Real Time
Marketing
Self-learning, intelligent customer
scoring offers financial service
providers a made-to-measure
forecasting model for individual
customers
Content
Management Summary
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3
Intelligent, modern customer scoring
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4
CRISP-DM – a methodical approach ... 4
Self-learning, intelligent model development ... 5
Management Summary
Digital transformation is continuing to affect a whole host of areas of society. New technologies only serve to fuel this development, resulting in sweeping changes in customer behaviour. Digital and mobile channels continue to gain in importance and more and more customers now use them to communicate with their bank and insurance companies. In parallel to this, customer expectations with respect to the customer journey have now shifted. Modern customers want offerings that match their needs without compromise, independent of the channel they decide to use to get in touch with their financial services provider.
Alongside this trend of more and more clients deciding for themselves how to get in touch (inbound communication), one increasingly important development is real-time marketing.
Banks have to be in a position to respond to contact requests from clients with the right offers and services – in real time, through all possible channels of communication. The big challenge here is – wherever possible – to predict exactly what the customer need might be or how they will behave, ideally by using customer scoring based on predictive modelling tools.
Most existing customer scoring systems draw on expert knowledge and they are typically created manually, using analysis as a basis to write predictive models. This approach has been established for decades, but it is resource-intensive, time-consuming and expensive. It is also difficult to find experts with the highly specialised skills required, the software is extremely complex and using the software is also resource-intensive. As a result, GFT has been working with a variety of innovative specialists in the field of software development to provide automated help with the problems posed by digital real-time digital communication.
State-of-the-art software takes care of big and important tasks and the integral, transparent parts of existing processes. This method has been tested using a variety of benchmarks to reflect actual client situations. This proved that there is a significant opportunity to improve efficiency and reduce costs by minimising the resource-intensive and manual processes of analysis.
The advantages with the innovative method developed by GFT – especially compared to traditional modelling techniques applied to customer scoring – were found to be: ¬ Qualitative benefits: the best-possible modelling results
can be provided for a variety of business tasks within specialist departments. Thanks to specialist models, which are matched to specific tasks in each area, it is possible to optimise the entire marketing portfolio. The models have also been improved to deliver precise predictions and provide higher conversion rates for more effective campaign management, thus resulting in cost and efficiency benefits.
¬ Cost benefits: instead of relying on cost-intensive implementation projects to overhaul and optimise existing models, work can be broken down into smaller tasks. Existing models can be partially automated, recalibrated and put to more productive use. It is also much easier and quicker to introduce data from other sources and take additional factors into account within models.
¬ Competitive advantages and improved ‘time to market’:
reductions in the time invested in all key processes relating to Business Intelligence(BI) and data mining, based on the semi-automated development, adaptation, extension and optimisation of scoring models using special software. This also reduces the burden on specialist departments and IT so they can concentrate on the core business.
By reducing time-to-market cycles, a significantly higher volume of tasks can be processed within a short space of time. Because processes become much more flexible, it is easier to react quickly in any given situation, for example when launching a product or innovative services, or if the bank is losing more customers.
Modern customers want offerings
that match their needs without
compromise, independent of the
channel they use to get in touch
with their financial services provider.
Intelligent modern-day client
scoring
CRISP-DM – a methodical approach
‘Classic’ analysis and conventional model development typically rely on CRISP-DM methods, which are now practically the industry standard. The overall approach is iterative but the quality of the results is strongly dictated by the expertise of the people involved. Also, the model only starts to become more accurate over time – assuming there are no changes in market conditions.
By continually repeating individual steps within the process, the idea is to expand the model sequentially and make improvements, through reconciliation and manual fine-tuning.
Figure: CRISP-DM (Cross Industry Standard Process for Data-Mining)
• Diagnostics • Requirements / measures • Recommendations Data understanding • Sources • Quality Data preparation • Data cleansing • Data formatting • Data transformation Modelling • Evaluation • Creation Evaluation • Model testing • Recommendations Deployment Business understanding
Classic and intelligent
software-based methods differ in speed,
model definition and resource
investments.
Self-learning, intelligent model development
A comparison between classic and intelligent software-based methods (also sometimes called adaptive methods) does reveal parallels in the overall approach, but there are significant differences in terms of speed, model definition and resource investment.
By using specialised data mining techniques and intelligent toolsets, laborious manual analysis and modelling tasks can be automated in most phases of the process. The role of the experts is to assess models and results rather than carry out the modelling themselves, which is time- and cost-intensive. This allows for significant improvements in efficiency and quality.
In the following sections, the individual phases of the data mining process are examined to explain the underlying differences between the classic scoring process and the self-learning, intelligent approach.
1. Diagnosis
The aim during this initial phase is to bring the people involved in the project together to evaluate and reconcile the fundamentals: the object to be modelled, objectives of data mining, definition of success factors and other important issues pertinent to the overall task. Determining fundamentals is important for both kinds of approaches, so in itself there are no significant differences at this point.
2. Data analysis
During data analysis, data that is available for the modelling is analysed, evaluated and put into categories. This involves looking closely at operational and planning systems and investigating the relevance of available data. The classic approach during this phase revolves closely around the knowledge and experience of the data mining experts, whose job it is to analyse the data. The experience of these data mining experts and the quality of the legwork carried out by their colleagues in specialist departments play a decisive role in the quality of the output of this phase. Typical rules and filters are looked at from previous modelling projects with the aim of pooling, evaluating and categorising available data (especially the model variables that will be used), before matching data to the planned users of the model.
Self-learning model development – and the intelligent tools it involves – offers important advantages during this phase, especially when it comes to analysing and evaluating the variables that will be used. Drawing on standardised, tried-and-tested analytical methods and routines, data undergoes comprehensive evaluations, documentation processes and quality checks. Any possible quality issues with the data are captured and analysed based on expert knowledge so that it can be prepared for modelling. By the end of this phase, this tried and tested procedure delivers superior results when preparing and evaluating model variables.
Furthermore, new insights can be gained and logged as executable rules and filters for further modelling projects, thus establishing a basis for continually accumulating knowledge and experience.
Table: Time and quality benefits offered by self-learning, intelligent model development
Key: Time benefit Quality benefit
Phase Classic Modern
1. Diagnosis 2. Data analysis 3. Data enhancement 4. Data transformation 5. Model generation 6. Model comparison 7. Model testing 8. Model results 9. Data updating 10. Calibration
3. Data enhancement
When data is prepared using the classic approach, the data mining experts check and evaluate available sources of data and model variables according to their content. Under ideal conditions, further important information and insights can be derived or generated from existing model variables. In classic model development, deriving and – where applicable – generating further model variables to be taken into consideration are based on transformation rules. The data mining experts then apply these to data.
With the approach based on self-learning, intelligent toolsets and software, this part of the process is similar to the previous phase in that it is supported by existing standardised routines. This results in corresponding efficiency improvements, which can also be realised when looking at future model variables that have not yet been considered.
4. Data transformation
The available model variables are also transformed during the data preparation phases, in accordance with the type of model being used, as required. For example, continuous variables are used to establish corresponding interval variables and these are used in subsequent modelling. The classic approach again involves asking the data mining experts to take on this comprehensive task. They carry out a qualitative analysis of the available data and establish and use corresponding transformation rules.
At the end of this complex and important phase of the data mining process, the experts pull together a selection of the model variables to be used and these are looked at in the next part of the process.
Self-learning, intelligent software-based model
development offers significant efficiency advantages during this phase, especially when it comes to the analysis and evaluation of key variables. By using highly automated dynamic and adaptive classification techniques – involving filters, rules, clustering methods and feature detection algorithms – decomposition of the model variables is optimal and indeed comprehensive. This approach offers previously unachievable efficiency in determining the primary influences of the data mining model under consideration, thus making an important contribution to model accuracy.
With self-learning, intelligent
toolsets, a solid foundation can
be established for the ‘optimum’
model.
It is during this phase that one important factor becomes noticeable, relating directly to the efficiency improvements that can be achieved with self-learning, intelligent model development. Unlike the classic approach, in which expert knowledge dictates the evaluation and selection of the model variables to be examined, with the self-learning, intelligent approach and toolsets, the overall number of available model variables is looked at to determine the optimal set of model variables. Using highly automated methods that have proven statistically reliable makes the otherwise highly complex model significantly less complex. The following examples show the extent of complexity reduction: (n = number of attributes per data set)
This shows that even with small data structures (like core customer data), an unmanageable number of models have to be examined to establish the optimum model. With the classic approach, there are only a limited number of possible options that can be covered, both in terms of the number of possible models and in terms of the time required to calculate individual models.
This contrasts with the use of self-learning, intelligent toolsets, whereby a solid foundation can be established for the ‘optimum’ model by taking the maximum number of possible models into consideration during model development.
5. Model generation
One of the most important and most resource-intensive stages of data mining has been found to be the model generation phase. It is during this phase that the experts look at different model classes that may come into consideration for the task at hand. This involves classification, clustering, decision trees, classification or regression trees, neural networks, and similar. Of course the types of models that should be examined are also taken into consideration during the previous stages of data mining, but it is not until this phase that they are actually calculated and compared, using the selected model variables.
Under the classic approach, it is the data mining experts who decide which methods and algorithms to use: depending on their previous experience and level of expertise with different types of models, different models are examined, calculated and evaluated as part of the modelling phase, concentrating on a certain type of model in order to pinpoint the best possible option for the task at hand.
This fundamental, iterative and adaptive approach to data mining is also used with the self-learning, intelligent method and tools. There is one key advantage with this approach, however: Rapid-Modelling is used to determine the optimum, minimum-error model more quickly and more efficiently. By using autonomous learning techniques, proven statistical methods and model accuracy metrics, it is much quicker and easier to move towards the optimum model.
Combined with the results of the subsequent model comparison phase, this innovative, intelligent self-learning method makes the process of model generation much more efficient, as is the case with model calibration. Data mining experts can use these modern techniques as an automatic process, or intervene at any stage of the model development or model calibration process. As a result, this method is nothing like black box evaluation, it is simply using pre-defined and proven functions to avoid time-consuming manual tasks and unnecessary resource investments in programming.
n = 30 n = 100 n = 1000 n = 10‘000
Number of possible models (theoretically):
Formula: 2n-1-1
536 mill. 6.33E29 5.36E300 9.97E3009
6. Model comparison
If the specialist task of analysis previously involved using different techniques and algorithms, calculations are carried out during this phase and the different models are compared using champion/challenger analysis methods. To do this, the data mining experts contrast and compare the modelling results of the different model options and hold meetings with specialist departments to discuss the findings, evaluate them and set priorities within the different types of models.
With the self-learning, intelligent approach, the champion/ challenger comparison is automated based on standard methods and functions. This makes it possible to carry out rapid modelling and significantly improve efficiency (cf. previous section).
The advantages with the self-learning, intelligent approach during the model comparison phase are summarised in the following:
¬ Improved quality: transparent comparison of different types of models and computational algorithms
¬ Continued flexibility to control optimisation algorithms, for example with the possibility to exclude unwanted statistical routines and algorithms
¬ Enhanced efficiency: analysis, data enrichment and transformation are possible within short timeframes, as are model generation and model comparison
¬ Lower costs: even if champion/challenger comparisons are needed, this uses up less capacity in specialist departments and IT
7. Model testing
After defining and calculating the optimum model, in this phase the task is to verify and validate the quality of forecasts and reliability.
With both approaches – the classic technique or the self-learning, intelligent method – this part is similar. If things went positively and model generation resulted in a model capable of producing sufficiently accurate and reliable numbers, then few significant differences are to be expected.
It is a different case if model validation showed that the model is not of sufficient quality to meet the desired accuracy and reliability criteria. In such cases, the self-learning, intelligent approach offers clear advantages in terms of efficiency. During data mining, the process jumps back to the previous analysis and model generation phases and this routine is carried out again and again until an optimised, minimum-error model has been identified.
During data mining, the process
jumps back to the previous analysis
and model generation phases and
this routine is carried out again and
again until an optimised,
minimum-error model has been identified.
Again, this is where the self-learning,
intelligent approach has significant
efficiency and time advantages
– and advantages gained in the
previous phases can be enjoyed
once again by being repeated.
8. Model results
At this point the resulting model is integrated into the customer-specific system infrastructure and operative processes are aligned to match.
GFT believes this phase should also involve evaluating the overall approach and the benefits of implementing both methods, and that this should be according to the specific type of customer.
It is not possible to make generalisations about the advantages of one or other method. For example, different issues should be included such as real-time analysis requirements, or whether different points of customer contact will result in different usage scenarios.
9. Data updating
This part of the data mining process involves ensuring that what was learnt from the model is fed back into the productive, customer-specific, operative and planning systems. Both dependent target variables and independent model variables have to be updated according to the newly generated data. The steps that need to be taken to make possible improvements to the model currently being used can be explored, evaluated and implemented in close cooperation with specialist departments.
Similar to the previous phase of the process, GFT believes that no significant advantages are to be expected with either approach. If there are advantages or disadvantages, these have to be analysed and assessed within the context of specific types of customers.
10. Model calibration
Model calibration plays an important role with highly iterative data mining processes. Drawing on qualitative model results (expert opinion) and quantitative model results (model accuracy metrics), with the classic approach an attempt is made to improve the quality of the model by introducing highly specific measures. This can involve making fundamental adjustments to the model in productive use and quite possibly even involve complete remodelling.
Again, this is where the self-learning, intelligent approach has significant efficiency and time advantages – and the advantages gained in the previous phases can be enjoyed once again by being repeated.
Conclusion
The self-learning, intelligent customer scoring solution offered by GFT provides a basis for predicting client behaviour accurately, flexibly and efficiently. The approach to modern predictive modelling is crucial for real-time (inbound) marketing. If financial service providers want to keep managing clients efficiently in the future, especially through digital channels, they will need to look carefully into this issue – sooner rather than later. Established forecasting models, which have been elaborated on repeatedly during round after round of discussion and development over several weeks, if not months, are simply not suited to the requirements of Generation Y1, let alone their successors in Generation Z1.
Spotting offers that will fit customers like a glove –from next-best offers (NBO) to next-best activities (NBA), for example as part of cross-selling, up-selling or churn prediction campaigns – requires forecasting models that are sufficiently accurate, efficient and up to date. To do this, communication must be consistent and coherent across all channels – be they digital or non-digital.
Self-learning, intelligent customer scoring offers financial service providers the following specific benefits:
¬ Superior quality:
▪ Model development based on statistical and
mathematical methods delivers vastly superior results versus modelling based on intuition, and this remains so in the long term during on-going business. ▪ Using specialised models for special tasks will
improve the quality of forecasting, avoid introducing suboptimal measures to approach customers and improve conversion rates achieved through campaign management.
▪ Integration and deployment of the optimum models within the necessary client environment in IT systems, can be carried out by GFT experts. The experience and expertise of GFT – during all project phases (concept development, implementation, integration into processes, IT integration, change management, coaching and training) – ensure safe fulfilment of project deliverables.
¬ Lower costs:
▪ Gaining faster results during the early stages of data cleansing, data analyses and model generation reduces project ramp-up costs.
▪ There is no need to set up a team of highly specialised and highly expensive experts – it is enough to have a small, specialised core team which only works on projects as required.
▪ Fewer employees and resources are tied up in model creation, calibration and expansion.
▪ Dependence on technical departments can be reduced to an absolute minimum based on requirements. ▪ Model adjustments, extensions and calibrations can be
made more quickly, easily and inexpensively; the same applies to integrating new specialist requirements or including other data.
¬ Improved flexibility
¬ Competitive advantage and improved time to market:
▪It is easier to develop and apply new model variations quickly
- Throughput times accelerate markedly, making it possible to identify and implement quick wins in a variety of areas
- Ranking models for sales and marketing
- Cross-selling and up-selling scores for further products and services
- Churn management scores to help avoid customer attrition
- Uplift modelling
The self-learning, intelligent
customer scoring solution
offered by GFT provides a basis
for predicting client behaviour
accurately, flexibly and efficiently.
About the author
Georg Hildebrand
Senior Account Manager Business Development
In his role as a consultant, project manager and account manager, Georg Hildebrand has been working with companies in the financial services sector and other industries for over 25 years. The digitalisation of business processes that started many years ago has brought sweeping change to the way companies and customers interact with one another, and this change will continue in the years to come. As a project manager, Georg Hildebrand has been involved in realigning the planning and technical processes of client and service management right from the beginning, fulfilling a number of key roles for a variety of companies.
Georg Hildebrand is currently working with a number of clients on leveraging digitalisation as an opportunity to modernise existing business models, supported by a team of technology experts, the GFT laboratory and FinTech companies.
About the GFT Group:
GFT Group is a business change and technology consultancy trusted by the worlds’ leading financial services institutions to solve their most critical
challenges. Specifically defining answers to the current constant of regulatory change - whilst innovating to meet the demands of the digital revolution.
GFT Group brings together advisory, creative and technology capabilities with innovation culture and specialist knowledge of the finance sector, to transform the client’s businesses.
Utilising the CODE_n innovation platform, GFT is able to provide international start-ups, technology pioneers
and established companies access to a global network, which enables them to tap into the disruptive trends in financial services markets and harness them for their out of the box thinking.
Headquartered in Germany, the GFT Technologies SE achieved consolidated revenue of around EUR 365 million in 2014 and is represented in eleven countries with a global team spanning 3,300 employees. The GFT share is listed on the Frankfurt Stock Exchange in the TecDAX (ISIN: DE0005800601).
