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Case Studies

These are a couple of case studies where we used online advertising and website optimization strategies

Case Study #1: Local Restaurant Promotion

A local restaurateur was in need of increasing the amount of visits and orders online. Our proposed solution focused on enhancing their digital presence through increased posting on Instagram and Facebook, alongside the design and launch of a streamlined online ordering portal. Visually, we concentrated on featuring their wide selection of Italian dishes and featuring specials to order pizza, wings, salads or sandwiches.​​​​​​​​​​​​​​​​​​​​

The new website was optimized for speed and ease of use, with a strong emphasis on local SEO to attract customers within their immediate neighborhood. The marketing approach prioritized pickup and delivery, aligning with the owner’s plans to expand dine-in service once a liquor license was secured.

Our integrated media marketing strategy featured branded content promotions and the launch of new social media channels to expand reach. We maintained high engagement through consistent organic posting and amplified visibility with targeted paid media campaigns. Search-based marketing was aligned with audience intent to drive high-conversion traffic, while lift-based ROI tracking and precision targeting ensured continuous optimization and measurable growth.

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Solar Panels
Case Study #2: Predicting Solar Installs

A solar company wanted to gain insights on their in-market shoppers, who are likely to install a solar equipment at their homes. Insight Que Solutions developed a propensity-to-buy model based on customers’ historical attributes, which allowed the company help decide where to focus on their marketing efforts to close-in consumers.

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Approach

Various consumers’ attributes such as customers demographics (e.g., marital status, gender), housing attributes (# of bedroom, # of bathroom), financial information (e.g.,Net worth, estimated median income, etc), geographic information (e.g., city, zip-code, zip+4, etc.), solar attributes (e.g., current consumers has interest in home improvement, solar sq-foot, has a solar target etc.) were used to build the model. The dataset involve consumers information from one of the mountain states within united States contains over 3000 samples with multiple zip-codes.

 

  • Initially, Missing values were imputed using statistical modeling

    • CART (Classification and Regression Trees were used)

 

The original data-set contains 302 observations (8.13%) for Has_Solar_Target.

 

Initial dataset was split into Train and Test datasets.

 

Multiple Machine Learning models were trained; these models were tested and validated against historical data. Finally, the best performing model was selected.  

 

Results

 

Variables with highest predictive power to determine if a home owner wll

 

1. Land Value            

2. Home Purchase Price            

3. Estimated Income Amount            

4. Solar sq. ft. Roof Space            

5. Estimated Home Value  

 

Final Model:

Gradient Boosting Method (GBM) Accuracy: 84% -- Model was able to identify the if a consumer is likely to install a solar product at their homes with 84% accuracy.

Concluding Remarks

Applying data-driven predictive marketing, Insight Que Solutions provided insights to the solar company about their in-market shoppers. The precise accuracy of the Machine Learning model helped to pinpoint the consumers if they are likely to install a solar product in their homes in the near future. ​​

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Abingdon
Case Study #3: A Multi-Channel Holiday Campaign​

Insight Que Solutions executed a multi-channel marketing campaign for a women’s online watch merchant. The objective of the campaign was to increase their online sales and to move their old inventory SKU’s so that new inventory could come-in and replace the old inventory of the merchant.

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Approach

Insight Que Solutions used data driven strategic marketing execution, to execute this campaign for the watch merchant. The process was executed using best-in-class campaign design, strategic use of analytics and data and marketing automation.  ​​

The campaign was executed on primarily on three objectives:

a) A Landing Page  

b) Strategic execution of multiple email campaigns  

c) Miscellaneous Website Promotion Integration

 

The overall campaign execution included on the following components:

  • List of SKU’s for all items

  • Collateral provided (Images, videos, promo codes)

  • Messaging

  • Page setup/layout   

    • Image creation, editing, copy   

    • Programming 

    • Testing

    • Tracking  

 

Campaign Details:

1. A  Holiday Gift Guide

  • The Timeless Holiday Gift Guide

2. 6  Strategic Email Campaigns Campaign

 

Duration: 35 Days  

At each milestone, we collected and compared the core metrics for the campaign in order to evaluate the reach, engagement and conversions. Before executing the campaign, the client did not have the eCommerce tracking in place within analytics tool to understand the consume journey. We executed tracking enhancement during the campaign execution.  

Campaign Results Compared to Previous Year's Conversion Stats

  • Within first 2 weeks of campaign orders increased: 300%

  • Number of order increased compared to same period of 2017: 16%

  • 2018 sale ($) increased compared to the same period of 2016*: 21%

 

It is important to note, merchant decided to sale the product at a discounted price, otherwise, the campaign will had a larger impact on the net amount ($) sold.

First Week Performance During the Campaign Execution

At the beginning of the campaign launch, the main website recorded a 49% sessions increase from the previous year’s same period.  Overall Reach and Engagement Stats (full campaign duration) 

​Concluding Remarks

Applying data-driven strategic marketing execution, in this case, Insight Que Solutions improved the overall sales and number of total orders for the merchant significantly. The process was executed using best-in-class campaign design, strategic use of analytics and data and marketing automation. The process allowed us to learn customer behaviors at multiple levels, such as new customers’ behavior vs. repeat customer behavior, their propensity to respond to a campaign, interact and respond to campaigns through landing page and emails website elements during the holiday seasons. 

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