Is it time to transform to analytics-driven Buying & Merchandising?
Inspired by how retail disrupters operate, a new analytics-driven Buying & Merchandising (B&M) operating model is evolving that is rapidly generating incremental business value.
New analytics applications, delivered with a compelling employee experience (EX) and transformation methods based on agile techniques, are being applied across the end-to-end product lifecycle to deliver both performance uplifts and team benefits.
This blog outlines the five key components of the new operating model, the impact on retail B&M ways of working, team roles, capabilities and solutions, and how transformation can be accelerated in your retail business.
What are the key components of the new analytics-driven B&M operating model?
1 Automation of key meeting reports: Leading retailers are automating reporting for key meetings (e.g. weekly trading meetings, range selection meetings) using new generation data visualisation tools, which enable B&M professionals to become analytics and insights experts, without input from data scientists.
2 Delivering actionable recommendations via analytics platforms: Another key component is the creation of an analytics platform to deliver “next best action” recommendations based on exceptions to business rules and predictive analytics. These platforms support functions, such as range planning and markdown modelling, and can also be used to combine data from standardised spreadsheet templates allowing consolidated cross-category and channel views.
3 Product, channel and customer data blending: New analytics solutions enable the combination of data from different sources to deliver new customer-focused, omni-channel insights for strategic range development and performance optimisation. The tools overcome legacy system constraints and make use of all available data, both internal and external, to look for segments and trends down to a local level. These capabilities can facilitate everything from space optimisation to hyper localisation.
4 Data attributing and quality: Analytics needs high quality, enriched data. The new analytics-driven operating model focuses on data attributing and quality, which ensures that everyone is working from the same “golden record”. Furthermore, the tools built into analytics applications track data quality on an ongoing basis. Data attributing can include product, supplier and customer data.
5 Critical path collaboration: Optimising performance relies on cross-functional communication and collaboration. The new model enables progress tracking via analytics on all key business milestones and critical paths, allowing retailers to address potential problems early.
How does this new operating model impact the B&M team and improve performance?
Adopting the new B&M analytics-driven operating model leads to an enhanced culture and ways of working, which require new skills, capabilities, organisation and tools.
- New culture and ways of working: These include changes such as the standardisation of processes across category teams, a more customer-focused planning and forecasting approach and quicker reactions to trends via predictive analytics. Ultimately, a more collaborative mindset is needed when implementing this operating model.
- Role and capability changes: Teams become more engaged as repetitive, administrative tasks are reduced through automation, and data from new sources is available. Merchandisers change from “data manipulators” to “analytics and insights experts”; buyers evolve from “supplier managers” to “new product developers”.
- Organisation structure development: New skills and capabilities are needed across the business, especially for data quality and governance.
- New tools and techniques: New analytics applications require new techniques, eventually facilitating self-service analytics.
How can you accelerate B&M transformation, and ensure user adoption?
Several steps are needed to transform B&M processes. Depending on the size of the team and the scope of the prototypes to be developed, this journey can take from a few intensive days to several weeks:
1 Business engagement and understanding: Start and end with the users, and involve them at all stages of service design to ensure adoption. Conducting research is important to build an understanding of needs, pain points and aspirations, while making sure that stakeholders are fully committed and engaged.
2 Business benefit priorities: Capture KPIs and benchmarks against peers and industry leaders, and model potential benefits of adopting an analytics-driven approach.
3 Data availability and quality assessment: Identify any major data gaps, and be sure to assess data quality using a sample data set, addressing data issues early to ensure project success.
4 B&M analytics strategy and concept ideas: Run a 1-2-day workshop to bring the main stakeholders together to agree the strategy for B&M data and analytics. Generate tangible concepts for analytics applications, and agree the focus for the design sprints and prototype development.
5 Design sprints: Use Design Thinking and agile methodology to create new B&M analytics solutions, working in 2-week sprints to build initial prototypes, validate with users and feedback improvements.
6 Data blending and visualisation: Use new generation analytics tools in prototype development, such as Alteryx and Tableau, to blend data from various sources, create workflows to automate data preparation and present using data visualisation, such as dashboards.
7 Analytics-driven operating models: Take the opportunity to think through how these new tools and techniques impact ways of working, and what the new B&M analytics operating model will look like.
8 Prototyping: Focus on delivering rapid prototypes to provide tangible solutions using own data for trialling.
In summary, an analytics-driven operating model, with flexible analytics tools that are implemented using rapid prototyping and a focus on employee experience, is the best way to generate incremental business value and compete with retail disrupters.