It comes as no surprise to many that enterprise IT projects typically run over budget or are never even finished. One of the most challenging IT tasks is around customer data and analytics. Centralizing customer data is difficult, as it requires a tacit understanding of each business unit’s data structures and IT architecture. Furthermore, analyzing such data and dispersing analytic results across the enterprise requires a paradox of organizational excellence: a centralized analytics team serving a decentralized set of business units, with the analytics team actually understanding and prioritizing its work across those business units.
Canopy Labs is fortunate to work with numerous businesses across a wide range of revenue scales. From ten-person ecommerce startups to billion-dollar retail operations, businesses of all sizes struggle with the idea of centralizing customer data, standardizing analysis, and prioritizing actionable insights.
The key differentiator between success and failure in this domain is often a clear structure around approaching customer analytics, data, and reporting. To help achieve project success, we have developed the Customer Data Framework (CDF). The CDF is a means of testing an organization’s readiness to use and analyze customer data and analytics. This framework helps build an understanding of how data exists within a corporation and how analytic results can be used.
Using this framework, Canopy Labs scores an organization on a 5-point scale within each of the eight categories. Businesses just developing a basic capability fall as a “1” while best-in-class practices are scored as a “5”. For a full breakdown of the framework and scoring system, please contact firstname.lastname@example.org.
The first step of the customer data framework is data collection. It is important to list and analyze all the customer data that passes through a company’s various IT systems, and understand what it actually represents. A best-in-class company tracks all its customer data and is able to collect a large portion, if not all, of the data.
In many ways, collection is about the organization being able to access the customer data flowing through its systems. The “storage” capability focuses on the organization’s ability to actually maintain a historical account of the customer data that passes through its systems. Storage focuses on the core data itself, as well as relevant metadata, such as time stamps and quality assurance metrics (e.g., whether the data actually follows any prescribed standards).
Note that data passing through a company’s IT systems is often left uncollected. Imagine a retail operation: while actual purchases are tracked, most customers who enter a store and leave are unknown to the sales agents or staff. This is why some restaurants and retail operations have contests and campaigns to collect business cards or encourage people to register for free services.
The core of the customer analytics strategy is the “analysis” step. Customer analytics is impossible without actually taking stored data and running it through a set of business rules, statistical methods, or other tools. This step scores companies on the sophistication of their analytics capabilities. Those using simple heuristics or rules score low, while organizations with sophisticated software and teams with strong statistical backgrounds score well.
Analytics without action is a theoretical exercise and does not actually help an organization in any way. Organizations need to act on whatever findings they discover through the analysis process. A strong score in this category shows a company regularly take models or analytics results and actually tests them, and ensures that marketing, sales, and other business functions have access to the latest model results to inform their campaigns.
Analytics-enabled business activities are scientific in nature: a company tests hypotheses using available data, and proceeds to apply findings on hypotheses that are most likely to succeed. The optimal process for hypothesis testing is an iterative one: you test, collect data, and try again. This process is what underpins any successful scientific effort and is analogous to best practices with customer data. Every model and every campaign should be tested rigorously, and the results themselves should be included in future analysis. This iterative approach (i.e., feedback loop) will build on successes to drive even bigger performance gains.
There are three crosscutting themes to customer data. These crosscutting themes focus on the broader, often organization-wide efforts to optimize and improve on customer data capabilities.
The first of these is security. Any customer data strategy requires a strong knowledge of which data is valuable not just for analytic models, but to potential threats and criminals. Furthermore, ensuring this data is safe and secure is crucial to maintaining a sustainable business strategy based on customer data. Otherwise, the risks are too great.
Related to security but much less tactical is “governance”. Governance around customer data often relates to understanding how to make decisions around investments, and how to prioritize projects related to customer data. Not every data set is created equal, and not everything can be optimized: knowing which is and which is not, and knowing how best to make and communicate these decisions, is crucial.
Finally, developing a strong culture around analytics and optimization underlies the entire customer analytics process. A strong culture results in employees understanding the importance of secure data, respecting decisions made around optimization, and trusting analytic models to help them in their day-to-day decision-making. A strong culture enables the activity and feedback loops that lead to performance gains through analytics.
Enabling a customer analytics and data strategy is challenging but immensely rewarding. Introducing the rigorous and performance-based mindset around analytics often leads to noticeable performance gains, and helps drive businesses forward. Using the eight step framework above will enable a sustainable and successful customer data strategy.
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