The core element of any business application is data. Better data produces better results, and this is particularly relevant when it comes to critical functions like customer relationship management (CRM).
What Is a CRM Database?
While it is still vitally important to build a CRM platform around the features and functionality of the software, the true challenge will be to construct a CRM database that can deliver customer information in the proper context and in an acceptable format.
But exactly how is the enterprise supposed to accomplish this feat? What sort of criteria should be used to convert raw data, much of it likely to be unstructured, into useful knowledge?
Components of CRM
One of the best ways to approach this challenge is to determine the key components of CRM so as to reverse-engineer the database from that perspective. Individual implementations will vary, of course, but in general, the CRM database should be optimized to support the following functions:
- Salesforce Automation
- Human Resource Management
- Lead Management
- Customer Service/Case Management
- Workflow Automation
- Business Reporting
Clearly, each and every one of these functions requires data that is up-to-date and accurate (name, address, etc.). Beyond that, however, a lead management process, for example, will also require purchasing and browsing history, certain financial information, broader market trends and other data. For business reporting, purchasing data must be combined with data related to supply chains, inventory, related overhead costs and the like.
Types of CRM Databases
One key issue that many enterprises keep running into when it comes to the CRM database is that it is often separate from the databases used by finance, marketing and other departments. This leads to a wide range of problems given that everyone is working off a different version of the truth, all of which must be combined in order for top-level management to make informed decisions regarding core business functions.
This is leading many organizations to integrate these disparate databases, which not only improves workflow but also tends to streamline infrastructure. But while this does solve the problem of disjointed data sets, it also tends to blur the lines between business functions so that no one is really sure where CRM ends and, say, business intelligence begins. In an interview with ITBE earlier this year, Israel Greene of subscription services firm cleverbridge noted that integrating all of these back-office databases is often the most challenging aspect of CRM deployment.
Ways to Utilize a CRM Database
But once this master database is established, how should the enterprise leverage it for CRM? Many of the key functions and benefits of CRM, in fact, depend heavily on properly structured and conditioned data. This would include:
Deep, granular data on individual buyers can help guide a highly personalized sales approach that comes across as warm and friendly, not a hard sell. This data could include the buyer’s likes and interests, spending levels, goals and objectives.
Identify Most Valuable Customers
Past purchases alone do not determine value. Proper data management can also reveal successful outcomes, repair and service histories and the individual’s outlook on the brand. And this data can always be used to turn low-value customers into high-value ones.
Related products, and sometimes seemingly unrelated products, move more quickly when the proper data is used to connect them. But this requires strong coordination between a wide variety of data sets, including trends, supply chains, logistics, etc.
Brand loyalty is one of the top priorities of CRM. Using customer feedback data, social media monitoring, and a host of other sets, organizations gain a deep understanding of how faithful their customers are, and how to maintain high levels of satisfaction.
The biggest challenge with the CRM database, however, is management. With the pace of business being what it is these days, manual data input and manipulation is becoming a thing of the past. Even cutting-edge automation systems are giving way to platforms empowered by artificial intelligence and machine learning that are taking over many of the ingesting, conditioning and analytics functions currently performed by highly paid data scientists.
This is a net positive for everyone involved, however, in that the data experts can now focus on higher-level strategic and operational tasks, as opposed to the rote, repetitive functions of database management, while access to highly complex data analytics tools is being federated across the non-technical side of the workforce. Going forward, any salesperson or marketing professional who requires access to highly specialized data can get it from their own laptop or cell phone.
For CRM and a host of other business functions, the technology may be becoming more complex, but the processes are getting simpler and the results are getting better.
Arthur Cole writes about infrastructure for IT Business Edge. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly. His contributions have appeared in Communications Today and Enterprise Networking Planet and as web content for numerous high-tech clients like TwinStrata and Carpathia. Follow Art on Twitter @acole602.