Kugamoorthy Gajananan

Pricing IT Services Deals: A More Agile Top-Down Approach

Information technology service providers bid on high valued services deals in a competitive environment. To price these deals, the traditional bottom up approach is to prepare a complete solution, i.e., know the detailed services to be offered to the client, find the exact costs of these services, and then add a gross profit to reach the bidding price. This is a very time consuming and resource intensive process. There is a business need to get quick (agile) early estimates of both cost and price using a core set of high level data for the deal. In this paper, we develop a two-step top down approach for doing this. In the first step, we mine historical and market data to come up with estimates on the cost and price. We provide some numerical results based on industry data that statistically shows that there is a benefit of using historical data in this step beside the traditional way of using market data. Because the bidding price is not the sole factor affecting the chances of winning a deal, we then enter the different price points in a predictive analytics model (step two) to calculate the relative probability of winning the deal at each point. Such probabilities with the corresponding prices can provide significant insights to the business helping them reach quick reliable pricing.

A Top-Down Pricing Algorithm for IT Service Contracts Using Lower Level Service Data

Information technology (IT) service providers competing for high valued contracts need to produce a compelling proposal with competitive price. The traditional approach to pricing IT service deals, which builds up the bottom-up costs from the hierarchy of services, is often time consuming, resource intensive, and only available late as it requires granular information of a solution. Recent work on top-down pricing approach enables efficient and early estimates of cost and prices using high level services to overcome and complement these problems. In this paper, we describe an extended pricing method for top-down pricing using the secondary service level. The method makes use of data lower level services to calculate improved estimates, yet still requires minimal input. We compare the previous and new approaches based on industrial data on historical and market deals, and demonstrate that the new approach can generate more accurate estimates. In addition, we also show that mining historical data would yield more accurate estimation than using market data for services, experimental results are in consistent with our findings in previous work.

Top-Down Pricing of IT Services Deals with Recommendation for Missing Values of Historical and Market Data

In order for an Information Technology (IT) service provider to respond to a client’s request for proposals of a complex IT services deal, they need to prepare a solution and enter a competitive bidding process. A critical factor in this solution is the pricing of various services in the deal. The traditional way of pricing such deals has been the so-called bottom-up approach, in which all services are priced from the lowest level up to the highest one. A previously proposed more efficient approach and its enhancement aimed at automating the pricing by data mining historical and market deals. However, when mining such deals, some of the services of the deal to be priced might not exist in them. In this paper, we propose a method that deals with this issue of incomplete data via modeling the problem as a machine learning recommender system. We embed our system in the previously developed method and statistically show that doing so could yield significantly more accurate results. In addition, using our method provides a complete set of historical data that can be used to provide various analytics and insights to the business.

A Method for Selecting Peer Deals in IT Service Contracts

To respond to requests for proposals from clients requiring complex Information technology (IT) services, IT service providers have to prepare a solution composed of the multiple services requested by the clients and price that solution. Then, each provider competes in a tender-kind of process trying to convince the client with their solution. Pricing these solutions/deals, using historical and market data, is a complex task that we studied in our previous works. In our prior pricing approaches, we used a simple algorithm for selecting similar historical and market deals to the one we are trying to price, before we mine the data of these deals to estimate the costs of that latter deal. However, there are multiple limitations to that algorithm that we overcome in the novel approach that we present in this paper. These limitations include missing on some similar deals due to the way we chose them. Our new approach involves an iterative algorithm that selects peer deals at different levels until a pre-specified number of deals is determined. We present a proof-of-concept implementation of our approach, using real-world data, to illustrate its efficiency.

A method for estimating annual cost reduction of IT service deals

Highly valued Information technology (IT) service contracts involve the delivery of complex IT services, such as migrating the clients IT infrastructure to the Cloud, Mainframes, among others. IT service providers usually compete to win these IT service contracts. In order to bid on such deals, IT service providers need to price/quote the solution that they propose to the client, trying to convince him to use their services. A few analytical methods in the literature have been provided for pricing these deals. However, these methods ignore an important characteristic of these services; that is they are typically characterized by a decreasing cost profile in subsequent years to the first year. Typically, these methods require solutioners to manually input these annual cost reductions. In this paper, we present an analytical way for calculating this cost reduction, if applicable, via mining historical data. We show that using our methodology could achieve significant increase in the accuracy of estimating the costs and prices of IT service deals.

Bug Localization by Learning to Rank and Represent Bug Inducing Changes

In software development, bug localization is the process finding portions of source code associated to a submitted bug report. This task has been modeled as an information retrieval task at source code file, where the report is the query. In this work, we propose a model that, instead of working at file level, learns feature representations from source changes extracted from the project history at both syntactic and code change dependency perspectives to support bug localization. To that end, we structured an end-to-end architecture able to integrate feature learning and ranking between sets of bug reports and source code changes. We evaluated our model against the state of the art of bug localization on several real world software projects obtaining competitive results in both intra-project and cross-project settings. Besides the positive results in terms of model accuracy, as we are giving the developer not only the location of the bug associated to the report, but also the change that introduced, we believe this could give a broader context for supporting fixing tasks.