Pareto Principle in the world of Analytics
A principle, named after economist Vilfredo Pareto that specifies an unequal relationship between inputs and outputs. The principle states that, for many phenomena, 20% of invested input is responsible for 80% of the results obtained. Put another way, 80% of consequences stem from 20% of the causes. Also referred to as the “Pareto rule” or the “80/20 rule”.
Originally, the Pareto Principle referred to the observation that 80% of Italy’s wealth belonged to only 20% of the population. The Pareto Principle can be applied in wide range of areas especially in the technology dominated enterprise environment of today.
Pareto Principle in general is the observation (not law) that most things in life are not distributed evenly. It can mean all of the following things take on “80% Vs 20%” relationship:
“Results Vs Inputs, Production Vs Workers, Revenue Vs Customers, Crashes Vs Bugs, Usage Vs Features, Interruptions Vs Employees, Problems Vs Issues, Advertising results Vs Campaigns, Instructors time Vs Students, Customer Complaints Vs Products/Services, Our Phone Calls Vs People in our Address Book, Decisions made in meetings Vs Meeting Tim, Outfits worn regularly Vs Individual’s wardrobe collection, Website Traffic Vs WebPages on the site,………”
There is a common misconception that the numbers 80 and 20 must add to 100 – this is not necessary !
This 80/20 rule is a rough guide about typical distributions. There could be scenarios which are contrary to this rule. Depending on the situation, this ratio could change. The take away is that most things in life are not distributed evenly and some contribute more than others. The Pareto Principle aids in realizing that the majority of results come from minority of inputs. This knowledge helps to focus on the key contributors, deviants, customers, technology etc. The idea is to identify and focus on the attributes that make a difference instead of wasting the energy on the vast majority with minimal outputs.
The value of this in Data Analytics is highly overlooked. If the enterprises have a detailed look at their data, it can be extremely useful to derive hidden intelligence. In the scheme of Big Data with high volume, velocity, variety, context, noise and other complexities the value of the data is in itself a part of the challenge. The data is no more restricted to the structured formats of Application data and Machine data. The quantum of unstructured data in the form of human generated content, transactional data, real time interactions, feedback etc is overwhelming.
Facebook alone is estimated to be generating more than 500 Terabytes of data a day and is growing at a break neck speed. With the high volume of human interaction online, increased automation of processes, interconnectivity of systems, mobile business app transactions, the new dimension is a hybrid mix of human and machine generated data. This dramatic sea change with the ‘second order’ data from social graphs, emoticons, mesh of contacts, reviews & recommendations etc have added to the complexity with a subtle capability for sentiment analysis.
The Computer generated data as a byproduct of interactions with other devices and humans comes in varied formats from semi-structured log files to unstructured binaries. These ‘exhaust fumes’ of the data can be extremely valuable to understand and track behavioral patterns for user experience or statistical patterns and correlations to generate insights.
Companies can harness the information hidden in this data, make informed decisions and stay ahead of competition. If only the ‘data in hand’ is analyzed, then there is a lost opportunity with the prospective clients on whom there is no data. This data resides with the competition. Predictive analytics thrives on the fact that the future of a business is not only with retaining the top performing clients/customers but also from the clients you are yet to know about!!!
As an example, putting Pareto thinking into practice in Advanced Customer Analytics can help separate high value data with reference to customers, predict the action for increased customer loyalty and execute the action at the right time while optimizing the resources. Predictive analytics extracts the top 20% and intelligently identify the highest value customers, their sentiments and predict reactions using traditional, nontraditional and innovative data sources. Identifying the right strategy for the individual customer using predictive analytics is the key. Unlike traditional behavior scores which treat groups or segments of similar customers, Predictive solutions execute targeted actions. This customization allows companies to handle customers to align with their business goals and strategies.
In today’s highly competitive marketplace, customer retention is the mantra. Progressive companies realize that it only takes one mistake, such as a mishandled inbound service call, to lose a customer to a competitor. Adding sophisticated analytic products to existing pre-agent routing systems is one of the best ways to identify and service high-value inbound calling customers. Analytics can dynamically manage call queues. Moving away from the traditional ‘first come, first served’ call center model which does not support the 80/20 rule, predictive analytics updates this standard operation by continually and dynamically sequencing the customer calling queues. This highly flexible capability helps companies ensure that their highest value customers always receive the ideal level of loyalty-boosting service.
Companies that leverage cutting edge advanced analytics can easily maximize the Pareto law and identify customers with a high propensity to purchase cross sell and up sell offers, conduct continuous retention activities and/or deepen engagement with high-value customers, reduce the risk of attrition with superior service and deliver a better customer experience. This may sound too futuristic but with the technological advances, nothing is impossible. The first company to do this will emerge as a Gold Medalist in the ‘Global CRM Olympics’.