It’s one thing to procure fashion finds in the latest styles, but another to make sure those items are discovered and purchased by customers shopping in your eCommerce store.
According to Google, “Retailers need to be ready to anticipate customer needs by analyzing customer trends and applying automation tools to keep pace with both large and small demand shifts in real-time.”
We’ve all seen how effective automated recommendations have been for larger online retailers like Amazon, but how can smaller Shopify retailers do the same and align with their customer’s needs, automatically and in real-time?
For most, an integration with SaaS or plug-ins is far easier than building their own software or mapping hundreds of products based on what merchants may guess is important to their customers. But which technology should they choose?
Argoid provides a no-code solution for eCommerce sites looking for real-time automated product recommendations, and we’ve discovered that it works extremely well with online Fashion storefronts - in fact, in A/B testing Argoid outperforms most other recommendation technologies.
With most site personalization software, the platforms are constructed on “Rule-Based” Personalization. This approach relies on domain expertise or manual data analytics. Personas are segmented based on a general understanding of user behavior. Also, a domain expert is needed to set any new rules periodically in order to strengthen results.
Argoid is different in its approach, where AI-driven 1:1 personalization replaces rule-based algorithms or manually-added data. We take segmentation to the next level with Micro Segmentation, providing a deeper understanding of site behavior. And rather than needing a domain expert to set new rules, Argoid automatically predicts changes in user behavior with constant learning, building on streaming algorithms.
The Argoid Personalization Engine is built with data from six different variables:
Real-time changes in the same session
Argoid changes recommendations in real-time, depending on the user’s intent in the current session.
To illustrate, a parent may have initially begun their session looking for a shirt for themselves, but if that user’s intent shifts to purchasing clothing for their child Argoid will catch on quickly, recommending choices for both parent and child within the feed.
Ensemble of models approach
Argoid implements many different machine learning models, not just one, to serve recommendations. Each of these models varies from one fashion store to another fashion store. The right model or set of models is picked automatically depending on the consumer’s behavior.
AI models with deep-level understanding of apparel product catalog
Fashion items have many sub-categories by which they can be described - type of apparel, fabric, fit type, closure, the occasion for wear, pattern, size, region suitable for, color, brand weight, price - Argoid applies 15+ product attributes for each one of these sub-categories.
This allows Argoid to have a deeper understanding of what may make a shirt or skirt appealing to someone, and then instantly uses the data to inform its recommendations moving forward.
Argoid understands the differences & nuances in buying behavior
Retailers know that wool socks are usually sought out during the winter and in cold locations. They know that dress trends are likely to shift after a big-name designer launches a new design. They also know socks are bought all year long by men, without variation. Knowledge like this is built into our algorithm.
The result? These nuances that would be understood by a seasoned salesperson in a brick-and-mortar store can now be a part of your eCommerce store’s up-selling strategy, simply by implementing Argoid.
Differences in the buying behavior of men, women, parents...
Argoid extracts behavioral observations from your site’s data to inform its recommendations. Examples of such observations include:
a. How frequently does this demographic visit the website for buying vs browsing?
b. How much time does this demographic spend on the website, looking for items of their choice?
c. Does the demographic prefer similar or different items based on their previous purchase?
Differences in buying behavior for each individual for each sub-category
Remember the sub-categories in point 3? Argoid models understand the spending budget of each individual, along with brand and color affinity, the likelihood of an individual to purchase a similar product again, coupled with preferred sizes (and a key point - whether or not there is corresponding availability in your inventory) and what fabrics the user prefers.
Essentially, Argoid is doing all of the thinking for your customer, taking multiple needs and preferences into account and presenting a product that your customer will actually want.
To create a recommendation engine as intuitive as Argoid in-house for a single Shopify fashion retailer would require additional hires, take years of development and come at a hefty price. We offer the ability for fashion storefront owners, marketers and website programmers to take that effort and cost completely off their plates, and replace it with a product that would likely surpass what most in-house teams could develop. And unlike other personalization engines, we offer AI-driven 1:1 personalization and Micro Segmentation, and require no outside intervention in order to keep your product feed current and relevant.
We encourage you to reach out and learn more about implementing Argoid - our turn-key, no-code, SaaS solution that will give your customers what they want with every visit, quickly and accurately. Talk to us!