Embracing Real-time Analytics for Proactive Business Management

This is a framework for implementing big data advanced analytics that is technology neutral.

Marketing Analytics: Understanding Segmentation and Prediction

By Alberto Roldan, Copyright 2010 Alberto Roldan
Introduction
Campaign management, media mix optimization, cross-sell, and up-selling are some of the terms that are commonly used in marketing analytics. Although those terms are understood in a business context, the advanced analytics techniques behind those terms are not as well understood. The purpose of this article is to explain some of the analytics techniques used in marketing analytics, such as segmentation and predictive analytics, so executives understand the capabilities and limitations involved. A secondary objective is to help analytics professionals explain concepts to businesses.

Segmentation
Segmentation is the process of dividing a large market into groups that have similar characteristics. There are two main issues that I have found in explaining this process to businesses: 1) the different processes in arriving at granular vs. aggregated segmentations; and 2) the difference between “similar” vis-à-vis “equal” characteristics within a segment.

Granular vs. Aggregated Segmentations
Companies need to be able to separate the clusters of data and identify the driving factors for marketing and sales purposes. Hence, a limited number of segmentations that is directly related to the core business is necessary for strategic and tactical decision making. The limited number of segmentations (about 6) is what I refer to as aggregated segmentations. Examples of aggregated segmentations are best customers, next best, infrequent buyers, and power buyers. The process of arriving at a consensus of aggregated segmentations is a combination of business knowledge and experience with statistical data analysis of granular segmentations. Aggregated segmentations allow businesses to efficiently analyze large datasets and make decisions that will impact profit, revenues, and costs.

Granular segmentations refers to the separation of the clusters of data using advanced analytics techniques like hierarchical partition, k-means clustering, distribution, and correlation analysis. Therefore, the main difference between the process of distinguishing granular and aggregated segmentations is that the first is determined by using proven mathematical techniques, while the later employs business knowledge and advanced analytics techniques. A frequently asked question is, Why do we need to do granular segmentations before arriving at aggregated segmentations? The answer is that analytics requires following scientific methodology (observations, theory, experiment, and outcome). The scientific method allows for objectivity, reduces bias in the interpretation of results, and brings measurable precision to the process. Moreover, the accuracy of a prediction is based on granular segmentations rather than aggregated segmentations.

Similar Characteristics vs. Equal Characteristics
A challenge encountered in attempting to explain segmentation to businesses is the difference between similar vs. equal characteristics in a segment. The members of a granular segment might have equal characteristics and, hence, be homogeneous. The members of an aggregated segment will have similar but not equal characteristics. The granular segment characteristics are determined by using advanced analytics techniques; therefore, the homogeneity of the segments is determined with mathematical precision.

On the other hand, aggregated segments are determined by combining homogeneous granular segments with business value and experience. Since multiple distinct granular segments are combined in aggregated segmentations, those segments should have similar but not equal characteristics. For example, the best customer segment will have customers with similar characteristics such as frequency of purchase, but not all the frequencies will be the same (i.e., once a week, twice a week, or daily).

The importance in understanding the differences within an aggregated segment allows the decision makers to be precise in their strategic decisions while simultaneously considering a manageable set of segments.

Predictive Analytics
Availability and Data Quality
Predictive analytics refers to the ability to accurately predict an event or occurrence, for example, that a customer will purchase a product or service at a set price or within a certain price range. This area is so broad that I am only going to address two issues: 1) data availability and quality; and 2) accuracy of prediction. A baby must first learn to crawl before it can run. Although this concept is fairly obvious, sometimes its application in marketing analytics is not well understood. In order to make a prediction, data must be available and of acceptable quality. When a new product is launched into the market, predictions are difficult because of lack of data. Sometimes the initial analytics outcome is limited to comparing similar characteristics of a new product with an existing product. The next step is to make an inference (weighted value) that the new product may perform similarly to an existing product. As the new product gains traction into the market and that data becomes available, the accuracy of any prediction will substantially improve. The availability of this new data will prove, disprove, or modify the inference that was initially made. Availability of data also means that the data is accessible in the correct format for analysis.

Data quality refers to the percentage of individual variables that have correct information, as well as how the aggregate data quality issues impact the accuracy of any prediction. The old computer science axiom “junk in, junk out,” is true in marketing analytics. Therefore, it is crucial that a thorough ETL (extraction, translation, and load) process, including a data quality hub, be in place prior to attempting any enterprise predictive analytics. In other words, this is the seam where best practices in business intelligence (BI) and advanced analytics meet. It is important to remember that the accuracy of any prediction is directly correlated to the quality of your data. Therefore, executives should address data quality issues at the beginning of any analytics project.

Accuracy of Prediction
There are two issues that I would like to address regarding accuracy of prediction: variables and analytics tools. In the IT and BI world we speak of fields or data elements. In the analytics world, we talk about variables. A dataset may have hundreds of data elements, but analytics uses a limited number of relevant and pertinent variables. In order to understand whether their company can successfully implement a predictive analytics project, business decision makers must be able to distinguish the fundamental differences between the IT and Analytics languages. I like to think about this as the difference between learning how to say “food” in English and in Chinese—both words are necessary if you want to eat in each country.

One of the most common mistakes in predictive analytics is thinking that if we input data elements into a predictive analytics tool such as SAS, SPSS, or KXEN, we are going to obtain accurate predictions. This is a lack of understanding of the internal workings of regression algorithms. Regression works on a set of independent variables and a dependent variable. Therefore, regression reads independent variables as separate from one another. If a ratio between two independent variables is pertinent and relevant to a prediction, that ratio must be created as an independent variable. For example, if the variables are “date of first purchase” and “date of last purchase,” and you think that the relevant variable is “days between purchases,” then you need to create this variable. Otherwise, the regression algorithm reads those separate variables as independent from one another. Experience in variable creation is one of the areas business decision makers should examine when evaluating a predictive analytics project. The accuracy of a prediction is directly related to the variables used in the analytics model.

Analytics Tools
I have found that companies want to talk analytics tool evaluation right away when considering a predictive analytics project. This tendency is driven by IT experience with estimating cost of software, hardware, and staffing with qualified resources. Although analytics tool selection is an integral part of any predictive analytics project, it is neither the most important consideration, nor should it be the driving motivation for an analytics project. For example, I can go to a hardware store and buy the best carpenter tools, but if I do not understand their proper use, my success rate in building anything will be significantly reduced. A master carpenter with an old hammer and a hand saw will build a house faster and better than a novice with the best power tools.

One of the most important analytics tools that decision makers tend to ignore is to create a separate environment for advanced analytics. I have found that on occasion executives do not understand that predictive analytics consumes a large amount of internal memory and tends to negatively impact performance of current operational systems. The solution is fairly simple: build your analytics engine in a separate environment.

Conclusion
A recent survey found that three out of four executives understand that predictive analytics are essential to the operations of their business. Decision makers can and should use proven advanced analytics techniques to improve profitability. If executives learn the fundamentals of business analytics, its possibilities and limitations, they will be able to make better informed decisions in the investment of these new technologies.

Implementing Business Analytics

By Alberto Roldan - Copyright 2010

Introduction
Companies are eager to implement business analytics to help them reduce costs and increase revenues. These companies face an unchartered territory in the area of analytics and need assistance in how solve implementation issues. Among those issues are mapping business objectives to analytics, resources, budget, data understanding, and planning. The objective of this article is to assist companies in dealing with those issues.
For the purposes of this article, we will equate analytics with the ability to predict the probability an occurrence or an event in the future. Companies are using analytics in many ways to predict the probability of
1. Transactions that have a potential to be fraudulent in market surveillance institutional trading and in health care claims processing.
2. Clients that should be targeted to buy different software products that are bundled together.
3. A CPG competitor’s product gaining track in the marketplace through the use of data from social media and its internal data.
4. The patients that are more susceptible to multiple chronic diseases.
5. The failure of machine parts within a specific product at the customer site and during the manufacturing process.

Organization Structure
Companies want to know the number of resources needed for a successful analytics project. Although the number of resources varies from project to project, the rule of thumb is that the core team consists of three people: a statistical modeler, an analyst, and developer. A fully functional team is also going to include a leader, a manager or project leader, business analysts, and evaluators/testers.
One of the main issues is how to find and budget for the proper resources. Individuals with statistical backgrounds are hard to find in the marketplace, as well as developers in specialized analytics software. A common mistake is to equate a developer with a statistical modeler. These skill sets are different, and understanding this difference is one of the keys for successfully implementing business analytics.
Outsourcing the analytics project is a good and cost-effective solution if the outsourced company has industry-specific knowledge and statistical modeling experience. There are different business models that allow a company to be successful using this approach:
1. Start with a proof of concept (POC) and move to larger projects.
2. Ramp up with the outsourced company, but then move to bring the analytics area into the full control of the company.
3. Fully outsource for the long term using a revenue-sharing model.
Other companies prefer a staff augmentation model. Personally, I have only seen this model work for companies that already have a well-organized organization structure for their analytics.
Budgeting for these resources and the analytics software is another challenge. It is a traditional market forces issue: high demand for these resources but low supply in the marketplace. The result is that you are paying a premium for these resources. In order to justify these resources, you need to show a return on investment (ROI). It is difficult to show an ROI in an area that you have no historical experience. The best solution is to start with a well-defined (including the budget) small project and use the results to extrapolate an ROI. Outsourcing the POC is a potential solution to this issue.

Mapping Business Objectives
One of the keystones in the successful implementation of business analytics is to specifically map the business objectives to the question that you want to solve. Although on its face this seems like a fairly simple issue, it is recurring flaw that I see. Companies want to know how they can use predictive analytics instead of defining the issues that they are seeking a solution. The first step in implementing analytics within a company is to have a clear business understanding of the issue. Analytics are based on a combination of mathematics and business knowledge. This knowledge is precise to solve a specific question.
The leader of the analytics team must ensure that the best practices are followed, including mapping out business objectives to the specific questions that analytics answers. The manager of the analytics team is responsible for identifying the metric for success used to evaluate the project. An analytics project should always be measured in business terms. How much potential revenue or cost savings does it identify?

Data Understanding
Two of the main issues are data quality and availability. The results of an analytics project are directly related to these two issues. The best practice in dealing with a data-quality issue is to deal with these issues before undertaking an analytics project. The data does not need to be perfect for an analytics project to be successful, but leaders and managers must understand and agree upon the limitations facing the project. Also, there are statistical techniques that can be used to refine the results and minimize data-quality issues like segmenting the results into “expected” results or those with a high probability of data-quality issues (“unexpected” results) for evaluation purposes.
The issue of data availability is more complex since there are some issues that we can extrapolate results even without having the data available, and sometimes this is impossible without additional data. A CPG company could extrapolate the results of an ethno demographic trade promotion analytics project into stores that they do not have data, but that falls within the same classification as similar stores where that data is available. The issue to validate the extrapolation is whether the segmentation or classification is valid or not. For example, a small store (by sales volume) in Southern California may not be comparable with a small store in Miami because although they both are catering to Latino customers, the characteristics of both populations may be different. On the other hand, a segmentation of small stores within a specific geographical area of Southern California could be useful to extrapolate the results within that geographical area in stores that do not have data available.

Implementation Planning
Implementation is essential in analytics because that is the actual transformation of data into actionable information. Planning how you intend to use analytics and the nature of your audience becomes essential to maximize your ROI. An understanding that analytics has different meanings within a company is fundamental. Executive management may want to know whether tactics are properly aligned with strategic goals. Line management would like to know how to best accomplish their specific monthly objectives. The employee on the field would want to know how to accomplish today’s target.
The visualization of analytics results, through dashboards, provides the means for decision makers within a company to quickly grasp the meaning of the information. Companies should think about how they want the information layer to be presented before embarking into an analytics project. The information layer should be directly correlated to the business objectives. Also, it should be flexible to add new requirements.
Companies should consider utilizing the advances in visualization techniques when planning a dashboard. This includes the ability to see the condition of a company through business metrics in a 3-D manner. The utilization of a 3-D graph that incorporates dollar value, statistical control process comparison, and predictive analytics is a powerful tool that allows using analytics in strategic and operational areas. The utilization of 3-D graphs increases perception and the ability to detect patterns by over 40 percent. Companies should ask, “Is there a value in increasing our ability to detect patterns in the data by 40 percent?”

Conclusion
As companies embrace and streamline analytics projects into their operations, they should plan to face issues that can derail their goals. Just a few years ago, it was common to hear that 50 percent of all IT projects were never completed. One of the lessons learned during that time was the importance of limiting scope, budgeting for resources, and project planning. A modified version of those lessons should be used when planning analytics projects. Think big, but start with small measurable projects. Avail yourself of best practices, identify potential issues early, and use experienced resources to ensure the success of your project.

About the Author - Alberto Roldan, the author, is responsible for the Enterprise Analytics Practice- North America, at Cognizant Technologies. He a published author with over 18 years experience in business analytics. He has a BA from the University of Michigan and a JD from the University of Puerto Rico Law School. He can be reach at alberto.roldan@cognizant.com

Copyright 2010

Advanced Business Analytics for 2010 - The Reset Economy

This article, Turning a Sow's Ear Into a Silk Purse, is how to use an analytics Center of Excellence to achieve cost savings, increase profitability, and promote efficient innovation for companies in the reset economy of 2010.