Insights & Analytics
Advanced analytics offers in-depth actionable insight from a complex bigger data set and helps business to be more responsive and carryout data driven decision-making.
We use data science beyond traditional business intelligence (BI) methods to predict patterns, clusters, trends and generate recommendations to form effective strategic decisions.
Descriptive Analytics - Descriptive Hindsighting
Helps to understand ‘what happened’. Here at ebp Global, we use various techniques to summarise and highlight patterns in current and historical data to better understand the areas of strength and weakness as well as to obtain a holistic view of the performance and trends on which to base business strategy.
We provide hindsighting abilities using interactive reports and visualizations such as pie charts, bar charts, line graphs that enable companies to track performance and other trends.
Helps to understand ‘Why has it happened’. Here at ebp Global, we perform root cause analysis on your data to identify the origin of any business issues and advises and recommends solutions to prevent them from happening in the future through KPIs and business metrics setting.
With the dawn of technological advances, pandemic disruptions, supply chain uncertainty, traditional forecasting methods fails to cover the breadth of complexity; instead, we are forced to consider innovative ways to tackle the issue.
Here at ebp Global, we are constantly working on and building a number of custom algorithms and methodology based on the context of the forecast, the relevance and availability of historical data, product maturity and lifecycle, the level of accuracy desirable and finally the time-period to forecast (forecast horizon). One such algorithm we offer is Probability based forecasting for shorter life-cycle items with limited history. We also offer services leveraging statistical as well as Machine learning algorithms with an ebp touch.
A core merchandising principle is to stock the right product, in the right amount, at the right place, at the right time; tailoring an assortment to remain customer-centric based on localised customer need, while reducing the stock holding is of utmost importance in today’s world of retail. The traditional method is more intuitive than scientific and are/maybe prone to bias.
Here at ebp Global we apply unsupervised Machine Learning and clustering insights to effectively build and manage your tailored assortment and the buy planning needs. Our approach is to provide a ‘attribute’ based clustering which not only helps remove the guesswork from the merchandiser’s side as to where to send what but also manages open-to-buy effectively by reducing the inventory holding and improving the turnover.
Clustering and Classification
Here at ebp Global, we use clustering (a form of unsupervised data mining technique) to identify hidden patterns and structures in the data at a more granular level removing any bias or without forming any hypothesis.
Clustering in retail involves grouping stores with similar characteristics to tailor their assortments based on localized customer need thereby enabling businesses to be more customer-centric. Here, we can combine both performance-based attributes and non-performance based (climate, store size, store ethnicity, income level, age group, fashion preference etc.) attributes to generate clusters and effective assortment planning whilst reducing inventory holding and improving margins.
Product clustering involves grouping similar products together based on their attributes and helps retailers make effective planning decisions involving inventory, pricing, promotions, and markdowns.
We also offer classification methods to assign new products/stores to a set of category or classes based on their attributes.
Range plans expresses retailer’s revenue targets in terms of how the range should be built up : the depth, width and length of range, what proportion of the total business mix it should take, what the initial store allocations would look like and the replenishment requirements in the DC leading to an effective buy planning.
Here at ebp Global, we help build an effective range plan by measuring the effectiveness of their ranges against delivered margins, promotional activity and markdown and terminal stock. We use predictive analytics to derive at an ideal product matrix and effectively manage the lifecycle of a product.
Size Planning and Analytics
Getting size curve right is often challenging as history can only tell us how each size has performed in relation to the inventory holding and fails to capture any missed opportunity. Another age-old problem retailers face is stock fragmentation across the network and due to the cost implications, consolidation of the fragmented sizes are always considered as the last option, if that.
Here at ebp Global, we offer predictive size curve to enable effective planning as well as size-based forecasting to answers the most frequently asked questions such as:
How do we get the size curve right?
Should the launch size curve be same as the steady state size curve?
Should the size curve be customized based on the product lifecycle?
How do I ensure that past sales and stock-outs are not impacting the size curve allocated to stores?
How do we ensure the correct size curve is available to influence and maintain the sales, as well as the brand experience and brand loyalty?