Enterprises are increasingly operating in a dynamically changing and fluid environment. They are constantly changing gears just to keep pace. CXOs are constantly looking for ways to overcome or create disruptions in a world becoming increasingly complex. Infosys Consulting Blog gathers a community of subject matter experts who are driving pragmatic conversations around that which is changing and that which needs to be rethought, redefined and redesigned for enterprises to achieve market-leading performance roadmaps.

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April 15, 2013

Analytical Models...Design Right and Monitor Well

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Due to ever-changing business conditions and technological advancements, the predictive power of an analytical model can fade unless there is a mechanism to monitor and calibrate it, thereby keeping it in sync with decision variables. This raises few important and related questions. Can models be calibrated to ensure longevity? Is it feasible to design a self-learning model sensitive to business environment? Are there some model design and management principles which can address future performance needs?

Analytical models vary in context, complexity and implementation. However the core process of building models in terms of getting the right input, applying the relevant algorithm and producing the desired result, remains the same. Getting the right data to the model is an important step but not an easy one. There is a huge surge in data as more devices are getting connected and firms are trying to capture every bit of data by building appropriate infrastructure. Data used for developing models are changing on an ongoing basis. Applying models based on old data structures may not yield an optimized result as customer decision process is evolving and becoming contextual based. An example, you are looking for a restaurant to have a quick lunch with your friend, mobile based apps can provide local search results of restaurants near your vicinity with special offers. You can also review the feedback of restaurants before picking up that menu card. It is a different approach of connecting with potential customers, being context specific and personalized. These light weight models (so called business apps) have a quick design to deploy cycle, mostly driven by business rules and a thin layer of analytical engine. They are usually part of the analytics game plan for many of the leading firms.

On the other hand we have these complex models applied in critical systems such as those used in banks to predict fraud behavior of transactions to alert the customer or the ones used in airline industry to predict maintenance interval of aircraft components. These models have a lot at stake and hence demand complex algorithms with champion-challenger methodology for self-learning and benchmarking. If designed correctly, the model can be calibrated by updating the business rules and associated components without having to update the analytical engine. The best practice is to design the model right and design for future. Some of the well accepted design principles are:

  • Input Strategy: Understand, filter and integrate current and expected future data structures and its relation with business function decisions.
  • Business Rule Aggregation: Design for future by separating the business rules and constraints from the core analytical engine.
  • Analytical Engine:  Implement advance mathematical and statistical algorithms relevant to business function with built-in fallback methods. Ensure the engine is built to handle structured and unstructured data as the case demands. Some of the common modeling techniques are logistic regression, neural network, decision tree, time series analysis, and principal components analysis.
  • Output Strategy: Outputs are presented for quick and effective decision making through appropriate medium and format.
  • Self-Learning components: Design procedures to capture False Positive and False Negative cases for analysis and feedback into the model for self-learning and enhancement.


Once a model has been successfully developed and deployed, it is important to monitor and calibrate it on an on-going basis. With multiple models being commissioned and decommissioned, it is easy for an organization to lose track of the same. One of the best practices of model management is to implement a Model Validation Framework at the business function level. Inventory of all the models are listed down with attributes such as model purpose, metrics impacted, financial impact, complexity, validation frequency, model owner, date commissioned. Doing so would provide complete visibility on all the models and their current status.

One of the key questions of model validation is to ask if the model still serves the purpose for which it was developed. A model validation group can oversee the validation process and provide appropriate recommendations. The validation report would contain discussion on technical aspects of the model including discriminatory and predictive power, mathematical/statistical algorithms, benchmarking and back-testing results. This way, the models can be tracked to ensure its relevance and performance as desired by the business.




April 9, 2013

Social CRM Tech Trends -- Facebook Integration


For the past decade and a half, I have been involved with CRM implementations in various capacities. In that time, I have participated in the evolution of CRM from the days of in-house overly customized implementations to today's cloud based hosted CRM applications and ever evolving social media landscape. After mulling over the idea of sharing my experiences and thoughts through blog posts for a few months, I have finally decided to throw my hat in the ring.

I wish to present through my blog posts, my perceptions / opinions on the management, business and technical facets of CRM applications. I look forward to your  comments  and ideas.  The point of blogging, after all is to engage with people of like interests in a community atmosphere.  In that way, we can all benefit, regardless of whether you are a CRM consultant, a user or just someone interested in CRM and related technologies.

Let's begin with an interesting development in the 'Social for CRM' space. In a Q&A article posted on the Oracle website recently, Aaron Shidler, Oracle's Vice President of CRM Product Strategy and Management, mentioned that they are enabling the creation of service requests and leads from their Social Listening solutions. This implies that users of Oracle's Siebel CRM application will have greater flexibility in tracking customer complaints as well as generating leads from competitors' sites.

While such features are already available by integrating Siebel with tools like Buzzient Enterprise, this adds to an important question in the ongoing "CRM Wars" among the major technology vendors: How simple will this feature be for organizations with CRM software installations to enable and use?

For an example of what Oracle's competition in this space already offers, don the hat of a 'non-technical administrator' and log into the Developer Edition of  With the help of a 'how-to' document, set up a connection between an Account/Contact in with a specific account in Facebook and you'll see how powerful this feature really is.

Say, for example, you are a dissatisfied customer and you've posted your comments on your Facebook Fan Page.  The Salesforce application will automatically generate a case for the attention of a customer service representative without you having to call the customer support line or send an email or letter. The key points here are the simplicity and ease of use.

I look forward to getting my hands on the aforementioned Siebel features and see how they compare to the ease and convenience of the features. I'll be posting my findings in a future blog. 

In the meantime, I look forward to your comments and suggestions.  Is there a CRM solution you are interested in or features and functions that you'd like to see?