Congratulations! Your Appliance Is IoT-Enabled. Now What?

By Andy Chinmulgund, CEO, Bruviti

Sensor and connectivity technology built into the current generation of appliances has the potential to dramatically improve customer experience. But to maximize this potential, manufacturers must combine appliance telemetry data with existing customer and product information.

Like most businesses, appliance manufacturers have been driving broad digital transformation initiatives to improve customer experience and drive brand loyalty. Two interesting trends have become apparent as a result of these digitization strategies:

1. The emergence of IoT-enabled smart appliances that generate information about usage, error codes, and service intervals that potentially could enable predictive service;
2. The development of artificial intelligence and predictive applications aimed at turning that machine telemetry data into actionable insights.

When it comes to analytics, the devil is truly in the details. The tsunami of telemetry data being generated by these machines is often unstructured, raw, and in a native format. Manufacturers are struggling to blend this machine telemetry data with existing customer and service data to derive useful insights to enable predictive maintenance, rapid diagnostics, better field service capabilities, parts inventory optimization, and a better overall customer experience. All this information —including the machine telemetry data— is part of the “digital thread” — an integrated view of everything about an asset or product throughout its lifecycle.

Machine data can help personalize the customer experience

Much has been written about the need personalize the customer experience (CX). Most appliance manufacturers are finding ways to create a customer-specific journey map to understand every interaction, or touchpoint, between them and customers over the course of the product lifecycle. Understanding these interactions as part of the customer journey lets us see opportunities where digitization makes the most sense and where customers, and business, have the most to gain from digitally enabling these interactions.

CX can be measured during the research and selection of a product, through the purchase/ordering phase, and throughout the ownership phase. Customer- and field-service operations can have a huge impact on CX during the ownership phase. There are three areas that are driving the costs of delivering excellent support and service. Each of them stands to benefit tremendously if we can combine machine telemetry data with the wealth of customer, service, and product data that we’ve already amassed as part of our digital thread.

Here are four areas where we can align our digital thread and our customer journeys to transform CX:

1. Call-center efficiency: Running a call center is costly. It requires hiring and training hundreds of personnel to support numerous products and models. To do their job effectively, call-center agents must become proficient in different tools that access diverse data sources. To resolve a customer issue and minimize truck rolls, the agent must refer to CRM information, product manuals, service bulletins, and various other knowledgebases. The complexity and challenges associated with accessing myriad data sources while an impatient customer is on the line can lead to a mismatch between the call-center diagnosis and the actual problem that encountered by the field technician during a service call. The digital thread can improve call-center efficiency with better deflection/resolution tools, and by equipping agents with AI-guided diagnostics powered by the synthesis of all available information?
    
2. Field-service efficiency: Field-service teams rely heavily on the accuracy of diagnostic information collected by the call-center team to schedule service calls and to stock/provision the relevant parts needed to resolve a ticket. If service technicians have not been trained on a particular model, they may not have the expertise to identify and resolve the issue. Advanced technical expertise and more sophisticated knowledgebases are needed for more complicated problems, which results in longer MTTR and additional truck rolls. The digital thread can help us create a digital service twin to enable faster and more accurate problem diagnostics.
    
3. Parts inventory management: The cost of maintaining a parts inventory is one of the major challenges for both consumer and commercial appliance manufacturers. Parts requirements for both in-warranty and out-of-warranty products must anticipate future needs to avoid service delays and customer satisfaction issues. Flawed or inadequate parts inventories can increase truck rolls and delay problem resolution. Here, the digital thread can help us become better at predicting which part will fail, and when, for specific models and serial numbers.
    
4. Omnichannel CX: Customers expect the same experience across all channels — website, mobile app, chatbot, or phone support. What’s key here is to make the experience consistent and the data (or context) persistent — so if a customer switches from one channel to another, the previous information should be available. The objective is to drive deeper connections between customers and a brand they already know and love, not to create experiences that feel like they’re coming from an entirely different company. Again, the digital thread equips us to better understand customer intent across all channels.

Connected machines and the telemetry data they generate are an underutilized asset. By combining this real-time data with existing knowledge bases, it’s possible to transform post-sales customer experience.

About The Author

Andy Chinmulgund is CEO of Bruviti, an IoT applications company he co-founded in 2012. By combining artificial intelligence and machine learning, Bruviti’s digital twin and predictive analytics applications enable machinery and equipment manufacturers to automate their support and service teams. Andy is also chair of the AHAM Supplier Division. Contact Andy at andy@bruviti.com.