Peter had come to this ecommerce store acme.corp looking for jogger pants for his newfound love for fitness. While he searched and browsed for the pants he was looking for; he was recommended more options that other users with similar shopping interests had viewed. This helped him choose from options he was more likely to buy from. As he landed on the product display page, he came across "Bought Also Bought" suggestions. And he added a matching set of tees to his cart, along with a pair of gloves. As he moved ahead to view his cart, he found a smart and chic duffle bag with a free sipper bottle under complete the book recommendations.

While he had come to the online store to buy just jogger pants, he ended up buying more than what he needed but all that he could have wished for. And the ecommerce store ended up making more money per order only if this could be true every damn time! But, alas, it is not!

Recommendations – howsoever easy and integral it seems to the online shopping journey, in actuality, it is not. It needs a lot of data analysis and much more manual work to set things up and suggest relevant products to online shoppers across the shopping journey. Every time we talked to our customers – we could hear the challenges they were facing with enabling recommendation widgets on their platform (read ecommerce store) – despite it being a great tool to see a lift in conversions and Average Order Value.

How did it work earlier?

While everyone was convinced about the utility and value recommendations brought to the product discovery journey, we saw an exorbitant adoption rate for our offering. There were inhibitions regarding the complexity of changing and customizing the algorithms. This need for change meant external dependency on IT to learn (and unlearn) the existing logic and tweak the algorithms as per the changing business needs, always. All this meant more delay in bringing merchandising changes alive in real-time. We were all ears to these challenges being faced by our customers, and we wanted to do something about the same – to make it super simple and fun to make recommendations a part of the product discovery journey for our customers and their shoppers.

What has changed now?

We at Unbxd took it upon ourselves to design and devise a Recommendations engine which is:

1. 100% customizable – plug and play with various recommendation algorithms as per your business needs

2. Zero IT dependency – let merchandisers and marketers be in more control of running the recommendations engine for your ecommerce store

3. Faster Go-to-Market – with quick and easy onboarding and a self-serve environment

What's new in Recs 2.0?

With these new Unbxd Recommendations, we have ensured a faster go-to-market for the recommendations engine for an ecommerce store. It comes with power-packed features as listed below:

23. Faster Onboarding and Go Live

24. Strategy-based pre-defined Algorithms

25. Custom Algorithms

26. Hybrid Algorithms

27. Preview Debugger

28. Create experiences

Let's look at this short video and experience the Recs2.0(as we love to call it) before we jump into the details of each feature individually.

Book a demo to know how Unbxd Recommendations can drive sales.

Faster Onboarding

All you have to do to start is upload your catalog through API to instantly populate Unbxd Recs with the product feed. With an intuitive interface, you will experience seamless onboarding from start to finish to getting activated! (AJAX Integration – Coming Soon!)

Strategy-based Algorithms

Unbxd will offer 12 pre-defined algorithms to target shoppers across the shopping journey. This will allow you to personalize the recommendations based on popularity, the wisdom of the crowd, the catalog, and the past activity of the shoppers. With this, you can shorten the time to purchase and achieve targeted upsell and cross-sell opportunities.

Custom Algorithms

As the video above shows, your ecommerce team can create filter rules incorporating brand, price, category, and other product attributes. At the same time, fallbacks can be set in case of no matches, and dynamic filters can be set to match the shopper's intent at the time of the purchase. This will allow you to target multiple customer segments with different affinities.

Hybrid Algorithms

With this, you can combine multiple algorithms into a single recommendation widget. It implies you can always use a fallback option irrespective of the stage of the shopping journey. This feature allows you to utilize widget space effectively and showcase a wider selection of products.

Preview Debugger

As the name suggests, you can see the look and feel of how the recommendations widget (and products in the suggested slots) would look in real-time. It allows you to visualize and change the recommendations widget space before going live.

Create Experiences

Unbxd allows you to create a differentiated customer experience by swapping one algorithm with another. You can choose from a pre-defined set of algorithms or create a hybrid algorithm within a few clicks and custom-build the recommendation widget. Now that we have seen all the magic Unbxd Recommendations can make, let us know what business impact you can see out of using this slick and smart offering of ours.

Ecommerce visits where shoppers click on recommended products fetch 24% of orders and 26% of revenue. Enabling Recommendations on an ecommerce store results in 49% spontaneous purchases, and these shoppers are 2x more likely to return to your ecommerce store. So, why not? Millions of products are being suggested to millions of shoppers across channels, including web and mobile. In this plethora of options, uniqueness and engaging with shoppers at a 1:1 level remain the most challenging bit of the shopping journey. It demands that ecommerce stores offer a contextual and behavior-driven product discovery and shopping experience. And this is where our new 2.0 version of Unbxd Recommendations comes in handy. Unbxd Recommendations allows you:

1. Establish contextual relevance and better product discovery experience – meaning the shoppers can find the most relevant products (the ones they are most likely to buy) with ease and fluidity
2. More upsell and cross-sell opportunities – with 12 pre-defined algorithms and the ability to create custom and hybrid algorithms, you can now target various personas of shoppers with different affinities without any hassle
3. Higher Conversion Rates – You can show relevant recommendations to both new and repeat shoppers, thereby increasing the likelihood of purchasing and growing conversions
4. Increased Customer Retention – By offering a differentiated customer experience, you motivate shoppers to keep coming back to you and hence increase the overall customer lifetime value

We at Unbxd are constantly brainstorming, innovating, and gravitating toward building world-class products that mean more value for our customers. Book a demo to learn more about Unbxd Recommendations!