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Big data, BI e Advanced Analytics nel Fashion & Retail
Production and sales & marketing forecasts thanks to the analysis of Big data in Fashion & Retail
From Trend forecasting to supply chain optimization, to improve the customer shopping experience
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- Optimize production and sales processes thanks to Big data
- Management of the entire data life cycle: from data governance to data visualization
- Lutech Big data services in Fashion Retail
Discover your road map for digital transformation Sales & Marketing
Season after season, fashion retailers and the top players in the fashion industry create collections which ever-increasingly reflect the needs and desires of their customers: colors, patterns, materials and fit are the factors which, in the hands of the right fashion designer, give rise to new and distinctive items in an ever-more competitive market.
Up until a few years ago, the creativity of the stylists and the retail and wholesale distribution capacity were the key variables for success, alongside historical sales data.
But today this is no longer sufficient: knowing how to anticipate trends and analyze consumer insights through big data analysis in the world of fashion is the difference between having a decisive role in the sector or remaining at its margins.
Optimizing production and sales thanks to big data analysis
Big Data & Trend
Discovering and developing trends is the lifeblood of the fashion industry, and in the past historical sales information has been one of the key determiners of the popularity of trends
But historical sales information is no longer sufficient, because more than ever, customers expect a personalized purchase experience.
The fashion industry uses big data both to generate forecasts and the responses to corporate problems which can turn around declining trends from production and sales data, and to flesh out new business models.
Improving the Customer Experience is naturally at the center of any synergy between big data and the world of fashion; this is reached through other collateral goals, which range from the area of customer satisfaction through to operations and supply chain management, in order to allow customers to obtain the styles from the shows available for omni-channel purchase:
- Identifying the market and the target
- Carrying out predictive trend analysis in order to discover and develop new trends and direct new designs thanks to the data gathered in the previous seasons
- Managing and designing physical stores which improve customer involvement and experience, thanks to analysis of traffic and POS purchase behavior data, supported by RFID, Wi-Fi and Beacon technologies
- Measuring the impact of influencers on brand sales and awareness
- Improving cross-selling and promotional logic, with proposals customized on the basis of customer interests and behavior, both in terms of products and content, making marketing campaigns more effective and engaging
- Optimizing inventory management through a reduction in over-inventory, and savings in terms of transport, storage and over-production costs thanks to the more precise demand forecasting provided by big data analysis. Supporting fast and flexible supply chain and fulfillment processes on the basis of requirements is a key mission of big data analysis
- Reducing returns by ensuring the right size even for online sales, thanks to machine-learning algorithms which identify the shape of the customer's body or feet via a simple photo and identify the most suitable size for them, alongside data-driven insights gathered from returns and refunds, which contribute to improving production decisions
- Dynamic management of prices, thanks to big data analysis of currency rates and raw material prices, strategic analysis above all for players in the fast fashion sector
Big Data & Loyalty
Big data analysis can be key to allowing luxury brands to identify, connect with, understand and build a long-term commitment with their customers
Management of the Entire Data Lifecycle
From mapping data sources to defining KPIs with the business, big data analysis follows well-defined phases:
Assessment & Advisory
- Definition of the IT and Business requirements, analysis of the source systems in order to extract the most significant data and the KPIs on the basis of sector-specific processes in order to gather the correct production and sales data
- Consultancy on the best systems and solutions, security checks, performance optimizations, and on cloud migration
Data ingestion & Data repository
- Mapping out the requirements on the source systems, conversion of the requirements into technical specifications and data mapping
- Data modeling, definition of the structure and standardization of the data warehouse or data lake, on the basis of the data visualization requirements, aiming to achieve the best possible performance both in terms of loading and use of data, in order to export the data into the BI tools in the business dashboard
- ETL Development: engineering of the analysis performed in terms of flows and modeling of the processes, structuring of job templates to support the development in order to facilitate real-time ingestion, and the robustness of the software releases
Data science
Analysis of historical data in order to understand the consumption dynamics and guide corporate production and sales strategies, with:
- Machine learning & AI services
- Data preparation and Data Discovery in order to identify patterns and anomalous values through data visualization or advanced analytics
Data visualization
Focus on the best systems and architecture in order to guarantee performance for use of the data by the business
Data governance
- Mapping out the source and route of all data (data lineage) and all related entities thanks to data catalog tools
- Selection of implementation methods and components which can be incorporated into data governance tools in order to guarantee the evolution of the BI environment
- Definition of the processes and templates that the development team must follow
- Data quality with definition of rules defined to set automation logics and alerts in the event of data errors
- Data protection & data compliance
Lutech Big Data Fashion & Retail Expertise
Lutech's Big Data, BI and Advanced Analytics team is able to take on end-to-end management of the data lifecycle in the Fashion & Retail world, thanks to its vertical knowledge of the sector and consolidated knowledge and expertise in the following:
- The main transaction processing systems used in retail (SAP, Stealth, Salesforce, navision, jde, POS systems …)
- The corporate processes specific to the sector, with complete projects on the payables and receivables cycle
- The main on-premise (oracle, sqlserver, postgresql, greenplum), and cloud (redshift, bigquery) databases, and those of the big data universe (Cloudera)
- The most important ETL / ELT tools (Informatica, Talend, Oracle ODI, IBM Datastage, Microsoft SSIS/data factory)
- Big data analysis logic for the Fashion&Retail sector, price, sales, demand forecasting, upsell opportunity analysis, from a predictive point of view
- The best AI & machine learning and advanced analytics solutions and relative languages: Dataiku, LoopAI, Tensorflow, jupyter, python, …
- The main BI tools (Qlickview, Tableau, Oracle BI, PowerBI) and main real-time entry techniques in the presence of in-memory tools
- Delivery on market-leading BI platforms, in "data-intensive" enterprise contexts
- Matters relating to sensitive customer data and sales data (anonymization, retention time and profiling restrictions ...)
- Effective data control for business exposure
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