Managing Customer Data Analytics
A 10 step guide for business managers to conceptualize and assess customer data analyses
In data driven environments the analysis and interpretation of data is a prerequisite for decision making. However, transforming raw data into management analyses is a complex process both in terms of technical programming skills as well as abstract thinking. This requires well trained data analysts and data savvy business managers.
The articlce series Managing customer data analytics serves as as a guideline for business managers in data driven environments. The articles offer insights into conceptual and statistical specialties of customer data analyses that help managers with limited previous experience in analytics to define, assess and interpret customer data analyses.
The ten steps of managing analytics cover conceptual and practical insights related to the scope of customer data analysis, the conceptual analysis setup, the generation and processing of customer data and finally the transformation of customer analytics data deliverables into customer insights.
#1 Who works on customer data analytics?#2 What is the business question of the analysis?#3 What customer data is needed for the analysis?#4 Which data subsets are relevant for the analysis?#5 Which KPIs are suitable for the analysis?#6 How do benchmarks facilitate interpretation? #7 How can sufficient data quality be ensured?#8 Which formats exist for analytics deliverables?#9 What makes a good analytics interpretation?#10 How does data become a customer insight?
Ensure the relevance of your data analyses and interpretation for the business management decisions by considering the ten steps when managing customer analytics projects or briefing data analysts.
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Hackathon Setup for Customer Analytics
Business decisions based on customer data have entered all departments of modern corporations. While the benefit of customer-centric, data-driven business decisions is widely recognized, the analytics value chain, i.e. the process to transfer customer data into customer insights is marked by several pitfalls.
One major challenge for businesses is finding a balance between the daily operative work on analytical processes and the continuous progress of strategic analytical projects: The day-to-day handling of data and related processes often binds the majority of analytical resources in a company. Attempts to push progress on strategic analytical projects, let alone analytical innovations, on top of the daily workload all-too-often end in project stagnation and frustration amongst employees.
The Hackathon workshop concept offers aims to generate short-term project progress despite high daily workload in analytics teams. It also fosters creative analytical thinking amongst the Hackathon participants and allows for technical development of the analytical workforce on all levels and thereby often increases motivation of the participants.
A Hackathon is especially suitable in corporate environments with
a need for data-driven innovations and/or existing, strategic analytical projects
projects, or general business challenges, require no in-depth technical onboarding
very limited time of analytical resources for those projects due to daily business
A successful Hackathon setup should consider the following:
Project selection & preparation
Hackathons can be used for two different purposes: First, to brainstorm on general business challenges regarding customer-centricity and data-driven innovations. Second, to ensure progress on well-specified projects, e.g. from a project backlog list. The difference between those approaches is the extension and detail of the pre-defined scope of the Hackathon project and therewith, the guidance that the Hackathon participants can receive during the Hackathon. A brainstorming and creative analytical approach to data-driven innovation needs to have a clear description of the business goal and specifics of the deliverable format. Briefings of well-specified projects should be much more extensive, including project and business goals and specifics of the deliverable format, but also prepared datasets of cleaned data, descriptions of previous (unsuccessful) approaches or other information that might be relevant for the Hackathon participants.
Participant selection & team building
Transforming raw data into management analyses is a complex process both in terms of technical programming skills as well as abstract thinking. This requires well-trained data analysts and data savvy business managers. The responsible organizer of the Hackathon needs to ensure that the skillset of the Hackathon participants ensures efficient work on the chosen Hackathon projects. In case the Hackathon consists of several projects, the group of participants should be divided into subgroups or pairs with pre-defined allocation to specific projects. Important aspects to consider for those subgroups:
Ensure a broad perspective within each pair or subgroup, e.g. by including employees from different departments.
Allow for professional development, e.g. by including employees from different seniority levels and with different technical expertise in one subgroup.
In case of existing analytics projects in a Hackathon, the participants should ideally not have worked on the project before.
Workshop timeline & location
As with any workshop, the Hackathon needs to have a clear timeline and a moderator who is available to answer any questions the participants might have. The minimum time for project work depends on the Hackathon projects. Given an introductory phase and minimal get-to-know and onboarding into the different projects, the suggested time planned in for a Hackathon workshop should be no shorter than two working days and not longer than four days. Typically, taking the Hackathon participants out of the daily operative work for two or three days offers a reasonably good trade-off between sprints of project progress at minimal impact on the daily operatives that can be a worked on in a business week.
There is a variety of technical setups available for free to organize a Hackathon remote with main rooms and separate, remote collaboration spaces. In case the workshop takes place in the office, the organizer should book dedicated rooms or locations for the Hackathon where undisturbed project work is possible for all Hackathon participant groups.
Presenting deliverables
Towards the end of the Hackathon, the participant groups should present their project results and deliverables to the round of participants. Optionally, the organizer may include representatives from the management or leadership team in the presentation round so that immediate feedback on the deliverables and the discussion of potential next steps is possible. By requesting every participant from a group to be involved in the project presentation, this part of the Hackathon can also help to train data storytelling and open communication skills. At the end of the Hackathon, the groups should each document and save their project work so that the deliverables are accessible within the company for further usage.
Workshop debrief
Debriefing the Hackathon is an essential part of the workshop setup. Feedback on the workshop experience itself is helpful to learn about preferences for e.g. remote vs. in-office Hackathon setups or adjustments in the timeline for the next round. A debrief on the usage or progress of the Hackathon project deliverables is also essential as it helps to assess the business contribution from the time and resources invested in the Hackathon.
The success of a Hackathon depends on several factors: Besides the five steps of preparation, moderation and debriefing, the capabilities of the Hackathon participants are ultimately the essence of efficient and effective collaboration on analytical projects. It is important to acknowledge that working on a dynamic, project-driven Hackathon requires skills such as analytical creativity and fast acquaintance with new data and business questions, which employees might need to develop over time. Therefore, the Hackathon workshop concept should be repeated in regular frequency, two to four times a year. This increases efficiency and quality of the project deliverables and maximizes progress of analytical projects over time.