Data is at the heart of every marketing organization. Not sure how an initiative performed? Check the data. Looking for clarity on when and where to launch a new campaign? The data can guide you. Eager to prove real return on investment to leadership? The data will deliver.
Despite data's very meaningful impact on revenue generation, disagreement remains around the best way to enrich data. Is more data better? Or should marketers focus only on the right data? And what is the "right" data in the first place? In this post, we will define data enrichment and detail what success looks like when appending, enriching, or augmenting data sets.
What is Data Enrichment?
Data sets are generally incomplete. Privacy initiatives make it (rightfully so) impossible to understand everything about individuals - at an individual level. Data enrichment is the process of filling in holes with supplemental data so that the dataset is more accurate and complete.
There are two main ways to enrich data: manually and through a third-party data.
Manual enrichment is done by analyzing data and looking for patterns. Once patterns are identified, more data can be added to the dataset to fill in the gaps. This process can be time-consuming, but it allows for more control over the data that is being enriched.
Data from public records is most common for manual data enrichment. From social media profiles to company websites, sources of data for manual enrichment are plentiful. The downside of manual data enrichment? The time it takes to do it properly and the chance of human error when dealing with data at scale.
Third-party data identifies patterns in data and then adds supplemental data to fill in the gaps. This data can be lifestyle, behavioral, intent, or purchase data. This approach is less time-consuming than manual enrichment and offers a wide variety of applications and insights to be pulled and utilized across marketing. Using third-party data can be a great way to quickly append or enrich data.
Success with Data Enrichment
There are a few general goals that are commonly pursued when enriching data.
This goal is pretty self-explanatory. The purpose of data enrichment is to make the dataset more accurate by filling in the gaps with supplemental data. All forms of data enrichment have a margin of error when it comes to accuracy. The goal of marketers is the most accurate dataset possible even though most realize that 100% accuracy is impossible.
As mentioned above, most first party data is demographic or purchase based. Third party enrichment can append lifestyle, behavioral, or other intent based data to provide a better understanding of your customer base for creative, targeting, or media purposes. A successful enrichment initiative not only fills in the gaps in a data set, it also uncovers new data worth considering.
When datasets are enriched, it can be easier to identify patterns and trends that were not visible before. This can lead to improved insights into customer behavior, marketing strategies, and more. Insights around buying patterns, how long customers stay on a webpage, or some similar data point can be instructive.
Is it your mandate to get the most value out of every data point? Of course not. It is important to recognize that the more accurate, the more complete, and the more insightful your data sets, the more options you have for the future.
Demystifying Data Enrichment
Data enrichment gets complicated or confusing when marketers tie themselves in knots looking for insights or discoveries that are not there. Data is a tool, and like most tools, it comes with limitations in its utility. Some marketers get frustrated when data does not clearly surface the answers to their most pressing questions. Instead of such aggravation, those marketers might be well-served to ask what the data DOES show and what actionable insights can be gleaned from the data that is available. Asking how enrichment is moving knowledge and wisdom forward instead of lamenting what enriched data sets do not do is productive.
More to the point, marketers would be wise to ask themselves what is complicated, what is simple and straightforward, and what is realistic when thinking about enriching data.
What is Complicated?
Data enrichment can be a complicated process, particularly when it comes to manually enriching data. There are a lot of factors to consider when looking for patterns in data and adding supplemental data to fill in the gaps. It takes time and effort to do this correctly, and there is always the risk of making the dataset less accurate instead of more accurate.
Are you enriching the right data? Is the data accurate? Are you creating a complete data set? The best way to make enrichment less complicated is to detail clear success metrics and test your enrichment against a small subset of data before you being enriching a full data set.
What is Simple?
Third-party data automatically identifies patterns in data and adds supplemental data to fill in the gaps and provide a simplified way of enriching data. This approach is less time-consuming than manual enrichment and does not require as much expertise.
Using data sources like Affinity Enrich makes planning for data enrichment and completing the work simple.
What is Realistic?
When it comes to data enrichment, it is important to set realistic goals. Not every business can achieve the same level of success with data enrichment. The goal should be to improve accuracy, completeness, and insights as much as possible without making the dataset less accurate.
Solving for what is realistic is sometimes counter to what is ideal. However, with data changing so frequently and found in so many disparate places, its is important to recognize that "perfect" is the enemy of "good enough." Being realistic means that your data can be enriched in meaningful ways even if it is not perfect.
How to View Enriched Data
When data has been enriched, it is important to view it in a positive light. The glass-half-full approach means making the most of the data that has been enriched. Viewing the data in this way allows for improved insights and decision-making.
The glass-half-empty approach, on the other hand, means only seeing the gaps in the data set. This can lead to missed opportunities and poor decision-making. Perhaps the worst possible outcome when enriching data is failure to recognize the immense value that comes from appending even a small amount of data to existing records. Every insight about customers and cohorts can help shape a decision or show ROI.
Enriching allows for marketers to make use of their existing data sets to increase insights and trends discovered. It also makes it possible to combine two disparate but related data sets (e.g., CRM prospect email lists with social media behaviors) which can provide powerful insights into future behavior or buying patterns. With that said, it's important to remember that without cleansing the resulting enriched dataset, retailers risk running into issues with generating accurate reports -- making it difficult for their business to act on this information.
Solving the Mystery For Good
Not every business can achieve the same level of success with data enrichment. The goal should be to improve accuracy, completeness, and insights as much as possible without making the dataset less accurate. This is a difficult balancing act, but it is achievable with careful planning and execution.
When viewing data that has been enriched, it is important to remember that there are always gaps in any dataset. However, by using data enrichment techniques, these gaps can be filled in, and the dataset can be made more accurate, complete, and insightful. Demystifying data enrichment comes down to realizing there is not really a mystery at all. To learn more about how Affinity Answers Enrich can make enriching your data simple and straightforward, contact us here.