Poor data quality can impact the reach and reliability of your digital marketing campaigns. This can lead to bad product recommendations, less effective new customer acquisition strategies, and decreased customer retention and engagement.

Malformed data records will be replicated across systems, compounding the negative effects. Backend systems responsible for running AI algorithms will be compromised and show diminished confidence. In some cases, they will be unable to return meaningful insights.

Looking at the Data Workflow and Life CycleMost data problems stem from the source: data entry errors, typos, spelling errors and, for one merchant, I recall seeing intentional jabs aimed at customers ... “This lady thinks she’s all that” and “Green is not your color.” NOTE: Those were supposed to be email addresses entered at the time of checkout but apparently, the cashiers weren’t all that impressed. Shame on them.

Systems and services can have bad moments as well, and there goes the data pipe from your corporate network to your CRM. These gaps in time could remain unnoticed and are often discovered unexpectedly. Or - worse yet - it turns out to be the root of that nagging data discrepancy for which your boss just lambasted you. Thankfully, there are ways to monitor, shed light on and deal with data quality issues.

Introducing Listrak’s Data Quality Progra

Don’t fear! We understand the responsibility and inconvenience involved in managing and maintaining a healthy data enterprise. The Listrak CRM offers merchants a dashboard to help diagnose common data quality issues, notify administrators, and proactively repair some issues.
Here’s a sample of the offering:

• An order total outlier plot makes it easy to spot and eliminate that invalid $1,000,000.00 order;
• A plot showing the daily number of orders and any obvious visible gaps in data or reduced volumes hinting at an underlying system issue;
• A listing of suspect duplicate orders that could throw off customer metrics or overall order volumes totals if not dealt with swiftly by the local authorities;
• A breakdown of how many products are missing categories, brands or other important data points (this decreases the potency of classification algorithms and leads to bland customer taste graphs); and
• A listing of products with missing image and link URLs. This is not very convenient for your email campaigns and could eliminate that product you should be recommending.

Most of these issues require correcting the problem at the source. Sometimes the data you have on hand is old, harmless reference data that isn’t relevant to your current operation, but having regular data quality checks will yield an occasional surprise.