Demystifying Enterprise AI for Retail

> : / / / W H I T E P A P E R

W W W . R U B I K L O U D . A I

P A G E | 1R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

Today’s retailers face unprecedented competition. Traditional retail models are being upended by new channels, players and technologies. Entrenched brick and mortar retailers are making significant strides into eCommerce, while dominant eCommerce retailers are aggressively pushing into the brick and mortar world, experimenting with new store formats, and aggressively growing their store footprint through M&A activity. National brands are expanding their retail network, while multi-brand retailers are building portfolios of private label brands to compete. All of this means that customers have more ways than ever to buy the goods and services they want, and their expectations have risen accordingly.

This is not a new phenomenon, the customer has always been the greatest change agent in retail; from the pioneers behind the mail order catalogue through the proliferation of mega shopping malls, there has been no shortage of disruption. What has changed in recent years is the pace of innovation and the pressure to evolve or be left behind. Credit the exponential growth and adoption of new technologies, and blurring of online and brick and mortar merchants. The speed of evolution has picked up and the margin for error or underperformance has all but disappeared. In today’s hyper-competitive environment, the rules of engagement seem to be changing daily and the risk of failure is much higher than before. One of the keys to survival will be the adoption of new technologies that intelligently automate decision making, freeing up retailers to focus on the core elements of their business that define their competitive advantage.

Despite the hype surrounding Artificial Intelligence and Machine Learning, the technology is far from a pipe dream. The technology is here today and is solving real business problems for retailers, driving incremental sales, improving margins, and saving costly employee time.

Read on to learn how AI can improve your supply chain, drive profit margin growth and build loyal customer bases.

3 W AY S A I C A N T R A N S F O R M R E TA I L , T O D AY.

A C C U R A C Y A U T O M A T I O N S C A L E

P A G E | 2R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

RETAIL IS CHANGING. WHAT CAN RETAILERS DO ABOUT IT?

Most retailers look at Amazon and Walmart as a primary threat to the long-term stability of their core business. These companies utilize their unparalleled scale and operational excellence to dominate existing lines of business and successfully enter new ones.

In order to compete with players like Amazon and Walmart, retailers must be prepared leverage their data to the same extent. Combined with new technologies and best practices, this data can be used to deliver better experiences, drive more relevant offers, and create more efficient supply chains. AI is helping retailers to move from business rule driven, to intelligent decision automation, at a scale that would not be possible with human effort alone.

Retailers would be wise to follow early adopters embracing AI and systematically integrating it into their operations. Although the topic of AI conjures images of autonomous robots stocking shelves and running checkouts, the most tangible application of this technology is in building systems that learn and adapt to eliminate waste and inefficiency throughout organizations. Used strategically, AI can tackle core business challenges from front facing client interactions all the way to the backroom. It can also increase supplier efficiency by reducing inventory and supply chain costs and even generate higher customer satisfaction and loyalty. All of this is possible without a large-scale technology re-platform and comes from simply making better use of the available data.

P A G E | 3R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

A I I S H E R E T O D AY AI is real, actionable, and achieving incredible results with many leading retailers. The opportunity is to apply focused and retail-specific AI that integrates intelligent decision automation to augment existing retail processes at scale. It can be difficult to differentiate between practical applications of AI and the rest of the vaporware in the market. Practical applications of AI should help you solve a problem faster, more accurately, or with less resources to justify the investment.

With this perspective in mind you can start to differentiate between applications such as computer vision for sentiment analysis, compared to forecasting using ensemble learning (forecasts where set of predictive models are created and its outputs combined to become the single output of the entire ensemble). The former falls into Gartner’s “Peak of Inflated Expectations” whereas the latter is driving real financial impact for retailers today.

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.

J E F F B E Z O S

P A G E | 4R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

S O U R C E 1 https://hbr.org/2004/05/stock-outs-cause-walkouts

Industry leaders are now leveraging AI to power everything from promotions to fraud detection. Retailers can - and should - use AI to better integrate streams of data, optimize inventory and supply chain, and all but eliminate dreaded stock-outs. According to a study done by the Harvard Business Review, stock-out rates worldwide are stubbornly lodged at about 8%. “Retailers can lose nearly half of intended purchases when customers encounter stock-outs. Those abandoned purchases translate into sales losses of about 4% for a typical retailer. For a billion-dollar retailer, that could mean $40 million a year in lost sales.” 1

For the majority of retailers, there is room for improvement. Retailers holding too much inventory are investing too much capital, pricing inefficiently and not anticipating customers’ changing demands when stocking shelves or online quantities. Leveraging machine learning to augment retailers’ intuition, the application of AI can result in double digit improvements in key measures, maximizing sell-through while minimizing the incidence of over and under stock. Retailers already have the data they need to anticipate customer demand and better fulfill their needs and wants, but they require the application of AI to optimize at scale.

How do AI systems help? Some proven examples have included streamlining disparate data sets to provide a complete view of all relevant data, improving product forecast accuracy for better SKU and store allocations, and right-sizing inventory levels.

A $6B U.K. health and beauty retailer, and Rubikloud client, spent 50% of their time manually planning promotions and forecasting promo sales. Inconsistencies in methodology across category teams, and business-rule driven approach to forecasting resulted in chronic stock-outs. Utilizing Rubikloud’s Price and Promotion Manager, this retailer was able to harness the power of machine learning to not only improve forecast accuracy (and reduce corresponding stock-out), but also reduce the manual effort required to build promotions. The integration resulted in a 30% increase in forecasting accuracy, a 50% reduction in hours required to plan promotions, and a 31% decrease in stock-outs.

E L I M I N AT I N G S T O C K O U T S

P A G E | 5R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

What is the most significant vulnerability for many retailers? The customer. Critical customer-related shortfalls include difficulty converting customers into loyal customers, personalizing customer interactions, and tracking and responding to changes in customer behaviour.

When customers shop with a retailer, they create a wealth of data and insight about their behaviours and preferences. Automated orchestration and operational systems already in use, such as CRM, CMS, and ERP do not provide the analytics or answers necessary for retailers to personalize their approach to individual customers, surprise and delight them, or enhance loyalty. Companies are becoming increasingly sensitive to the issue, with 50% reporting to consulting firm BCG last year: “We have the data but integrating and using it – that’s the hardest part.” 2 This lack of resources dedicated to personalization can also be addressed through the application of AI.

A recent report from Global Market Insights found that automated merchandising was the number one application of machine learning by leading retailers. The firm reported demand for automated merchandising is being driven by growth in demand for customer behaviour tracking solutions that assist companies in analyzing purchasing behaviour and providing relevant suggestions to increase sales.3 Data analytics and AI systems allow retailers to better anticipate customer needs, improve their experience and build loyalty. AI and machine learning can predict user preferences, optimize prices, and personalize communication.

For example, a $3B European luxury cosmetics retailer that experienced challenges in building customer loyalty and driving lifetime value across their network of 974 stores across the United States. Despite heavy investment into CRM and Marketing Automation capabilities, they lacked deep customer insights – leading to below average sales and ineffective use of campaign budgets. In addition, personalization at scale still required significant manual effort.

Through the automatic integration of Rubikloud’s Customer LifeCycle Manager with their existing campaign management platform, this retailer was able to predict customer intention and behaviour through machine learning, maximizing their customer lifetime value. They increased revenue by 11% over 107 campaigns and 23 million messages sent to members, resulting in $15M USD generated in incremental sales.

G R O W I N G C U S T O M E R L I F E T I M E V A L U E

S O U R C E 2 https://www.bcg.com/publications/2017/retail-market-ing-sales-profiting-personalization.aspx

3 https://www.prnewswire.com/news-releases/artificial-intelli-gence-ai-in-retail-market-to-hit-8bn-by-2024-global-market-insights-inc-682027561.html

P A G E | 6R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

Today’s retailers are all facing the same challenges to their bottom line: margin compression, ever-increasing retail wages, the entrance of disruptive competitors, and the increasing vulnerability to accelerate digital transformation of the industry as more sales move from physical retail locations to online.

What can retailers do to not only survive, but thrive? Get faster, smarter and more effective. Rubikloud’s solutions allow retailers to automate the promotional planning process to generate major time savings and cut overhead costs, reduce excess inventory in store, distribute AI insights and “future proof” the business to protect and grow margin.

Global Market Insights forecasts that AI adoption has reached a tipping point with retailers and predicts the rate of adoption will grow from $650M USD last year to $8B USD by 2024.

Category leading retailers who are the pioneers in the adoption of AI are raising the level of competition – and the expectations of consumers. This drives Global Market Insights’ forecast for growing AI investments as retailers seek to leverage fast-advancing technologies such as machine learning, and deep learning to better use their digital data. The payoff will come in improved operations, more nimble supply chain and business models that enhance sales and the customer experience.

One Canadian health and beauty retailer found that their lack of forecasting sophistication was driving investment into poorly performing flyer blocks that did not drive traffic and upsell opportunities, at the detriment to their margin. They leveraged Rubikloud’s Price and Promotion Manager solution to identify and remove low profit ads from promotional flyers, increasing cost savings, maximizing margin, and earning an incremental 13% margin lift per ad.

Another European health and beauty retailer found that their in-store visits from loyalty members were dangerously low, leading to below average sales and customer fatigue through CRM activities. By automating one-to-one personalized content, targeting their loyalty base and optimizing their marketing budgets with Rubikloud, they were able to reduce churn, reduce operational costs, and drive incremental sales by 5x their regular amount.

The financial impact doesn’t stop there. In order to realize the full benefits of digital transformation, and the wealth of data that comes with it, retailers must also recognize the need to scale intelligence to match. Access to elastic infrastructure and advances in Machine Learning mean it is now possible to for retailers to optimize their businesses in ways unimaginable even five years ago. Delivering on this vision requires partnering with someone who understands retail and has built tailored solutions to address specific retail pain points.

B E E F I N G U P T H E B O T T O M L I N E

P A G E | 7R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

Retailers know intuitively that the rules of the game are changing. The stakes are higher, the price of failure is greater, and the speed to adapt must be faster than ever. AI has already been adopted by leading players who value the myriad of benefits it brings to their business.

Finding the right AI partner is a crucial step in your digital transformation journey. Here are three E’s to remember when evaluating vendors.

1. Do they have deep retail expertise? Does the company intimately understand the magnitude of customer-centric marketing and the complexity of promotional planning, logistics and supply chain?

2. Are their solutions enterprise-ready? Are the company’s AI solutions designed to meet the business needs of global enterprise retailers?

3. Are their solutions easy-to-use? Can the company’s solutions offer the flexibility to ingest and integrate data from a variety of data sources and bring scalable infrastructure that can integrate with existing legacy systems?

At Rubikloud, we apply cutting edge AI and practical data science solutions to a single industry—retail. With more than 100 employees, we have deep expertise in AI and machine learning, enterprise software engineering, retail product development and retail customer experience. Most importantly, we build the tools we wished we had during our time working at retailers. Our solutions are enterprise-ready and deliver short time-to-value and continuous evolution through a software-as-a-service (SaaS) model. Our services expand and broaden across multi-billion-dollar clients with retail brands operating in North America, Europe, and Asia. Our solutions are simple, inherently flexible and easy to integrate with multiple legacy systems to begin delivering tangible results to our clients within weeks.

M A K I N G A I R E A L F O R Y O U R O R G A N I Z AT I O N

P A G E | 8R U B I K L O U D . A I

D E M Y S T I F Y I N G E N T E R P R I S E A I F O R R E T A I L W H I T E P A P E R

U N L O C K I N G I N T E L L I G E N T D E C I S I O N A U T O M AT I O N

Rubikloud is the world’s leading machine learning platform for retail. Our full-stack, cloud native platform, and two flagship applications automate and improve mass promotional planning and loyalty driven marketing for our multi-billion dollar retailers. We target mission-critical business problems by analyzing large amounts of data from multiple legacy sources as well as new and offline systems.This empowers retailers to take tangible actions with powerful results.

207 Queens Quay West, Suite 801, Toronto, Ontario M5J 1A7, Canada

A B O U T R U B I K L O U D

H E A D O F F I C E

L I N K E D I N/rubikloud-technologies

I N S T A G R A M@Rubikloud

T W I T T E R@Rubikloud

F A C E B O O K@Rubikloud

Y O U T U B E/Rubikloud

The pace of change - and the urgency with which retailers must evolve or be left behind – is speeding up as online giants overshadow brick and mortar brands and many established retailers expand their online presence. The acquisition of Whole Foods by Amazon represents a watershed moment that challenges retailers to acknowledge they can no longer operate “business as usual.” How individual retailers respond to this challenge will determine the future of their organizations.

Industry experts forecast massive investment in AI over the next five years as more and more retailers realize its massive potential. The first step to creating a more efficient and effective retail business is to make intelligent decision automation a reality for your business.

Book a complimentary feasibility workshop for your business today to learn how Rubikloud’s solutions can automate mass promotional planning and create consistent, personalized customer experiences across channels to drive customer lifetime value.