Services organization manage a pipeline of sales opportunities with variable enterprise sales engagement lifespan, maturity levels (belonging to progressive sales stages), and contract values at any given point in time. Accurate forecasting of contract signings by the end of a time period (e.g., a quarter) is a desire for many services organizations in order to get an accurate projection of incoming revenues, and to provide support for delivery planning, resource allocation, budgeting, and effective sales opportunity management. While the problem of sales forecasting has been investigated in its generic context, sales forecasting for services organizations entails the consideration of additional complexities, which has not been thoroughly investigated: (i) considering opportunities in multi-staged sales pipeline, which means providing stage-specific treatment of sales opportunities in each group, and (ii) using the information of the current pipeline build-up, as well as the projection of the pipeline growth over the remaining time period before the end of the target time period in order to make predictions. In this paper, we formulate this problem, considering the service-specific context, as a machine learning problem over the set of historical services sales data. We introduce a novel optimization approach for finding the optimized weights of a sales forecasting function. The objective value of our optimization model minimizes the average error rates for predicting sales based on two factors of conversion rates and growth factors for any given point in time in a sales period over historical data. Our model also optimally determines the number of historical periods that should be used in the machine learning framework to predict the future revenue. We have evaluated the presented method, and the results demonstrate superior performance (in terms of absolute and relative errors) compared to a baseline state of the art method.

An Optimization Approach to Services Sales Forecasting in a Multi-staged Sales Pipeline

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.

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

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

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 Selecting Peer Deals in IT Service Contracts

One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.

Augmenting missing values in historical or market data for deals

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.

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

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

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