Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Ensuring quality data from service providers

For those of us that have lived, eaten, and slept with data quality and data management it is hard to fathom that there are still pockets of those that have yet to define a solid foundation of data quality and data management best practices.  It is even harder still to take a step (leap) back into the roots of how data quality and data management issues all began.  Well, let me tell you, those pockets of organizations are alive and well in the most unlikely places – those companies that are providing data.

To be fair, there are some amazing companies out there that provide information and data that we use to improve and enhance our own data or take to analyze independently.  They may not be perfect (no one is!).  Though, they have defined themselves as servicing organizations with “better quality data” and stand by it with best practices of their own.  But, as enterprise organizations and even mid-sized companies have jumped on the band wagon and adopted sophisticated processes, solutions, and people that are dedicated to better information, there are still a significant number of services providers that lack the skills, tools, and practices that would ensure reliable information to measure our performance, understand our market, and take advantage of new opportunities.

At the end of the day, the data and information we source needs to be reliable.  It is important to guard yourself when both contracting with service providers and when you receive data.  Simply relying on the fact that the data is of high quality when you receive it is not good enough.  You need to be vigilant during the sourcing of providers as well as clearly defining how you can ensure what you received is what you paid for.  Here are some things to consider and ask when working with data providers:

  • How do they collect their information?
  • How do they verify that the information is valid?  What process, sources, and analysis is used?
  • Are they providing data to other customers for the same purpose you need the information for?  How many/what portion?
  • What is their repeat business rate?  Who are their top customers?
  • What purposes are their customers using their data?
  • What do they do to verify and validate your data prior to providing it to you?
  • What do they do to verify that the data they are providing is complete?
  • What guarantees do they or will they provide that the data meets your specifications and quality standards?
  • What is required on your end to validate that the data is accurate and reliable?
  • If you are purchasing tracking data (real time/period feeds), what initial and regular testing processes used to verify proper data transfers?
  • What is required on your end to ensure the data transfer is working initially and ongoing?

What have you done to ensure data from service providers is what you want?

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B2B CRM: The Right Contact Mix for Your Customer Relationship

You’ve spent years gathering contacts into your databases.  You’ve implemented a data quality practice that is now starting to give you a solid picture of your universe.  It is now time to classify your contacts.

Invariably, your database is more than just purchasing/decision maker contacts.  All departments have gathered people’s information depending on the purpose.  It offers a window into your business dealings.  It also offers a window on your ability to market and sell.  Just as you consider vehicles, content, and message to deliver to your database, you also think about who you are reaching and who can be converted.

SOA and MDM initiatives are great because they bring together a full picture of interactions with the customer as well as who is part of those interactions.  But, not all contacts are created equal.  Just as not all customers or companies are created equal.  It is the first thing that is considered when determining targeting strategies.  The size of a database is typically determined based on the silo it is intended to help.  Marketing wants decision makers, finance wants accounts payable, customer support wants end users, investor relations wants analysts and media.  By themselves, these data silos serve a purpose.  Together, they can show a picture of where your awareness, message and brand really are.

A good  test once consolidation of data bases is done, or even within your CRM system alone if it receives lists and feeds from other internal sources, is to classify contacts based on their primary interaction with your company.  Everyone in your database has had a reason to connect.  Bringing these reasons into a standardized category will help determine the value they bring to a marketing program, customer relationship, or evangelist role.  Monitoring the ratios of these groups within a cusotmer relationship and firmographic data can give insight into the ability to grow a relationship, if it is at risk, or there is no relationship and the company serves another purpose.

While as marketers we typically look at the entire size of our database to determine if we have enough contacts to convert to leads, if those leads are weighted towards a low number of companies, or they are not the right contacts, then our efforts can be wasted.  With the cost to acquire customers and contacts expensive, having a mechanism to determine when to purchase lists and how much to purchase will refine the amount of resources and budget needed.  In addition, messaging and engagement strategies can be modified to align to the type of relationship outcome you intend.

So, rather than thinking about personas when you need to target, think about them strategically and as an indicator of the strength of relationship with your customer.

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How to Measure the Business Impact of Data Quality

So, you want to invest in data quality but you need to prove ROI before you get the resources. Intuitively you know that data quality is impacting your business. How to measure that to make a case is the test.

Many businesses focus on data elements that are easy to see and understand like company and contact information.  However, as obvious as some of these elements may be, they don’t always lead to the highest bang for the buck.  Data elements have priority levels within processes depending on the desired business outcome.  In addition, data elements have dependencies outside of how the information comes into the system.  You need to take this into account as you conduct your business analysis and map your data across your business processes.

During business analysis it pays to establish a foundation that validates recommendations and shows ROI through case studies.  You can do this through data analysis and pilot programs.  Data analysis can be applied through meta data segmentation within processes where you look at the existing state of the data.  You can also improve portions of the data and perform the segmentation and analysis.

These steps will prepare your case but will also help establish dashboards to allocate resources for future projects.

1) Identify the processes you think are most impacted by poor data quality . The processes should be tied into key business functions. For instance, in marketing you may want to look at lead qualification and management. Processes that are well defined and have a tangible link to businesses objectives work best as they are most likely mature and revenue has been tied to them.
2) Pinpoint smoking guns in the processes.  There are bound to be several points in a process that are key indicators of success where data quality has negatively impacted the outcome.  Your business analysis will or should show this clearly.  These smoking guns should be called out clearly in the processes.  What you should determine is which data elements are impacting the most and can be easily focused on or addressed.
3) Select data quality issues that you can segment the process into influence tracks.  This step is critical to measurement.  You need to dissect the process to create scenarios of what good vs. bad looks like in process outcomes.  In the lead management process suggested earlier, it could be the point where you would qualify a lead to move into the sales pipeline.  
4) Measure performance success with good quality vs. poor quality data.  At this stage you should be able to run an analysis that shows the difference in process outcomes and performance when you run scenarios between good quality data and poor quality data.  

The real benefit is that at this stage you’ve provided the dashboard to measure improvements to the business.  Rather than wait until the data quality projects are completed, this provides the foundation for predicting where you will get the most impact from your investments. Instead of focusing solely on metrics that measure the completeness, accuracy, and uniqueness of records, you can focus on how these metrics within processes influence business outcomes.  Now you have a case for linking data quality with ROI.

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Starting Your Business: Data From the Ground Up

data managementIt is easy when starting up a business to think about selling first, marketing and database management later.  Afterall, revenue is the most important thing to focus on.  Though, once you get over the hump and begin to groove, you realize that data is important.  Now you have to sort through it and it feels worse than diving into list of 300 emails in your daily inbox.  Well, if you have a method to deal with your email inbox, create one for managing customer and contact data.

Here are some simple things you can do up front to stay organized and be better prepared when you are ready to look at and manage your customers and the business in depth.

  • Be consistent about how you collect customer data – There are usually several layers to the importance of customer information elements depending on your relationship.  What you want to do is determine the information that is most critical and collect this consistently across all methods.  Keep in mind that what is mandatory to transaction may be different from what you need to follow-up with customers after a purchase.  So, make sure that you take this into account at the point in time you collect the information.  It is harder and more costly to collect after the fact.
  • Save data elements into dedicated fields – The biggest issue I find with new businesses and small businesses when they need to convert to more robust systems is that data elements are merged together into a single Excel cell.  When collecting contact names, break apart the first and last name into separate fields.   Do the same for addresses having fields for street address, city, state, country, and postal code.
  • Determine what platform has the Master data – The second biggest issue when migrating customers to a robust system is the inability to determine which record is the most valid of duplicate entries.  If you are saving contact and company information between your mobile phone, laptop, website, and company server, which will you consider the single source of record?  Once you determine this, make sure you sync your lists to that source.  I recommend you do this weekly at the least and use your primary server.  Then, include the database in a weekly back-up process.
  • Save, Save, Save – You may have caught this recommendation in the previous bullet.  Backing up is critical.  It is mandatory.  I’ve watch small businesses loose business critical information because they didn’t back up or back up often enough.  There are easy services today that make backing up our information simple.  At the very least, invest in a USB storage device and plug into daily when you sit down and get to work.  Before you do anything, back up.  Make it a habit.

Managing your customer and company information does not have to be difficult or cumbersome.  With a little forethought, when you business gets off the ground and you are ready to invest in better platforms and reporting, you will have a great foundation to do so.

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