Roadmap For Successful CRM Implementation

Traditional approaches to customer relationship management have yielded weak results because they have been technology-led. Ideally, the prime movers of a CRM strategy should be line functionaries in marketing, finance or operations. Indian banks, just moving into CRM, can learn from the mistakes made in the West

About a decade ago, the marketing research industry in the United States seemed poised to take off on an altogether new technological flight. Research clients in manufacturing and marketing organisations of customer goods and services seemed to readily accept the notion that strategy building needed some significant inputs from customer data collected from tracking services. These data were different from the ones supplied until then by research suppliers through one-off survey analysis.

Tracking services supplied information not only about consumer attitudes but also their actual behaviour "" and that too on an ongoing basis. Behavioural data was considered more useful for developing strategy since it provided true insights about a customer's likes and dislikes rather than slice-in-time customer surveys which provided stated preferences. This led to the usage of marketing research techniques as a planning tool for the future rather than using it to merely report "nice-to-know" customer reactions in posterior.

Strategy development exercises turned more data savvy and scientifically tuned and corporate America was talking precision in estimated earnings rather than simply directions. Prediction modelling came into vogue and a proper calibration of the marketing mix to induce an appropriate customer response became necessary. Mere directional insights were not enough.

Large marketing organisations such a Kraft, Pepsi, Coke, General Mills and the like invested in customer data management initiatives, recruited and trained technicians to mine the databases and focused on interpreting past customer behaviour along with individual characteristics to fine-tune marketing strategies directed at specific customer groups. This technique became popularly known as micro-marketing based on data analysis.

The emphasis on customer data analysis and the subsequent use of the analytical output for strategy building evolved more dramatically in the banking services sector where the preponderance of customer transaction data is overwhelming. The entire business of banking services is about building and maintaining individual customer relationships through value-added service. This spurred the need for developing individualised customer care strategies to ensure that each customer (at least the high value ones) was kept satisfied perpetually. Customer retention was important especially in the context of the keen competition prevalent in the western markets.

The route to ensuring a high degree of satisfaction was to devise individualised strategies for every important customer. Thus evolved the concept of customer relationship management (CRM) which not only incorporated the micro-marketing strategies based on data analysis but advanced further to customise marketing efforts to individual customers based on past behaviour.

Similar CRM strategies evolved in several service-based industries which have access to large-scale transaction data of individual customers. Goods and services that lend themselves to repeated usage and require the customer to directly access the provider to make a purchase are amenable to CRM-type strategies. In order to build a database of information of customer transactions, adequate computing infrastructure is required to identify and store individual transactions.

EFFECTIVE STRATEGIES

To devise effective strategies based on past customer behaviour, a thorough understanding of each customer with respect to his/her profile and past consumption pattern is required. While this may sound fairly prosaic, there are some significant operational issues for organisations resorting to CRM initiatives. These can be categorised as:

* Customer data acquisition from all customer contact points

* Data mining for building customer insights and prediction modelling

* Developing appropriate initiatives on a proactive basis to improve customer satisfaction.

Perhaps the core of this process is the development of a data acquisition plan and its implementation. The quality of the raw material used to develop customer insight determines the ultimate effectiveness of any strategy. The subsequent stages in the CRM process development are of secondary importance. Yet, there are many instances of organisations investing less in the planning of data acquisition and relying more on efficient data mining to drive their CRM initiatives. Unfortunately, this has resulted in less than marginal success.

RETAIL BANKING: CASE STUDY

To substantiate this point, the case of implementation of a customised debt collection initiative at a credit card issuing bank in the United States is presented here. The objective of this CRM initiative was to identify and assign appropriate delinquent customers to a litigation process to accelerate the collection of pending dues from them. It was estimated that this method would significantly improve the debt collection amount of the bank since it had over $5-6 billion of delinquent balances.

According to the bank, all delinquent customers were not suited for coercion into paying their dues. Delinquent customers, who had pending disputes with the bank or had a genuine grievance, were definitely not candidates for litigation. Hence, there was a need to search the history of past interactions of customers with the bank and deselect customers with such records.

After deselecting the cases with a dispute or grievance, a second stage of classification was attempted to segregate appropriate customers for litigation. The logic used was that it would be viable to initiate action only against those delinquent customers who tended to pay up only when litigation proceedings were launched. Past behaviour and profiling information was required to identify this segment.

It was presumed that customers who were unwilling to pay up their dues, but had to the ability to pay, would generally satisfy the criteria for selection for litigation. Hence, it was necessary to determine the financial solvency of delinquent customers in order to identify the appropriate cases for litigation. Available customer information regarding possession of assets or a job that fetches a steady income were found suitable to make this assessment.

Unfortunately, this type of information is not readily available with most banks for a variety of reasons. First, they may not have instituted a procedure to collect this information at the time when customers apply for credit cards. Some banks do have procedures to collect information about the customer's income. However, this information is not updated on a periodic basis since there are no instituted procedures to monitor changes in the financial status of customers over time.

Also, there are instances of data corruption causing a high level of missing information in the databases. As a result of the non-availability of appropriate information, identification of customers to be sent to litigation becomes non-trivial. Less than perfect information about customers imposes constraints on the identification of the right candidates for litigation.

A similar problem was encountered at the bank while attempting to re-engineer the debt collection practices. A decision-tree analysis was employed based on available surrogate information such as past behavioural data of customers in lieu of information on the customer's financial status, which predicted who would be a likely candidate to be sent to arbitration. Instead of definitively identifying financially solvent cases, statistical modelling was used to predict the true financial solvency of delinquent customers with the help of information of their past behaviour. Cases with a high probability of being sound financially were assigned to the litigation process.

It was not viable to assign all delinquent cases to litigation because of the significantly high cost associated with these proceedings. There was a $100 court fee for filing every case. The result of developing sophisticated prediction models was just about average. The bank was able to identify about 40 per cent of the true cases that were appropriate for litigation.

Even with a comprehensive search across multiple databases in the organisation, the bank was unable to improve the identification process using the sophisticated prediction-modelling tool. The silver lining was that given the scale of operation at the bank, even with this moderate success in customer identification process it was able to save over $40 million annually.

This illustration is not an isolated case of low to moderate success in developing successful business strategies based on customer information. Based on the author's experience from the banking sector in the United States, no major bank in the past five years has recorded a high degree of success in formulating customer transaction data-driven business strategy (with the possible exception of Capital One).

While it is conceptually very appealing to devise systems to track consumers and fine-tune future strategies based on an understanding of their past behaviour, many organisations have yet to evolve internal processes that can capture and bank appropriate customer information for future use.

Unfortunately, this remains a major roadblock in the effective implementation of CRM or database-driven strategy initiatives. The stress is on the appropriateness of the data captured and not on the scale. The experience of the banking industry in the United States has exhibited the importance of strategic planning of data acquisition for its effective use in the future. Without such proactive steps, managers will have to reconcile themselves to moderate successes in any database-led strategy development initiative.

KEY ISSUES

Traditionally, the technology and systems group in an organisation have spearheaded information acquisition and management. In reality, this tends to be a hurdle since there is a significant disconnect between the tasks of data management and data use for strategic purposes. The former is handled by the more technology-savvy IT and systems personnel in organisations who may not be looped in with the mainstream business functions of the organisation.

While management and development of databases and information flows is strategically important, the responsibility of ensuring the acquisition of good quality information based on their potential applicability to strategy development activities has to be assumed by the user groups. Ideally, this would mean that prime movers of a CRM strategy in an organisation should be the line functionaries, such as marketing, finance or operations, who would benefit the most from effective database-driven business initiatives.

Hence it important that firms initiate a planning process for acquiring appropriate information resources prior to investing enormous amounts of funds on the development of database management systems. Proactive planning by line managers regarding their future information needs should ensure tracking of the required information resources that will drive effective business strategy.

THE PREREQUISITES

Strategic CRM initiatives will require the following:

* User groups in organisations must develop a proactive plan to capture appropriate customer information

* This plan can then be implemented by the systems and technology group, which includes customer tracking and data management issues

* Appropriate data mining activities need to be planned by user groups to support various customer management strategies as required in a timely manner. This activity will be supported by the systems group

* User groups must develop appropriate customer development strategies based on the insights available from the data mining exercise.

The general conclusion reached is that effective usage of customer data for developing business strategies will require more active participation by line managers in the entire CRM development process.

In reality, this remains an illusory goal. In the banking sector in the United States, most CRM-based strategies are still driven by systems and IT functions with a low involvement of the actual user group. One would suspect that the scenario is not very different in other industries.

CRM IN INDIAN RETAIL BANKING

Not surprisingly, India is still at a preliminary stage in the systems development cycle with respect to CRM strategies. This is a boon in more than one way since it provides Indian managers an opportunity to learn and avoid the pitfalls identified in more developed markets. Based on a limited survey of the Indian retail banking sector, we are able to classify organisations based on their CRM initiatives into three categories:

THE TRANSPLANTS: Many foreign banks operating in the Indian market have adopted syndicated CRM processes that have been developed by their principals in more developed western economies. Most bank managers in this group admit that the success rate of their CRM strategies is at best moderate. They also have no significant plans to customise such processes for the Indian market primarily because of perceived low marginal benefit. This is mainly because of the low degree of competition in the Indian market that does not make it viable for foreign banks to invest in customising their CRM processes for India.

THE PERFORMERS: Among "Indian-managed" banks, this group has acquired the so-called first mover advantage of cornering the profitable premium segment of customers. High performance has generated large fund surpluses, which bestows a high degree of confidence among the managers of these banks. Early initiatives at targeting premium customers along with low levels of competition from other banks are the prime reasons for their good performance.

However, managers of these banks have no reason to be complacent. With growing levels of competition, a single segment focus may not remain an effective strategic option in medium term. Micro-marketing strategies driven by consumer insights will determine successful businesses of the future.

Therefore, in order to remain market leaders, they have to equip themselves with appropriate technology to track and analyse customer behaviour. A more challenging task will be to reorient managers to think of customers at the micro-segment level or even at the individual level rather than as a single large entity.

THE FOLLOWERS: Many domestic banks, both private and state-run, fall in this category. Not having had the benefit of the first mover advantage that the "performers" had, they are reconciled to building competitive advantage by developing CRM-type infrastructure in their organisations to target micro segments and provide customised value propositions, which the "performers" have not yet initiated.

From a systems lifecycle perspective, they are ahead of the "performers" on two major dimensions. First, they have realised the need to fine-tune the marketing strategy towards micro-segments or even to the extent of looking at individual customers. Second, most of these banks have started collating the transaction data from all customer "touch points". As pointed out earlier, this exercise in data management is fallacious since it is driven primarily by the systems group in many banks without significant involvement of the line functionaries.

Managers at one such "follower" organisation, a large quasi-nationalised bank, pointed out that the primary responsibility of CRM activities was vested with the technology group of the bank. Data collation from various customer service points was the most significant task that the technology group was currently engaged in. The general perception was that issues related to possible strategic use of data and information were of a secondary nature, since the primary activity was to get the data organised. It was clear that the management had adopted a sequential approach to CRM development.

Significant investments had been made in developing data conduits for transfer and storage of data without much attention being given to the quality and nature of the data being organised. The team leader of the technology group recognised this as a drawback, but he expressed his inability to develop a more collusive CRM initiative with the involvement of the line functionaries.

According to him, some organisational hurdles impeded the formation of constructive collusion among the various functional managers of the bank. First, there was a general lack of awareness regarding the mechanics of executing CRM-based strategies in the organisation, especially among managers who were responsible for customer interfacing. While most recognised the need to customise product offerings to retain customers, the process of gathering and analysing customer data to develop insights for building strategy was perceived to be too technical and beyond their purview.

There was also a political dimension that seemed to have caused obstacles in healthy CRM implementation. Given the heavy reliance on technology for building CRM capabilities, the systems group was the natural choice for championing such initiatives. This may have been perceived by other managers in organisations, albeit erroneously, as the personal turf of a functional group and hence may not have spurred active participation from them.

To date, the impact of CRM-related activities on the bank's performance remains unclear since the development of various processes is still on. However, the top management has definitely concluded that any potential gain would be limited for the reasons cited above.

I suspect that the above reasons could be attributed to the lack of clarity in planning CRM initiatives in many other organisations. This has serious implications since the banking sector in India can ill-afford to spearhead a costly data management initiative for CRM-related activities without proper planning of the potential use of information resources.

FROM TECH TO CUSTOMERS

It is quite evident that technology is not the focus of a CRM-initiative in an organisation. It is also amply clear that technology has a significant role to play in ensuring smooth implementation, but effective strategy development requires a thorough understanding of customer behaviour.

This is possible by tracking customer responses to past and current marketing initiatives, developing insights about key customer groups, being aware of changing trends in customer preferences as observed from their past behaviour, and finally, using this knowledge base to judiciously develop segment-level strategies to enhance business performance.

This requires a more broad-based employment of organisational resources compared to what is presently being deployed. Effective CRM-type initiatives will have to be led by functional areas which have the most interaction with the customer, i.e. the marketing function. Marketing managers, therefore, need to be more technology savvy to employ the current tools for data management and tracking. They must integrate their knowledge about customers with careful collection and analysis of appropriate historical transaction data to evolve better customer-specific product offerings.

The mantra for the long-term viability of CRM initiatives in organisations is obvious:

* Involve line managers in data collection and management initiatives

* Think of customer data as an investment in the future. Evolve a dynamic plan to capture the "right" data over time

* Be customer-focused. CRM software is often one of the many dimensions to achieve customer focus.

Fortunately, commitment to CRM initiatives in India is still fairly insignificant. It is, therefore, important for Indian business organisations to avoid the pitfalls uncovered by the experiences in more developed economies. This would significantly improve returns on investments made in this area.

ABOUT THE AUTHOR

Arindam Banerjee is an associate professor in marketing at the Indian Institute of Management, Ahmedabad. Prior to joining the faculty at IIMA, he was a management consultant associated with the Mitchell Madison Group at their Chicago office. The survey mentioned in this article was conducted with a research grant from the Indian Institute of Management, Ahmedabad.

(This article was published in the July 2002 issue of Indian Management magazine)