I have seen that an effective approach to this question is to attack the problem through a Data ROI Financial Simulation Analysis. The basic process begins by creating a comprehensive list of business needs, data sources, and marketing applications.
- Identify your critical models and marketing programs, and the specific data assets that feed them. These core data assets are your baseline sources.
- Before moving on, create a baseline performance level of the impact they have on your business can be estimated using the model build lift charts and quarterly campaign results.
Next, define the metrics by which performance improvement is to be measured, and the potential thresholds to be used to evaluate each data source that will be incremental to the core assets. For each additional data source, consider these criteria in this stage:
- The supplier’s data compilation process and standards, accuracy, coverage, and variability.
- Any derived data opportunities – potential constructs that incorporate multiple data elements within new data sources and with core data assets.
- Linear relationships within data and unique relationships with predictive potential.
Then put the incremental data through its paces within your modeling environment.
- Select data extracts and load data into test bed – and be sure to include random samples and modeling samples to avoid sample bias.
- Assess predictive and insight capability by measuring additional lift and marketing insights attributable to test data
Finally, monetize these insights to estimate the incremental benefit from the data source:
- Estimate net present value of investment in additional data through its impact on lift charts and previous campaign performance to determine its incremental ROI as part of the marketing data suite.
- Simulate a range of assumptions on marketing usage, targeting objectives and financial scenarios to clarify ‘sweet spots’ of added value (and conversely, when the data stops paying for itself).
This approach allows sophisticated marketers to consider the incremental value of each data source and make informed decisions on where redundant/low value data creates inefficiency in the marketing process, while freeing up data investment dollars for higher value or emerging sources.
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I recently had a conversation with an experienced marketer in a major corporation who described their challenge with marketing data as an “embarrassment of riches” issue. Their company, having the ability to acquire multiple external data sets to meet short term needs, finds itself with too many options and now would like to rationalize its data purchases to increase efficiency.
The issue at hand here is this: How do you determine which data assets are worth their investment?
I have seen....