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Is it time to jet wash your CRM data?

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Is it time to jet wash your CRM data?

Monday 9th May 2016

As long as there have been databases there have been issues about data quality. The old saying ‘Rubbish in, Rubbish out’ applies here.   Think, for a moment, what this means in real terms.  Poor data quality can be more than just a thorn in your side, it can hinder business success, risk your corporate reputation and cost you a great deal of money.

What is bad data?

 

Duplicates – having the same person/organisation more than once in the database.

Missing information – this means gaps in core data – for example, missing postcodes or industry sectors.

Inaccurate or old information – incorrect addresses or people that have left the organisation.

What impact does this have?

 

You will be emailing to bad addresses (bounce backs and potential risk of spam flags)

Incorrect personalisation (addressing a mailing to Dear <> or even the wrong name entirely!

If you’re not communicating effectively with your customers, ultimately they will become disgruntled and move elsewhere.

You may create sales conflicts within your organisation if it’s not clear who is dealing with which lead.

You won’t be able to track marketing spend effectively if you’re not sure where lead sources are originating from.

You will be spending cash unnecessarily to market to duplicated records.

 

How do I clean up my database?

 

Analyse the existing data.

The devil is in the detail… exactly how dirty is your database?

You need to run queries or reports to assess the state of the enemy.  These can look for duplicate information, missing information and permutations and combinations of the same data.

One of the biggest bug bears is usually a duplicate problem. The queries that are employed should use standard matching that allows you to test on any field or combination of fields, and also combine match criteria with OR conditions.  Next, you’ll want to consider Fuzzy Matching – this lets you handle dirty data, where names may be misspelt for example, and to review possible matches before committing to changes.

Starting the big clean

Unfortunately, there’s no ‘one size’ fits all solution to data cleansing. Bad data comes in many guises and so does cleaning it. 

Address cleaning can be automated using an integration with an online postcode addressing solution. Prior Analytics have developed a tool which links with PCA Predict to clean up existing addresses. What’s more, if you then continue to get your data correct at the time of capture you will reduce the need for ongoing cleansing.

A lot of bad data comes down to human error. If an organization doesn’t have any sort of standards or policies to articulate how the data is entered into their CRM, different iterations of your data will exist within CRM. The solution is data standardisation which creates order out of chaos in your CRM.  For example, how do you address someone when you mail them? Do you write, “Dear Bob” or “Dear Mr Jones”?   Is the “Dear” field completed consistently?  Do you use a lookup to enter in the town to avoid misspelt entries?  Translating “bonkers data” into a standard list gives you the ability to take actions that otherwise would be impossible to do properly. Think about how you’d target a mailing to a specific industry sector if you have seven permutations and combinations of the same industry for example.  Once you’ve set standards then data can be globally replaced to match agreed criteria. Moving forwards, lookups and validations can be set on fields to ensure that data is correctly entered and remains clean.  It is vital to routinely check your data validity against a set of validation rules and highlight or correct any anomalies.  Generally, this sort of clean can be automated by an administrator with global replace tools.

How do I create a data plan to protect my data?

Without a protection strategy, your data will continuously be at risk. People move companies, companies move offices and organisations grow or cease to exist.

Start by listing all the data fields (names/headings/descriptions) that you collect in CRM.  Check the entries that you’d expect to be mandatory and create a data dictionary of standards that are allowed.

Define all your field options.  Which fields do you really need? Which are redundant?

Merge multiple similar fields into one where you can.

Now, you need to enforce your new standards. Make data entry as simple and fast as possible. No one really likes data entry!  Look for any tools or automated routines that will help you to avoid manual or repetitive entry.

Protecting your data includes a strategy to prevent or identify duplicate records (your CRM should be set to match on entry and warn users of possible duplicates before they are entered).  Ongoing standardisation can be enforced by simple sets of agreed standards with validation constraints to simply not allow blank or erroneous entries. Try to use automation solutions if you can – for example, Postcode addressing software integrated with CRM.

 

 

   

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