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Summary: An exploratory multiple-case study of how organisations learn to generate customer intelligence, based on 11 semi-structured interviews with practitioners in different countries and sectors.

Context: Master's thesis, Strategic Business Development, University of Vaasa (2019); exchange study at Lund University.

Keyword: Case Study · Organisational learning · Strategic Marketing · Sense-making

Skills: Qualitative research · Interviewing

Official title: An Exploratory Study of Customer Intelligence Process in Multinational Enterprise Case Studies

Access: : 🔗 Link’s here

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Research question

How do organisations learn to generate customer intelligence?

Two sub-questions supported it: what customer intelligence is, and why and how organisations choose particular market sensing techniques for a customer intelligence project.

Approach

• Exploratory multiple-case study.
• 11 semi-structured interviews, conducted in July and August 2019 face to face and online.
• Practitioners from 11 organisations in sectors including fintech, banking, hospitality, fashion retail, product design consulting, education and digital marketing, based in Vietnam, Thailand, Singapore, Hong Kong, Sweden, the US and Canada.
• Roles included data analyst, data scientist, research manager, senior designer, CMO and COO.
• Participants are anonymised.

Theoretical lens

Organisational learning and strategic sense-making, including the organisational learning framework of Crossan et al. (1999), absorptive capacity and strategic learning, combined with strategic marketing literature.

What I found

• Customer intelligence is a continuous learning process that needs cross-functional collaboration.
• Knowledge moves from individuals to teams, departments and the organisation through everyday conversation and sense-making. Dashboards and transactional data offer clues, but insight is sparked through interaction.
• Small insight teams can sit in silos, so valuable knowledge is hard for others to reach.
• CRM is a rich source of intelligence, and real-time CRM data needs to be combined with traditional research.

In participants' words

“If something doesn’t make sense, ask someone who is more experienced than you… Same for projects that I don’t have experience, then I would seek people to ask for some advice.” Statistician, higher education, US (Int. 9)

What it shows: when data does not match expectations, analysts turn first to the tacit knowledge of more experienced colleagues.