Marketers would like to unlock the power of data — but with so many platforms to choose from where do you start? If you are in that boat, you may be asking yourself what data format helps you best analyze and visualize data associated with customer experiences.
Let’s take a look at how to streamline your analysis and visualization efforts and elevate your marketing strategy.
The ability to seamlessly move between data types has changed the game in recent years. With a plethora of tools, from cloud options to no-code and low-code applications, data management has become more versatile than ever.
But with so many options, it can be hard to determine which one is right for you — Excel, SQL or Python. Each has its own strengths and limitations, but the new workarounds available for intermediate data storage have created both new opportunities and confusion. To truly grasp the impact of this evolution, it's essential to examine the traditional roles of Excel, SQL and Python.
The Traditional Role of Excel
Excel, with its roots dating back to Lotus-123 and VisiCalc, is the go-to tool for creating data tables. Its familiar tabular format is widely used by professionals in business, nonprofits and government agencies.
Even with the emergence of Google Sheets, Excel remains the first choice for many. Its user-friendly interface makes it ideal for developing basic data table structures, acting as a whiteboard for organizing data. However, Excel's manual workflow can be a downside, slowing down decision-making and fostering silos of workflow techniques. Despite the addition of cloud integration and collaboration features, manual workflow can still be an issue.
The Traditional Role of SQL
SQL, like Excel, has a long-standing history in organizations. It allows for well-defined relationships between and within tables, making it a powerful language for exploring data hosted in a backend computing environment.
This leads to relatively efficient data standardization. However, the rise of open-source development has led to new accessibility to data structures, altering the way tables are accessed. With the advent of apps via APIs, data requests became more frequent, leading to the emergence of new protocols such as noSQL and in-memory data storage.
However, the complexity of SQL's tabular relationships can be intimidating for analysts who need a simple way to curate their data. The influx of many data sources can increase the steps and complexity needed to deliver metrics for a dashboard, hindering rapid development environments that need to structure data explorations. This can lead to longer investigation times for product or service features with data than teams have available.
The Traditional Role of Python
Just as open-source development revolutionized SQL, it also rejuvenated programming languages that work with data. Languages like Python and R, though dating back to the 1990s, are relatively new to marketers. Originally designed for front-end or server-side computing environments, developers have discovered new ways to expand their capabilities by creating dependencies and introducing new data sources, such as through APIs.
This has allowed for the application of statistical models and iterative analysis for predictive insights on customer behavior data, such as Markov Chain analysis for identifying the likelihood of sustaining a service or trend.
This has made Python one of the most popular languages in business intelligence. However, its success also has a downside for some analysts. The abundance of resources and tutorials on a wide range of applications, such as games, data visualization and machine learning models, can be overwhelming for those without programming experience, making it difficult to navigate dependency and modeling choices. The abundance of options could make it challenging to select the right data product development in an extensive programming environment.
Related Article: How AI-Driven Data Enhances CX
Evolution of Data Tools for CX
The roles of Excel, SQL and Python have evolved as analysts have gained access to tools that have expanded the capabilities of the platforms, allowing them to be used in new ways and in combination with each other. This has led to a cross-adoption of syntax concepts and data storage, as well as an increased use of intermediate storage for data comparison and cleaning. Automation features have also been added to make it easier to access and work with data across different platforms.
For example, tabular data is being placed more frequently in intermediate storage for extensive comparisons, such as examining IDs for hashing rows in a data cleaning room. Features to automate cross-language access and calculations in SQL, Python and R programming have been added. Libraries added to R programming permit SQL queries and Python syntax without changing the programming environment. Python has similar options. A flood of these dependencies simplified repeated data access.
As a result, it has become more difficult to determine the specific skill needs for teams working with these languages. Do you really need someone only dedicated to SQL or can the team who prefers Python make do with a few SQL skills?
What can these tools do to improve customer experience and marketing outcomes?
1. Solve Problems
Today's marketers must rely on data architectures that can quickly and efficiently solve business problems. The choice of technology must align with the specific needs of stakeholders, whether it be real-time data visualization for a dashboard, a sales pitch based on real-time data or an operational analysis such as ranking sales leads.
2. Build Trust
The technology must also be reliable to build trust in the final product. However, the underlying technology of these features can be complex and difficult to understand, making martech stack decisions challenging even for experienced technology teams. One decision can lead to multiple additional technology objectives, resulting in increased time and costs for delivering customer experience support.
3. Make Informed Decisions
Establishing an operational strategy is crucial for identifying opportunities to improve customer experiences, such as delivering personalized upsell offers. The right data management approach provides a comprehensive view of customers and their associated data, enabling marketers to make informed decisions.
Marketing analysts should recognize that different problems require different data mediums and workflows for efficient problem-solving. These considerations also play a role in shaping long-term martech platform and analytics choices.
4. Understand How Data Is Consumed
By getting these considerations right, marketers can act in real-time and on the customer's interests and needs, be it through cohort analysis or real-time dashboard updates. Understanding how data is consumed and utilized is crucial for achieving the desired outcome.
Related Article: Is Bad Data Ruining Your Customer Experience?
Unlocking the Potential of Advanced Analytics
When choosing a platform for your analytics workflow, it's important to consider the tasks that go beyond default reports and basic tasks and how frequently these tasks are performed. This can indicate which platform is best suited for real-time adjustments. Here are some considerations:
1. Align Your Data Needs
Align your data needs with the dashboard and data management activities being managed by your team. This can lead to opportunities for combining and strengthening your team's technical skills with Excel, SQL or Python-related data models. Each choice offers different capabilities for prioritizing data tasks and delivering results.
Some tasks, such as exploratory data analysis, are best handled through Python or R programming. Python is designed for real-time calculations and Excel is better for advanced data calculations before scaling them in an open-source program. SQL is good for delivering data queries based on the relationships among tables and maintaining certain data needs, such as a table of customers accessed by customer service for service plans and registrations.
2. Focus on Data Literacy
It's also important to focus on data literacy, the ability to read, analyze and draw insight from data. A focus on data literacy can reveal best practices for a model and help identify the right level of data literacy for a platform and its data rather than a one-size-fits-all approach. Marketing teams can help frame the right data tasks that create better team productivity and cost management for campaigns and associated projects.
3. Review Decisions
Finally, review your decisions quarterly to see where your team can adjust. Are the data sets being examined growing in size? Are there simpler ways to call data? These topics can be addressed over time.
Conclusion: Data Management Tools Fuel Customer Experience
Choosing the right platform for managing customer experience data can be overwhelming, but by considering the tasks, frequency and data needs, as well as focusing on data literacy, marketers can simplify the process and empower customer-facing roles with the information they need to serve customers best.