The art of choosing Excel, SQL or Python for Better CX
Take a look at how marketers can unlock the power of data analytics using traditional spreadsheet software and computing languages.
Marketers want to unlock the power of data – but with so many platforms to choose from, where do you start? If you’re in that boat, you might be asking yourself which data format will best help you analyze and visualize data related to customer experiences.
Let’s take a look at how you can streamline your analytics 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 difficult to decide which one is right for you – Excel, SQL or Python. Each has its own strengths and limitations, but the new solutions available for intermediate data storage have created both new opportunities and confusion. To truly understand the impact of these developments, it is important to examine the traditional roles of Excel, SQL, and Python.
The traditional role of Excel
Excel, with roots back to Lotus-123 and VisiCalc, is the tool for creating data tables. The well-known table format is widely used by professionals in business, non-profit organizations and public agencies.
Even with the rise of Google Sheets, Excel is still the first choice for many. Its easy-to-use interface makes it ideal for developing basic data table structures, and acts as a whiteboard for organizing data. However, Excel’s manual workflow can be a disadvantage, slowing down decision-making and promoting silos of workflow techniques. Despite the addition of cloud integration and collaboration features, manual workflow can still be a problem.
The traditional role of SQL
SQL, like Excel, has a long 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 effective data standardization. However, the rise of open source development has brought new accessibility to data structures, changing the way tables are accessed. With the use 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 table relationships can be daunting for analysts who need an easy 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 the teams have available.
Python’s traditional role
Just as open source development revolutionized SQL, it also rejuvenated programming languages that work with data. Languages like Python and R, although 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 extend their capabilities by creating dependencies and introducing new data sources, for example through APIs.
This has enabled the use of statistical models and iterative analysis for predictive insights into customer behavior data, such as Markov Chain analysis to identify the likelihood of sustaining a service or trend.
This has made Python one of the most popular languages in business intelligence. However, the 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 can make it challenging to choose the right computer product development in an extensive programming environment.
Related article: How AI-powered data improves CX
Development 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 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 more often placed in staging for extensive comparisons, such as examining IDs for hashing rows in a data cleanroom. Features to automate cross-language access and calculations in SQL, Python, and R programming have been added. Libraries added to R programming allow SQL queries and Python syntax without changing the programming environment. Python has similar options. A flood of these dependencies facilitated repeated data access.
As a result, it has become more difficult to determine the specific skill needs of teams working with these languages. Do you really need someone dedicated only to SQL, or could the team that prefers Python get by with a few SQL skills?
What can these tools do to improve customer experience and marketing results?
1. Solve problems
Today’s marketers must rely on data architectures that can quickly and efficiently solve business problems. The choice of technology must match the specific needs of the stakeholders, whether it is real-time data visualization for a dashboard, a sales pitch based on real-time data, or an operational analysis such as ranking of sales leads.
2. Build trust
The technology must also be reliable to build trust in the final product. However, the underlying technology of these functions can be complex and difficult to understand, making Martech stack decisions challenging even for experienced technology teams. One decision can lead to several additional technology goals, resulting in increased time and cost to deliver customer experience support.
3. Make informed decisions
Establishing an operational strategy is critical to identifying opportunities to improve customer experiences, such as delivering personalized upsell offers. The right approach to data management provides a comprehensive view of customers and their associated data, allowing marketers to make informed decisions.
Market analysts should recognize that different problems require different data media and workflows for effective problem solving. These considerations also play a role in the design of long-term martech platforms and analytics choices.
4. Understand how data is consumed
By getting these assessments right, marketers can act in real-time on customer interests and needs, whether through cohort analysis or real-time dashboard updates. Understanding how data is consumed and used is crucial to achieving the desired result.
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 standard reports and basic tasks and how often these tasks are performed. This can indicate which platform is best suited for real-time adjustments. Here are some considerations:
1. Adjust your data needs
Customize your data needs with the dashboard and data management activities managed by your team. This can lead to opportunities to combine and strengthen the team’s technical skills with Excel, SQL or Python related data models. Each choice offers different options 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 in an open source program. SQL is good for delivering data queries based on the relationships between tables and maintaining certain data needs, such as a table of customers accessed by customer service for service plans and registrations.
2. Focus on computer skills
It is also important to focus on data literacy, the ability to read, analyze and gain 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 design 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 easier ways to call data? These topics can be addressed over time.
Conclusion: Data processing tools fuel customer experience
Choosing the right platform to manage customer experience data can be overwhelming, but by considering the tasks, frequency and data needs, in addition to focusing on data literacy, marketers can simplify the process and empower customer-facing roles with the information they need to best serve customers.
