Generating new leads and timely insights are only as good as the data you feed your machine. And a mish-mash of non-standard, unresolved, un-translated and un-matched data signals is a waste of money. Get data standardization right and you can:
- Increase your total addressable market
- Reach more targeted marketing qualified leads globally.
- Increase your ROI
Data standardization:
Standardization is the process of matching data signals within the database records, such as roles, responsibilities, and experience, to ensure that marketers and product developers receive:
- The most accurate data datasets
- Increased reach by searching across a wider range of languages.
- A longer tail of critical job titles by uncovering new decision-makers in roles that otherwise wouldn’t have been captured in a generic search
And that’s what discovery is all about, whether you’re looking for new sales leads or recruiting targets or trying to analyse buyer and market trends.
In the United States, the title of “CEO” is well understood. By normalizing the data, you will capture the “Chief Executive Officer” equivalents within the database. However, what normalization will not catch are the titles of “PDG”, or “Président Directeur Général”, which are the CEO equivalents in France – this is where standardization comes into its own. “CFO” is another well-known title in the US, and normalization will ensure to recognize “Chief Financial Officer” as the same thing. But once again standardization is required if you also want to capture titles such “Director of Finance”, which is the CFO equivalent in many other parts of the world.
Whatever insights you’re trying to get, or problems you’re attempting to solve, data standardization enables you to properly understand your data across languages and geographies.
From normalization to standardization, a three-steps process:
Data normalization
Data normalization, in its simplest definition, refers to the process of data cleansing; Cleaning up data and removing the noise and mistakes to ensure format consistency. For example, it’s very common for databases, especially those in CRM systems, to contain records that have different data formats. Let’s look at titles, for example. One record may say “Sr. Engineer”, while others may contain alternative titles – such as “Sr Engineer” (no period), “Senior Engineer”, or “Senoir Engineer” (typo intended). Normalizing the data cleans this up for future processing – such as using “Senior Engineer” consistently in the examples above. Normalization is the first step in the data hygiene process.
Enrichment
The second step is enrichment. This step improves the fill rates of data fields of most value to you such as firmographics, technographics, “do not call” registrations and contact data. That contact or company data enrichment will add more context to each opportunity and the more you know about your target, the more you are able to convert leads. With enrichment, your segmentation, targeting and email personalization will improve.
Standardize your data
Now, you need to standardize your data – the third step. Once you have clean and consistent data across your database, it is important and valuable to standardize it. By doing so, you will increase the number of sales qualified leads in your data base because you have increased your addressable market with new contacts standardized and translated globally.
Now you know the differences between normalization, enrichment and standardization, make sure your data provider does too!
Normalization, enrichment, and standardisation concepts are often confused by data providers. Now that you know better, ensure your data provider can help you on each of those elements and that they deliver real and tangible data standardisation benefits. Caveat data emptor, as you might say.
