The latest in our regular expert blog from industry analyst firm, Cambashi reflect on methods for collecting customer requirements for product differentiation
We believe that the customer experience is the key success factor in product differentiation. However, few of today’s applications support collecting information about the customer’s needs and wants and ensure that these are implemented to create the customer experience desired.
This Silos Changing series explores how manufacturers are likely to deploy new software applications that enable enterprises to implement business initiatives in the new economy.
This blog looks at applications that support business initiatives to respond to the product differentiation business driver. We will review a few of these applications and how they can help enterprises implement business initiatives. We identify interlinked themes that these applications address rather than propose clear product categories. We’ll also look at one in detail – collecting customer requirements.
The danger is that when the product is designed, it won’t give the customer experience they expected. Part of the reason is that it is difficult for a manufacturer to describe the innovative functions, benefits and usage cases of a novel product. They can often be misunderstood. When responding, inevitably, the customer falls into using terminology that reflects older, existing technologies that partly meets their needs and this can mislead evaluators of customer feedback. The data types are complex, including audio and video streams. There is a mountain of poorly- structured or unstructured data that is difficult to evaluate.
Collecting and managing all the data, then transforming that into a specific set of requirements for a product, and then designing the product takes time. New data mining applications can help make sense of this data. Certainly, applications that visualize and simulate the product while it is still in virtual, rather than physical, form can resolve many misunderstandings.
It is often the case that version 1.0 of an innovative product can’t do everything that customers suggested they need. It is important to handle customer expectations. Providing the product road map is clear, there is good evidence that building a family of products with similar values and a range of different functional levels and price points, is a better proposition.
There are application solutions to handle two aspects of this opportunity. Portfolio planning helps executives to manage R&D activities. Requirements management helps project teams transform customer statements into requirements for specific products that form parts of the family. The requirements will have to support the different phases of product development, including testing.
Another aspect of product differentiation occurs in the detailed design of the product. Applications can help designers find cost-effective generic components that meet requirements to minimise the number of newly-designed components in the product. That allows engineering effort to focus on the components that deliver the differentiating factor of the product.
Let’s start with data mining. Autonomy was recently controversially bought by HP. Autonomy knows how to handle vast quantities of unstructured information – an important part of what is sometimes referred to as “big data”. It can apply its generic technology, with significant professional services effort, to address the problem of transforming customer input into specific requirements. They call this “Voice of the Customer Discovery and Analysis” – rather a mouthful but at least it’s not been given a Three Letter Acronym!
Autonomy Explore, which uses their IDOL technology to make sense of and extract meaning from a heap of unstructured data, can be built into a powerful tool to understand customers’ experiences, with both a producer’s and competitor’s products. This enables an enterprise to combine qualitative information they have collected through market research exercises with notes from storefront representatives, social media such as Twitter and Facebook and similar web chatter.
While not a manufacturer example, there is an interesting case study of how using these tools improved customer experience.
At the Kaiser Northern California (NCAL) Pharmacy Call Center, which handles approximately 2.5 million member calls annually, the objective was to improve the experience of customers who typically order prescriptions. That means agents interact with ailing, frustrated, or elderly members who require specialized attention. Frustrated customers led to extra steps to provide those members with their services so Kaiser knew that better customer satisfaction would lead directly to lower costs. Kaiser already invested in e-learning training courses for agents to raise member satisfaction scores.
By collecting free form survey information and analysing the unstructured information Kaiser realized that a specific group of Kaiser Permanente members needed more effective and empathic interactions. As a result, the contact centre’s online training content was immediately tailored to include best practices for interacting with these members.
We think that these same techniques can equally be applied to manufacturers, both to understand customer wants and needs and to understand how to generate after-sales revenues.
In future blogs we will go on to talk about other potential deployments that support product differentiation in Portfolio Planning; Requirements Management; Cost effective sourcing; and visualisation and simulation.