🎯 Unlocking the Power of Effective Campaign Management: 


Are you in search of a campaign management strategy that seamlessly aligns with your business objectives? Welcome to Pro-Data Science, where we specialize in guiding organizations towards a robust, enduring campaign management system.

🌟 What Sets us Apart: – Our expertise stems from over a decade of successful campaign management implementations, delivering sustained performance year after year.

Key Insights for Effective Campaign Management:

🔍 Navigating Audits with Ease: A well-designed system ensures clarity and structure, facilitating an organized workflow with identifiable campaigns, approved changes, and automated closure processes. We streamline this from the outset with automated standardized campaign ID generation and versioning protocols.

🎯 Execution Efficiency: We understand the importance of executing campaigns across diverse scenarios. Our approach involves campaign base selection using straightforward fields or logical aggregate queries, simplifying data management and enabling effortless campaign selections.

💾 Database Integrity at its Core: Database integrity forms the bedrock of a stable campaign infrastructure. Our policies prevent unnecessary table proliferation and implement automated archival processes, ensuring continuous high performance.

📊 Reporting Engine Elegance: Our solution design promises high-performance reporting engines, adept at creating report templates, fetching and combining data effortlessly, even from archival databases, ensuring accurate insights for informed decision-making.

📈 Deep Dive Analysis Expertise: we excel in data modeling practices, enabling comprehensive campaign analysis with tagged data (test/ control), providing historical insights spanning years for impactful campaign strategies.

🔐 Data Governance Mastery: Our design principles encompass comprehensive data governance practices, ensuring integrity, format consistency, and proactive measures to maintain database stability and performance.

🎯 Custom Personalization Engines: For organizations seeking tailored user experiences, our solutions design include personalization engines, delivering unique content based on predefined logic, enhancing user engagement and satisfaction.

Your quest for streamlined campaign management ends here! Let’s collaborate to design a bespoke, audit-proof system that propels your business towards unparalleled success.

ProData Consultancy Benefits

Some Best Practices of Campaign Management

  • All Campaign management solution struggle over time. A good campaign management infrastructure has to take care of many a things
    • from audit perspective.
      • Like campaign should be identified without any confusion
      • Changes in campaign should be duly approved and it should be clear that how changes in logic has taken place
      • Campaign cannot be executed without due approval
      • Campaign closure – should be an automated process to make it manageable
    • Execution perspective
      • Campaign can execute on variety of situation – not just by selection but also running logic on aggregate queries – otherwise a nightmare to manage data creation jobs
      • Campaign execution efficiency
    • Campaign management practices
      • Workflow for
        • Standardization of campaign ID
        • Storing campaigns details for audits.
        • Ensure no campaigns can be fired unless it is part of the approved campaign. You need to ensure a database entry after final approval
        • Show campaigns, which are not expired and approved from database for campaign ID selection in the campaign execution tool
        • Versioning – all subsequent changes in campaigns should lead to closure of a version and start of new version. This will help in tackling audits
        • Campaign closure process
      • Campaign execution engine
        • Make sure of running campaigns
          • by selection or
          • Or running campaigns based on aggregate query, which runs only for a set of accounts / decided logic and for a period only. Otherwise query management / data management is a nightmare
        • Make sure that you can change campaign ID in the entire flow with minimal effort
        • Possibility to show only active / not active / all campaigns
        • Intelligent querying of data as per the design (index / partition) of database table – so that query is fast executed
      • Campaign database management
        • Poor guy database – at the end of the day, this is what does the job. All are giving instructions (making queries) only
        • Database – should not allow anyone to create temp tables in the same area otherwise you will reach from 75 tables to 7500 tables in 4 years. Allocate space for users to make their own table and let them manage their space
        • Archival database – a must so that you live database can give performance
        • Dataflow has to be well documented –
          • How data is flowing
          • How data is getting treated in between
        • Data management practices and auto archival from main database and auto delete from archival database is must
        • Data governance policies has to be place – details below
        • Always sync using logs – < 10% impact and a database ready for querying
        • Ensure automated data reconciliation practices
        • Avoid file based data transfer – use API, even dB link may not be a good idea
        • If too much load, create an operational database and a running database of last 90 days / 180 days for operational purposes
      • Reporting engine – has to operate without causing performance issues
        • May keep a specialized system to create reports and insert data into database for final report
        • Develop reports templates on these database, which can automatically fetch figures based database insertion
        • Develop a mechanism so that reporting templates can fetch data easily from archival database – ensure logic to print, if any column not found in the archival table (quite possible that new database has a new table / column, which is not part of old database)
      • Modelling practices for deep dive campaign analysis
        • Ensure to keep campaign level – account level data with tagging like test / control , responder / non responder, active / inactive, responders activities (like spend/ transaction etc) , active guys activities, runner= active – responder, activities
        • Ability to fetch any such data for last 2 years quickly / last 7 years from archival database
      • Data governance practice
        • No one should be allowed to create new temporary tables in production area
        • Match data in source system and uploaded data in the database. If source system has 1.5 million records and database table had
          • Say 0.5 million (partial update) records
          • Say 0 records (missing update) records
          • Say 3 million (duplicate upload) records
          • It is an issue
        • Similarly if input data is of 2012-12-Dec and some one uploads it with 2012-12-Nov, it is wrong
        • Also, if source data has
          • Too much missing values
          • Irrelevant values (like city field having values 1, 2 etc)
          • Junk values (like containing square brackets / # etc)
          • There is sudden changes in the data format like Gold is coming like gold or GOLD, it will cause some programs to fail
          • All production database backup for DR activity should be taken from sister database rather than main database
          • There has to be an automated procedure to drop data from user tables after a gap of prescribed period
          • Every database should have an automated procedure to check
            • Index stability
            • Fragmentation level
            • Read only Lock
            • Long running queries (say which has taken more than 360 minutes)
          • Every six months, take list of top 10 worst performing queries and get to the solution around the same. It will require fine tuning tables / queries for performance
          • There has to be automated data load governance practice, which can detect
            • Each data load procedure executed or not
            • The count of record modified / updated and does it follows the statistical trend
            • The system should not generally allow older data insertion (usually less than t-2 days) to avoid data getting loaded for wrong dates (like 20-sep data getting loaded as 20-jul).
            • Keep a watch on trend of query execution time. It will help to proactively maintain /enhance table design for performance
          • Personalization system – at times organizations need a mechanism to show different content to different user based on predefined logic. Personalization engine comes into picture in this aspect.