Skip to main content

Marketing analysis enables digital marketers to drive meaningful business growth.

Marketing analysis enables digital marketers to drive meaningful business growth. In today’s competitive digital marketing landscape, leveraging data analytics is crucial for driving business growth. As a digital marketer cum analyst, mastering Excel analytics can transform raw marketing data into strategic insights that drive revenue, optimise campaigns, and enhance customer experiences. This article delves into a real-world Excel analysis of a 10,000-entry marketing dataset (marketing_data_xlsx), focusing on the 'Electronics' product category. We'll examine year-over-year (YoY) purchase amount growth segmented by marketing channels, alongside customer satisfaction trends and their correlations, all based on actual filtered data from 2023 and 2024. By incorporating tools like pivot tables , the FILTER function , and correlation formulas , marketers can identify high-performing channels and make data-backed decisions to fuel growth. Why Excel Analytics Is Crucial for ...
Post a Comment

Marketing analysis enables digital marketers to drive meaningful business growth.

Marketing analysis enables digital marketers to drive meaningful business growth.


Marketing_Analysis


In today’s competitive digital marketing landscape, leveraging data analytics is crucial for driving business growth. As a digital marketer cum analyst, mastering Excel analytics can transform raw marketing data into strategic insights that drive revenue, optimise campaigns, and enhance customer experiences. This article delves into a real-world Excel analysis of a 10,000-entry marketing dataset (marketing_data_xlsx), focusing on the 'Electronics' product category. We'll examine year-over-year (YoY) purchase amount growth segmented by marketing channels, alongside customer satisfaction trends and their correlations, all based on actual filtered data from 2023 and 2024. By incorporating tools like pivot tables, the FILTER function, and correlation formulas, marketers can identify high-performing channels and make data-backed decisions to fuel growth.

Why Excel Analytics Is Crucial for Digital Marketers

Excel remains a powerhouse for digital marketers, offering cost-effective ways to analyse customer purchase data, campaign performance, and satisfaction metrics. With features like dynamic filters and pivot tables, you can quickly segment data—such as filtering for 'Electronics' purchases in specific years—to reveal trends that inform budget allocation and strategy. For example, in this analysis (conducted as of August 31, 2025), we used real data showing Search Engine and Email channels leading in YoY growth, with a moderate correlation to customer satisfaction. Such insights help marketers prioritise channels like Search Engine (10.84% growth) to maximise ROI and business expansion.

Key Benefits of Excel Analytics in Marketing

  • Data-Driven Decisions: Identify growth drivers, e.g., Email's 9.69% YoY increase in purchase amounts.
  • Efficiency: Use formulas like FILTER for quick segmentation without advanced coding.
  • Scalability: Handle large datasets (e.g., 10,000 entries) to spot patterns in demographics, locations, and channels.
  • Actionable Outcomes: Correlate metrics like purchase growth (overall +3.41% from 2023 to 2024) with satisfaction to refine customer journeys.

Mastering these techniques empowers digital marketers to boost conversions and sustain long-term growth.

Step-by-Step Excel Analysis Using Real Marketing Data

We'll walk through the analysis using the marketing_data.xlsx dataset, which includes columns like Customer_ID, Age, Gender, Location, Income_Level, Product_Category, Purchase_Amount, Purchase_Date, Campaign_ID, Channel, Conversion, and Customer_Satisfaction. The focus is on 'Electronics' purchases in 2023 and 2024, revealing insights like Search Engine's 10.84% YoY growth and a 0.419 correlation between purchase and satisfaction trends.

marketing_data_image


Step 1: Filter the Dataset for 'Electronics' and Year 2023–2024

To isolate relevant data, use the FILTER function in a second worksheet. Assuming the original data is in Sheet1 (headers in row 1, Product_Category in column F, Purchase_Date in column H), the formula in a new sheet (e.g., cell A1) is

=FILTER(Sheet1!A2:L10001, Sheet1!F2:F10001="Electronics")

This extracts rows matching the criteria, such as a 32-year-old female from Tokyo purchasing ₹277.45 via Email on 27-10-2024 (no conversion, satisfaction 4) or a 35-year-old male from Los Angeles buying ₹430.01 via Search Engine on 03-09-2023 (no conversion, satisfaction 3). The filtered subset shows diverse locations (e.g., Tokyo, Paris, New York) and channels, enabling targeted analysis.


marketing_analysis_data_image

Step 2: Calculate Total Purchase Amounts by Channel and Year

Create a pivot table from the filtered data:

  • Rows: Channel
  • Columns: Purchase_Date (grouped by Years)
  • Values: Sum of Purchase_Amount
  • 2023 Total: ₹1,023,493.36
  • 2024 Total: ₹1,058,425.46
  • Channel Breakdown (2023 vs. 2024 in ₹):
  • Direct: 210,537.38 vs. 204,171.56
  • Email: 211,923.51 vs. 232,458.87
  • Search Engine: 183,168.3 vs. 203,027.16

Real results:

Insight: Email and Search Engine drove the overall 3.41% increase, with Search Engine showing the strongest absolute gain (₹19,858.86).

total_purchase_pivot_table


Step 3: Compute Year-over-Year Growth in Purchase Amounts

Add a column to the pivot table output for YoY growth:

=((2024 Amount - 2023 Amount) / 2023 Amount) * 100

Actual YoY Growth Rates (%):

  • Direct: -3.02
  • Email: 9.69
  • Referral: -3.71
  • Search Engine: 10.84
  • Social Media: 2.60

Insight: Search Engine (10.84%) and Email (9.69%) outperform others, indicating effective SEO/SEM and email campaigns for Electronics.

purchase_yoy_growth_pivot_table_image


Step 4: Analyse Average Customer Satisfaction by Channel

Another pivot table:

  • Rows: Channel
  • Columns: Purchase_Date (grouped by Years)
  • Values: Average of Customer_Satisfaction
  • Direct: 3.04 vs. 3.03
  • Email: 3.01 vs. 3.09
  • Referral: 2.97 vs. 2.86
  • Search Engine: 3.09 vs. 3.01
  • Social Media: 3.11 vs. 3.06
  • Grand Total: 3.04 vs. 3.01

Real Averages (2023 vs. 2024):

customer_satisfection_pivot_table


Calculate YoY Satisfaction Growth (%):

  • Direct: -0.39
  • Email: 2.63
  • Referral: -3.70
  • Search Engine: -2.45
  • Social Media: -1.48

Insight: Email's satisfaction rise (2.63%) aligns with its purchase growth, while Referral's drop (-3.70%) may explain its decline.

customer_satisfection_yoy_growth_pivot_table_image


Step 5: Calculate Correlations

Copy growth rates into a table and use:

=CORREL(Purchase Growth Range, Satisfaction Growth Range)

Result: 0.419 (moderate positive correlation).

Insight: Higher satisfaction growth (e.g., Email's 2.63%) tends to support purchase growth, but the moderate value suggests other factors like campaign IDs or demographics influence outcomes.

growth_data_image

correlation_result_image


Step 6: Visualise Insights with Charts

From the combined report:

  • Bar chart for Purchase Amounts: Shows Search Engine's leap from ₹183,168.3 (2023) to ₹203,027.16 (2024).
  • Line overlay for YoY Growth: Peaks at Search Engine (10.84%).
  • Satisfaction Chart: Email's upward trend vs. Referral's decline.

These visuals highlight opportunities, like boosting Search Engine investments.

customer_satisfection_graph_imagepurchase_amount_graph_image


Driving Business Growth with These Insights

This analysis uncovers actionable strategies:

  • Channel Optimisation: Prioritise Search Engine (10.84% growth, despite -2.45% satisfaction dip) and Email (9.69% growth, 2.63% satisfaction rise) for Electronics campaigns—e.g., enhance Google Ads or email personalisation.
  • Address Underperformers: Referrals' dual declines (-3.71% purchase, -3.70% satisfaction) signal the need for better referral programs or feedback loops.
  • Leverage Correlations: The 0.419 link emphasises improving satisfaction to amplify growth; investigate low scores in Referral via demographic filters (e.g., low-income customers in Houston).
  • Broader Applications: Extend to other categories or segments (e.g., high-income females in Tokyo) for comprehensive RFM analysis.

Regular Excel analyses like this can reduce ad waste and increase conversions by 10–20%.

Conclusion:

In today’s competitive market, understanding this kind of analysis is not just helpful but necessary for digital marketers. It allows them to see what is working, what needs improvement, and how to reach customers more effectively. By using these insights, digital marketers can make smarter decisions, create stronger strategies, and drive continuous business growth.

Files:

Raw Excel File: click here

Excel Report File: click here


Labels:

Comments

Post a Comment

Popular posts from this blog

The Timeline That Defined the Rise of Artificial Intelligence.

The Timeline That Defined the Rise of Artificial Intelligence. Introduction The beginning of artificial intelligence (AI) started with a legend. There were tales of machines as human as can be, such as Talos, a giant made of bronze, and the Golem, a clay creature to be awakened. Inventors created simple machines imitating human and animal movement over the years, keeping the vision of AI alive. In the 1940s, the concept of a thinking machine was becoming serious.  The development of digital computers led people to feel that human thought could be replicated by machines. In 1956, a team of researchers formally began AI research at the Dartmouth Workshop.  Governments provided them with plenty of m...
Post a Comment
Read more

The History of Marketing: From 1.0 Basics to 6.0 Smart Tech

The History of Marketing: From 1.0 Basics to 6.0 Smart Tech Introduction Marketing is always in motion. It's an ever-changing narrative. Just treat it like a living entity, constantly evolving with new technologies, how people live, and the evolution of society. That's something any business has to understand. It keeps you part of the game and on your toes in a busy world. We will talk about the early steps, Marketing 1.0 to 3.0. Those early steps are what the present is based on. But then what? What will marketing in the future consist of? We will talk about that as well. Marketing 1.0: The Product-Centric Era Marketing was easy. There were factories that produced a lot of products, and all the businesses wanted to do was just sell them. They just wanted to sell their goods to as many people as possible. The History of Mass Advertising Early advertising was mass-targeted. Imagine a Ford Model T commercial in the newspaper. This period employed print, radio, and e...
Post a Comment
Read more

Why Your Marketing Strategy Needs Machine Learning Now

Why Your Marketing Strategy Needs Machine Learning Now Introduction Marketing for decades depended on guesswork and general assumptions. Things are however changing with the advent of machine learning (ML), focusing attention away from guesswork to data-driven decision-making. Machine learning algorithms are no longer tomorrow's novelties; they're already transforming the manner in which marketers interact with audiences, optimize campaigns, and ultimately drive business outcomes. It's a model proving to be priceless with the ability to personalize, predict customer needs, and optimize ROI. Let's examine how some algorithms are disrupting the game in different marketing fields. Customer Segmentation: Getting to Know Your People on a Deeper Level At the heart of effective marketing is understanding your audience, and it starts with segmentation. Segmentation used to depend on coarse demographics. Now, machine learning algorithms, and K-Means Clustering in particu...
Post a Comment
Read more

Back to Home

© 2025 Saikat's Blog. All Rights Reserved.