Jonathan Ansell

Identifying cross‐selling opportunities, using lifestyle segmentation and survival analysis

Purpose – To demonstrate the successful use of lifestage segmentation and survival analysis to identify cross‐selling opportunities.Design/methodology/approach – The study applies lifestyle analysis and Coxs regression analysis model to behavioural and demographic data describing 10,979 UK customers of a large international insurance company.Findings – There are clear differences between the lifestage segments identified with respect to customer characteristics affecting the likelihood of a second purchase from the company and the timeframes within which that is likely to take place. The “mature” segments appear to offer greater opportunities for retention and cross‐selling than the “younger” segments.Research limitations/implications – The study was limited by the type of data available for analysis, which related mainly to life insurance and pension products characterised by low transaction frequency. Different results might be expected for banking or credit‐and‐loan products. The findings could be enh...

The stability of an optimal maintenance strategy for repairable assets

Abstract Equipment in the process industry is often subject to decay and requires maintenance, repair and eventual replacement. The challenge of competition and the accompanying regulatory regime requires that actions be integrated and cost effective. Ansell and colleagues in 2001 explored an approach to the assessment of asset life of maintained equipment in the process industry using a semiparametric approach. Using stochastic dynamic programming techniques an approach to find the optimal strategy was also developed by Ansell and colleagues. The approach was illustrated using data from the water industry. A major aspect in the development of optimal strategy for repair and replacement is the cost of these activities. Often the costs can only be ascertained roughly in terms of hours expended on the activity. Detailed costings are rarely available. The discount factor will depend on the interest rate in place. Generally a conservatively high level has been taken but with current low rates one needs to explore the sensitivity of solution to the discount factor. Also there is a need to explore the sensitivity of the solution to changes in the costs of the maintenance activities involved. In this paper we explore the stability of the results to changes in the relative costs. It is seen that two costs seem to be more important than the others; hence the accounting effort appears to be best directed towards these costs. One concern that arises from these results is the impact of the maintenance events. In past modelling the impact of maintenance has been assumed to have a fixed effect. This was derived from study of data. It is felt that this assumption may be too simple and so a differing model is explored to examine this issue.

Modeling of a Small Transportation Company’s Start-Up with Limited Data during Economic Recession

This paper presents a modeling method for analyzing a small transportation company’s start-up and growth during a global economic crisis which had an impact on China which is designed to help the owners make better investment and operating decisions with limited data. Since there is limited data, simple regression model and binary regression model failed to generate satisfactory results, so an additive periodic time series model was built to forecast business orders and income. Since the transportation market is segmented by business type and transportation distance, a polynomial model and logistic curve model were constructed to forecast the growth trend of each segmented transportation market, and the seasonal influence function was fitted by seasonal ratio method. Although both of the models produced satisfactory results and showed very nearly the same of goodness-of-fit in the sample, the logistic model presented better forecasting performance out of the sample therefore closer to the reality. Additionally, by checking the development trajectory of the case company’s business and the financial crisis in 2008, the modeling and analysis suggest that the sample company is affected by national macroeconomic factors such as GDP and import & export, and this effect comes with a time lag of one to two years.

'Birds of a feather' fail together: Exploring the nature of dependency in SME defaults

This article studies the effects of incorporating the interdependence among London small business defaults into a risk analysis framework using the data just before the financial crisis. We propose an extension from standard scoring models to take into account the spatial dimensions and the demographic characteristics of small and medium-sized enterprises (SMEs), such as legal form, industry sector, and number of employees. We estimate spatial probit models using different distance matrices based only on the spatial location or on an interaction between spatial locations and demographic characteristics. We find that the interdependence or contagion component defined on spatial and demographic characteristics is significant and that it improves the ability to predict defaults of non-start-ups in London. Furthermore, including contagion effects among SMEs alters the parameter estimates of risk determinants. The approach can be extended to other risk analysis applications where spatial risk may incorporate correlation based on other aspects.

Time-to-profit scorecards for revolving credit

This paper defines and models time-to-profit for the first time for credit acceptance decisions within the context of revolving credit. This requires the definition of a time-related event: A customer is profitable when monthly cumulative return is at least one (i.e. cumulative profits cover the outstanding balance). Time-to-profit scorecards were produced for a data set of revolving credit from a Colombian lending institution which included socio-demographic and first purchase individual characteristics. Results show that it is possible to obtain good classification accuracy and improve portfolio returns which are continuous by definition through the use of survival models for binary events (i.e. either being profitable or not). It is also shown how predicting time-to-profit can be used for investment planning purposes of credit programmes. It is possible to identify the earliest point in time in which a customer is profitable and hence, generates internal (organic) funds for a credit programme to continue growing and become sustainable. For survival models the effect of segmentation on loan duration was explored. Results were similar in terms of classification accuracy and identifying organic growth opportunities. In particular, loan duration and credit limit usage have a significant economic impact on time-to-profit. This paper confirms that high risk credit programmes can be profitable at different points in time depending on loan duration. Furthermore, existing customers may provide internal funds for the credit programme to continue growing.

Issues in Optimal Asset Management

Abstract In previous presentations the authors have discussed optimal asset management within the water industry. This paper extends this work by developing a case study around optimal asset management. Whilst earlier work has been focused on the development of theoretical models, this paper focusses on issues in application of the optimal asset model. It presents the steps to address the application of the model. It does this by addressing a specific piece of equipment within the water industry subject to replacement and refurbishment. Hence it describes a practical solution for dealing with the trade-off between capital expenditure (CAPEX, replacements of an asset) and operational expenditure (OPEX, refurbishment of an asset). The case study is based on experiences with two Water Companies.