Create Personalized “New In” Recommendations instead of the One-Size-Fits-All Approach

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Home page is the storefront

Regardless of the city, if you walk on any high street around the world, the physical stores will showcase timely products of new selections and lines, enticing you to visit their store if you spot something interesting. The same methodology also occurs in the online context, especially in fashion & garment. The main motivation is simple to extrapolate: Seasonality matters and hugely affects consumer preferences as the majority of consumers are not after winter parkas in the mid-summer anymore – nor do they shop boardshorts for Christmas presents (at least on Northern hemisphere).

A seasonality example below: Just before NYE (when this article was written), Windsor promoted flashy looks for New Year celebrations.

As it’s very likely that you work in, or are closely related to retail if you read this, we’ll just mention that new products typically yield a higher margin and that it’s a common target to sell at least half of the new stock (in fashion) before the sales season begins. An important and slightly less known factor is the need to cultivate new best sellers out of the new inventory as each product (or range) has its limited lifespan, and mainly because it affects the long-term success of nearly any retail business. As a further read on the topic for true geeks, I recommend this book delving into depths of merchandising analytics.

There are arguably numerous other reasons and factors affecting what is promoted especially on the homepage, like manufacturer deals and sales targets but to support the case of New Inventory merchandising and to add a personalization layer and touch, Nosto supports this by toggling one switch: New In (example below) Next we’ll cover how it can adjust recommendations depending on the intended use case.

Apply tactics based on the target

We get back to our example site Costo which has a fairly limited inventory and adds to it fairly regularly. More so monthly than weekly, new products arrive to its inventory and due to seasonality – it’s winter when writing this – the generic best sellers on the homepage would be like in the picture you can see below that focuses on: Beanies. Most of the items represent the evergreen products year after year, except of the hot top seller Wau Teal and Wau Rose, indicating the fun fact of how the majority of shoppers stick to safe black, gray and burgundy color options, while shoppers often click something more exciting in social ads or in other marketing.

In the next example below, when the simple New In inclusion setting is enabled with a relatively long time period of 60 days, (note also the highlighted ribbon in the menu), the recommendation output changes quite dramatically, as recommended products are now limited to products that were added to the inventory during the past two months. Wau Teal and Rose still climb up the popularity ladder while other options take the place of the evergreens, which have been the bread & butter to Costo for many years.

Since the line was added roughly two months ago and because my own personal visit to the site dates back just a week or two ago, if the option New In Since My Last Visit option is enabled, the best seller recommendation output looks into my past visit and recommends products that were added between this time period, consequently surfacing product ranges that I didn’t even know Costo sells: Bow ties.

To add even more personalization effects, if the recommendation type is changed from Best Sellers to Browsing History Related by still applying the New In rule, the recommendation looks into my individual preferences (hats with caps) and gives more weight to the most recent actions on the site. Hence, the output is a combination of what’s new and noteworthy and personalized for you and looks like the below:

Finally, the most advanced use case would be creating New In Recommendations based on category and/or brand affinities, meaning that the New In Recommendation would look into segment affinities and customize the recommendation based on (for example) category affinity and later into what’s new and noticeable in that range. In the below picture you can see the Asmat segment and an example of how the output could look for the segment.

Learn more

To conclude, this article’s goal was to demonstrate multiple different options and use cases which a simple New In rule could cover. Due to the sheer amount of options, what is good is that getting started is literally as easy as toggling one switch called “New In” under the Inclusion and Exclusion settings. As always, reach out to our product specialists in case you would like to ask what specific setting you should use to cover your current business target!

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