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3Q Digital’s monthly Decision Sciences office hours series last month covered the impact of poor and incomplete data collection. Here are the main takeaways; for the full recording, click here.

Data quality is becoming increasingly important and should be a top initiative at every company. Poor and incomplete data collection can lead to a loss of revenue, wasted media dollars, and inaccurate decision making. A lack of quality data causes inability to accurately assess performance, sales, and the converting customer. A good way to think about it is garbage in, garbage out; poor data input leads to poor decision making and has a direct impact on performance.

bad foundation

Bad data is like cracks in a foundation; building on it is beyond risky.

As the consumer journey becomes increasingly more complex, there are more ways than ever for a consumer to be exposed to the brand.  If you aren’t able to accurately track which channels or campaigns are driving conversions, you lose visibility into optimization insights, potential revenue sources, and the ability to properly assess why conversions are or are not occurring. For instance, missing data may lead to undervaluing certain channels that may be actually be driving more conversions at a lower cost. However, without insight into the entire data set, you may shift budget into underperforming or more expensive channels, which limits sales potential and increases media spend.

By not being able accurately assess performance, you risk limiting brand growth. Without a full data set it becomes impossible to measure the relationships between channels and better understand conversions trends to find growth opportunities. It also becomes impossible to know if the media investment was worthwhile.

To recap, some implications of poor and incomplete data collection are:

  1. Inaccurate data outputs, which prohibit or contaminate data-driven decisions
  2. Wasted media dollars
  3. Distorted campaign success
  4. Lack of visibility into the consumer
  5. Lack of brand growth
  6. Impact on revenue, sales and profitability
  7. Increase in operational costs
  8. Operational inefficiencies