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Description: This application scenario demonstrates how Big Data in retail can be used for operational decisions as well as for day to day operations. The goal is to automatically collect

and analyse in-store data, like product placements, customer-product interactions, customer- staff interactions and cash-point data.

Example Use Cases: In-store data like heatmaps of customer movement can be used to optimize floor plans, cash point management, employee schedules and advertisement areas. Product data can be connected to different manufacturers and real-time information on delivery processes. Warehouse information allows more control on the inventory, intelligent shelves can help to control the inventory and direct staff to the most efficient actions. More complex information in a database can be useful for comparisons between similar products, more specific usage data for a single product, product ratings based on characteristics like dwell time or service requests and comparisons via online reviews. The combination of these product details can be useful for stocking decisions, pricing strategies and other operational decisions.

User Value:

 User Impact:

o high impact for retailers as profound warehouse / in-store design is essential o the customer benefits from a better shopping experience

o manufacturers get more detailed feedback on their products

 Maturity: partially implemented today (e.g. generation of customer movement heatmaps)

 Financial Impact: high financial gain for the retailer through the more efficient shop design. The manufacturers benefit from more precise details

Prerequisites:

 Data Acquisition: Collecting information inside the store requires the installation of hardware e.g. sensors in shelves or on shopping carts. To get more detailed information about products, like ingredients, pictures or reviews appropriate data sources need to be found.

 Data Analysis: A semantic analysis is necessary to find information about products in the web.

 Data Curation: It must be checked if the acquired online / third party data is trustworthy. E.g. it must be verified that the picture really matches the product. Furthermore, it might also be necessary to remove noise from in-store data.

 Data Storage: Different kinds of data have to be stored which are partially unstructured.

 Data Usage: It is necessary to run automatic inventory checks to be able to advise staff in real-time. The queuing behaviour needs to be simulated to optimize the staffs’ workload. The impact of a location on the product needs to be analysed in order to find the most efficient placement.

Data Sources: In-store data like customer movements, customer-product interaction, customer- staff interaction and existing product databases and online / third party data like ingredients or pictures.

Type of Analytics: Advanced Analytics for analysing comprehensive in-store data sets and online data.

Required Big Data Technologies: To be able to give the staff real-time advices software for dealing with large amount of data, like e.g. hadoop, is necessary. Furthermore crowd-sourcing platforms for data curation are required to ensure that the online data is trustworthy.

6.6. Requirements

In future application scenarios several key functionalities are needed (see Deliverable 2.4.1). Many use cases e.g. require a user model to understand the individual customer requirements. With this specific user model it is possible to provide ad-hoc context-aware customer support like individual recommendations or advertisements.

Besides an individual customer model, smart embedded systems in shopping environments play a major role. These are necessary to be able to e.g. detect customer-product interaction or to automatically discover if products are out-of-shelf.

Implementing these key functionalities is very challenging as different data sources need to be matched, online data has to be validated and data mining and analytics must be privacy preserving.

The following sections represent the current situation in the different steps of the data processing chain and point out the requirements associated with these steps in future.

6.6.1 Data Acquisition

The interviews revealed that currently two types of data are collected: the classical data for the accounting and controlling department (detailed sales volume), and data for the marketing department (information about the consumer and his behaviour).

The acquired data includes all information that is relevant for the business cases. Besides data for the accounting department and product information, the acquisition of information about customers for campaign optimization increased in the last few years.

Data from different sources and decentralized databases are stored in a data warehouse or a central repository. The amount of data of a full-range retailer has an overall volume of more than 2 petabyte. For future application scenarios, information from a lot of different sources has to be acquired and merged. Examples are data from social networks or RFID sensors. This data is often unstructured and therefore requires intense pre-processing to be able to perform analysis.

6.6.2 Data Analysis

Today, for statistical and controlling purposes, standard Business Intelligence software is used very often (e.g. Microsoft BI Server). By using cubes, database queries can be made task- oriented by composing rules. This type of analysis is especially used for controlling and business management. For marketing purposes, customer information is analysed by special marketing software for campaign optimization and customer acquisition. As an example, software by SAS was mentioned and especially the packages Enterprise Miner and Enterprise Guide. These software tools work fine for structured data analytics, but additional unstructured data sources call for new techniques that also have to be easy to use. Furthermore the software has to be able to deal with huge amounts of data to perform large-scale reasoning and large- scale machine learning.

6.6.3 Data Storage

Different database systems are commonly used to store different data sets. Which one is used, depends on the processing and analysis steps performed on the data. The data itself is often stored in multi-dimensional cubes instead of traditional relational databases. The advantage of cubes is the rule-based summarization and grouping of dimensions which makes the

processing and analysis of Big Data sets manageable. The growing amount of data makes the usage of new data storage technologies like e.g. NoSQL databases and cloud storage necessary.

6.6.4 Data Curation

Nowadays, data curation is handled by the IT department of the retail company, which is mostly located in the headquarters. With the growing amount of data, it is necessary to automate many curation techniques to be able to ensure data validity. In future, it is imaginable to let the customers perform the curation of e.g. product information using crowd-sourcing platforms.

6.6.5 Data Usage

Sales volume and receipt analysis is used for reporting and management purposes. Data about consumers and their behaviour is used for marketing optimization, e.g. dialog marketing and tailored recommendation that should be situation and context-aware in the future. The need of ad-hoc data analysis to provide situation-aware recommendation and advertising is another point that was mentioned by interview partners. Most interfaces today can only be used by specialists, in future these interfaces should be able to adapt to the person using it.

6.7. Conclusion and Recommendations

The task of the retail sector can be summarized as: reorganizing existing Business Intelligence for retail analytics and lift it up to the next level towards a more context-sensitive, consumer- and task-oriented analytics and recommendation tool for retailer-consumer dialog marketing.

Availability of Smart Data

The accessibility of Big Data, and respectively of Smart Data, in an easy way is a key prerequisite for most of the stakeholders within the value chain of the retail sector and also in cross-sectorial application scenarios. Not only the data itself, but also time plays an important role. The evolution from traditional history-based data analysis to real-time processing based on heterogeneous data sources becomes an important new value creating parameter. At the moment there are still rudimental effort wasting problems based on the lack of digitized and tagged data that is often not shared between stakeholders. For example, the data set of a single product can consist of more than 200 attributes and there is still no standard data format to share them with retailers and their heterogeneous data warehouse architectures. Especially in the growing era of Multi-Channel commerce, the availability of data across channels becomes an essential success factor. There is an urgent need for a standardization platform like the Global Data Synchronisation Network that is initialized by GS1.

Big Data for transparency in price and sales forecast

Besides services that are based on product specific and personalized recommendations, retailers are interested in price experimentations. The real-time comparison of promotion and prices among online and stationary competitors can be used to adjust inventory and prices for best traffic and sales ratio. Such a transparency has not only a benefit for customers, but also for retailers because the competitive nature can be used to identify and observe best practices and best in class that can be adopted for better self-performance. New algorithms operating on Big Data sets will be able to optimize decision processes like pricing in response to real-time in- store and online sales as well as managing automated disposition and smart inventory systems.

Big Data as the key to efficient consumer response

The highest potential for Big Data in retail can be identified in targeting services for individual consumer response. Providing customized services for consumers in an individual way by using knowledge about the behaviour and personal wishes will be a success factor in the near future. The aggregation of customer specific data has the target of analysing and segmenting customers as individuals by combining knowledge from different data sources. These sources include demographic data, shopping behaviour, purchasing metrics and digital footprints from social networks. The challenge is to combine and to analyse these unstructured data in real- time to open e.g. a channel for marketing-customer interaction in a dialogue and efficient way. Smart usage of Big Data in retail will become a powerful tool towards an extremely efficient customer-retailer-partnership.

6.8. Abbreviations and acronyms

BI Business Intelligence

CPE Cyber-Physical Environment IoT Internet-of-Things

6.9. References

BITKOM (2012). Big Data im Praxiseinsatz – Szenarien, Beispiele, Effekte. Leitfaden des BITKOM, Berlin.

McKinsey Company (2011). Big Data: The next frontier for innovation, competition, and productivity. Lebensmittelzeitung (2012). Top 10 Händler Europa 2012. http://lebensmittelzeitung.net/business/daten- fakten/rankings/Top-10-Haendler-Europa-2012_347.html#rankingTable