AI and ecommerce go naturally hand in hand. Being a digitally native service, ecommerce platforms have a wealth of data at their fingertips just waiting to be analyzed and automated by a competent algorithm.
It’s no secret that 2020 has changed the retail sector, with customers across the globe switching to or increasing their use of online shopping channels, and there are countless ways that AI can supercharge the success of ecommerce platforms during this boom. For example, recommendation engines that offer customers tailored product suggestions that truly match their tastes, to create a personalized, pain-free shopping experience.
Although some groundwork is required to get an effective algorithm up and running, it is possible to do so without breaking the bank by using a ‘build vs buy’ methodology during product development.
Here, we’ll explore the benefits of AI tools such as recommendation engines in the ecommerce space and explain how to adopt a ‘build or buy’ approach to take advantage of this technology.
Recommendation Engines: a refined model for ecommerce
Although customers may find some pleasure in browsing for products in-store, time and convenience is of the essence when it comes to online shopping. Personalized product recommendations ensure customers receive an optimized, convenient experience, in turn helping boost sales. A study from MyBuy found that recommendation engines boost conversion rates by a staggering 915% - but only if they’re able to accurately predict which content they should serve each user segment.
Here, a laser focused approach to customer segmentation is essential to ensure that recommendation engines make relevant suggestions by training on accurate data. For example, a fashion brand with a focus on cutting edge styles could investigate potential buyers in the 21-28 age range living in Berlin. By examining the social media habits of this segment and tracking, comments, photo shares, and interactions related to fashion trends in this scenario, the algorithm could discover that a certain style of a shoelace is a hot trend. With this intelligence to hand, the recommendation engine can serve buyers in this demographic with a range of trainers that have similar laces when they land on the app, increasing the chance of a sale.
Although large and established players may have sophisticated recommendation engines at their disposal, innovators shouldn’t think an AI model such as this is out of their reach. Let’s take a closer look at how such a model can be built swiftly with a ‘build or buy’ mindset.
Build or Buy: How to decide
There are huge opportunities in the ecommerce sector at present: online retail orders in the US skyrocketed by 146% in April 2020. But with this also comes increased competition which means speed is of the essence for any vendor hoping to launch or expand into digital.
Spending years training and developing an AI tool isn’t feasible as any truly disruptive ideas will be long obsolete by the time the model is ready. However, it is possible to swiftly create AI tools such as recommendation engines without sacrificing on quality.
There are many ready-to-use tools on the market that use repeatable solutions to address commonly occurring software challenges and requirements. In addition, many pre-trained AI models exist which avoid the need to build and teach the algorithm from scratch. The foundational model can then be tailored to the required use case with data found on the public online data markets, purchased from the data services, or gathered from social networks.
Ecommerce platforms can keep the ‘build or buy’ question in their minds to analyze which parts of their AI strategy can be supported by off the shelf solutions, then invest in fast-track development for the unique elements of their service.
Refining the choices:
When starting a new AI project the following variables should always be considered closely:
Next, existing market solutions should be explored to see if any of these can address any development tasks within the project. For example, there are many chatbot frameworks with natural language processing capabilities that can be easily repurposed. Building a custom or proprietary framework from scratch for a chatbot that delivers a standard service would not be a wise investment of time or money.
The nature of the AI project will also influence the best approach. For example:
The project needs to verify whether a recommendation engine will improve customer satisfaction levels: buy a turnkey solution that allows for adaptation to build a proof-of-concept model.
The minimal viable product can’t rely solely on the cloud: if existing solutions can’t be customized to support this, the project warrants a unique build.
A bespoke, market-ready product needs to be delivered after a successful proof-of-concept: creating specific deliverables such as offline support to very high standards justifies a bespoke solution.
It’s important to remember that any disruptive or particularly innovative solution will likely need a custom build. However, existing tools can work as solutions accelerators if they’re utilized as foundational building blocks. This cuts down on the amount of bespoke development and allows resources to be focused on the elements that are most likely to deliver a competitive advantage.
Optimizing the online shopping experience with AI
By using AI to create an optimized online experience, ecommerce brands stand to fuel sales conversion rates and transform customer retention. A strategic approach to software development can help to cut down the time to market and prioritize resources, but the answer on whether it's best to build or buy will always depend on the context and goals of the project.
Engaging with an AI consultancy such as intive during a time when speed is a vital factor helps to ensure the most direct approach to a high-quality product is found.