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Seven Disastrous Data Don'ts for Marketing and Sales

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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.

2. Unorthodox Flow of Data Through Marketing Technology Ecosystem

Companies can set up their marketing automation and CRM systems in a way that works for them, but which may or may not be optimized for scaling. Then, when they want to ramp up their efforts, the entire process has to be changed for the scaling-up effort because it wasn't set up correctly in the beginning.

Essentially, there is no foresight into the eventual scaling up of the systems, and the initial incorrect/unorthodox setup creates massive problems down the road because of a lack of understanding of how the systems work.

3. Incorrect or Absent Metrics

Your team must have goals. Just make sure that they are within reason.

If you don't know where to start, begin by looking at your existing data and use averages to form a baseline. Then, let's say that you want to increase (or decrease) those metrics 5-10% each quarter. If you make it, brilliant! Take note of what made it successful and then ratchet up the effort.

If you don't reach the goal, then start dissecting the data to see where the error occurred. Once you know, you can make a better plan.

Working toward a goal is much more rewarding than just simply collecting data. It also gives you something concrete to show your CXO team.

4. Inability to Effectively Analyze Your Data

Another big "don't"? The inability to effectively analyze your data. Often, marketers see a spreadsheet with numbers and letters, and aren't sure what they mean and what to do with them.

Categorization and standardization of your data is absolutely key to deriving meaning from it.

5. Wrong People

The wrong set of hands on your data can be catastrophic. You must have a team member capable of looking at all of your systems and understanding the data possibilities in each.

A complete, end-to-end picture is absolutely crucial to getting the most out of your data.

If you don't know how it moves from start to finish, it's unlikely the team will know how to perform analysis, prescribe repairs, or suggest meaningful change that will optimize a workflow.

Diversification is the name of the game, and you should trust your data to people who see the big picture and understand how to make all the parts work together.

6. Wrong Tools

Digital marketers are only as good as the tools they use. There are so many tools out there that you can harness and combine to creatively find ways to gain better insights into your data and business processes.

Having someone who keeps up to date on all of these tools is crucial to the continued success of your data management.

7. Wrong Attitude

Having the wrong attitude toward your data will greatly influence the experience you have with it. Data is like fine art, often fragile and extraordinarily beautiful, but the appreciation of it differs depending on the sets of eyes looking at it.

* * *

As our data-driven world continues to turn, data will play a larger and larger role in marketing operations big and small.

Sales and marketing data holds so much power, but only if it's used correctly. So take note of these seven common data mistakes and how to avoid them, and turn last year's biggest challenge into this year's biggest achievement.

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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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