A single view

Cross- and up-selling can only happen if companies have a single view of the customer. To achieve that, they have to embrace master data management (MDM).

Many companies, chief among them financial institutions, want to sell more of their goods and services to existing customers. But they are inhibited from doing so by their inability to enjoy a single, unquestioned, unified view of their customers - who they are, what products they already hold, the key details of their life (birthday, education, partner), and when they are due for a new car, home, or other major purchase.

Customer profiling, the process of gathering data about customers such as age, income, gender, education, and buying patterns, enable companies to capture and analyse critical customer demographics and behaviour.

In theory, this leads to the ability to identify new business opportunities, and develop and retain customers through reports and analysis. The more information gathered about a company's customers, the better equipped that company is to anticipate and cater to its customers' needs, so they can produce targeted marketing messages, campaigns, offers, and products.

The problem is, however, that effective customer profiling depends on a unified view of the customer's information collected via each one of an organisation's sales channels. Most companies are prevented from obtaining such a view due to the nature of corporate systems, which are silo-based in nature.

Companies invest heavily in integration software, or in contracting external service providers, to try to achieve this single view, but that's neither a long-term nor an effective solution. The only way to address the problem is through master data management (MDM), which provides a framework for unifying that view and enabling a comprehensive profiling process.

* How does master data management promote cross-selling?
Cross-selling uses a customer's buying history to select them for related offers that they may consider purchasing. The idea is that should someone be willing to buy a product, they may also be willing to spend additional cash.

Say, for example, the customer buys a pair of leather boots - heel guards and protective polish will add to the longevity of their purchase. A flat-screen television will require something that may not have been considered by the customer, such as a high-definition decoder. To be able to afford that television, the customer may need credit services that allow for longer-term financing and incremental payments. Customers may also be drawn to upgrade their existing product or service. Customers with similar profiles may be interested in similar sets of products.

Online retailers such as Amazon have caught onto this and offer suggestions about what customers with similar buying patterns have purchased.

It's clear that cross-selling can only work if there is access to the appropriate information.

In a small business environment, one-on-one interactions help in driving the cross-sell. The salesperson at an electronics store knows the customer will need help getting that flat TV up on the wall, so that's an easy one.

Effective customer profiling depends on a unified view of the customer's information collected via each one of an organisation's sales channels.

Mervyn Mooi is director of Knowledge Integration Dynamics.

In larger organisations, however, such as multichannel Web vendors, financial services institutions and office suppliers, the more data that is available, the better, both from customer profiling and sales pattern points of view. Customer profiling can drive cross-selling by clustering similar customers and evaluating the items they purchase at the same time - their “market basket”.

This type of analysis requires customer data and transaction data. The customer profiling collects demographic information about the client base. The transaction evaluation looks at what was purchased, when, with which other products and by whom, for example.

This is where MDM comes into play. Large organisations with many lines of business and catalogues of products may have multiple customer-facing channels through which people interact with the company and buy products; effective customer profiling depends on a unified view of the customer characteristics collected via each one of those sales channels.

Master data management provides a framework for unifying that view and enabling comprehensive profiling in preparation for the next phase, which includes collaborative filtering and market basket analysis.

Collaborative filtering looks for common purchase patterns among segments. Any time a Web site or an individual suggests that the same kind of customers who bought product "A" are also interested in product "B", the inference is that many people like to buy both products together.

Making the buyer aware of that correlation paves the way for the cross-sell pitch, but there must be access to all of that data to suggest those correlations. MDM makes this practicable by providing a master view, usually within the data warehouse environment, that takes into account all the organisation's sales channels.

Similarly, market basket analysis evaluates the products that customers purchase together to identify any patterns that suggest the potential to cross-sell. Some of these opportunities may simply not have occurred to businesses; further, this analysis may appeal to core affinities that customers themselves may not even consciously recognise. Again, MDM provides the methods for accumulating a standardised view of the products and services purchased, thus simplifying market basket analysis.

These concepts become especially critical in environments where there is not a known relationship across products that exist in soloed lines of business. The current consolidation in the financial services industry is a good example. As more banks acquire less conventional financial services organisations, they will have a growing need to understand how to combine their customer databases, understand who their customers are, what types of products they are interested in, and the kinds of patterns that emerge from the combination of sales histories.

* Mervyn Mooi is director of Knowledge Integration Dynamics.