Ecommerce ops · Production

Dawn Foods improves B2B e-commerce search accuracy and sales with Algolia

The problem

Dawn Foods' simple keyword search could not handle complex multi-keyword B2B queries across a catalog of well over 10,000 SKUs and 10 million different price points, resulting in rage clicks, high search abandonment rates, and lower-than-desired conversions.

First attempt

The original search solution could not match complex multi-keyword queries to the correct products, and it provided only limited visibility into how customers were interacting with search.

Workflow diagram · grounded in source
1
Complex B2B query submitted
trigger
“if a bakery is looking for ingredients for "gluten-free german chocolate cake", it needs to be directed to the two correct products offered, not thousands that include one of the four keywords in the search term”
2
Algolia interprets user intent
ai_action
“Algolia gives our users the answer appropriate to their query, understanding their intent, and matching what they're asking for. And that's before conducting any sort of tuning activities on the solution. We just put all the searchable d…”
3
Personalized catalog and custom SKUs
ai_action
“Search must address custom SKUs available to individual customers, such as packages. Algolia has made all this possible, and easy to implement.”
4
Unified multi-content results returned
output
“Dawn Foods' new Algolia Search implementation made all their content — products, recipes, insights, marketing content, information and support pages, and education — available and accessible through search”
5
Replacement ingredient routing
routing
“quickly directing customers to replacement ingredients as needed”
6
Search data feedback loop
feedback_loop
“The digital team plans to use that data to create a feedback loop to further refine search and improve conversions”
Reported outcome

Algolia was deployed in six weeks and delivered immediate improvement: search-influenced sales greatly increased, adoption rate targets were exceeded, and both internal and external complaints were replaced with strong praise for the fast and accurate results.

Reported metrics
product SKUs indexedwell over 10,000 SKUs
Price points in product catalog10 million
personalized catalog coverage (share of total SKUs)25%
Implementation timesix weeks
Show all 8 reported metrics
product SKUs indexedwell over 10,000 SKUs
price points in product catalog10 million
personalized catalog coverage (share of total SKUs)25%
implementation timesix weeks
search influenced salesgreatly increased search influenced sales since implementation
adoption rate vs targetexceeded its adoption rate targets
targeted lift delivereddelivered a targeted lift
customer and employee complaints about searchcomplaints replaced with strong praise
Reported stack
Algoliacommercetools
Source
https://www.algolia.com/customers/dawnfoods
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Algolia was deployed in six weeks and delivered immediate improvement: search-influenced sales greatly increased, adoption rate targets were exceeded, and both internal and external complaints were replaced with stron…

What tools did this team use?

Algolia, commercetools.

What results were reported?

product SKUs indexed: well over 10,000 SKUs; Price points in product catalog: 10 million; personalized catalog coverage (share of total SKUs): 25%; Implementation time: six weeks (source-reported, not independently verified).

What failed first in this deployment?

The original search solution could not match complex multi-keyword queries to the correct products, and it provided only limited visibility into how customers were interacting with search.

How is this ecommerce ops AI workflow structured?

Complex B2B query submitted → Algolia interprets user intent → Personalized catalog and custom SKUs → Unified multi-content results returned → Replacement ingredient routing → Search data feedback loop.

Related ecommerce ops cases