Today’s marketers are becoming technically savvier. They understand the need to improve customer experiences or implement digital marketing strategies to engage consumers across channels. Customer retention and acquisition, Big Data, social media marketing, and content marketing are just a few of the goals and strategies in today’s marketing toolbox.
However, perhaps not so widely discussed are some important fundamentals – high quality marketing data.
As marketers we understand that without data, there are no insights. But managing the quality of the data and applying analytics are key to successfully implementing all these other great marketing goals.
When data goes bad, even the best laid out strategies are doomed to fail. After all, “Garbage In, Garbage Out”, right?
So let’s take a step back from the world of Big Data, Digital Marketing, and Customer Experience Management to focus on the basics – the data.
Let’s first check out some of these great stats by Halo Business Intelligence:
The implementation of a data quality initiative can lead to reductions of:
And increases of:
Unfortunately, many organizations take a reactive approach to data management, only taking action when something negative occurs. For example, multiple messages sent to the wrong person damages customer relationships, revealing a need to improve customer information.
However, choosing NOT to fix dirty data can be extremely damaging. Consider these data quality horror stories:
While these examples are extreme, the negative impacts of dirty data on your business are very real.
As part of any new data initiative, a business needs analysis should be performed to understand what is required of data moving forward. A business needs analysis focuses on understanding business objectives, strategic goals and business drivers.
For example, what information is required to meet these objectives and how accessible is it to end users? Are data gaps occurring, limiting the availability of required information to support decision-making? What data issues may be impacting revenue, increasing costs, or causing inefficiencies in operations?
Documenting business objectives helps determine what data should be captured, how the data is related, and how data should be structured to create value.
Once a data management project is approved, data must be properly cleansed and integrated to ensure information is of the highest quality to drive smarter decision-making across departments.
Begin by evaluating the quality of your data with a data assessment. Many vendors offer a complimentary assessment to help identify areas where data quality can be improved, what types of data may be missing, and other problems that may be affecting optimal data performance.
Data must also be integrated and placed into a central repository for a complete, 360-degree view of the customer or other business area. Data quality software automates integration processes and improves data quality by performing the following functions:
Business processes should also be established to ensure data manually entered into systems is of the highest quality possible. As we learned previously in our example of the pregnant men, many organizations experience data errors when information is manually entered, at a rate of 2% and 8%. Even one wrong number entered incorrectly can cause a payment to fail, a wrong part number to be shipped, or apparently a man to become pregnant.
Data validation controls can be integrated into on-line forms, using rules to check the validity of data sets. For example, an on-line website form may require a visitor to enter data in specified formats. Or an IRS form may utilize controls to check that positive numbers are being entered into fields. Training employees to be more aware of the importance of data quality is also a crucial step to achieve a company-wide awareness of maintaining high quality information.
With an integrated and clean database in place, customer analytics can be applied to target customers with the most relevant offers. Begin by creating customer segments. Customer segmentation refers to dividing customers into groups who share similar characteristics, such as age, gender, lifestyle, and so on.
Any number of segments can be created as long as each segment is:
Large Enough to be Profitable: The segment should not be too narrow, making it cost-prohibitive to reach these customers. The segment should be worthy of the marketing efforts.
Accessible: The consumers must be able to be reached through channels already established, such as a website or store, or by new channels that can easily be created.
When creating customer segments, a company must consider a wide range of customer characteristics, such as:
Based on any combination of these characteristics, companies can develop key customer segments and develop marketing strategies designed to generate the most profit from each unique customer group. A company may want to enhance loyalty, increase customer value, or provide products and services to a particular geographic area.
Consider the following examples:
In the following example, a regional retailer has identified three segments
and the marketing strategy for each.
Similar to segmentation, predictive modeling allows marketers to develop very precise, targeted campaigns. Both techniques examine the characteristics of customers and prospects, however modeling takes this one step further by also predicting future behaviors. Modeling is the practice of forecasting consumer behaviors and assigning a score based on the likelihood of completing a desired action, such as purchasing a product. For example, which customers are most likely to spend the most across a 6-month cycle?
Check out the following examples of how a predictive model may be used:
Data quality and business intelligence are critical for success in today’s economy. More companies are increasingly investing in data management and business intelligence solutions to maintain high quality data and target multi-channel consumers. And with better data insights, marketers are better able to focus on today’s data-driven, technically-savvy marketing strategies.