Software Center. Customer Data and Ecosystem Driven Development

Transcription

1 Software Center Customer Data and Ecosystem Driven Development

2 Research Themes 1. Continuous Delivery 2. Continuous Architecture 3. Development Metrics 4. Customer Data and Ecosystem Driven Engineering

3 Theme 4: Objectives Shorten feedback loops to customers and enable continuous validation of customer value Advancement of agile practices Data- driven development Feature experiments Strategies and infrastructures for managing business ecosystems and maximize co- creation of customer value Ecosystem orchestration and management Ecosystem assessment methods From products to services Theme coordinators: Helena H. Olsson (Malmö University) Fredrik Hugosson (Axis Communications)

4 Theme 4: Projects Project 5: Fast Customer Feedback In Large- Scale SE Prof. Jan Bosch, Dr. Helena H. Olsson, Aleksander Fabijan Ericsson, AB Volvo, Volvo Cars, Jeppesen, Axis, Grundfos Project 9: Strategic Ecosystem Driven R&D Management Prof. Jan Bosch, Dr. Helena H. Olsson Ericsson, AB Volvo, Volvo Cars, Jeppesen, Axis, Grundfos Project 11: Ecosystemability Assessment Method Dr. Eric Knauss, Dr. Imed Hammouda Volvo Cars, Axis

5 Software Center: Project 5 Fast Customer Feedback In Large- Scale SE

6 Objectives What? Shorten feedback loops to customers Continuous customer validation Why? Increase accuracy of R&D investments Improve data- driven development practices How? Identify techniques for collection of customer feedback Initiate, run and evaluate feature experiments

7 Feedback Loop Slow Rapid

8 Companies

9 The Open Loop Problem Learn (?) Build Measure

10 Interview Quotes (1/2)??? We DON T know what features our customers use. We have an idea on what functionality that is used based on sales but we DON T really know. We get feedback only on things that DON T work things that are problemtic. This is not necessarily an indication of what is used the most. Does silence mean that things are OK? We DON T know.

11 Interview Quotes (2/2) there are a lot of assumptions when questions are often answered with we belive, or we think this is what the customer wants. we have such a vast amount of functions that we collect data from and not a very structured way of harvesting this data, so in the end, it is very difficult to learn from the data.

12 Featuritis

13 Next version Slow Feedback Loops

14 Limited Use of Data New feature development Feature improvement Feature usage Diagnostics Operation

15 The HYPEX Model Business strategy and goals Strategic product goal generate Feature backlog Feature: expected behavior (B exp ) select implement MVF B exp no gap (B act = B exp ) relevant gap (B act B exp ) Gap analysis Develop hypotheses actual behavior (B act ) Experimentation implement alternative MVF Product abandon extend MVF

16 On- Going Feature Experiments Existing/new feature No usage/lack of usage Wrong usage Uncertainty on right implementation Feature focus R&D level The$HYPEX$Model$ Business strategy and goals Strategic product goal generate select Feature: expected behavior (B exp ) implement MVF Feature backlog B exp no gap (B act = B exp ) relevant gap (B act B exp ) Gap analysis Develop hypotheses actual behavior (B act ) Experimentation implement alternative MVF Product abandon extend MVF

17 Qualitative And Quantitative Customer Feedback Techniques* (CFT s) *Fabijan et al (2015). Customer Feedback and Data Collection Techniques: A literature review.

18 Qualitative/quantitative Customer Development Model (QCD) Qualitative and quantitative feedback techniques. Requirements are treated as hypotheses that are continuoulsly validated with customers. The validation data is used to decide whether to run another validation cycle, whether to have the hypothesis put back into the backlog, or whether to abandon the hypothesis. Continuous and dynamic prioritization of hypotheses

19 Customer Feedback Techniques (CFT): Qualitative data: Surveys Interviews Participant observations Prototypes Mock- ups Quantitative data*: Feature usage Product data Support data Call center data New hypotheses Hypotheses backlog - Concepts - Ideas Product R&D organisation Selection of hypothesis CFT Data Selection of CFT Hypothesis Customer Feedback Technique (CFT) QCD validation cycle Products in the field Product data database Selected customers Deployed products New hypotheses based on: Business strategies Innovation initiatives Qualitative customer feedback Quantitative customer feedback Results from QCD cycles Abandon CFT Data *Loop in which decisions are taken on whether to do more qualitative customer feedback collection.

20 The Key Opportunities Increase frequency of delivery Increase accuracy of development efforts Anticipate future customer needs Improve requirements prioritization Help customers optimize use of product

21 Thank you!