The “retail apocalypse” and “the death of brick and mortar” are common phrases echoed in the industry as pressure mounts for enterprise retailers scrambling to compete with the likes of Amazon. As one of the FANG companies (Facebook, Amazon, Netflix and Google), Amazon has passed the tipping point in its ability to collect trainable proprietary user data to the point where they understand more about their customer base than any other company in the world. Using artificial intelligence (AI) and machine learning technology, Amazon uses the massive amounts of data they collect to do everything from making their current products more successful to investing in new areas.
As identified by Jeff Bezos in his 2016 Amazon shareholder letter, “Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.”
Traditional retailers can’t keep up and must realize the imminent need to infuse AI with their processes. However, while AI and machine learning are hot topics, there tends to be a lack of understanding of how AI can actually be applied to solve retail business challenges. Using AI is a relatively new concept for retailers, especially those that have been around for decades and use the same legacy technologies and processes that are now the main factor behind why they’re falling behind. It’s not as simple as deploying “AI solutions”; this generalization of AI hinders a full understanding of what AI actually is, let alone how to apply machine learning-enabled software to make optimized retail business decisions. AI without operationalization capabilities is useless.
Amazon is a prime use case for revolutionizing retail through technology and retailers shouldn’t need convincing that automation is the way of the future. In fact, Amazon’s retail takeover is exactly the catalyst the industry needs to energize everyone to move faster. In today’s environment, AI is just better automation and better rules, which lead to better decisions, more quickly.
Rubikloud is dedicated to changing retail with “intelligent decision automation.” Focused on the retail industry, Rubikloud has built world-leading cloud-native, machine learning enabled solutions that address the unique business issues that retailers face (i.e. promotion planning, regular pricing, inventory overstock and understock accuracy, loyalty growth, etc.) to improve performance and positively impact their P&L.
Customer LifeCycle Manager (CLCM) applies machine learning to predict customer intention and behaviour through intelligent decision automation. This solution yields deep understanding of offer and communication effectiveness to deliver the most appropriate 1:1 personalized content, with relevant and timely offers, through the most optimal channel, for each customer to maximize engagement and deliver growth. CLCM uses AI to automate customer-centric decisions through marketing channels for retailers by focusing on generating incremental revenue.
CLCM was implemented for a $3 billion luxury cosmetic dealer for personalized campaigns across their channels and had the following results:
The application of machine learning in personalized campaigns yields more accurate predictions and better opportunities in generating incremental behaviour.
Promotion Manager applies machine learning to reduce the complexities of the promotion planning process to yield more accurate forecasting and automate decision-making. As the market changes, our solution continuously improves decisions for retailers to increase promotional effectiveness that drives incremental customer engagement. Promotion Manager automates the current mass promotions and merchandising process (the most expensive business processes in retail) to deliver:
a. the optimal mix of promotion mechanics,
b. a clear understanding of promotional effectiveness,
c. more accurate forecasts that reduce stock-outs and increase revenue, and
d. stronger customer experiences.
Using Promotion Manager, a $2 billion health and beauty retailer saw the following results:
The application of machine learning in the presence of complex promotion planning process yields more accurate forecasting and automated decision-making.
In order to minimize effort and resources for client deployments, Rubikloud developed tools to speed up and reduce the costs associated with onboarding new clients onto its products. These tools significantly reduce the time and resources it takes to ingest and control the quality of client data, configure the product to the specific needs of the client, and launch the product for full client use, by almost half. Each of Rubikloud’s products can be deployed in months and generate improvements in forecasting accuracy, inventory forecast efficiencies, and loyalty revenue.