Get Better Grades - Teaching Siebel to Improve its Own Performance!


Wouldn’t it be great if we could teach Siebel how to improve its own performance? Well now we can!

OK, first let me clarify, at this stage we are only talking about Siebel Product Configurator, there are other areas we are looking at but the performance of Product Configurator is of particular interest to many Siebel customers.

Siebel Product Configurator is critical in enabling commerce for many customers. These customers use the Siebel Product Configurator to sell large and complex products to millions of their customers with eCommerce sites, call centers, or through field sales. Of course in all these situations, performance is paramount.

So back to my opening line, if we could teach Siebel a way to improve its own performance of product configurator, giving it the ability to dramatically decrease response times of even the most complex product models then I think we may be onto something big!

We started down this path by accident. One of our customers was faced with the problem of supporting a peak load of 100,000 orders per hour through their eCommerce site. This was in addition to orders taken by their 4000 call center agents directly through Siebel. The product models and rules were to be held within Siebel and using the Product Configurator to generate quotes and orders. It quickly became obvious that Siebel would need a major injection of hardware and tuning to provide the necessary performance, scalability, and reliability that was needed to provide a 24/7 service, particularly to the eCommerce site. Standard tools to optimize Configurator performance weren’t going to cut it.

Upon careful analysis of product models, their constraints, and the end-customers’ buying patterns, we hit upon an Artificial Intelligence-based solution to address this customer’s requirements.

We have since benchmarked the performance improvements achieved through our AI-based approach for the product models of a multi-national B2B Telco. The AI-approach yielded an order of magnitude improvement in performance (approximately 8x improvement) across a wide variety of product models.

The intelligence provided by our algorithms enable the system to be self-learning – it’s the best way to improve the performance of the Product Configurator. The very data that Siebel has been collecting day in, day out, probably (and preferably) for years is one of the key ingredients driving the intelligence.

Based on this AI-based approach, new KPI’s could be set, with continual benchmarking carried out and performance outside of those KPI’s could be instantly flagged up. Newly introduced products, could learn from existing, long-standing products to achieve great performance from day 1.

There are many Siebel customers who face this daily battle with the performance of the Product Configurator. Wouldn’t it be great to be able to finally be able to leave it to make its own improvements?

If you have these problems, you are not alone! We have the solution, so get in touch.




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