How CRM Specialists Leverage Data to Drive Growth

As companies have grown larger they have become separated both geographically and culturally from the markets and customers they serve. Disney, an American corporation, has operations in Europe, Asia and Australasia, as well as in the USA. Benetton, the French fashion brand has operations across fi ve continents. In retailing alone it operates over 7000 stores and concessions. Companies such as these generate a huge volume of data that 

needs to be converted into information that can be used for both operational and analytical purposes. The data warehouse is a solution to that problem. Data warehouses are really no more than repositories of large amounts of operational, historical and other customer-related data. Data volume can reach terabyte levels, i.e. 2 40 bytes of data. A warehouse is a 

repository for data imported from other databases. Attached to the front end of the warehouse is a set of analytical procedures for making sense out of the data. Retailers, home shopping companies and banks have been early adopters of data warehouses. Watson describes a data warehouse as followsThere are a number of steps and processes in building a warehouse. First, you must identify where the relevant data is stored. This can be a 

Challenge When the Commonwealth

Bank opted to implement CRM in its retail banking business, it found that relevant customer data were resident on over 80 separate systems. Secondly, data must be extracted from those systems. It is possible that when these systems were developed they were not expected to align with other systems. The data then needs to be transformed into a standardized, consistent and clean format. Data in different systems may have been stored in different forms, as Figure 4.6indicates. Also, the cleanliness of data from different parts of the 

business may vary. The culture in sales may be very driven by quarterly performance targets. Getting sales representatives to maintain their customer fi les may be not straightforward. Much of their information may be in their heads. On the other hand, direct marketers may be very dedicated to keeping their data in good shape. CRM applications allow users to interact with customer-related databases for operational purposes. Sales representatives add data to 

customer records after a call is completed; CSRs in call centres log inbound calls on customer records; marketers update online brochures as product specifi cations change. In addition, CRM users want to interrogate data for analytical purposes, or receive management reports. There are three main ways of doing this – standard reports, database queries, and data mining. 11 Standard reports Standard reports are automatically generated periodically by the CRM system. Examples include monthly reports to sales management about sales 

Representatives activity and performance

against quota, and daily reports of call centre activity. OLAP technologies allow users to drill down into the data on a screen rather than resorting to a fl at, fi xedformat, report. Starting manager can drill down into data about individual sales representatives and their customers, to reveal where causes of underperformance lie. Special reports can also be produced when ad hoc queries are made of a database, data warehouse or data mart. Most database 

management systems incorporate some reporting capability. Database queries A number of different types of query languages are available to CRM users when they want to raise a database query. Some are graphical – users can click and drag the data they want, and then drill down until they reach the level of granularity they require. Database managers may prefer to use SQL, which is now the standard query language for relational databases. SQL queries 

employing standard commands, such as  DROP, can be used to access required data. Data mining In the CRM context, data mining can be defi ned as follows: Data mining is the application of descriptive and predictive analytics to support the marketing, sales and service functions. 12 Although data mining can be performed on operational databases, it is more 

Held in data marts or warehouses

Higher processing speeds, reduced storage costs and better software packages have made data mining more attractive and economical. Data mining can provide answers to questions that are important for both strategic and operational CRM purposes. For exampData mining has proven to be a successful strategy for the UK retailer Marks & Spencer (M & S). The company generates large volumes of data from the ten million customers per week it serves in over 300 stores. The organization claims data mining lets it build one-to-one relationships 

with every customer, to the point that whenever individual customers come into a store the retailer knows exactly what products it should offer in order to build profi tability. Marks &Spencer believes two factors are important in data mining. First is the quality of the data. This is higher when the identity of customers is known, usually as a result of e-commerce tracking or loyalty programme membership. Second is to have clear business goals in mind 

before starting data mining. For example, M & S uses data mining to identify ‘ high margin ’ , ‘ average margin ’ or ‘ low margin ’customer groups. The company then profi les ‘ high margin ’ customers. This is used to guide customer retention activities with appropriate targeted advertising and promotions. This technique can also be used to profi le ‘ average margin ’ or ‘ low margin ’customers who have the potential to be developed into ‘ high margin ’ customers. 

Conclusion

analysis. Then you can experiment with different treatments, making different offers and communicating in different ways to selected cells of the RFM matrix (see Figure 4.7 ). You can expect to fi nd that customers who have bought most recently, frequently or spend most with you are the most responsive in general terms. Another approach in data mining is clustering . CRM practitioners attempt to cluster customers into groups. The general objective of 

clustering is to minimize the differences between members of a cluster while also maximizing the differences between clusters. Clustering techniques work by using a defi ned range of variables to perform the clustering procedure. You might, for example, use all available transaction data to generate customer segments. There are a number of techniques, such as cluster analysis, which fi nd the hidden clusters. 14 Once statistical clusters have been 

formed they need to be interpreted. Lifestyle market segments are outputs of cluster analysis on large sets of data. Cluster labels such as ‘ Young working class families ’ or ‘ Wealthy suburbanites ’ are often used to capture the essence of the cluster. Finally, data mining can contribute to CRM by making predictions . CRM practitioners might use historic purchasing behaviour to predict future purchasing behaviour and customer lifetime value. These fi ve 

major approaches to data mining can be used in various sequences. For example, you could use clustering to create customer segments, then within segments use transactional data to predict future purchasing and customer lifetime value. According to Gartner Inc market leaders SAS and SPSS offer broad data mining solutions that meet most market needs