For a long time, field service was seen as a cost to the business, a necessary evil of the balance sheet that kept products working while they were under guarantee. And yet, field service engineers were on the frontline with customers, with knowledge not just of products but how they were being used and occasionally, how the customer was feeling about the product. Customers were important because they bought stuff—no one really cared what they thought.
Today, things are different, thanks in part to technology. IoT sensors, 5G, mobile devices, automation and cloud computing have reframed the customer as not just the source of cash but the source of market intelligence. Central to this is asset data and not just in terms of enabling predictive maintenance or self-service capabilities. Asset data can add incredible value across an organization, providing insights into how customers are using products, as well as machine life expectancies, upgrade timelines, and so on.
Unifying Around Asset Data
In short, organizations need to do more with this rich asset data to drive change and improve overall revenue performance. As Deloitte suggested in its report Next Generation Customer Service: The Future of Field Service, even though many companies are on the road to using digital technologies, the service ecosystem still doesn’t use knowledge on customer data and product information efficiently enough. Both customer and company often lack knowledge on the problem and each other. This is the problem that asset data can help solve, by unifying an organization around machine and device insights. This puts service teams front and center.
Benefits Beyond Service
The benefits should be felt across the organization, as data insights unify teams and enable more informed decision-making. Service teams can collaborate with supply chain teams, for example, to coordinate more closely on customer needs, ensuring the right supplies are available at the right time, matching real rather than predicted demand across the end-to-end asset life cycle.
Marketing departments can also use asset data to run revenue-generating campaigns, such as creating specific incentives for customers whose service contracts are nearing renewal. Sales can better understand and predict customer needs, leveraging analytics to upsell and cross-sell, identify new opportunities, and measure business impact. Customer success teams can use asset data to support the adoption of asset features, document the value of the service and business relationship, and grow the organization’s footprint at a customer site or account.
Finance teams too can use the data to align revenue and customer value. Insights into the asset and the customer are critical for billing accurately, writing contracts, assessing risk and forecasting – something which is also of great benefit to C-level execs trying to steer the organization towards growth and profitability. In addition, engineering and product design teams can use asset data to build a more complete view of customer interactions with assets. This includes quantitative data around usage, as well as qualitative data on how customers feel about using the asset, for example.
With service and asset data so relevant across the organization, it needs to be looked after as mission-critical business intelligence. The challenge, however, is for organizations working across variable, internal and external teams, different offices, geographies, supply chain partners and so on. How can organizations ensure consistency?
Establishing a Common Blueprint for Service
The key has to be in a common blueprint – not just for service data, but a standard set of processes and capabilities built around a standard platform and set of tools. This becomes increasingly important with an aging and retiring workforce. Knowledge drain is a real concern within service teams. How can organizations guarantee knowledge transfer? How can they ensure that increasingly essential asset data and service provision is running 24/7, consistently, regardless of location? Service teams and asset data can be a differentiator, but organizations need to be singing from the same sheet or otherwise miss a golden opportunity.