Data initiatives are finally finding the platform they deserve—and for good reason. Data is the foundation of every great marketing strategy and loyalty program. Yet another element is just as critical: the insights and analysis developed from data. Data only gets brands so far if they don’t have the insights and analysis to determine what all that data is saying.
An experienced marketing partner can show you how to create a customer database and unlock meaningful insights that make all the difference in planning and executing profitable campaigns. Brands need trusted partners that understand industry standards, employ a broad customer relationship management (CRM) methodology, and have an acute understanding of brand goals to tailor the analysis to unique programs.
Let’s discuss how to create a customer database:
Bringing Order to Chaos with a Customer Database
Data is naturally messy, unorganized, and fragmented. Brands often don’t realize the chaos that comes with data and the disparate sources, structures, and silos.
One of the biggest mistakes marketers make is producing insights and strategies from faulty data. No matter how well-derived those insights might be, if they’re produced from messy data, they won’t show positive growth when executed. That issue gets lost in the labyrinth of cross-functional teams and strategies, making trusted data even more imperative.
A common misconception about data is that it’s raw and pure. That’s not accurate—data is crafted, and it needs to be constructed with an experienced, unbiased, and meticulous hand. It requires constant hygiene and organization to make it actionable. More importantly, data needs validation so you can prove its quality and trust the foundation for all of your future insights, marketing strategies, and measurements.
Wondering how to bring order to chaos and ensure customer data is always organized, accurate, and validated? Create a customer database that makes messy and unstructured data usable.
Creating a Profile for Each Individual Customer
Every brand thinks it knows its customers. It has an idealized version of the customer that usually is quite different from reality and the story that data tells.
Insights aren’t relevant if they don’t have context and a foundation of customer knowledge to build on. Everything is connected, and brands need a comprehensive understanding of customer behaviors, customer types, and trends that impact customer relationships.
If brand data is organized, clean, and validated for accuracy, learning how to build a customer database with individual profiles should be the next step toward generating insights. Customer profiles help brands gain an in-depth, contextual understanding of customer health, which is crucial to know before investing in customer marketing initiatives or loyalty.
Loyalty programs are unique to each brand based on strengths, weaknesses, brand position, financial modeling, and—of course—customer health. Customer profiles are an important cog in that wheel; they can identify where investment needs to be allocated to support brand challenges while boosting brand strengths.
Basic profiles should include at least the following analyses:
Channel Health and Performance
Channel analysis should dive into each channel’s attribution to numerous key performance indicators (KPIs), not just sales. By breaking down channels on an in-depth level, there are usually important revelations about the differences in customer journey, how and what products are sold, and how best customers utilize each channel.
Purchase Behavior
Purchase analysis is an overview of which customers are buying, what products they are buying, and how often they are purchasing. It gives brands a breakdown of reliability and visualizes how often customers return to purchase, which can impact the customer lifecycle and communication strategy.
Best Customer
There’s a commonly known rule in customer marketing that 80 percent of sales come from just 20 percent of customers. This analysis will show marketers how important their best customers truly are to their brand. It’s also paramount to understand best customer analysis if a brand is considering loyalty.
Segmentation
This should be a breakdown of the customer file from top to bottom, showing the distribution of lapsed customers, the performance of deciles in existing customers, and the migration levels from each segment.
Retention and Acquisition
Retention and acquisition analysis measures the number of customers coming in versus the number of customers lapsing. How well does a brand retain customers, and can it support those levels with its current acquisition efforts? This analysis guides brands on how to distribute investment for acquiring and keeping customers.
Loyalty Readiness or Performance
Loyalty isn’t right for everyone. Trusted partners performing brand analytics need to be open and honest on the loyalty front. Analyzing the customer file, producing a financial model, and understanding the needs and requirements of customers, technology, and brand investments can help to identify if loyalty is a good fit—whether the brand has an existing program or is considering investing in a new one.
Discovering Actionable Insights to Inform Your Strategy
One of the biggest fears for marketers is guessing and creating strategies from intuition. Whether that stems from a lack of humility and a biased approach or a marketer not having the data and analysis to make educated decisions, relying on intuition can be the catalyst for failed initiatives.
The good news is that insights are intertwined with customer analysis; the insights come naturally if the analysis is comprehensive and done with expertise. With a solid foundation in trusted data and an exploration of the customer, well-established agencies produce insights with a clear path forward. Data is the foundation, but insights are the fuel for great strategies—and a customer database is where that fuel lives.
Want to learn how to create a customer database that provides valuable, data-backed insights? Download our free eBook, Customer Analytics 101: How to Derive Data-Backed Insights.
