AI: the hottest two-vowel acronym on the internet these days.
With the rise in chatbot tools like ChatGPT, Bard, and Jasper, these easily-accessible artificial intelligence systems have brought what felt like a futuristic science fiction reality to everyday life.
In this new reality, more organizations are looking at how they can leverage the power of artificial intelligence to streamline their own operations. Forward-thinking brands are asking themselves questions like…
(Ignore that last question, it only pertains to anyone from the Terminator movie series).
The e-commerce industry in particular has reaped the benefits of AI in a variety of ways.
In a recent McKinsey report, 79% of respondents stated that integrating AI into marketing and sales has increased business revenue. While the increase in revenue varied across organizations, the report found that businesses were able to generate at least 20% additional revenue by using AI strategies.
While AI is not necessarily a new concept, the technology has seen a dramatic increase in use and implementation across various industries over the last year.
So as we look to the rest of the year and beyond, how can AI help 3PL companies?
Before we dive in on the how, let’s get a clearer understanding of what exactly is AI.
AI is broadly defined as using machines to imitate intelligent human behavior. These replicated behaviors are certainly growing in number as technology continues to advance, but common examples include search recommendations, speech recognition, and chatbots. With access to a machine (in most cases, a computer), users can direct the technology to analyze and learn from data sets to produce a desired output. Additionally, AI can help to build algorithms that have been designed to predict patterns from learned behaviors.
AI is the overarching concept that embodies a subset of technologies such as:
The main differentiator between ML and DL is the way in which the two technologies process data to make predictions.
DL can eliminate much of the data pre-processing that is typically required from ML techniques and can often automate feature extraction to remove human-identified guidelines. A DL algorithm will be able to determine specific features that are deemed “most important” from a set of photos that might need to be pre-identified in an ML model.
For example, a DL algorithm could analyze a set of flower photos and categorize each flower based on important characteristics (i.e., color, petal shape). An ML algorithm may need intervention from a human in the pre-processing stage to be able to characterize each feature for the appropriate categorization.
AI has come a long way since the first successful program was written in the 1950s. These days the goldrush on AI business applications seems unending.
This is great news for many industries, including e-commerce. With so much online retail data now available, brands can automate and extract useful patterns in consumer behavior to dictate crucial decisions. Key examples include smart product bundling based on consumer behavior, personalized product recommendations for individual shoppers, or smart product discovery based on product similarities and consumer preferences.
AI technology can be extremely valuable for brands that are looking to increase conversions and decrease cart abandonment. By recording conversion and cart abandonment events, users can train ML models to populate relevant pages on a website with smart product recommendations and offerings, such as cross-sells, upsells, or special offers (i.e., exclusive discounts and gift with purchase).
With data-driven offers in place, brand owners can then measure which products are most likely to convert and ensure that these products are recommended throughout the shopping experience, thus maximizing the likelihood of conversion.
Since the data that brands use as input can take multiple forms, logistics processes can also be optimized. Instead of inputting data related to onsite clicks and purchases, companies can rely on product inventory and logistic metrics (such as shipping times) to build models that determine which products to recommend based on the stock levels in a certain area. If stock levels are low at a number of fulfillment centers, the ML model could learn to create more diversity in product recommendations to avoid depleting certain products.
Whether brands decide to make decisions based off automatic models or via rules engines, metrics such as the amount of stock available and the location of customers can be considered by AI technology when recommending products. With a capable algorithm in place, brands can simplify business decisions and be sure that their fulfillment processes are running smoothly.
The personalization platform, Rebuy, uses collaborative filtering ML techniques to surface timely and relevant product offers to onsite shoppers. Using Data Sources, or rulesets, Rebuy enables users to leverage AI endpoints to recommend items based on browsing behavior, previous actions, and purchase history.
While Rebuy clients have the ability to create custom guidelines using product tag, collection type, and URL rules, users can also deploy pre-built AI-based rules.
Available AI endpoints include:
These available endpoints do not require any coding or technical expertise, so brands can get up and running with AI-powered personalization in a flash.
Additionally, Rebuy’s Data Sources empower users to stack rules to achieve a fully-personalized setup. For example, if an online apparel brand wants to surface top sellers and products from a specific collection, users can apply both rules to the same widget and the platform will intelligently recommend appropriate products based on an AI algorithm.
Other AI-based Data Source capabilities include:
After identifying which AI endpoints make the most sense, e-commerce teams can surface personalized product recommendations through the entire, onsite customer journey.
Rebuy’s available widgets include:
Rebuy’s personalization widgets seek to improve metrics such as average order value (AOV), customer lifetime value (LTV), and conversions while simplifying brands’ tech stack to declutter one-off apps.
Additionally, the platform’s suite of powerful integrations enables users to connect with their favorite tech partners and offer offsite product offers to optimize omnichannel strategies.
The Smart Cart™ is a powerful e-commerce tool that brands can leverage to deploy a number of personalization features including:
Proven to increase conversions and decrease cart abandonment rates, the implementation of Rebuy’s Smart Cart™ encourages customers to fully complete their purchase(s).
However, after checking out, customers are expecting faster and faster delivery times. Now more than ever, it’s crucial for online retailers to deliver a satisfactory post-purchase experience.
An omnichannel fulfillment strategy is now just as important as an omnichannel personalization to determine whether or not customers repurchase.
3PL organizations, like Ryder E-commerce by Whiplash, can ensure that products are delivered successfully and on time. Additionally, 3PL fulfillment services can deliver brands peace of mind when it comes to the full-service logistics process.
In today’s day and age, it’s difficult for brands to scale if they don’t have an efficient logistics process in place. Even with the best marketing and sales tactics, customers won’t repurchase if they have a poor shipping experience.
Rebuy’s AI-powered platform, alongside their highly engaging features like Smart Cart™, enhance customer satisfaction and increases conversion rates, but when combined with Ryder E-commerce by Whiplash’s advanced order fulfillment and logistics technology, brands can offer customers an unparalleled end-to-end purchase experience.
Find out more about the Rebuy + Ryder E-commerce by Whiplash partnership.