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Seven Disastrous Data Don'ts for Marketing and Sales

by George Verey  |  
April 14, 2016
  |  3,123 views

One of the biggest challenges Marketing Operations faced in 2015 was Big Data—where it comes from, what it means, who takes care of it, and what to actually do with it.

But once you figure out what to do with the influx of data, it's just as important to understand what not to do.

Here are seven disastrous data don'ts for Marketing and Sales.

1. Wrong Data

Adding big bad data to your marketing automation, email or CRM tool is one of the most disastrous things you can do. It's hard enough keeping these systems running smoothly, but if they get clogged with bad data... the struggle is greatly amplified.


Among the actions you can take to avoid this problem:

  • Get data from reputable sources.
  • Make sure to give the data a proper cleansing.
  • Make sure the data is matched up to CRM values perfectly.
  • Make sure the data you're importing is worth importing (ask, Is this data that sales actually wants?).

You've got to keep in mind the quality-versus-quantity debate. At some point, the "x number of leads gets me y in revenue, so doubling the input of leads should double my revenue" way of thinking will begin to fail.

Take a deep look into your data to find out what you are really trying to get from it; you may just discover some new insights.


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George Verey is the marketing operations manager for marketing automation provider Act-On Software, He turns vast amounts of data into digestible and insightful narratives to enable his executive team to make strategic decisions.

LinkedIn: George Verey

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  • by Ann Feeney Thu Apr 14, 2016 via web

    Great points!

    I'd also add:

    8. Balance between data and human judgment/knowledge about context is key. Classic example: If you look at the number of Elvis impersonators over the years and run projections, by 2030, one in three humans will be an Elvis impersonator. At the same time, be sure that you aren't letting biases or emotions tell you that only the data points that confirm your judgment are valid.

    9. Make sure everybody involved is data-literate. The teams looking at the data should all understand the concepts of sampling bias; when to use medians, modes, percentiles, and means; correcting financial data against inflation; what p values do and do not demonstrate, and so on. They also need to know when rules of thumb are and aren't adequate. For many analyses, overlap in margins of error for two data points is a good enough indicator that a difference isn't significant, but from a pure statistics perspective and under some circumstances, it's not good enough.

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