“You can have data without information but you cannot have information without data”
Daniel Keys Moran
Database completeness and accuracy are not only vital components of a marketing strategy but also impact the efficiency of your marketing automation platform.
What is data normalization?
It’s a process of the structuration of various attributes holding information with a focus on satiating the need of having clean, accurate, and consistent data. Data normalization helps in saving the cost associated with managing a database, the time utilized in looking for missing information, better analytics, and effective decision-making.
Why do we need normalized data?
1. Database Status: Clean and normalized data makes the database more graspable and organized. This also helps us identify which fields are empty or inconsistent. Enriching/normalizing information increases lead quality, further resulting in a hike in conversion rates.
2. Effective Decision-Making: Availability of accurate information helps in preparing marketing plans, streamlining business processes, and better analytics.
3. Segmentation: We can create various segmentations in the marketing automation platform, based on a lead’s demographics for targeted marketing outreach. This reduces the efforts required in creating dynamic assets. Accurate segmentation makes content planning and orchestration of demand generation strategies easier.
4. CRM Sync: Sync fails due to the unavailability of a field value and marketers commonly experience this issue. Keeping clarified data not only results in seamless syncing but also increases the efficiency of marketing campaigns.
5. Cost-Effectiveness: Marketing automation subscriptions are dependent on data sizes. Keeping blank and junk records may cost you unnecessarily. Paying for clean and normalized data, that is complete and relevant, makes more sense.
What is Grazitti’s approach towards building a normalization set-up?
1. Database Assessment: The database is keenly reviewed for determining its actual state and identifying bad values entering the system and then into operational workflows. We then cite observations around empty, junk, or inconsistent field values.
2. Normalization strategy: This component covers a quick assessment of demographic details that are being tracked for leads (Country, Industry, Job Role, etc). Based on this assessment, we document recommendations around the demographics that should be targeted for normalization along with recommendations for keeping good and bad data separate.
3. Marketing Automation Process: In this component, we cite observations on how operational workflows are performing and what would the best approach be to automate the incoming data and its inflow through various backend and CRM-sync operations.
4. Analytics: This component is inclusive of recommendations around outcomes of the normalization set-up. We keep regular checks for ensuring the set-up is performing in the desired manner and devise strategies on optimizing it periodically.
It makes perfect sense to normalize data as the ultimate goal is to improve lead quality. The more organized the lead management system, the more qualified the lead. The more qualified the lead, the higher the priority for outreach. All of this translates into better conversions and higher ROI.
Have you leveraged normalization for a spic-and-span database? Let’s talk!
Feel free to drop us a line at firstname.lastname@example.org and we’ll take it from there.
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