WordCustomRefiner2#cdfade37-5562-4d72-84f0-aa62bd64e13c

Your CRM Is Only as Good as Your Data

Last Modified 05 February 2014
Top Solution

Download

Why do CRM rollouts so often fail to meet expectations? Sometimes it's the result of organisational issues, outdated business processes or the CRM application itself. But one of the most pernicious problems is the quality of the data underlying the initiative.

Sign In Popup
Lock Icon

The content of this page is locked.

To access this information log in to CustomerSource.

Unable to log in to CustomerSource?

Visit our CustomerSource Help Page.

Close

Lock Icon

The content of this page is locked.

To access this information log in to CustomerSource.

Unable to log in to CustomerSource?

Visit our CustomerSource Help Page.

Lock Icon

Your current service plan does not allow access to this information.

To learn more about Microsoft Dynamics Service Plans review our
service plan offerings.

Related Links

I Want My QVC

Creating a Positive Customer Experience: Dos and Don’ts

Your CRM Is Only as Good as Your Data

Web Seminar: The CRM ROI Blueprint

CRM Success Story: KeyArena Fills Seats with Help from CRM Technology

By Bill Hobbib for ComputerWorld

Data problems are like sand in the gears. They don't stop you in your tracks, but they can slow you down and erode the integrity of the endeavour until it all falls apart and you need to start over. Simply put, your CRM can be only as good as your data and it's critical to ensure that data is available and reliable.

 

Consider this example: You're a vendor of electronic components and you've sold your products into multiple divisions of a $40 billion manufacturer but have only 20 customer records for the company -- all seemingly in one division and half of which are duplicates. Some records show the same name but different addresses and even worse, you have no context for the relationship history with contacts in the other organisations using your products. This disjointed view will tarnish your professional image, cripple up-sell/cross-sell opportunities, and threaten the entire customer relationship. In fact, there are myriad ways bad data can make you look foolish.

 

For example, you're a financial services company with millions of customers. You want to target high-net-worth clients across all investments, such as equities, cash, CDs, household accounts and lines of credit. But when the data has come together from different systems, many e-mail addresses or postcodes are missing and 20% of the fields in the CRM system now have conflicting or missing data. How do you execute an effective direct-marketing campaign when you are hitting some customers five times and missing others entirely?

 

Or, if Jack Jones phones in and your customer service representative pulls up three records for him, how can the rep determine who's who and give Jack the service he deserves as a returning customer and possibly even sell him something new? Does the rep know how profitable the customer on the phone is, how he has responded to up-sell offers in the past or whether your current promotion brings him value?

 

Is your call centre trying to sell your best customers products they already have? With customer information becoming obsolete at a rate of 2% per month (25% per year), are you spending $500,000 or $1 million annually sending mailings to addresses long since abandoned? Do your client-facing applications help you determine how many unique customers you really have and which 10% are the most profitable? What is the true lifetime value of a customer across all purchases and what is the cost to your company if the 5% of your best customers is misunderstood?

 

Bottom line: Fixing data problems can go a long way toward recouping your CRM investment and saving your customer relationships. Here are 10 ways to shore up your data and make you the kind of CRM statistic you want to be: the hero who made CRM pay off quickly.

 

  1. Know before you go. Before connecting pipes haphazardly and flooding your new CRM applications with raw data, profile all source systems to capture data content, quality and dependencies. Tools can automate this tedious, error-prone process and they cut as much as 90% of the time off manual profiling in the initial deployment. Plus, detecting and correcting problems early dramatically reduces the costs and risks of failure down the road.
  2. Build in data quality. Standardise all data (such as customer names, addresses, customer identification and product information) to ensure consistency and match records with statistical logic to eliminate duplicates for a 360-degree view of all customer activity. Appropriate procedures and processes around data quality must apply to batch data cleansing and to all new data being input regularly through Web transactions and other mechanisms.
  3. Take something old, something new. Anticipate marrying old data, such as customers' transaction histories, with real-time information, such as order status, to better determine who is most likely to buy which product, respond favourably to an offer, cancel a service or churn.
  4. Share common metadata. With data from separate sources often having conflicting meanings, it's critical to share metadata across end-to-end integration processes to have one enterprise data "dictionary" for all users, such as one meaning and context for customer information such as definitions of "customer", "revenue" and "profit."
  5. Abandon hand coding. Tools exist to automatically extract data from disparate sources, repurpose and transform it into the required form and load it into the proper target application. Coding all of these steps manually wastes time, money, morale and the opportunity to focus limited resources on something more valuable. Moreover, hand coding limits your ability to quickly respond to changes in the business.
  6. Architect for "right-time." Use a data integration engine with one set of business rules for both bulk and real-time processing, so that all customer data is managed consistently, regardless of channel or source.
  7. Implement a highly scalable foundation. If necessary, use parallel processing to accommodate growing data volumes and allow integration tasks to scale without requiring costly rewrites.
  8. Ensure interoperability. Since a number of tools are required in order to integrate and homogenise data from disparate sources, teams should avoid the additional burden of integrating the integration tools. Rather, give consideration to suites that offer data quality, profiling, integration, metadata management and other critical functions on one interoperable platform, so you can integrate the enterprise.
  9. Embrace open standards. With enterprises needing to do more with less when integrating best-of-breed applications with legacy systems, use of Web services, Java 2 Enterprise Edition and other open standards makes it much faster and easier to connect multiple data sources with CRM systems and to extend data integration capabilities throughout the enterprise.
  10. "Futureproof" your investment. Bringing together data and standardising and repurposing it is an ongoing process and your CRM needs tomorrow will be vastly different from today's. Put appropriate customer data management programs in place to maintain the integrity of data over time, taking into account inevitable growth in the variety and volume of online connections with customers and partners. That way, you can transform your data into a driver of profits, not an inhibitor.

Bill Hobbib is executive director of industry/solutions marketing at Ascential Software Corp., a leading enterprise data integration company in Westboro, Mass.  

https://mbsauthor.partners.extranet.microsoft.com/sites/CS/uk/News/Pages/022404_CRM.aspx
No
Migrate