Faster Customer Analysis; Faster Growth
Resolve 3 major data bottlenecks to reduce time to customer insights and gain up to 126% more revenue quickly
In 2019, Mckinsey & Co quantified the impact of customer analysis for revenue growth in their report, “The new growth game: Beating the market with digital and analytics.”
Only now is the promise of advanced analytics catching up to the hype. Take customer analytics. Companies that use it extensively see profit improvements 126 percent higher than competitors who don’t. And when it comes to sales improvements through the extensive use of advanced analytics, the difference is even larger: 131 percent.
Most marketing and sales teams were already hungry for customer insights, knowing that quality Customer 360 profiles can be leveraged to grow business rapidly.
If you’re in the midst of a customer analytics initiative, your organization will need to tackle 3 main bottlenecks to ensure your efforts are streamlined and maximized.
Data in silos or disparate systems
From point-of-sale systems to CRMs, to support/customer service portals; from digital touchpoints to in-person interactions, etc. your customer demographic and behavioral data may be in a bit of a “data sprawl,” which could be a major speed-bump in the analytics process.
Conquering data sprawl and harnessing the power of customer data can be a daunting task. Your IT team will need to develop or purchase tools that will allow them to connect to a variety of distributed and siloed data sources (including cloud and on-prem data), and easily add these sources to a data catalog.
Congregating all of your customer data in one place is the first step to speeding up the analytics process and driving growth with customer insights.
Learn more about conquering data sprawl with these resources:
- Customer Golden Records: How to build them from disparate data sources – Blog
- AWS Cloud Data Management: Conquer Your Data Sprawl – Blog
- Customer-Centric DataOps for Golden Records at Bremer Bank – Webinar
Data is ungoverned and low quality
Another obstacle your analytics team might encounter is low-quality data due to poor or unstandardized data governance across the organization. Even if customer data can be centralized for analysis; errors, outdated or incomplete information, and duplicate records might prohibit your team and your leadership from gaining meaningful insights – or worse, they could be making critical mistakes due to misinformation.
Data governance becomes especially important for highly regulated industries such as healthcare or financial services, where private customer information must be masked and tokenized or the organization will risk non-compliance with laws and regulations.
For quality customer analysis, and to unlock the potential for high growth, your IT team must prioritize data governance and ensure the data can be traced from ingestion to consumption.
Learn more about customer data governance with these resources:
- EndZone Governance Architecture – White Paper
- Customer Data Governance: Be the Air Traffic Controller of Your Data – Blog
- Visibility and Data Control: Two Factors Influencing Data Governance Success – Blog
Limited access to, or difficulty finding, the right data
If your organization was able to tackle the first two hurdles by centralizing data ingestion and ensuring quality and governance checkpoints, a final step in gaining quick customer insights is to enable self-service access to the “analytics-ready” data.
Imagine that your data scientists don’t need to submit a request week in advance in order to access data for their analysis. Imagine that these end-users don’t have to search for and guess which data sets are ready for consumption. Enabling self-service access only to “trusted” data will ensure that your customer analytics is not only timely but accurate.
Learn more about self-service data access with these resources:
- Self-Service Data for Your Organization in 3 Logical Steps – Blog
- Self-Service Data Marketplace to Accelerate Analytics – Zaloni Arena Demo
Think of the weeks to months it may take your IT or data teams currently to fulfill your customer insight requests. What if that could be reduced to days? What advantage could you gain with that time?
Analytics can only function as quickly or effectively as the data behind it. A data management system that can ingest data from any source, apply governance and quality checks with full visibility across the data pipeline, and unlock self-service access to trusted data will shorten the lifecycle to customer insights and analytics-based growth drastically.
Hungry for more DataOps Content? See these resources:
- DataOps 101 – What is DataOps? Where do you fall on the DataOps Maturity Model? And more.
- Webinar: Data Governance Best Practices – A DataOps Approach with Alexion Pharmaceuticals – Register Now
- The DataOps Virtual Event – Register Now