurrences, assess events that are alike and predict upcoming behavior and outcomes. From the data on existing campaigns, businesses can use predictive analytics to find out how certain consumer groups are similar in behavior or identify aspects that are significant to a particular group. Based on these attributes, AI will predict the outcome for each consumer in that subset.
- Prescriptive analytics: This type of AI uses predictive analytics to plan the next course of action. For instance, if a group of consumers have chosen a product and not yet taken the step to purchase, AI determines the best approach to persuade the customer to take the leap forward, sometimes in real time by coming up with an offer or discount.
A mix of AI and Analytics can therefore greatly increase a business’ capabilities from helping with root cause analysis, assessing campaign performance, identifying risks and positive outcomes and building automation.
