Case Studies
Welcome to our Case Studies section, where we highlight real-world examples of how our data analytics consultancy has empowered businesses across various industries. From optimising store locations for fashion retailers to enabling FMCG start-ups with operational reporting, our data-driven approach drives growth, efficiency, and better decision-making.
Each case study showcases the challenges our clients faced, the customized solutions we provided, and the remarkable results achieved. Join us on a journey through the transformative power of data analytics, uncovering how it can unlock new possibilities and drive measurable success for businesses of all sizes.
Case Study 1: Empowering a Small FMCG Startup with Operational Reporting and Business Insights
The Challenge
Our client, a small Fast-Moving Consumer Goods (FMCG) startup, was facing challenges in effectively tracking customer acquisition and sales data from their Shopify store.
They were experiencing difficulties in obtaining timely and accurate insights, which hindered their ability to make informed business decisions.
Manual data extraction and reporting processes were cumbersome and time-consuming, leading to missed opportunities and potential inefficiencies in their operations.
To achieve sustainable growth and competitiveness in the dynamic FMCG market, they recognized the critical need for a robust data analytics solution to streamline their operations.
The Solution
Ignite Insights designed a tailored solution to address the FMCG startup's challenges and optimise their operational reporting. Our team worked closely with the client to understand their specific requirements and pain points. The solution involved the following key steps:
Automated Data Extraction: We set up automated processes to extract relevant data from the client's Shopify store. This included sales data, customer information, and acquisition metrics. By automating the data extraction, we eliminated manual errors, reduced processing time, and ensured data accuracy.
Custom Database Development: To house the extracted data securely and efficiently, we designed and implemented a custom database. This database was optimised for performance and scalability, ensuring it could handle the growing volume of data as the business expanded.
Dashboard Development: Leveraging the data stored in the database, we developed intuitive and interactive dashboards. These dashboards provided real-time insights into sales and customer acquisition metrics on a daily, weekly, and monthly basis for each city they operate in. The user-friendly interface enabled the startup's team to access critical information with ease.
The Result
Our data analytics solution had a transformative impact on the FMCG startup, empowering them with actionable insights and operational efficiencies. The results achieved were as follows:
Accurate and Timely Reporting: With automated data extraction and real-time dashboards, the client gained access to accurate and up-to-date information on customer acquisition and sales. This enabled them to make data-driven decisions promptly, avoiding delays in responding to market changes.
Improved Business Insights: The comprehensive dashboards provided a holistic view of the business's performance across different time periods and locations. The location-based analytics shed light on regional sales patterns, helping the client make strategic decisions about inventory management, marketing campaigns, and expansion plans.
Enhanced Operational Efficiency: By automating data extraction and reporting processes, the FMCG startup saved significant time and resources that were previously dedicated to manual data handling. This allowed their team to focus on core business activities and value-added tasks.
Case Study 2: Enhancing Customer Engagement for a Women's Fashion Retailer through Personalised Email Marketing
The Challenge
Our client, a women's fashion retailer, faced challenges in effectively engaging their diverse customer base through their email marketing campaigns.
The emails they sent out were generic and lacked personalisation, leading to lower open rates, click-through rates, and an increasing number of unsubscribes.
The retailer recognised the potential of leveraging their customer data to create more targeted and personalised email campaigns.
They sought a data-driven solution that would enable them to identify customer attributes, preferences, and behaviours to tailor their marketing efforts and foster stronger customer relationships and as a result sales.
The Solution
Ignite Insights collaborated closely with the women's fashion retailer to devise a data-driven solution for their email marketing challenges. The solution involved the following key steps:
Data Analysis from Point of Sale (POS) System: We performed an in-depth data analysis on the transactional data from the retailer's POS. By analysing this data, we identified several customer attributes, including preferred store or channel; preferred brands and sizes; customer value; and purchasing patterns that can inform personalisation.
Customer Segmentation: Using the insights gained from data analysis, we segmented the customer base into distinct groups based on various criteria. These segments were designed to capture the different customer profiles and behaviours, allowing for more targeted and personalised marketing strategies.
Product Recommendations: Leveraging the transactional data, we developed product recommendation algorithms that suggested relevant products to individual customers based on their purchase history and preferences. This ensured that the emails provided valuable and enticing content to the recipients.
Development of Tags and Audiences: With the identified customer attributes and segments, we created tags and audiences within the retailer's MailChimp platform. This allowed the client to efficiently organise their customer data and tailor their email campaigns to specific customer groups.
Content Personalisation: Armed with the customer segments and product recommendations, we worked with the retailer to create personalised and compelling content for their email campaigns. This content aimed to resonate with the specific interests and preferences of each customer group.
The Result
The implementation of the data-driven solution yielded significant improvements in the women's fashion retailer's email marketing campaigns. The results achieved were as follows:
Increased Email Engagement: By personalising the email content and targeting smaller, more relevant customer groups, the retailer observed a substantial increase in email engagement. This was reflected in higher open rates and click-through rates, indicating that the recipients found the emails more relevant and engaging.
Reduced Unsubscribes: The personalised and targeted approach to email marketing led to a decrease in the number of customers unsubscribing from the retailer's email list. The improved content relevance ensured that customers were less likely to opt-out of the email communications.
Enhanced Customer Loyalty: With tailored product recommendations and personalised content, the retailer successfully nurtured stronger relationships with their customers. This, in turn, fostered increased customer loyalty and retention.
Optimised Marketing Spend: By targeting specific customer segments with personalised content, the retailer experienced a more efficient use of their marketing budget. The campaigns were better aligned with customer preferences, resulting in a higher return on investment.
Data-Driven Insights: The data analytics approach provided the retailer with valuable insights into their customers' preferences and behaviours. These insights could be used not only for email marketing but also for overall business strategies and inventory management.
Case Study 3: Streamlining Data Migration for a Footwear Retailer's In-Store and Online Platform Transition
The Challenge
Our client, a reputable footwear retailer, faced the challenge of migrating its in-store and online point of sale (POS) system to a new platform.
The migration involved complex tasks such as cleaning and merging customer profile data, loyalty points, and segments from the existing system. Additionally, the retailer needed assistance in extracting product images and details from their current online platform to be seamlessly loaded into the new system.
Moreover, creating an accurate inventory of stock on hand was crucial to ensure precise product availability for both in-store and online customers.
The absence of an option to export data from the existing website further complicated the data extraction process.
The retailer required a data-driven solution to handle these intricate tasks efficiently and ensure a smooth transition to the new platform.
The Solution
Ignite Insights collaborated closely with the footwear retailer and its new platform partners to develop a comprehensive solution for data migration. The solution comprised the following key steps:
Custom Data Extraction Tool: To overcome the limitation of exporting data from the existing website, we developed a custom data extraction tool. This tool allowed us to extract all necessary product images, descriptions, and details from the current online platform efficiently and systematically.
Data Cleaning and Tagging: The extracted product data was then meticulously cleaned and tagged to ensure it adhered to all the requirements of the new platform. This process involved standardising product attributes and categorising products accurately.
Customer Profile Cleansing and Standardisation: We cleaned and standardised the customer profiles to ensure consistent and reliable data was available for loading into the new platform. This step involved resolving data inconsistencies; removing duplicates; and unifying customer attributes.
Loyalty Points and Segments Integration: We integrated the loyalty points and customer segments into the new platform, ensuring that customers' loyalty rewards and segment-based benefits were preserved during the migration.
Historical Transaction Data Migration: By standardising customer transactions, we facilitated the loading of previous purchase history into the new platform. This enabled staff to access and utilise customers' historical data, which enabled them to provide a personalised service and recommendations to the customers.
The Result
The implementation of the data-driven solution resulted in numerous benefits for the footwear retailer's platform transition:
Efficient Platform Setup: The streamlined data migration process enabled the retailer to quickly set up and switch over to the new platform with minimal interruption to their business operations. This reduced downtime and allowed them to resume normal business activities swiftly.
Data Accuracy and Consistency: The cleaned and standardised data ensured that customer profiles, product details, and inventory information were accurate and consistent on the new platform. This promoted a seamless shopping experience for customers across both in-store and online channels.
Preserved Customer Loyalty: By successfully integrating loyalty points and segments, the retailer maintained its customer loyalty program without disruption. This retention of customer benefits strengthened brand loyalty and encouraged repeat purchases.
Improved Customer Service: The availability of historical purchase history in the new platform empowered the staff to provide personalised service to customers. This personalised approach enhanced customer satisfaction and overall shopping experience.
Increased Operational Efficiency: The automation of the data extraction and cleaning processes saved significant time and effort for the retailer. This increased operational efficiency allowed the team to focus on core business tasks and customer engagement.
Case Study 4: Unveiling the Impact of Click and Collect on In-Store Sales for a Large Homewares Retailer
The Challenge
Our client, a prominent homewares retailer, sought to explore the impact of their Click and Collect service on In-Store sales. Specifically, they wanted to investigate the hypothesis of whether customers made additional purchases when visiting the physical store to collect their online orders.
Understanding the relationship between Click and Collect and In-Store sales was critical to gauge the effectiveness of this service and its potential impact on overall revenue.
To achieve this, the retailer required a comprehensive data analysis that could merge information from their Point of Sale system (POS) and online ordering platform to reveal customer purchasing behaviour accurately.
The Solution
Ignite Insights undertook a thorough analysis of customer purchasing behaviour to address the retailer's objectives. The solution involved the following key steps:
Data Integration from Point of Sale and Online Ordering System: We merged data from the retailer's POS and online ordering platform. This allowed us to link Click and Collect orders with subsequent In-Store transactions by the same customers.
Identification of Additional In-Store Purchases: By analysing the integrated data, we identified customers who made additional purchases in-store while picking up their online orders. This enabled us to determine the proportion of Click and Collect customers who engaged in supplementary buying during their store visit.
Analysis of Additional In-Store Purchases: Further, we conducted a detailed analysis of the specific items that customers purchased in-store in addition to their online orders. This provided insights into the types of products that attracted customers' attention and resulted in additional sales.
Calculation of Impact on Sales: Based on the findings, we calculated the impact of Click and Collect on In-Store sales. We determined the ratio of additional In-Store purchases to the total amount spent on Click and Collect orders, highlighting the incremental sales generated by this service.
The Result
The data-driven analysis yielded valuable insights into the impact of Click and Collect on the large homewares retailer's In-Store sales. A number of the beneficial findings included:
Enhanced Understanding of Customer Behaviour: The detailed analysis provided the retailer with a comprehensive understanding of customer purchasing behaviour. This knowledge allowed them to tailor marketing strategies and product offerings to better cater to customer preferences and needs.
Validation of Click and Collect Strategy: The analysis validated the retailer's investment in Click and Collect as a valuable service offering. It confirmed that the service not only fulfilled customer convenience but also stimulated additional in-store purchases, driving incremental revenue.
Increased Customer Engagement: By identifying additional in-store purchases, the retailer gained insights into customer preferences for complementary products. This enabled them to create cross-selling opportunities and foster greater customer engagement.
Data-Driven Decision-Making: The data analysis provided concrete evidence of Click and Collect's positive impact on In-Store sales. This data-driven approach empowered the retailer to make informed decisions about the future expansion and optimisation of the service.
Boost in Overall Revenue: The findings demonstrated that Click and Collect customers made additional In-Store purchases amounting up to an additional 70 cents per $1 spent on their online orders. This translated into a significant boost in overall revenue for the retailer.
Case Study 5: Optimising Store Location for a Fashion Retailer through Data-Driven Analysis
The Challenge
Our client, a fashion retailer, faced a crucial decision when their existing store lease was nearing expiration.
They needed to determine whether to renew the lease at the current location or explore new store locations that could potentially retain their customer base while reducing operating costs.
The retailer recognised the significance of choosing the right store location to maintain customer loyalty and profitability.
They sought a data-driven solution to analyse customer data, identify potential new locations, and evaluate the impact of each location on their revenue and profit.
The Solution
Ignite Insights collaborated with a retail leasing consultant to develop a comprehensive solution for the fashion retailer's store location challenge. The solution involved the following key steps:
Customer Location Data Extraction and Cleansing: We extracted customer location data from the retailer's database and cleansed it to ensure accuracy and consistency. This data allowed us to understand the geographical distribution of the existing customer base.
Evaluation of Potential New Locations: Working closely with the retail leasing consultant, we identified and evaluated several potential new store locations. Each location was assessed based on factors such as foot traffic, visibility, accessibility, and leasing costs.
Customer Catchment Area Analysis: By analysing the customer location data, we determined the catchment areas for each proposed new store location. This analysis helped estimate the potential customer reach and accessibility of each possible new store.
Sales Projections and Customer Migration Modelling: Leveraging the customers' purchasing history, we developed a model to project potential sales from existing customers who would migrate to the new store location. Additionally, we estimated the potential for acquiring new customers in each location.
Cost-Benefit Analysis: With the projected sales and leasing cost information, we performed a cost-benefit analysis for each potential store location. This analysis weighed the potential revenue and profit against the leasing expenses to determine the overall financial impact.
The Result
The data-driven analysis enabled the fashion retailer to make an informed decision about their store location, resulting in numerous benefits:
Optimised Revenue and Profit Contribution: The selection of a new store location based on customer data and sales projections significantly improved the store's contribution to the retailer's revenue and profit. The optimised location attracted existing customers and new customers, driving sales growth.
Enhanced Customer Retention: By strategically choosing a new store location with customer catchment in mind, the retailer retained a substantial portion of their existing customer base. This strengthened customer loyalty and ensured ongoing business success.
Cost Savings and Efficiency: The relocation to a more cost-effective location reduced leasing expenses, positively impacting the store's profitability. The retailer could allocate these cost savings to other areas of the business to drive further growth and improvements.
Strategic Expansion Opportunities: The data-driven approach to store location decisions provided the retailer with valuable insights into customer behaviour and preferences. This information could be utilised for future expansion strategies and the development of additional stores.
Competitive Advantage: By making informed decisions backed by data analysis, the fashion retailer gained a competitive edge in the market. The optimised store location allowed them to efficiently serve their customer base and attract new shoppers.
Risk Mitigation: The data analysis mitigated the risk associated with choosing a new store location. The retailer could be confident in their decision, knowing it was based on robust customer data and projections.