The retailer possesses one of the richest retail data assets in its market, every day significant volumes of data are generated as shoppers swipe their store loyalty card, which offers loyalty discounts on certain products and personal loyalty rewards.
The function of the retailer’s insights team is to enable better business decision-making using data, information and insight. Several years ago a growing sense of frustration began developing that data was not being used anywhere near its maximum potential. The key challenge was how to transform the retailer’s raw data, into accessible comprehensive insights – at speed.
The retailer’s commercial chief, says the true value in analytics and insights is the ability to influence future business decisions.
“As one of the largest retailers in the country, it is important that we understand our customers preferences and behaviours through our data. By effectively analysing and transforming our dataset into actionable insights, we can effectively enable our business to make better and more informed decisions that meet our customers’ needs.”
With 11Ants deployed, the lead time required by the team to answer customer-centric questions has dropped to minutes rather than days. It has also been able to take new and innovative ideas to the retailer’s merchandising team by utilizing 11Ants' pre-built modules. These modules are based on customer-centric retail best practices and are fully operational from day one.
One of the most widely used modules is Key Measures. This module contains 50+ retail KPIs that can be applied to any corner of the business, from all stores all SKUs, down to a single SKU, in a single store, on a single day, bought by a single customer segment – or anything in between.
This flexibility is made possible by 11Ants proprietary AntScantechnology, which allows rapid scanning of the data at the transaction level, so the user is effectively building their cube as they build their query.
The experience of improved flexibility of queries without IT intervention was transformational. It opened entirely new possibilities as to the type of questions that could be answered ad hoc. As 11Ants’ CEO Tom Fuyala observes:
“The difference between the ability to answer a question in five minutes versus two days is generally the difference between bothering to ask the question or not.”
Another popular module is Basket Contents. This module empowers business users to understand which products are sold with other products, either at a basket level or a customer level. This also can be very broad, or extremely specific – for example ‘what is most likely to be found in the basket with 2L Coca-Cola on Monday mornings, by a female shopper, in store x, when sold on promotion y?’ This module allows the retailer to make decisions around co-location, cross-promotion and product bundling as well as measure the impact of these initiatives on shopper behaviour.
Busy Times is another well-utilized module. This module provides a very clear understanding of transaction time distribution across categories, stores, products, or customer types – by hour of day and day of week. Understanding these patterns enables better merchandising decisions.
These modules are just a fraction of the modules contained in 11Ants, which offers a diverse cross-section of retail analytics modules designed to serve merchandizing, marketing and operational requirements.
Using 11Ants allows this national retailer to unlock valuable data the supermarket chain was finding difficult to translate for business benefit.
Answers to analytically complex questions delivered often in minutes rather than several days.
As new modules are added to 11Ants, the retailer obtains the benefit of using them.
11Ants has made analytics accessible to many in the retailer, not only those with computer science degrees.
Tangible Business Benefits
Key business benefits realized by the retailer include:
Efficiency improvements and the ability to answer questions fast;
Understanding of shopper buying behaviour and translating this understanding to appropriate business decisions on price, range, promotion opportunities and availability;
Understanding of impact on customers behaviour post operational challenges and making appropriate remedial action – e.g. availability and product recall;
Superior understanding of promotions and their impact on specific customer segments, SKUs, category and whole-store spend.
Grocery Store Analytics Case Study
While rich in shopper data, this retailer was poor in insights. The key challenge was how to transform the retailer’s raw data, into actionable insights – at speed.