Componentes principales

The European Retail Industry is dominated by huge retail companies especially from Germany, France and the UK. The following table gives an overview of the Top 10 retailers in Europe.

Company Country Turnover in 2011

(Mio. Euro (pre-tax)) Managing Directors

1.Carrefour France 74.169 CEO: Georges Plassat

CFO: Pierre-Jean Sivignon

CIO: Hervé Thoumyre

2.Schwarz Gruppe Germany 69.986 CEO: Klaus Gehrig

3.Tesco UK 64.933 CEO: Philipp Clarke

CFO: Laurie McIlwee

CIO: Mike McNamara

4.Auchan France 47.813 CEO: Vianney Mulliez

CFO: Xavier de mézerac CIO: Daniel Malouf

5.Metro Germany 46.542 CEO: Olaf Koch

CFO: Mark Frese

CIO: Silvester Macho

6.Edeka Germany 44.421 CEO: Markus Mosa

CFO: Martin Scholvin CIO/CTO: Michael Wulst

7.Aldi Germany 44.038 CEO: Marc Heußinger

(Aldi Nord)

CEO: Norbert Podschlapp (Aldi Süd)

8.Rewe-Gruppe Germany 41.458 CEO: Alain Caparros

CFO: Christian Mielsch CIO: Frank Wiemer

9.Leclerc France 38.696 CEO: Michel-Edourad

Leclerc

CFO: Laurent Leclerc

10.Intermarché France 29.216 CEO: Jean-Pierre Meunier

Table 8: Ranking of the 10 biggest retail companies in Europe by turnover (Lebensmittelzeitung, 2012).

6.4.2 Market Impact and Competition

Concerning new technologies, the retail sector in Europe can be seen rather as a follower than as a pioneer in contrast to other core sectors of the European economy like manufacturing. Due to the fierce competition, the retail sector with its competitors is under high pressure. When we have for example a closer look into the stationary retail market, we can see that most of the retailers are focused on just a few countries. This limited expansion strategy is a result of country specific market conditions and the strong competition in the sector. For example there

are big margin differences in some product segments, e.g. in the food sector, within European countries.

The impact of Big Data on retail has constantly increased in recent years. The main advantages are higher efficiency and growing margins. A McKinsey Global Institute Report (McKinsey Company, 2011) identified five potential ways to create value from Big Data that are a result of an evaluation of the US retail sector, but based on the general nature of the findings, the results can also be adopted for the retail sector in Europe:

1. Big Data can unlock significant value by making information transparent and usable at much higher frequency.

2. As organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to staff management, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time.

3. Big Data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. When retailers exactly know in what their customers are interested in, offers and services can be provided more personalized. 4. Sophisticated analytics can substantially improve decision-making.

Big Data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).

6.4.3 Available Data Sources

According to BITKOM, Big Data sources can be classified according to different categories. Important categories for Big Data application scenarios in the retail domain are Cloud Computing, Sensor Technologies, Digitization of Business Models and Social Media / Collaboration. The following overview presents the relevant data sources for the retail domain according to these categories.

Cloud Computing

 demographic data

 psychographic data

 weather data

 upcoming regional events

 potential natural disasters

 local special data Sensor Technologies

 visual data from cameras o movement heat maps o facial expressions / gender

o product interaction (how long does the customer interact with the product? does he become a buyer?)

 RFID (Radio Frequency Identification) data o positioning

o inventory

Digitization of Business Models

 POS (Point-of-Sale) data o general: products, yield

o individual: shopping history (loyalty program)

 inventory

 placement and floor plan

 staff data o workload o traffic

o interaction staff-consumer

Social Media / Collaboration

 upcoming regional events

 local special data

 personal details

 consumer feedback

 product reviews

Most of the sources mentioned provide information in form of unstructured data in the web. Extracting information from these sources requires intense analysis on huge data sets.

When using sensors to acquire information from the environment, a huge amount of data is collected that needs to be interpreted, evaluated and, where appropriate, visualized to be able to extract specific information.

Sources: Stakeholder Interviews and (BITKOM, 2012)

6.4.4 Drivers and Constraints

Big Data in retail can be seen as an innovation topic that must be extremely supported by organizational processes within the retail company including effective sharing of information among departments and interest groups as well as a flexible organizational structure. An important organizational driver is the willingness to invest resources in the new technology to improve the effectiveness of and between marketing, merchandising, supply chain, business analytics and store operations.

Retailers have to build up Big Data knowledge not only on the operative level, but also on strategic, organizational and technological level as well as on human resources to educate qualified Big Data experts for retail. This new type of an interdisciplinary IT expert will have the competence to identify retail specific opportunities caused by Big Data.

6.4.5 Role of Regulation and Legislation

There are country-specific regulations and legislations that directly or indirectly affect Big Data in the retail sector. Privacy regulations e.g. directly effect the storage and usage of consumer related data. An important point that has to be taken into account is the transparency. It has to

be defined what kind of data is collected and how it is going to be used. Besides these direct effects, there is also an indirect effect on the retail sector caused by regulations. For example, the intention to restructure urban areas has also an effect on stationary retailers and their business model. They have to become more innovative with smaller stores installed in the centres of cities. Additional consumer services, and in doing so by using Big Data knowledge, have to be implemented. When we talk about regulation and legislation in the retail sector, we also have to think about regulations concerning distributors and producers that are often international operating companies.

6.5. Big Data Application Scenarios

There are several scenarios for the retail sector in which Big Data plays an important role. The findings that are described in the following are the result of the conducted interviews with decision makers from the retail sector. Their statements not only reflect personal opinions but also give evidences for sector representative positions. The results of the findings based on the interviews are the following:

Improving Enterprise Resource Planning and management of product database.

This includes retrieving and assembling of different product information (e.g. ingredients, nutrition information, best before dates, pictures) from different and heterogeneous data sources. These unstructured data sets have to be reliable, up to date and trustworthy, to mention just three important values. Especially in the food sector, the required product information is often not provided by manufacturers. This information must fulfil the requirements for multi-channel merchandising and they have to be up to date at every time. To get an idea of the data dimension, a full-range retailer has information of more than 2 million products stored in his data warehouse.

Planning of store and shelf location.

For stationary retailers, the planning of a new store requires access to different data sets, such as demographical distribution in the task region and detailed information about potential customers. These parameters have to be taken into account for planning a new store. Within a store, the shelf locations, the so called floor planning, is based on path analysis and heat maps, and requires additional information of the customer’s behaviour. Measuring and analysing this massive amount of data can also be seen as a Big Data challenge.

Better customer service and dialogue marketing.

Collecting a comprehensive knowledge about the customer seems to be the most interesting aspect for the retailers. Information about the customer including his behaviour allows the retailer to setup ad-hoc adaptive context-aware customer recommendation systems to provide smart shopping services. To fulfil this task, retailers need to know who their customers are. This must not be only on a cluster or segmentation level, but also on a personalized and individual level. In addition to classic data acquisition, social platforms provide a novel knowledge base that needs new evaluation techniques. The challenge is to identify, to acquire (with respect to legal restrictions) and to analyse these heterogeneous data sets and at the end to semantically interpret the results.

Out of these findings we defined the two application scenarios “In-store precision retailing” and “Operational Decision Management in Retail” which are presented in detail in the following.