Service Value Sales Programme

Creating more value for customers and suppliers

Why

Many companies struggle with the challenge of Product-Sales people selling services or Service people to developing revenues. The reality is not a specific answer, but for Sales & Service to work together to develop solutions for their specific business context. 

The Service Value Sales programme brings together these stakeholders to deliver a customised plan.

Key is developing a Service Value Sales mindset that enables customer touching professionals to engaging conversations with customers that drives solutions that brings success to customers. Then ensuring those people have great support in terms of systems, marketing materials, training and coaching.

The Service Value Sales programme will transform professionals from Feature to Value Selling!  – From a customer ‘push to a pull’ mindset!

Who should be involved

Business Unit Managers, Sales & Service Managers, Team Leaders, System & Product Sales, Service Sales, Field Service, Customer Support, Technical Services, Marketing, Product Management

How the programme works

Si2 Programmes are tailored to the specific needs of situation, pulling on a numbers of methods which Si2 partners have found to be good practice in their careers. They are very practical, typically collaborative and all have the goal of quickly delivering value for the programme:

  • Assessments: Service Sanity Check, Service Porfolio Check,  Installed Base Valuation, Capabilities Evaluation 
  • Facilitated Workshop(s): To launch programme, define direction, engage with leaders review results, develop mindset
  • Customer Segmentation: Identify relevant customer groups
  • Service Value Chain Analysis: Identify stakeholders and understand the flow of value and data that drives success
  • Customer Value Iceberg: Identify what makes individual stakeholders successfulul and identify pain points
  • Blue Printing: Identify how customer touch points, processes and support tools interact with the customer journey
  • Design Thinking: Ensure user centric agile methods or utilised on proosition development
  • Service Operations Manual: Define all service processes, management practices, people needs, functional requirements
  • Service Sales Manual:  Define all propositions, benefits, how to promote & sell, questions to ask, assessments to be done. i.e. everything required to professionally promote the portfolio and close deals
  • Sales Training Workshops: practical,activity based learning for sales people
  • Service Solution Sales Process: Structured solutions approach to service sales process
  • Points of Selling: An analysis through the lifecycle of when the customer is ready to buy
  • Service Business Ecosystem: Understanding how the key elements of a business link together is fundamental to managing change in culture, change in processes and ensuring sustainability.
  • Agile: Role out programmes using agile project management methods
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Service in Industry

Deep Dive into the industrial service business.

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Service Innovation for value-driven opportunities:

Facilitated by Professor Mairi McIntyre from the University of Warwick, the workshop explored service innovation processes that help us understand what makes our customers successful.

In particular, the Customer Value Iceberg principle goes beyond the typical Total Cost of Ownership view of the equipment world and explores how that equipment impacts the success of the business. It forces us to consider not only direct costs associated with usage of the equipment such but also indirect costs such as working capital and risks.

As an example, we looked at how MAN Truck UK used this method to develop services that went beyond the prevailing repairs, parts and maintenance to methods (through telematics and clever analytics) to monitor and improve the performance and  fuel consumption of their trucks. This approach helped grow their business by an order of magnitude over a number of years.

Mining Service Management Data to improve performance

We then took a deep dive into how Endress + Hauser have developed applications that can mine Service Management data to improve service performance:  

Thomas Fricke (Service Manager) and Enrico De Stasio (Head of Corporate Quality & Lean) facilitated a 3 hour discussion on their journey from idea to a real working application integrated into their Service processes. These were the key learning points that emerged:

Leadership

In 2018 the Senior leadership concluded that to stay competitive they needed to do far more to consolidate their global service data into a “data lake’ that could be used to improve their own service processes and bring more value to customers. As a company they had already seen the value of organising data as over the past 20 years for every new system they already had a “digital twin” which held electronically all the data for that system in an organised fashion. Initially, it was basic Bill of Material data, but has since grown in sophistication. So a good start but they needed to go further, and the leadership team committed resources to do this.

  • The first try: The project initially focused on collecting and organising data from its global service operations into a data lake.  This first phase required the development of infrastructure, processes and applications that could analyse service report data and turn it into actionable intelligence. The initial goal was to make internal processes more efficient, and so improve the customer experience. E+H looked for patterns in the reports of service engineers that could:
    • Be used to improve the performance of Service through processes and individuals
    • Be used by other groups such as engineering to improve and enhance product quality.
  • Outcome: Eventhough progress was made in many areas, nevertheless, even using advanced statistical methods, they could not extract or deliver the value they had hoped   for from the data. They needed to look at something different.
  • Leveraging AI technologies: The Endress+Hauser team knew they needed to look for patterns in large data sets. They had the knowledge that self-learning technologies that are frequently termed as AI, could potentially help solve this problem. They teamed up with a local university and created a project to develop a ‘Proof of Concept’. This helped the project gain traction as the potential of the application they had created started to emerge. It was not an easy journey and required “courage to trust the outcomes, see them fail and then learn from the process”. However after about 18 months they were able to integrate the application into their normal working processes where every day they scan the service reports from around the world in different languages to identify common patterns in product problems, or anomalies in the local service team activities. This information is fed back to the appropriate service teams for action. The application also acts as a central hub where anyone in the organisation can access and interrogate service report data to improve performance and develop new value propositions.
  • Improvement:  The project does not stop there. It is now embedded in the service operations and used as a basic tool for continuous improvement. In effect, this has shifted the whole organization to be more aware of the value of their data.

Utilizing AI in B2B services

Regarding AI, our task was to uncover some of the myths and benefits for service businesses and the first task was to agree on what we really mean by AI among the participants. It took time, but we discovered that there are really two interpretations which makes the term rather confusing. The first is a generic term used by visionaries and AI professionals to describe a world of intelligent machines and applications. Important at a social & macroeconomic level, but perhaps not so useful for business operations -at least at a practical level. The second is an umbrella term for a group of technologies that are good at finding patterns in large data sets (machine learning, neural networks, big data, computer vision), that can interface with human beings (Natural Language Processing) and that mimic human intelligence through being based on self-learning algorithms. Understanding this second definition and how these technologies can be used to overcome real business challenges is where the immediate value of AI sits for today’s businesses. It was also clear that the implication of integrating these technologies into business processes will require leaders to look at the change management challenges for their teams and customers.

To understand options for moving ahead at a practical level we first looked briefly at Husky through an interview with CIO Jean-Christophe Wiltz to CIOnet where we learned that i) real business needs should tailored drive technology implementation, and ii) that before getting to AI technologies, there is a need to build the appropriate infrastructure in terms of database and data collection, and, most importantly, the need to be prepared to continually adapt this infrastructure as the business needs change.