With Capital Markets firms facing a rapidly changing regulatory and competitive landscape, enlightened CRM systems developers are working on more sophisticated client engagement tools to help sell-side firms compete in what promises to be an even more demanding business environment.

These ‘next generation’ tools will enable banks, institutional brokers and research providers to manage and target their services more productively, expand the scope for building competitive differentiation, and ensure that service provision is consistently measured and recorded - both for regulatory compliance and to justify compensation.

Many sell-side firms already capture huge amounts of client data and put it to good use, but with the market disruption promised by research unbundling they will require significantly higher levels of data access and analysis in order to keep ahead in the race for actionable intelligence and superior business insights.

Answers

Advanced CRM analytics will give managers access to a new raft of business intelligence, with answers to key cross-reference questions such as: 

  • Which clients produce the most revenue and what level of service does it take to produce this revenue?
  • Which are our most profitable research sectors and analysts?
  • Where are the gaps in our coverage: where might we be losing votes and revenue?


And trend analysis will enable systems to reveal the answers to such questions as: 

  • Which clients are increasing their revenue over time and how is our service level changing?
  • Which sectors are growing and declining in terms of research consumption and associated revenue?


Alerts and Notifications

With systems capable of delivering the answers to these types of questions it will be possible to continuously analyse the data, and alert management according to pre-determined alerts e.g. 

  • Warn if a client’s service level falls below a certain measure over a four week period.
  • Warn if a key client’s revenue level falls below a certain measure over a three month period.


It will also be possible to build systems that proactively search out, and flag, notable events or data patterns whether or not a rules engine has been configured to do so. 

Analysts interactions and research distribution

This facility will be particularly applicable to research distribution, where targeting effectiveness is largely dependent upon individual sales persons’ assessments of an asset manager’s interests at a point in time. A research distribution engine could facilitate a much richer and more effectively targeted model than the current approach as it will be possible to set up profiles based on what people are actually reading.

Advanced analytics will provide a better picture of who should be targeted, based on evidence of their actual interests. Systems will read the content of the research that’s been produced and categorise it, then analyse individuals downloads to determine whether/how that interest changes over time.

Analysts coverage reports will provide a comprehensive picture of analysts’ interactions with their counterparts on the buy-side so that managers can determine who should be ‘marking’ who i.e. talking on a regular basis. They will provide detail of interactions between specific individuals and the means to see whether that person at that institution is reading the research published by this analyst. Over the year, quarter by quarter, the system will provide a picture of whether the firm is actually having all the interactions it should. 

This more dependable, actionable intelligence will power a virtuous circle i.e. outputs such as account planning and coverage management information that can be input to the CRM - with the option of manual review - so that the CRM can continuously ‘reconfigure itself’ accordingly. 

There will also be scope to generate valuable benchmarking intelligence (along the lines operated by McLagan), using participating clients’ data to anonymously benchmark various indices relating to client service and research consumption levels.