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CASE STUDY
Boutiques Daily touts itself as the platform for the boutique community of the world to discover, engage and collaborate with one another.
We were happy to help them through the folllowing channels :
  • Social Media Analytics
  • Influencer Aggregation
  • Community Platform Design
CLIENT
Boutiques Daily is a closed community marketplace and media center and has been reimagining the way boutiques are discovered, and how the global boutique community of brands, bloggers, designers, photographers, models, stylists and brand reps can discover and collaborate with one another.
PROBLEM
The most important online tool that the boutique community uses is social media. Boutiques Daily had realised that the buzz around the worth of data that can be aggregated from social media was worth exploring. But, they were unsure of how they could tap into this and how technology could assist them here. This is where we came in.
SOLUTION OVERVIEW
Our solution consisted of three parts:
Collect relevant data from Instagram, Twitter, Pinterest and Facebook. Relevance is based upon various factors such as location, hashtags, follower, viewers, likes etc.
Mine this data for valuable insights such as influencers, fashion trends, shopping habits etc. pertaining to each segment.
Present the result to the boutique community in an easy to grasp and useful manner. Provide the community tools with which they could use this data to interact with each other.
Resource Allocation
This project was more tasking on the analytical side of our team and was as usual lead by our design thinking principles. We assigned the following roles to the project:
1 Project Manager 1 Experience Designer 1 Interface Designer 1 Interaction Designer 3 Full Stack Developers 2 Data Analysts 1 QA Engineer
SOLUTION
Algorithms are not good at motivating people, but
they are great at finding people who do.
With the increasing reach of social media in newer markets, brands have the exciting opportunity of reaching out to them in unique ways. And to determine already established channels in the newer markets should be significant than trying to establish one.
Data Aggregation
Algorithms were developed that would fetch relevant data from Instagram, Twitter, Facebook and Pinterest. Out of these, except for the Facebook API, all others needed a lot of workarounds to get the desired results as the documentation on them were not as detailed.
Influencer Detection
Algorithms were designed and developed to mine out influencers in each segment from the data that was aggregated. A lot of matching, reverse engineering and sorting on large amount of data, meant some late nights for our data analysts and developers.
Community Platform
A web application for all the players to interact with each other. Experience designers had to frequently redesign the community aspects as the output from the developers on what can be achieved from the data varied from time to time. Usually we have our design decisions driving any project, but for this one, the technological constraints were somewhat out of our hands and we had to adjust accordingly.
IMPACT
80%
of the Boutiques that joined the community have now shown significant increase in their sales.
3x
Photographers, Stylists and Models have increased their outreach after joining Boutiques Daily.
94%
When Boutiques Daily packaged some features into their premium plan, the plan saw (an unexpected tbh) 94% acceptance among existing clients.
Excellent team! I have always felt like they were a partner rather than an agency. Their communication skills and adherence to schedules as well as cooperation, reinforced and built my feelings that they are a full-time partner. Another aspect I really loved working with is that they are honest, asks questions, suggests solutions and is always thinking a few steps ahead to try and foresee problems. I would recommend L8 N8 Labs to anyone needing their talents.
Josh Clemence
Josh Clemence
Founder & CEO, Boutiques Daily
APPROACH
Today’s problems cannot be solved with
yesterday’s worldviews.
Thinking out of the box and resisting conventional approaches to modern problems were core to our process here. As the saying goes, we didn’t want to bring a knife to a sword fight!
  • Listen
  • Research & Market Study
  • Brainstorming
  • Infrastructure design
  • Design-Dev-Test Sprints
  • Testing & QA
  • Product Launch
  • Analytics & Feedback Loop
Listen
For this project, we had been in talks with the client a good 40 days before assigning the full team on it. Among other things, we used this time to clear out any unreachable expectations that either party may have had.
Research & Market Study
The research mainly consisted of experimenting with the APIs from Instagram, Twitter, Facebook and Pinterest. Tools offerring social media analytic services were a dime a dozen. But, we needed ways to produce insights specific to the boutique community, and also something that will promote interaction among various players within the community.
Brainstorming
The chunk of the time here was spent on laying out the groundwork for the community aspects of the platform. Significance was given to increasing interaction among users and presenting valuable data in an intuitive form.
Infrastructure design
Need for large chunks of data demanded a highly scalable infrastructure for the solution. We considered various options such as Microsoft Azure, Google Cloud etc. Ultimately, Amazon AWS seemed to fit almost all our constriants and the scalability issue was also solved quite easily. Other major decisions made here were on databasing and application architecture.
Design-Dev-Test Sprints
Needless to say the agile method was used here. The sprints were oriented around social media platforms, data aggregation methods and data mining goals. Due to the need for a large amount of data spread over a good amount of time, a lot of "garbage" were intentinally mixed with real data to test out the eventual effects.
Testing & QA
Obviously the important factor to test out here was the data mining aspect. We had to know how much stress can our database stack handle at any instant and how to optimise the insight generation aspect of the tool. The testing process enabled us to categorise insights as instant and over-time. Insights belonging to the latter category were calculated over a period of time varying from 24 hours to 7 days.
Product Launch
The beta launch included around 2000 users. This expanded across all the user categories - Boutiques, Models, Photographers, Stylists and Brands. The users were socially onboarded 30 days prior to the beta launch so as to get some meat for the insights from their social media activities. The beta version was aimed at getting realtime feedback on the interaction aspect and we ran the beta for 120 days.
Analytics & Feedback Loop
Saying that the integral part of the system i.e. the social profiles of the users are unpredictable, is an understatement. So we knew beforehand that there were some really good feedback awaiting for us once we launch. One of the main pivots we did due to this feedback was to add more direct outlets from the community to the social media platforms.

Interested to learn more on our process? Here is how we approached the problem step by step.

APPROACH
Today’s problems cannot be solved with yesterday’s worldviews.
Thinking out of the box and resisting conventional approaches to modern problems were core to our process here. As the saying goes, we didn’t want to bring a knife to a sword fight!
Listen
For this project, we had been in talks with the client a good 40 days before assigning the full team on it. Among other things, we used this time to clear out any unreachable expectations that either party may have had.
Research & Market Study
The research mainly consisted of experimenting with the APIs from Instagram, Twitter, Facebook and Pinterest. Tools offerring social media analytic services were a dime a dozen. But, we needed ways to produce insights specific to the boutique community, and also something that will promote interaction among various players within the community.
Brainstorming
The chunk of the time here was spent on laying out the groundwork for the community aspects of the platform. Significance was given to increasing interaction among users and presenting valuable data in an intuitive form.
Infrastructure design
Need for large chunks of data demanded a highly scalable infrastructure for the solution. We considered various options such as Microsoft Azure, Google Cloud etc. Ultimately, Amazon AWS seemed to fit almost all our constriants and the scalability issue was also solved quite easily. Other major decisions made here were on databasing and application architecture.
Design-Dev-Test Sprints
Needless to say the agile method was used here. The sprints were oriented around social media platforms, data aggregation methods and data mining goals. Due to the need for a large amount of data spread over a good amount of time, a lot of "garbage" were intentinally mixed with real data to test out the eventual effects.
Testing & QA
Obviously the important factor to test out here was the data mining aspect. We had to know how much stress can our database stack handle at any instant and how to optimise the insight generation aspect of the tool. The testing process enabled us to categorise insights as instant and over-time. Insights belonging to the latter category were calculated over a period of time varying from 24 hours to 7 days.
Product Launch
The beta launch included around 2000 users. This expanded across all the user categories - Boutiques, Models, Photographers, Stylists and Brands. The users were socially onboarded 30 days prior to the beta launch so as to get some meat for the insights from their social media activities. The beta version was aimed at getting realtime feedback on the interaction aspect and we ran the beta for 120 days.
Sustained Product Analysis
Saying that the integral part of the system i.e. the social profiles of the users are unpredictable, is an understatement. So we knew beforehand that there were some really good feedback awaiting for us once we launch. One of the main pivots we did due to this feedback was to add more direct outlets from the community to the social media platforms.
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