Sanlam: Customer Relationship Management

Accenture helped Sanlam develop a single, central store of client information, allowing a number of reporting environments that helped enable high-performance customer relationship management.

Sanlam, South Africa's second largest life insurer with assets of R246 billion under management, aims to be the leader in wealth creation for institutions and individuals. It listed on the JSE Securities Exchange and the Namibian Stock Exchange after it demutualised in 1998. Established in 1918, the group boasts around 700,000 shareholders and provides financial services to more than 2 million investors. Its gross operating profits in 2002 was R2149 million.

Sanlam conducts its activities mainly through Sanlam Life, the group's provider of insurance and investment products to individuals and companies.

Sanlam's strategic objectives of being a client-centric organisation and growing its client base were being hampered by disparate information technology systems that made it difficult to gain a holistic view of clients across the organisation's product and business divisions.

This slow, inflexible access to client information added time to the design and development of marketing and direct sales campaigns, and meant long lead times in rolling out campaigns to exploit market opportunities.

Accenture, with its global experience in helping corporations unleash the potential of their legacy systems, was chosen to implement a client data warehouse. Client information would be drawn from multiple operational systems into the warehouse, checked for accuracy and updated to become a valuable marketing resource.

The project team, comprising Sanlam and Accenture experts, decided against a "big bang" approach, preferring to incrementally develop and introduce processes that would prove the benefits of the concepts and win user buy-in.

Workshops were held to determine the technical architecture of the data warehouse and to design and develop the solutions. Business users collaborated with the project team to check the currency and accuracy of data held in the legacy systems to ensure that only quality client information was transferred to the new system. More than 7 million records of 2.1 million clients were validated in this process.

The data warehouse was built with IBM's Visual Warehouse and implemented on a Unix platform using IBM's DB2 database. Client information was summarized and made available to business users through a number of data marts structured to support specific marketing initiatives.

In August 1999 work started on the first phase of the project—designing and implementing the basic data warehouse infrastructure—which had to be completed in six months to support marketing initiatives. Accenture's experience in large project management came to the fore to manage suppliers and, within Sanlam, convincing users that sharing customer information would be mutually advantageous.

The first business area to use the data warehouse was the direct marketing department, specifically in the execution of postal marketing campaigns. The success of this implementation was measured through improvement in the time it took to launch campaigns to the market.

The first two releases of the data warehouse were completed within 15 months, by December 2000. During the pilot phase in August 2000, 46 campaign selections were carried out in less than five days—a task which usually takes a month. At the end of this period, the warehouse was populated with all the client information relevant to marketing initiatives of the various business areas.

These campaigns included simple product offers, where a new investment product is offered to an existing client based on attributes such as age, income, marital status, etc. The campaigns also included more complex selections, where additional risk benefits are offered based on an existing qualifying policy. For example, a client would be offered accident coverage on a policy that currently has only an investment component.

For the first time, Sanlam's campaign planners were able to refine a target client group based on integrated information from all of the systems across the business. So, for example, clients could be analyzed and targeted based on integrated information from marketing systems (client demographics and lifestyle information), product administration systems (what products they hold with various product providers) and scores from data mining techniques (such as a propensity-to-buy score).

An innovative aspect of this project was the ongoing enrichment of information in the data warehouse. This provided Sanlam with insights to guide their interaction with the client:

• Through this initiative, Sanlam calculated a current-lifetime value for each client in its database, based on the current portfolio of products. Sanlam calculated a future lifetime-value by combining actuarial calculations with predictive data mining techniques.
  
  
• Clients were segmented based on the combination of their current and future lifetime values.
  
  
• Sanlam used data mining techniques to predict, for each client, their propensity to buy each of Sanlam's main products. This helped them to target clients more effectively.
  
  
• Campaign planners were able to perform online analysis of integrated information from across the enterprise to help them select the most appropriate clients for a campaign. This included client demographic and psychographic information (from marketing systems), product information (from disparate product systems), customer interactions (from customer contact systems) and derived information (from data mining techniques).

Sanlam's customer relationship management strategy has been put onto a dynamic footing. The data warehouse is now the platform from which initiatives are developed, managed and evaluated. Clients are more accurately targeted for sales drives as their propensity-to-buy score is calculated in the data warehouse through sophisticated data-mining techniques. Accenture's innovative solution is a tangible example of where customer relationship measures have been calculated and applied to re-engineer the client's client relationship process.

Client segmentation, managed in the data warehouse, is made available in the call center. Customer service representatives differentiate customer treatment based on a customer's lifetime value score. This helps retain high-value customers through differentiated service and loyalty campaigns while giving appropriate cost-effective service to lower-value customers.

Monthly client-focused trend reports enable executives to identify trends in customer behaviour and respond appropriately.

Through this integrated and enriched client information, Sanlam is better able to understand its client base and subsequently tailor its marketing programs more effectively, while analyzing the results accurately.

Business users enjoy prompt and direct access to relevant client information as they no longer need to request this from, and wait for the response from, information systems staff.

The insights and data segmentation model provided by the data warehouse means service levels can be adjusted according to customer value. Annual benefit statements, which are expensive to produce, are sent only to certain high-value client segments and lower-cost service is provided to the bulk of customers. This approach dramatically cuts costs.

The availability of a single, central store of client information has enabled a number of reporting environments to source consistent, clean information from a single source, thereby avoiding the cost of developing multiple, duplicated and inconsistent data extracts from the same systems.

Furthermore, the incremental implementation approach to rolling out data warehouse-based initiatives ensures that each release will deliver clear business value.