Turning Data into Dollars: The remarkable transformation Tunde achieved for a company’s marketing fortunes using only a pair of tools.
In the radiant city of Amphi-villa, there resided an enthusiastic data analyst named Tunde. His expertise was sought by Trendify, a mid-sized e-commerce company facing a complex marketing predicament. While sales remained consistent, customer engagement and retention struggled to keep pace. The marketing team grappled with the challenge of pinpointing the right audience for their campaigns and devising strategies to enhance customer loyalty. It was within this context that Tunde’s remarkable journey was set in motion.
Fueled by an unwavering passion for data and a resolute commitment to effecting change, Tunde embarked on a mission to address Trendify’s marketing dilemmas. The company possessed a wealth of historical customer purchase data tucked away in its database, a treasure trove awaiting exploration for valuable insights. Armed with SQL and Tableau, formidable tools renowned for data querying, visualization, and analysis, Tunde conducted his investigation, meticulously following a concise sequence of six steps.
Step 1: Inspecting the data
By implementing the SQL code provided below, Tunde conducted a comprehensive examination of the data, gaining an initial insight into its content. He ascertained that the company is engaged in selling seven distinct vehicle types across nineteen nations, spanning four global territories. Furthermore, he observed that the dataset encompasses a span of three years and that the company’s sales are categorized into three tiers based on deal size: large, medium, and small.
Step 2: Analyzing the data
Through the creation of additional SQL queries, Tunde delved deeper into the dataset, unearthing valuable insights. He unearthed that the company’s peak revenue was derived from Classic cars during the month of November. Moreover, the year 2004 stood out as a period of substantial sales activity. Additionally, a significant portion of the organization’s transactions were distinctly categorized as medium-sized deals.
Step 3: Customer Segmentation analysis through the RFM technique
Tunde knew that understanding customer behavior was key to solving the marketing problem. Hence, after discussion with the marketing team, he decided to employ the RFM technique to dissect and understand Trendify’s customer base.
RFM, which stands for Recency, Frequency, and Monetary value, is a methodological approach that delves into the purchase pattern of a group of customers.Recency pertains to how recently a customer has made a purchase. Frequency uncovers how often a customer engages in transactions and Monetary value outlines the amount of money spent by the customer during the course of transaction.
By synthesizing these three dimensions,Tunde systematically categorized customers into 6 different segments: Lost customers, Slipping away(cannot lose customers),new customers,potential customers,active customers and loyal customers
Step 4: Comprehending products with a strong correlation
Recognizing the significance of identifying frequently co-purchased products, Tunde acknowledges that such knowledge offers valuable insights to businesses in numerous aspects. These insights encompass the optimization of marketing, sales, and inventory approaches, ultimately leading to heightened revenue, enhanced customer contentment, and more streamlined operations. Thus, he proceeded to employ SQL codes for the purpose of exploring products that exhibited common purchasing patterns.
Step 5: Tableau Dashboard Visualization
He also acknowledges the power of visualization, he understood a Tableau dashboard holds immeasurable value for marketing and sales teams. By converting raw data into actionable insights, it empowers informed decision-making, facilitates focused strategies, and, in the end, plays a pivotal role in enhancing efficiency, fostering revenue growth, and bolstering customer satisfaction.
Consequently, he facilitated the development of two Tableau dashboards designed to visually represent sales data, the marketing team could now easily grasp the differences between segments and identify opportunities for targeted campaigns.
Sales_Dashboard_1 | Tableau Public
Sales_Dashboard_2 | Tableau Public
Step 6: Marketing Strategy implemented
Equipped with the segmentation insights derived from RFM analysis, the marketing team at Trendify embarked on a journey of crafting laser-focused marketing strategies. This strategic approach yielded remarkable results:
- High-Value Customers: These esteemed patrons were treated to exclusive offers and promotions on premium offerings, amplifying their potential to spend generously.
- Loyal Customers: A tailored approach was adopted, utilizing personalized emails to showcase new arrivals and updates tailored to their historical purchasing preferences.
- Lost Customers: With the aim of reigniting interest, re-engagement campaigns were meticulously executed, offering enticing discounts and incentives, thereby encouraging a triumphant return to Techville.
Conclusively,
Trendify’s marketing landscape underwent a notable transformation following the integration of the RFM technique and the invaluable insights gleaned from Tunde’s proficient data analysis and visualization expertise. Notably, customer engagement exhibited an upswing, while targeted segments witnessed a commendable surge in sales. The company’s rapport with its clientele grew more resilient and meaningful, culminating in heightened customer contentment and a surge in revenue.
Tunde’s expedition, from the intricate scrutiny of data via SQL to the vivid visualization of insights using Tableau, bore a profound impact on Trendify’s marketing strategy. This venture, propelled by dedication, technical prowess, and a profound grasp of customer dynamics, successfully surmounted the marketing hurdle, thus charting a trajectory of sustained triumph within the fiercely competitive e-commerce realm.
Thankyou.
Note: you can check out the sql codes and the visualization for this project on my github
