Sakshi’s team hit a bit of a wall during an ingredient-scouting project for a leading chocolate company. The key question was simple but tricky:<\/p>\n\n\n\n
\u201cHow do you even test ingredients that aren\u2019t finished product yet?\u201d<\/p>\n\n\n\n
The team needed a way to make ingredient performance more practical, visible, and easier for the client to evaluate.<\/p>\n\n\n\n
Instead of testing ingredients in isolation, the team identified and analyzed existing chocolates and confectionery products that already used similar ingredients. A list of products was prepared for procurement. Most were sourced from India, while a few came from the US. Shikhar and Rohit brought back the US products during their return trip. Another Greybian, Rishabh, who was in Europe, was also brought into the process to scout relevant products there, taste them, and share his reviews.<\/p>\n\n\n\n
This helped them:<\/p>\n\n\n\n
In short, the team turned something abstract into something they could actually taste, compare, and respond to.<\/p>\n\n\n\n
The usual route would have been to pay third-party agencies. However, the team decided to take a more creative route.<\/p>\n\n\n\n
Here\u2019s what they did:<\/p>\n\n\n\n
Now, one concern was obvious: Would Reddit responses affect the quality of the data?<\/strong><\/p>\n\n\n\n The team accounted for this by adding a simple filter:<\/p>\n\n\n\n It was not a perfect system, but it was an effective way to keep the responses relevant.<\/p>\n\n\n\n Over the course of the week, the team procured and tested several ingredients in their commercially available forms. Each product was evaluated for taste, mouthfeel, odor, aftertaste, solubility, and repurchase intent. These findings were then cross-referenced with what consumers had reported online.<\/p>\n\n\n\n All this was done even though neither product testing nor consumer surveys were part of the original proposal.<\/p>\n\n\n\n The team presented this on a client call yesterday. Before the deck was even opened, one of the client team members mentioned that she had reviewed the update beforehand.<\/p>\n\n\n\n Her first words on the call were:<\/p>\n\n\n\n \u201cI saw you guys did product testing \u2014 it is quite impressive. I’m curious to know, are you going to do this for all the ingredients?\u201d<\/strong><\/p>\n\n\n\n When a client asks for more of something before the team has even walked them through it, that is more than positive feedback. It is a signal.<\/p>\n\n\n\n It showed that the client was no longer seeing product testing as a one-time add-on. They were beginning to see it as a lens that could be applied consistently across the project.<\/p>\n\n\n\n For most ingredients, the testing confirmed what secondary research had already suggested. But for a few, it added a layer of insight that online data could not have surfaced.<\/p>\n\n\n\n For one ingredient, online reviews flagged color as a recurring complaint. However, the team did not experience this issue during testing.<\/p>\n\n\n\n This showed that the color concern was brand-specific, not inherent to the ingredient. For client, that meant it was a manageable formulation variable, not a red flag against the ingredient itself.<\/p>\n\n\n\n Without physical testing, this distinction would have been missed.<\/p>\n\n\n\n For another ingredient, consumer reviews online were broadly acceptable. But when the team tested the product, the flavor felt overpowering, artificial, and extra sweet.<\/p>\n\n\n\n The team also found internal color inconsistency. Some pieces were pink, while others were yellow, despite having the same flavor. No online reviewer had flagged this.<\/p>\n\n\n\n The team\u2019s consensus was clear: they would not buy it again.<\/strong><\/p>\n\n\n\n This insight would have been missed entirely if the team had relied only on secondary data.<\/p>\n\n\n\n Matcha was the most interesting case.<\/p>\n\n\n\n Online reviews for matcha were mixed, and the team\u2019s response was also mixed. At first, this looked like a simple confirmation of what was already available online.<\/p>\n\n\n\n But when the team looked more closely at who liked it and who did not, a pattern emerged.<\/p>\n\n\n\n The people who enjoyed the pleasant bitterness of green tea or matcha were also those who liked dark chocolate or preferred high-caffeine beverages. They already had a palate for pleasant bitterness.<\/p>\n\n\n\n This is not something an online survey would have revealed because reviewers usually do not describe themselves that way.<\/p>\n\n\n\n So, what looked like a generic \u201cmixed review\u201d online became a segment signal. Matcha\u2019s bitterness may not be a problem to fix. It may be a targeting lever.<\/p>\n\n\n\n This was especially relevant because the client already played in the dark chocolate space.<\/p>\n\n\n\n Most companies spend 18-24 months developing a single new product, with ingredient sourcing consuming nearly 40% of that timeline.<\/p>\n\n\n\n\n
Product Testing Became a Strategic Priority, Not a One-Time Add-On For The Client <\/h2>\n\n\n\n
Matcha Emerged as a Clear Segment Signal in Product Testing<\/h2>\n\n\n\n
1. A Color Concern Turned Out to Be Brand-Specific<\/h3>\n\n\n\n
2. Acceptable Online Reviews Did Not Match the Actual Taste Experience<\/h3>\n\n\n\n
3. Matcha\u2019s Bitter Edge Limits Mass Appeal, but Appeals to Dark Chocolate Lovers<\/h3>\n\n\n\n
Discover Ingredients That Match Your Product’s Vision<\/h2>\n\n\n\n