In a data-driven world, we enjoy data enrichment’s perks every day. From Google to Netflix and your favorite online grocery store, each online interaction you have is driven by data augmentation. But the power of data enrichment goes beyond suggesting your next favorite Netflix show or saving you time with Google’s autofill function. In business settings, data augmentation can give companies the competitive edge they need to be on top of customers’ minds and become industry leaders.
Here’s one recurring issue companies face when handling data: quality. Although all companies have data entry points across their channels (think marketing, sales, customer service, etc.), not all data entering their databases is accurate and valuable. A Global Databerg report finds that 85% of stored data is either dark or redundant, obsolete or trivial.
The data-hoarding tendency that companies have today raises new challenges for businesses. Capitalizing data and discerning obsolete information and valuable business insights can make a difference in profits, customer insights, meeting market and customer expectations, and so forth. Only 15% of data stored by companies is classified as business critical.
Data validation and enrichment tools have become the golden standard in business settings to eliminate the far-reaching impacts poor data has on operations and processes. Such software helps enterprises eliminate redundant and irrelevant information from the collection moment.
What Is Data Enrichment and How Do Companies Use It?
Data enrichment is the process used by organizations to merge first-party data with data sets collected by different internal systems and third-party data from external sources. The main goal of data enrichment or augmentation is to offer more insights and help brands make more informed decisions at an organizational level.
All customer interactions generate data. Each entry point can capture impressive amounts of data, from website traffic, email lists, form completions, or social media interactions. But in its raw form, it is useless and does not generate deep insights. In data enrichment, once information is captured and stored in a data warehouse, it is cleaned and structured to summarize customer characteristics and audience base. After that, it is compiled with data from third-party sources. Data augmentation gives more context, which helps companies offer personalized experiences across all customer interactions and make informed business decisions.
For example, marketing initiatives built on augmented data can fill essential gaps across the customer journey with content developed to educate and inform in areas where this is needed. Or customer support interactions that use data enrichment as a starting point can increase customer satisfaction by having deeper insights into each customer, their needs, and their behavior across multiple channels, internal or third-party ones.
Types of Data Enrichment
There are many types of data enrichment, but below, you have a short description of some of the most commonly implemented by companies delivering such services.
- Socio-demographic data. Aspects such as income level, the type of car driven, the number of vehicles per household, marital status, and the number of children influences purchase decisions. Socio-demographic data can be merged with internal information to craft personalized messaging for marketing campaigns or targeted paid ads.
- Geographic data. Enriching internal data sets with geographical information can improve business operations through geotargeting. In this scenario, businesses get accurate insights into areas with a high density of current or potential customers. For example, brands use this data enrichment type when deciding where to open new shops.
- Purchase intent data. Product view frequencies can reveal a prospect’s purchase intent. By merging such information with internal data, businesses can offer authentic experiences to prospects through targeted marketing activities. When using purchase intent data enrichment solutions, marketing departments can reduce campaign costs by only targeting purchase-ready consumers and shifting their buying decision.
- App usage generated data. This data augmentation helps businesses, especially in their Customer Experience (CX) efforts. By identifying which apps, operating systems, and even devices consumers use, brands can accelerate their efforts to create seamless experiences for app users. This information also allows them to develop better applications focusing on user experience.
Benefits of Data Enrichment
Having a laser view of your customer base and prospects is one of the obvious benefits of accelerating data enrichment efforts. However, there are more ways in which data augmentation can help organizations, regardless of their industry or profile.
- Reduce data management costs. In the data-hoarding culture, companies find themselves trapped in poor, redundant, and lost data costs them 15%-25% of their operating costs. Data enrichment protocols can help businesses eliminate redundant data and use the volumes generated by their systems and customer interactions in a profitable fashion.
- Enhance customer interactions. Accurate and complete data sets can help brands deliver more personalized customer interactions with the ultimate result of improving customer retention and boosting brand awareness and trustworthiness. When all departments have a single view of the customer, they can tailor more effective communication strategies. Data enrichment is not limited to enriching internal information with third-party data. It can also be in the form of compiling data from different departments’ systems within an organization. This ultimately results in a complete and correct view of the customer or prospect. Giving your customers the assurance that they are seen and understood will influence their purchase decision.
- Accelerate lead nurturing efforts. Lead nurturing efforts are sometimes ineffective due to failure to correctly identify leads worth promoting. Lead enrichment protocols based on augmented data have proven more effective and accurate.
- Targeted marketing campaigns through accurate lead scoring. Targeted marketing will soon become a staple as companies develop more precise segmentation strategies. Again, data augmentation plays a definitory role, leveraging information from various sources.
How To Implement Data Enrichment to Drive ROI
Database enrichment seems like a complicated business with data coming at you from all directions. It can be handled internally by using data management and augmentation software. Such software usually has all the features to deliver results at lower costs. Regardless of your route in your data augmentation journey, here are some best practices when implementing it.
- Establish your data enrichment goal. While improving your data’s quality and accuracy is the overall goal of data enrichment, it is too broad to help you get the expected results and improve KPIs. Instead, try to focus on data sets that you want to target in the process. You can establish these data sets at a departmental or organizational level. For example, if your immediate efforts are focused on increasing sales or improving customer experience, you should start with sales and customer support, respectively, data sets.
- Aim for reproducibility and consistency. Data enrichment processes need to be reproducible. They need to deliver the same result consistently. In business settings, new strategies appear routinely, so you’ll need the assurance that you’ll get consistent results no matter the steps you add to a business process or the fields you add to a form.
- Specific evaluation criteria. Getting the expected results will only be possible if you attribute clear data enrichment evaluation criteria to each step in the process. This will allow you to compare recent outcomes to past ones and ensure you generate accurate results.
- Scalability. Is your data enrichment strategy scalable in terms of allocated resources, desired timelines, and costs? Data volumes grow over time, so implement a process that allows your data enrichment efforts to grow without prejudicing your timelines and budgets. With this in mind, make sure to look into automated solutions.
- Completeness. Consider scenarios where new data sets enter your databases and anticipate all possible results, regardless of data type. By doing so, you ensure valid data outcomes in your enrichment efforts.
- Generality. Data enrichment tasks should be applicable to different data sets. Reusing the same logic in different scenarios and creating transferable protocols will help you ensure the consistency of your enriched data.
It’s a Wrap
Your business generates data at a staggering pace. While many brands face new challenges every day to leverage it at its full potential, data enrichment can help organizations get a clear picture of their current business position in the market, create more effective strategies and, ultimately, boost ROI.
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