Once I was chairing a conference where the speaker was explaining the business model for the licensing of the Peanuts cartoon characters - Charlie Brown, Snoopy and the gang - and how all that works when it comes to the balloons for the Macy’s Thanksgiving Day Parade (in case you are wondering, they cost about half a million $ each). The speaker was running well over his allotted time with the end nowhere in sight when he turned to me and asked, “How am I doing on time?” Looking out at how he had the audience spellbound in rapt attention, I simply said, “You’re doing fine – keep going”. There was no way I was going to prematurely interrupt this master class in media business models.
The greatest value I get from attending and chairing the many conferences that pass my way in this role comes from learning new business models such as this one involving Snoopy, typically arising from outside my own industry or segment. Having spent most of my career in high-tech manufacturing, I’m not as familiar with the business models associated even with heavy industrial manufacturing, let alone in media or health care. At a recent conference I learned something interesting from a heavy/mining equipment manufacturer: while their biggest machines have an upfront cost of about $2 million, their customers will typically spend about $6M more during the 10 to 20+ year life of the equipment in parts, maintenance and upgrades.
The “after-market” is their market, which is not unusual. Many of SAS’ clients report that 20-30% of their revenue and 40% of their profits come after the initial sale, and not just via parts, maintenance and upgrades (B2B) but also from cross-sell and up-sell oportunities in the B2C markets, remembering at the same time that it can be an order of magnitude costlier to gain a new customer than to simply retain and grow a current one.
Before I get to a few of the specific points to keep in mind regarding after-market service, I want to first mention the one key element to a profitable and effective after-market service model: an integrated data platform for after-market service, what I’ll call a Service Intelligence Platform. This is where the success starts.
Consider your primary after-market data sources: Product data (serial #, BOM), Service data (claims, tech support), Customer data, and Parts data (some progressive companies are even using product sensor data). These are almost always supported by separate systems and in many cases separate business operations. To make matters worse, each separate data silo has been optimized over time for performance within that function, not for enterprise-wide performance. Thus, rather than a smooth flow of information from engineering to manufacturing to marketing to service, there are discrete hand-offs, typically via email and spreadsheets.
To effectively run your service operations you must connect these sources and functions through business processes and a business intelligence system. For example, failure rate data is used by the service contracts team to price service agreements, and service parts information needs to be made available to the field service planning team for parts allocation. The outputs from one area become the inputs to another.
Advanced analytics can alert an organization to a relevant increase in warranty claim rates for a particular part. This early warning signal should reconcile with the sales data to identify the population at risk and predict the increased exposure. While the warranty team is working to resolve this issue, the output of the alert is fed into the parts forecasting system to ensure the right number of replacement parts are available and in the correct depots. The failure information is also fed into the resource forecasting system within the service operations area so that the company not only has the right number of call agents and field service technicians, but also that they have the right skills to efficiently diagnose and fix the customers issue.
To accomplish what was just described, a company not only needs good internal processes, but also a service intelligence platform that supports data integration (efficiently moving clean and aligned data), advanced analytics (predictive modeling, optimization, text mining), and business intelligence (a method to deliver the information in a manner which is actionable and easy to use). The service intelligence platform is the high-level dashboard that pulls everything together into a single comprehensive view so that companies can better monitor their service operations
Underneath this service intelligence platform could be any of several specific capabilities and components, such as:
- Parts optimization – have the right part in the right place at the right time
- Resource optimization - call center personnel and field technicians
- Failure detection and warranty analysis – find and fix the problems sooner
- Predictive asset maintenance – for higher yields, greater up-time and lower maintenance costs
- Social media analytics – your customers are talking, find out what they’re saying sooner rather than later
For me, from the financial perspective, where this all came together was the “first call fix rate”. A second trip to fix the same problem, because the right part or skill wasn’t available, was the profit killer. Since then I’ve come to appreciate the value of the broader service intelligence platform approach to after-market profitability, especially in the area of quality/warranty analysis, where faster detection of problems arising in the field can feed back to the design and production teams more quickly, eliminating the problem/defect before it can ever make it into the market. A ‘No-call fix’ beats a ‘first-call fix” every time.