Data Mining Case Studies - PDF
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The OnTARGET models estimate the probability of purchase at the product-brand level, and use training examples drawn from historical transactions, with explanatory features extracted from transactional data joined with company firmographic data. The objective of the second initiative, the Market Alignment Program MAPis to drive the sales allocation process based on field-validated analytical estimates of future revenue opportunity in each operational market segment.
The estimates of revenue opportunity are generated by defining the opportunity as a high percentile of a conditional distribution of the customer's spending, i. We describe the development of both sets of analytical models, as well as the underlying data models and web sites used to deliver the overall solution.
We conclude with a discussion of the business impact of both initiatives. Categories and Subject Descriptors H. Database applications data mining General Terms Algorithms, Management, Performance Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.
Keywords Propensity models, Quantile estimation, Customer wallet estimation, Sales efficiency 1. While hiring the best sales representatives is an obvious first step, it is increasingly recognized  that the realization of the true potential of any sales force requires that sales reps and executives be equipped with relevant IT-based tools and solutions.
The past decade has seen the development of a number of customer relationship management CRM systems [2, 3] that provide integration and management of data relevant to the complete marketing and sales process. Sales force automation SFA systems  enable sales executives to better balance sales resources against identified sales opportunities. While it is generally but not uniformly  accepted that such tools improve the overall efficiency of the sales process, major advances in sales force productivity require not only access to relevant data, but informative, predictive analytics derived from this data.
In this paper, we develop analytical approaches to address two issues relevant to sales force productivity, and describe the deployment of the resulting solutions within IBM. The first solution addresses the problem faced by sales representatives in identifying new sales opportunities at existing client accounts as well as at non-customer whitespace companies. The analytical challenge is to develop models to predict the likelihood or propensity that a company will purchase an IBM product, based on analysis of previous transactions and other available third-party data.
A second, but related business challenge is to provide quantitative insight into the process of allocating sales reps to the best potential revenue-generation opportunities.
In particular, we are interested in the allocation of resources to existing IBM client accounts. Here, the analytics challenge is to develop models to estimate the true revenue potential or opportunity at each account within IBM product groups. These models were developed as part of an internal initiative called the Market Alignment Program MAPin which the model-estimated revenue opportunities were validated via extensive interviews 1 An earlier version of this paper appeared online in July, in the IBM Systems Journal see Page: We describe this process, and the webbased MAP tool, later in this paper.
Both employ a data model that effectively joins historical IBM transaction data with external third-party data, thereby presenting a holistic view of each client in terms of their past history with IBM as well as their external firmographic information like sales, number of employees, and so on.Pianos Around Europe Part 5 - Budapest Maan Hamade
Both systems exploit this linked data to build the models described above and in the sections below. Given the different business objectives, the tools employ different web-based user interfaces; however, both interfaces are designed to facilitate easy navigation and location of the relevant analytical insights and underlying data. In the following section, we describe the OnTARGET project, its data model, and overall system design motivated by the business requirements. Section 5 describes the MAP revenue-opportunity models.
Finally, we describe the deployment of these systems, and discuss the operational impact against their respective business objectives. Since the broad market is likely to grow in aggregate at rates only slightly higher than GDP, companies will need to generate organic revenue growth at rates greater than the market overall to remain competitive. One approach is to pursue growth opportunities in emerging markets.
But it is also necessary to generate significant growth in a company s core businesses and markets. This requires a renewed focus on identifying and closing new sales opportunities with existing clients, as well as finding new companies that will be receptive to the company s core offerings. Improving sales force productivity is essential to both objectives. Early in the OnTARGET project, we spoke to a number of leading sales professionals and sales leaders about potential IT-enabled tools that they believed could enhance sales productivity.
One common sentiment is that sales people are often forced to use multiple tools and processes that not only fail to provide the relevant information needed to do their jobs better, but also take valuable time away from actual sales activities. While some cross-sell models were available for use by the sales teams, these analytics were often delivered via spreadsheets and lacked integration with important underlying data needed to understand the client sales history and potential IT requirements.
The sales professionals with whom we spoke were open to using a new tool, provided that such a tool 1. References a large universe of existing clients and potential new clients, 2.
Incorporates relevant data that may require multiple existing tools to access, 3. Includes analytical models to help identify the best sales opportunities, and 4. Integrates all such data for each company under a single user interface designed by end users to facilitate easy navigation.
As discussed further in Section 6. The success of OnTARGET is due in large measure to our ability to deliver these key capabilities directly to the front-line sales force. In the rest of this section, we discuss specific design decisions and implementations in light of these requirements. In particular, we discuss the types of data selected for inclusion in the tool, the integration of this data in the overall OnTARGET system, and the design of the user interface.
From a design perspective, this requirement drove decisions on the specific data and linkages to be incorporated, as well as the criteria to specify the universe of companies to be made available within the tool. After discussions with sales professionals, the following sources of data were selected for inclusion: All transactions executed by IBM with its clients over the past 5 years 2. Information on installed hardware and software at IBM client sites 4. Contact information for both customers and noncustomers 5.
Competitive information from external vendors 6. Assignments of companies to sales territories. Using the historical IBM transactional data, we select client companies for inclusion based on a minimum threshold of their spending with IBM over the past five years. Over 2 million sites are included worldwide. From the beginning, OnTARGET was developed with a webbased front end that would be flexible enough to allow end users to execute complex queries directly from the user interface.
It has a performance requirement of less than sevensecond response time for all transactions executed. The architecture somewhat isolates these elements to provide flexibility during the development and deployment process.
It also allows the transformation and refresh of data to occur in a staging area, with subsequent deployment to the production database. These operations are quite resource intensive, so executing them outside of the production environment eliminates any impact to the production application. The analytical models are developed outside the OnTARGET system, and are imported onto the staging server and integrated with the other data sources.
Separate cross-sell rules are specified by sales people, and are integrated in much the same way as the analytical models. Some of the data from each geographic region came from disparate data sources, so commonality of data elements had to be designed into the model. For example, the source contact entity from one region may have different fields and lengths from another or an element might have a common field name with different domains.
An analysis of the domain and length of each data element was done to ensure that a common data model could be created to allow the user interface to work more efficiently, standardize queries, and have a standard code base worldwide. All relevant pieces of data from each of the required entities were gleaned and assembled in a Computer Aided Software Engineering CASE tool from which a logical and physical data model was designed.
Hence, another key requirement of the common data model was that it readily support integration of new countries as data became available. The standardization of data structures allowed the user interface to remain untouched in many instances even as additional countries were being added.
IBM uses an internal reference number to identify customers, so it was necessary to introduce a unique database key in order to join internal data with the external reference data for each company. We developed a flexible process to transform all data to this common key. Transformation algorithms were developed using a transformation tool, WebSphere DataStage , to allow for consistent data presentation within the application. This helped to give the OnTARGET user interface a more consistent look and feel, regardless of the geography in which it was being used.
This tool also helped in documenting the data flows within the application and was useful for ongoing maintenance and training. All models are rebuilt using this data, and hence the model scores are always consistent with the latest financial and firmographic data. Updates to the other information, including company contact information and product installation records, are made more frequently.
The basic objective is to allow the user to build a focused customer targeting list composed of companies that meet criteria specified by the user. In the first step, the user defines a broad set of companies based on selections described in Figure 2. For example, one can specify a location e. New York stateand an industry e. Financial Services and immediately form a set of companies meeting these criteria.
Alternatively, a sales representative interested in a specific sales territory can select the territory identifier sand immediately build an initial set of all companies within these sales units. The second step allows the user to further filter this initial set of companies based on additional criteria.
It is possible to select only companies that have purchased in IBM product groups e. Tivoli software, System x servers. Hence, it is Page: The various selection criteria mentioned here are easily entered in the interface via standard pull-down and selection menus.
All companies that meet the specified criteria are displayed as the resulting targeting list. This list can be further modified by adding and removing companies directly, or by modifying the filter criteria in an iterative process that yields a list of key potential opportunities as a focus for the sales process. Selecting any company in the list takes the user to the company s detail page. This holistic view facilitates the sales process by providing all relevant information in one place, allowing a user to easily generate a downloadable report of this information.
Similarity is defined by a distance metric constructed using only firmographic information, e. This feature is useful in further identifying sales prospects, as well as understanding which IBM products have been purchased by other companies of comparable size in the same industry. The targeting list can be saved for future reference or as a basis for applying other criteria. Users can receive targeting lists and then refine the filters to meet their specific requirements.
In many cases, this function enables a sales operations person to define criteria and pass them on to representatives in their region. An essential feature of the user interface is the enforcement of appropriate security and privacy rules to ensure that all information is protected according to IBM and country-specific policies. This capability is managed from a separate administration interface that allows specification of rules to limit display of sensitive data to users with the appropriate authorization.
OnTARGET also includes the capability to collect usage statistics such as the number of logins by each user, as well as timestamped user accesses to each company detail page.
These data are essential to quantify both the acceptance of the tool, as well as some indication of the extent to which subsequent revenue for a specific client can be linked to usage of the tool.
We discuss these metrics in the section on business impact. For OnTARGET, we develop propensity models to predict the probability of purchase within a specific product group, while the MAP models are designed to estimate the potential revenue opportunity at each client account. The MAP models are described in a subsequent section. The goal of the propensity models is to differentiate customers or potential customers by their likelihood of purchasing various IBM products.
Rather than model at the level of individual products, our models are built to predict purchases within broad product groups or brands. Currently, we develop separate propensity models for ten product brands. We have at our disposal several major data sources to utilize in this task. The two major ones, which are available for the largest number of companies, are: Our goal is to make use of this data to build propensity models which are a widely applicable, and consider all potential customers, and b accurate in terms of differentiating the highpropensity customers from low-propensity ones, on a product-byproduct basis.
Companies that have already purchased Y in the past. These companies are eliminated from the propensity modeling all together. Companies that have a relationship with IBM but have never purchased Y. For these companies we can utilize both data sources 1 and 2 above, in building our existing customer model. Companies who have never purchased from IBM. For these companies we only have the firmographic Page: The model for these companies is termed the whitespace model. Since we have multiple geographies Americas, Europe, Asia Pacificwith multiple countries within each geography, and multiple product brands, we end up building a large number of propensity models currently about in each quarter.
In what follows, we summarize our modeling approach and the considerations leading to it, demonstrate its evaluation process during modeling, and show results of actual field testing. Finally, we discuss the modeling automation put in place to handle the overwhelming number of models built each quarter.
Our first step is to identify positive examples and negative examples to be used for modeling. In each modeling problem, we are trying to understand what drives the first purchase decision for brand Y, and delineate companies by the likelihood of their purchase. Assume the current time period typically last year, or last two years is t, then this leads us to the formulation of our modeling problem as: Differentiate companies who never bought brand Y until period t, then bought it during period t, from companies who have never bought brand Y.
Of the companies who never bought brand Y before period t, some will have bought other products before t. These companies form the basis of the existing customer model for Y.
The companies who never bought any brand before t are the basis for the whitespace model. Thus, for the whitespace problem, our positive and negative examples are: The definitions for the existing customer problem are similar, except that a previous purchase from IBM is required for inclusion. For some combinations of geography, brand and modeling problem, the number of positives may be too small for effective modeling we typically require at least 50 positive examples to obtain good models.
In that case, we often choose to combine several similar modeling tasks where similarity can be in terms of geography, brand, or both into one meta-model with more positives. In , we discuss in detail the tradeoffs involved in this approach and demonstrate its effectiveness. Next, we define the variables to be used in modeling.
For existing customers, we derive multiple variables from historical IBM transactions, describing the history of IBM relationship before period t.
Examples of these features are Total amount spent on software purchases in the two years before t Total amount spent on software purchases in the two years before t, compared to other IBM customers rank within IBM customer population Total amount spent on storage products purchases in the four years before t.
We then build a classification model more accurately, a probability estimation modelwhich attempts to use these variables to differentiate the positive examples from the negative examples. Our most commonly used modeling tool is logistic regression, although we have experimented with other approaches, like boosting.
For each example, the model estimates the probability of belonging to the positive class. We describe here a detailed example from a recent round of Existing Customer models built for North America, and discuss possible interpretations.
The example we give is the existing customer model for the Rational software brand. Figure 3 shows the predictive relationships found for this model. Predictive relationships between some derived variables and new Rational sales to existing customers Green arrows signal a positive effect i. The width of the arrows indicates the strength of the effect, as measured by the magnitude of the regression coefficient.
We show only statistically significant measured by p-value effects in the figure. We see several interesting effects, and most seem to be explainable: Industrial sector ITgeography California and company s corporate status Headquarters seem to have Page: This seems consistent with Rational being an advanced software development platform, which medium-sized IT companies in California and thus likely at the front of the hi-tech industry might be interested in purchasing.
On top of that, having a strong relationship in Lotus seems to afford additional power. While the total size of prior non-software relationship does not have a strong effect, some specific nonsoftware brands seem to be important. System p and System x, somewhat seem to encourage Rational sales, while System z relationship seems to discourage them. While this last fact may seem puzzling, it may be explainable by the particular nature of the software relationship with System z customers, who often manage their software relationship with IBM in conjunction with the System z relationship.
More analysis would be required to clarify this point. In all the hundreds of models we build, some of the variables are always highly significant see Fig. However, we do not perform further variable selection to save computation and avoid overfitting. In addition, we are not particularly interested in analyzing the "absolute" performance of our models, but care much more about the relative performance to a baseline model along the marketing-relevant performance metrics of Lift and AUC as defined below.
After repeating this 10 times, we have our whole data scored as leave-out by the different models, and we can use it to evaluate the modeling success. Japanese soundtrack was released applications is the production is the potential for in their rationalizing of.
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