1 Introduction
2 Background
2.1 Collection of Customer Feedback
2.2 Impact and Use of Customer Data
3 Research Method
Company and their domain | Representatives |
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Company A is a provider of telecommunication systems and equipment, communications networks and multimedia solutions for mobile and fixed network operators. The company has several sites and for the purpose of this study, we collaborated with representatives from one company site. The company has approximately 25.000 Employees in R&D. The participants marked with an asterisk (*) attended the workshop and were not available for a follow up-interview. | 1 Product Owner 1 Product Manager 2 System Managers 2 Software Engineer 1 Release Manager 1 Area Prod. Mng.* 1 Lean Coach* 1 Section Mng.* |
Company B is a software company specializing in navigational information, operations management and optimization solutions. Company B has approximately 3.000 Employees in R&D. All the participants attended the workshop and were interviewed. | 1 Product Owner 1 System Architect 1 UX Designer 1 Service Manager |
Company C is a manufacturer and supplier of transport solutions construction technology and vehicles for commercial use. The company has approximately 20.000 Employees in R&D. All the participants that attended the workshop were interviewed. In addition, one sales manager and one technology specialist wished to join the project at a later stage, and were interviewed. | 1 Product Owner 2 Product Strategists 2 UX Managers 2 Function Owners 1 Feature Coord. 1 Sales Manager 2 Technology Spec. |
3.1 Data Collection
3.2 Data Analysis
3.3 Validity Considerations
4 Findings
4.1 Data Collection Practices: Current State
Roles that collect customer feedback | Common customer feedback collection techniques | Common types of customer feedback collected | |
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Pre-Development | Strategy specialists, Product managers, Product owners | Reading of industry press, Reading of published standards, Reading of internal reports, Reading customer visit reviews | Customer wishes, Short/Long market trends, Competitors ability of delivering the product |
Strategy specialists, Feature owners | Telephone interviews, Face-to-face interviews, Conducting group interviews | Existing product satisfaction, Future product specification, Personas and User Journeys |
Roles that collect customer feedback | Common customer feedback collection techniques | Common types of customer feedback collected | |
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Development | UX specialists, Software Engineers | System Usability Scale Form, Asking open ended questions, Demonstrating prototypes, Filming of users’ product use | Acceptance of the prototype, Eye behavior and focus time, Points of pain, Bottlenecks and constrains, Interaction design sketches |
System managers, System architects, Software engineers | Consolidate feedback from other projects, Reading prototype log entries | Small improvement wishes, Configuration data, Product operational data |
Roles that collect customer feedback | Common customer feedback collection techniques | Common types of customer feedback collected | |
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Post-Deployment | Release managers, Service managers Software engineers | Reading of customer reports, Analyzing incidents, Aggregating customer requests, Analyzing product log files | Number of incid. and req., Duration of incid. and req., Product operational data, Product performance data |
Sales managers | Reading articles in the media, Sentimental analysis Customer events participation, Reading industry press, Performing trend analysis | Opinions about the appeal of the product, Performance of the product, Business case descriptions |
4.2 Data Sharing Practices: Challenges
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Fragmented Collection and Storage of Data
“… it is all in my head more or less.” -Product owner, Company B
“Information exists but we don’t know where it is.”–UX Specialist from Company C
“I do not know everyone… So I contact only the person who is next in line.” -Sales manager from Company C.
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Filtering of Customer Data
“It is like there is a wall in-between. There is a tradition that we should not talk to each other.” -Product Owner from Company C.
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Arduous to Measure Means Hard to Share.
“Maybe 10 % of information is shared. It is very difficult. It takes so much time, to, you need to write a novel more or less and distribute it” -Product manager from Company A.
4.3 Data Sharing Practices: Implications
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Non - evolving and Non - accumulating Data.
“I think now a lot of thing are developed in a sub optimized way.” -Technology Spec. from company C.
“We get feature which is broken down and then this value somehow got lost when it was broken down, then it is harder to understand what they really need it for.” –Software engineer from Company B.
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Repetition of Work.
“You cannot build on what is already there since you don’t know. You then repeat an activity that was already made by someone else.” –UX specialist from Company C.
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Inaccurate Models of Customer Value.
“You think one thing is important but you don’t realize that there is another thing that was even more important.” -Technology Spec. from company C.
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Validation of Customer Value is a “Self - Fulfilling Prophecy”.
Challenge | Description | Company implications | Product implications |
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Fragmented collection and storage of data | Sharing of data is limited across the development stages. | No evolving and accumulating of customer data and understanding. | Suboptimal products are being developed. |
Filtering of customer data. | Only roles that work in close cooperation exchange feedback. | Inaccurate assumptions on customer value and repeating work. | Risk of developing wasteful features. |
Arduous to measure means hard to share. | What can easily be measured and quantified is shared. | Validation of customer value is a “self-fulfilling prophecy”. | Product maximizes partial models of customer value. |