[MUSIC] Welcome back. Now that we have laid out the data and analytic framework, we need to answer the question, what analytic techniques can I use and what tools can help me. In this video, we're going to talk about five types of data analytics, and the tools used to help build our analysis. We categorize the types of analytics that one can do into five types. Descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and adaptive and autonomous analytics. These are the broad categories that will help you deliver on the data and analytics framework we discussed in the previous video. The framework is abstract, but these types of analytics will help you operationalize it. As we discussed earlier, one of the common uses of analytics in marketing is in cross-selling multiple products to customers. When we looked at the outcome of increasing the number of cross product holdings of our customers, we started with the descriptive analytics. We looked at their data to profile their customers with respect to one product holders, two product holders, etc. These profiles included the sociodemographics of the customer, their online and offline behaviors, their attitudes, life stages at which they bought these products, and so on. Descriptive analytics helps you understand the current state of affairs in an organization. It lets you look at what is happening today and it's what has happened in the past. This type of analytics typically provides summarized information to understand currently existing sales patterns or customer behavior, customer profitability, past competitor actions, etc. Specific techniques might include simple box plots, histogram charts with means, minimums, and maximums. Plotting the data in quartiles or deciles across a number of different variables. Or computing statistical measures like mean, mode, standard deviation, etc. Descriptive analytics is very powerful for understanding the current state of affairs and for developing the hypothesis to anticipate where business problems and opportunities may lie. It helps us answer the question, what happened? For example, from the descriptive analytics, it was clear that a large proportion of the customers of the insurance company we talked about in the previous video, had only one product, and a very small number of customers had four products or more. In addition, there were three products that all had more or less equal share within the one product customers. After we had a good description of what the cross product holding of the different customers were, we started investigating why the cross product holding was so low. It turned out that the insurance company had multiple channels to sell their products, captive agents, independent agents and telemarketing staff. Each of these channels sold a specific type of product well and this resulted in an equal number of one product customers across all three channels. Given the expertise of these channels, there was very little cross selling. Multiple product holdings was more a result of customers demanding the additional products, as opposed to any deliberate cross selling process. Diagnostic analytics helps you understand why it happened. It provides the reasons for what happened in the past. This type of analytics typically tries to go deeper into a specific reason or hypotheses based on the descriptive analytics. While descriptive analytics cast a wide net to understand the breadth of the data, diagnostic analytics goes deep, probing into the causes of issues. For example, we might look at creating a decision tree analysis of the cross product holdings to reveal the types of customers who have bought these products, the channels they use, the products they've bought and when they bought them. Once we knew what some of the major issues in a low cost product holding were, we started formulating hypothesis on what we can do to increase cross-sale of products. We built predictive model to rank customers on their propensity to buy a specific second or third or fourth product. This predictive model was built based on the understanding we gained from the two previous steps of descriptive and diagnostic analytics. Next we have predictive analytics. Unlike descriptive or diagnostic analytics, predictive analytics is more forward looking. Predictive analytics lets you envision what could happen in the future. This type of analytics can help the client answer questions like, what are my customers likely to do in the future? What are my competitors likely to do? What will the market look like? How will the future impact my product or service? Predictive analytics typically predicts what could happen based on the evidence we have seen. In our insurance case study, once we've built our propensity model, we were able to identify some of the high potential targets for cross sale, and what product they should be cross sold. Given the different propensities to buy, we computed the next best offer for each and every customer and how the cross sell message should be personalized for each customer and distribution channel. Prescriptive analytics goes beyond providing recommendations to actually executing the actions or taking the decisions that are right for a particular situation. It does this by looking at what happened in the past, the present state and all the future possibilities. Prescriptive analytics provides answers to the question, what steps or interventions need to be taken to achieve the desired outcomes? Often the intervention might be an optimal solution given the circumstances. Or the best possible action given the uncertainty in the environment and the limited information available. It frequently involves scenario analysis and or searching for optimal solutions. Prescriptive analytics is powerful in understanding the right actions needed today to address future possibilities and put an organization the best possible position to take advantage of future conditions. While we built a one off solution for the client to increase the cross-product holding of their customers, what they really needed was an adaptive and continuous system that learns from the behavioral interventions and actions taken by customers to automatically change the recommendations and try out new measures. Such an always-on insights platform where the system builds a model of the real world, takes actions, learns from the environment, and continuously adapts itself is the ultimate adaptive, autonomous solution. Adaptive and autonomous analytics is still in it's infancy, most systems today are either predictive or prescriptive. Very few of them are completely adaptive, or autonomous. However, there are a number of companies that are building more adaptive, or autonomous analytic solutions where we are eliminating the human in the loop. Autonomous car driving is a great example of an adaptive or autonomous analytic solution. Adaptive and autonomous analytics provides answers to the question how does the system adapt to changes? How can we run analytic solutions on a continuous mode? Constantly learning and correcting its behavior to optimize its performance. We may not want to build adaptive and autonomous systems in all cases. There may be instances where we may want to retain the human decision maker. But there may be other situations where the speed of decision making, is such that having a human in the loop may be counter productive. Algorithmic trading might be one such example. Now that I've given you a high level overview of the different types of analyses, let's look at the tools available to help you perform each one. Descriptive and diagnostic analytics usually rely on analytic tools that can handle manipulation of large sets of data or that help visualize and interact with summarized information. Examples include SQL, Oracle database or Oracle DB, Hadoop/Spark, Tableau, QlikView, Microsoft Access, SAS, R, Python and various statistical packages within them. Predictive and prescriptive analytics have traditionally relied on analytics tools that have significant mathematical modeling capabilities or scenario planning or simulation capabilities. Examples of these tools include SAS, R, SPSS, Python, and various packages associated with them. Optimization tools like Garrobi, ILOG, RiverLogic, etc. Simulation tools like Vensim, AnyLogic, STELLA. Machine learning and deep learning tools, like Scikit, TensorFlow, Caffe, Theano, etc. Natural language processing tools like NLTK or Natural Language Tool Kit or OpenNLP. In this video, we talked about five different types of analytics you can use to analyze data. Descriptive analytics helps you understand the current state of the problem and answer the question, what happened? Diagnostic analytics helps you understand why it happened or the underlying causes for the observed data. Predictive analytics helps you understand what could happen in the future given certain conditions. Prescriptive analytics helps you understand the right course of actions needed today to address future concerns. Adaptive and autonomous analytics helps you answer the question of how to continuously adapt to change. We also took a very high level look at the tools available to help you perform these analytics. Later in the course, we will take a much closer look at what some of these tools do and how they provide value. In the next video, you'll have the opportunity to hear some of our PwC professionals talk about data and analytics at PwC. And how it plays a role in over tax, assurance and advisory or consulting practices. [MUSIC]
