New research guide: The Confidence Economy, how AI is changing fashion commerce.
Read it →
Use case
The longer they use Elara, the faster they buy.
Acquiring a new fashion customer costs $30-130. A returning customer costs nothing to re-acquire. The Style Graph gives them a materially better experience on every return visit — which is why they return.
The business case
Retention is cheaper than acquisition. Elara makes retention compounding.
Acquiring a new fashion ecommerce customer costs $30-130 depending on channel. A repeat customer requires no acquisition spend, and buys more per visit than a first-time visitor. The challenge is making repeat visits feel worth returning for. Elara gives returning shoppers a concrete reason: the experience is meaningfully better because the system knows them better.
0 acquisition cost
A returning shopper who converts costs nothing to acquire, Elara’s Style Graph makes them more likely to return and convert
Compounds over time
The Style Graph is the only part of Elara that gets better without any merchant input, it trains itself
LTV multiplier
A shopper who visits four times and buys twice is worth more than four first-time customers, Elara is optimized for that shopper
How it works
Every visit, Elara knows more.
01
Style Graph
Brief
Outfit
Like / skip
First visit: the Style Graph initializes
The shopper’s first interaction, a brief, an outfit, a like or skip, initializes their Style Graph. Generic AI tools reset to zero on every visit. Elara compounds from session one, which is why a fourth visit converts faster than a first.
02
Deepens
Occasions
Aesthetics
Price points
Skips
Each interaction deepens the model
Every subsequent visit adds data: which occasions the shopper returns for, which aesthetics they consistently choose, which price points they buy at, which types of pieces they skip.
03
34%
visit 1 · accuracy
91%
visit 4 · accuracy
Returning shoppers convert faster
By the third or fourth visit, the Style Graph has enough signal to produce recommendations that feel like they came from someone who genuinely knows the shopper’s taste. Less deliberation. Faster checkout.
Explore more
See what else Elara does.
Questions, answered.
How is the Style Graph different from a standard personalization algorithm?
Standard personalization algorithms predict what a shopper might buy based on what similar shoppers have bought. The Style Graph builds a model of aesthetic preference: what this specific shopper considers beautiful, appropriate, and fitting for their life, tracking what they like and skip across occasion contexts.
Does the Style Graph carry over between Elara on different brands?
No, each brand's data stays exclusive to that brand. The exception is the Elara iOS consumer app: shoppers who use the app have a cross-brand Style Graph, making a first brand visit perform like a third or fourth visit.
How many sessions does it take for the Style Graph to be meaningfully better?
One session with a complete outfit interaction gives the Style Graph enough signal to be useful. Three to four sessions produce a model that meaningfully outperforms a cold-start recommendation.
Can I use the Style Graph data for email or remarketing?
The Elara merchant dashboard surfaces aggregated Style Graph insights, which aesthetic profiles your customer base clusters into, which occasions drive the most repeat purchases, and which taste segments have the highest LTV.
See what a shopper’s fourth visit looks like when the Style Graph knows them.
30-day free trial. Holdout-based lift report included. No developer required.
Live in under an hour · No dev required · Real lift measurement