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Crap Customer Data

Crap Customer Data

 

How much is your crappy customer data costing you?

 

We all know CRM customer data is crap – there’s 15 versions of P&G and 33 flavors of Starcom.  Experian estimates companies lose up to 23% of revenue due to low quality data.  Ok, that sounds high, but what if there’s an easy was 5-10% incremental revenue potential by getting it right?  Seems reasonable. 

 

We on-boarded over 10 media companies to the boostr platform in Q1 and see the same patterns with every customer.  Duplicates galore, wrong or incomplete hierarchies, no clear understanding of advertisers vs. agencies, agency employees linked to brands not agencies, contacts without emails, etc.  Contact us to get a customer data quality assessment.  All of this compounds in volumes by supposedly effective CRM add-ons like Yesware & Cirrus that increase the volume of bad data and brings to mind this great quote from management guru Peter Drucker…

“There is nothing quite so useless as doing with great efficiency something that should not be done at all.”

 

Let’s face it, the data you’re collecting is useless for making decisions and that’s costing you revenue and wasting your employee’s time.  You’re not able to:

  1. Understand which clients have gone inactive or are about to
  2. Where there’s cross-sell, up-sell or within large corporate structures like P&G, Unilever, Wells Fargo, where there’s brand whitespace
  3. Know where your pitching is working or not by advertiser & agency
  4. Get a full picture of the opportunity within agencies – who are not doing business with at Publicis holding company vs. Starcom Chicago.
  5. Prepare for an agency meeting and have credible facts on spend by client, product, etc

 

“If you want something new, you have to stop doing something old.”

Peter Drucker

So how do you go about fixing this?  Start with the right taxonomy to structure your advertiser and agency data – how they’re related, the hierarchies and how the contacts are related to both parties.  There’s a big difference between Starcom Chicago and Starcom New York.  Better to know the best practices than get it wrong again.  Next you need a  platform with a data model that supports the taxonomy and automation to prevent dupes at all the entry points.  Then you’ll need to clean and reformat your data into the new structure.  Once you’ve got it right the last mile is the analytics to light up your data like a christmas tree illuminating the revenue opportunities so you can start taking action to grow sales.  

To learn the best practices and see how media & ad sales companies are solving this problem, unlocking the power of their data contact us to see a demo of boostr.