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The cost of customer acquisition in many industries can be high, but some large retailers are investing in computer systems to help them get rid of customers.

These systems identify ‘devils’ – unwanted customers who cost the retailers money.

‘In all retail businesses there is a segment of customers which is unprofitable, and often this is far larger than expected,’ said Tony Stockil, chief executive of retail consultancy Javelin Group. ‘This varies by industry, and in some cases this segment may be as large as 20 per cent of the entire customer base.’

Devil consumers’ behaviour ranges from the legal to the fraudulent. At one end of the scale are devils who only visit a store to buy loss leaders. At the other end are criminals who carry out scams such as buying an item to get a valid receipt, then stealing the same item and returning it for a refund using the original receipt. Other devil activities include wardrobing – the practice of buying an expensive item of clothing such as a cocktail dress, wearing it for one night with the labels tucked out of sight, and returning it the next day for a refund; pack attacks – damaging the packaging of an article on display in the hope of buying it later at a discount; and excessive returning, which may involve buying the same item of clothing in many different sizes and colours with the intention of returning all but one item after a few days.

Excessive or fraudulent returning is a huge problem in the US, where it is estimated to cost retailers $16bn annually. ‘Wardrobing is an especially big problem for large retailers,’ said David Jones, president of New York based loss prevention consultancy Cost Benefit Consultants. ‘In the US it’s not unusual to get people who make hundreds of purchases from a store every year and then return them all,’ he said.

Mini case study

Along with the special characteristics of a data warehouse is the process of creating and maintaining that data warehouse, referred to as data warehousing. Figure 4.3 indicates the major steps in the data warehousing process. It can be seen that there are three main elements to the data warehousing process. Firstly the data warehouse takes information from internal

Data warehousing

To combat this, some American retailers are turning to a data warehouse service, operated by a California-based company called The Return Exchange, to identify customers carrying out wardrobing or fraudulent returns. Every time a return is made, relevant transaction data and customer identity information from a driver licence or other ID card is sent to The Return Exchange where it is stored in its database. By analysing large samples of customer returns data, the company helps retailers recognise the mark of a devil: specific patterns of returns behaviour that indicate excessive returning or return fraud.

When a customer takes an item to a store to return it, that customer’s previous return history at that store – which includes the number, frequency and value of returns that have previously been made – is examined to see if it matches the profile of a devil. If so, the retailer can decide whether the customer should be given a warning or refused a return. Devil customers who are consistently refused returns are thus forced either to change their habits and become profitable customers, or take their unprofitable custom elsewhere.

British high street retailers are far more restricted in the type of personal information they can store, but they can still spot devil behaviour by analysing data captured at the sales tills. Retailer John Lewis uses software from London-based loss prevention vendor IntelliQ to sift through transactions carried out at all of its shops. ‘We have business protection teams who identify the sorts of bad things that criminals do, and the software allows us to look at millions of transactions and identify suspicious ones that match the modus operandi of these criminals,’ says Peter Kaye, John Lewis’s head of business protection. ‘An experienced investigator can see what’s happening and say “Yes, that transaction is fraud”, or “this one is suspicious”.’

Many large retailers are also adopting software that produces barcoded till receipts. These make it harder for customers to manufacture or copy receipts, or to use them to return the same goods more than once.

But sometimes, low tech solutions can be the best way to avoid acquiring devil customers in the first place. ‘A number of the largest mail order companies in the UK sell on credit, so assessing accurately if a customer is creditworthy or not is key,’ says Javelin’s Tony Stockil. ‘One of the many things a company might look at is whether a credit application is filled out in pencil or pen. The use of a pencil is a good indicator that the customer may not be creditworthy.’

Source: Rubens, P. (2007) How to get rid of ‘devil customers’. Financial Times. 13 June.

© The Financial Times Limited 2007. All Rights Reserved.

Data warehousing The process of creating and maintaining a data warehouse.

Figure 4.3 The data warehousing process

Internal and external

data

Extract and transform

warehouseData data

analysisData Client

and external sources such as operational systems which record sales or transactions with customers. Data can come from sources such as legacy databases holding historical data (see Chapter  3), operational systems such as enterprise resource planning systems (ERP) (see  Chapter 6), electronic point-of-sale (EPOS) data from customer transactions (see Chapter 6), data from electronic data interchange (EDI) systems (see Chapter 5) and RFID tags (see Chapter 5). Data is then extracted from these databases and transformed into a suitable form to be placed in the data warehouse using software known as ETL (see below).

Extraction, transformation and load (ETL)

An important part of the data warehousing process is the requirement to transfer data from a variety of sources, put the data in a relevant format and place them in the data warehouse repository. ETL software extracts data from one or more databases, transforms that data into a suitable format for the data warehouse and loads that data into the data warehouse.

This involves processing the source data according to business rules defined within the data warehouse. These rules can include definitions of data attributes and calculation methods.

Part of the advantage of the data warehouse approach is that rules can be applied to data in a consistent way within the enterprise data warehouse. ETL software can be developed by the organisation but due to its complexity it is usually purchased from an ETL software provider such as Tibco, Oracle, Microsoft or IBM.

Extraction, transformation and load (ETL) software Extracts data from one or more databases, transforms that data into a suitable format for the data warehouse and loads that data into the data warehouse.

The configuration of the system that undertakes the data warehousing process previously outlined can actually take a number of forms depending on the current information systems infrastructure and the organisational requirements of the data warehouse. The objectives and capabilities of management can also lead to compromise when considering the implementation of enterprise-wide systems. Ekerson (2003) provides four options to build a data warehouse (see Figure 4.4):

■ Data mart centric. Data are linked to users through independent data marts. This provides a relatively easy implementation in technical and organisational terms but lacks an enterprise-wide view of the organisation’s data and can lead to inconsistencies of data across data marts.

■ Virtual, distributed, federated. This consists of linking users directly to data sources through the use of middleware (see Chapter 11). Whilst providing integration of systems there may be performance and data quality issues using this approach.

■ Hub-and-spoke data warehouse. This links users to dependent data marts, which are then linked to an enterprise data warehouse which in turn is linked to organisational data sources. This provides the ability for customisation to user needs through the use of dedicated data marts but can lead to redundancy of data and relatively high operational costs of running both data marts and the data warehouse.

■ Enterprise data warehouse. This links users directly to a data warehouse which is linked in turn to data sources. This provides a single and thus consistent view of the data across the enterprise. It does require leadership from senior management in order to implement an enterprise-wide solution.

Data warehouse architecture

Traditionally data warehouses are updated periodically, for example weekly, to provide decision makers with information for decision making. However real-time data warehousing, also known as active data warehousing (ADW) provides the capability to load and process

Real-time data warehousing (RDW)

Real-time data warehousing (RDW) The capability to load and process data as events happen into the data warehouse.

Figure 4.4 Alternative architectures for data warehousing

Data mart centric Virtual, distributed federated

Hub-and-spoke data warehouse Enterprise data warehouse Sources

Data marts

Users

Sources

Middleware

Users

Sources

Data marts

Users

Sources

Data warehouse

Users Data warehouse

data as events happen into the data warehouse. This permits the use of the RDW for real-time operational decisions such as process flow performance which require current as well as historical data. Although RDW widens the type of decisions that can be assisted by data warehousing systems there are disadvantages to this approach. On problem is the technical difficulty in extracting and transforming real-time data. Another issue is the inconsistency in results of queries which are constantly updated, for example report statistics may differ for different personnel during a working day depending on the report viewing time.

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Data are the raw materials of this age of information, whether in such structured form as ATM transactions and till receipts, or unstructured such as trends on social media feeds and sites.