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“The one book that clearly describes and links Big Data concepts to business utility.” Demystify big data and you can effectively communicate with your IT department to convert complex datasets into actionable insights.“This text should be required reading for everyone in contemporary business.”
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Focused on consumption patterns and associated business outcomesĪs you can see there are a lot of different approaches to harness big data and add context to data that will help you deliver customer success, while lowering your cost to serve.Backward looking, Real-time and Forward looking.This analysis is meant to help you know your customers better and learn how they are interacting with your products and services. Non-discrete forecasting (forecasts communicated in probability distributions)Īlso referred to as consumption analytics, this technique provides insight into customer behavior that drives specific outcomes.Description of prediction result set probability distributions and likelihoods.Focused on non-discrete predictions of future states, relationship, and patterns.Examples of predictive analytics include next best offers, churn risk and renewal risk analysis. The most commonly used technique predictive analytics use models to forecast what might happen in specific scenarios. Category development based on similarities and differences (segmentation).MECE (mutually exclusive and collectively exhaustive) categorization.Focused on descriptions and comparisons.
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Examples of descriptive analytics include summary statistics, clustering and association rules used in market basket analysis. Descriptive analytics provide insight into what has happened historically and will provide you with trends to dig into in more detail. This technique is the most time-intensive and often produces the least value however, it is useful for uncovering patterns within a certain segment of customers.
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It can also illustrate the implications of each decision to improve decision-making.
#BIG DATA ANALYSIS TECHNIQUES HOW TO#
It helps to determine the best solution among a variety of choices, given the known parameters and suggests options for how to take advantage of a future opportunity or mitigate a future risk. The most valuable and most underused big data analytics technique, prescriptive analytics gives you a laser-like focus to answer a specific question. In order to effectively work with your data scientists (if you have them) or your IT analytics teams, you need to understand the different types of big data analytics techniques and how to utilize them to get the actionable insights that you need to succeed. Arguably this is the most important, yet most difficult step in turning your oceans of customer data into valuable, practical and actionable business insights that will help your teams deliver value and expected customer outcomes. For Customer Success leaders, this step requires you to analyze data to identify key value drivers, important milestones and leading churn or loyalty indicators. The second step in the process is to ‘galvanize’ data-meaning to make something actionable. In the blog Steps to a Data-driven Revenue Lifecycle we outlined the steps required to transform your data into ‘ RLM Ready Data’, aka actionable data that drives customer success and revenue growth. However, big data analytics continues to be one of the most misunderstood (and misused) terms in today’s B2B landscape. Finding a way to harness the volume, velocity and variety of data that is flowing into your business is as critical to innovation and transformation initiatives today, as it was then. Well truth be told, ‘big data’ has been a buzzword for over 100 years. Predictive analytics and data science are hot right now.