INTRODUCING RETAIL INTELLIGENCE

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1 INTRODUCING RETAIL GET READY FOR THE NEXT WAVE OF ANALYTICS IN RETAIL By: Dan Theirl Rubikloud Technologies Inc. Prepared by: Laura Leslie Neil Laing Tiffany Hsiao

2 WHAT IS RETAIL? Retail intelligence is a new wave of analytics systems designed to transform traditional retailers into modern data-driven organizations. Using retail intelligence systems, marketers no longer need to spend hours analyzing campaign results. Executives no longer need to worry that an analyst missed a major trend in their business. The essential reports to show changes and trends no longer need to be manually created and explored to find anomalies and insights. Modern software engineering, machine learning, and big data technologies enabled this new wave of analytics. Retail intelligence systems are finally able to bridge the gap between machine and human. These systems are driven by intelligent machine learning algorithms that run on large volumes of data. These systems are capable of monitoring competitive pricing in real-time, finding actionable insights, automating the analysis of retail-specific data and much more. Retail Intelligence systems help retailers make critical decisions, remain competitive, and provide a better customer experience to their shoppers. CUSTOMER PERSONALIZATION SYSTEMS PREDICTIVE MARKETING RECOMMENDATION SYSTEMS PRICE Different types of retail intelligence systems include price intelligence, customer intelligence, predictive marketing, personalization and recommendation systems. Price intelligence systems help retailers find the optimal price compared to competitors in the market. Typically price intelligence systems scrape other competitive websites to find the listed price and track changes over time. Customer intelligence systems

3 collect and analyze customer information from shopper surveys, social media sources, and loyalty reward programs. Predictive marketing helps optimize marketing campaigns by attempting to predict customer response and lifetime value. Personalization systems learn customer behaviors and provide personalized product recommendations or personalized customer experiences. Recommendation systems analyze various external and internal data sources to provide actionable insights to management and marketing and merchandise teams of retailers. WHY RETAIL? Communication is the key to success for any size organization and utilizing a system that helps communicate the meaning of large diverse data sets is a game changer for most companies. Executive, marketing, and operations teams need to execute on dozens of priorities at once and in sync. They are also expected to measure, monitor and optimize these tasks. Business teams are increasingly finding legacy analytics systems such as; Coremetrics, Omniture and Google Analytics lacking to help make smarter decisions. Analyzing line graphs for the past 30 days does not help retailers take the most effective action. Without this understanding, communication decays within the organization to lean on best guesses from past experience. For example, in the competitive retail landscape every dollar counts, and 60% of marketing teams are feeling pressure to show higher marketing ROI according to this Forbes survey (http://www.forbes.com/sites/christinemoorman/2013/11/18/ does-pressure-to-prove-the-value-of-marketing-help-or-hurt-companyperformance/). In order to take action quickly, marketing and operation teams need to understand what the data means in a way that speaks their language. Without retail intelligence, retailers are blind in many areas such as price sensitivity, promotion effectiveness, competitive impact and customer behavior across each channel. Without this new wave of retail intelligence, retailers struggle to know how shoppers prefer to experience

4 their store. Shoppers may research certain products on mobile before making a purchase in-store, or they may browse in the store and buy certain brands online later. Understanding the ever-changing behavior of consumers across all channels remains a key priority for many retailers (http://www.sciencedirect.com/science/article/pii/s ). Customer Experience Crosses All Channels Price Sensitivity Promotion Effectiveness Competetive Impact Cross Channel Customer Behavior Data Cleaning & Processing Feature Induction Automation In order to take action quickly, multi-channel retailers need to have visibility into thier market, customers, and business.

5 WHY NOW? Many technology hurdles were overcome in the last few years that enabled an advancement in data science. The combination of Big Data, Machine Learning, and Cloud Computing technologies now allow for extremely fast methods of processing and modeling data (http://www. researchgate.net/publication/ _machine_learning_and_ Cloud_Computing_Survey_of_Distributed_and_SaaS_Solutions). These technologies were open sourced and advanced by a community of engineers expanding the capabilities past spreadsheets and pie charts. The cost and ease of use allow for rapid development of new software that enables a greater connection between machine and human insight. Some examples from Apache-based projects are Spark, Hadoop, Cassandra, Storm, and Kafka. The other side of this coin is the limited capacity for humans to analyze high volumes of data and make the connections that allow us to find valuable, actionable insights. Simple linear thought process causes humans to look for a single nugget of gold. In the data gold rush, human analysts place blinders on digging for insights one data source at a time. It s very difficult for humans to see all of the connections between data and to find true understanding of how one element impacts another. The recent merging of technology enables machine learning algorithms to comb through hundreds of data streams quickly to find these connections and discover true insight 24/7 (http://www.infoq.com/ articles/stream-processing-hadoop). Retail intelligence systems are ready today to provide this stream of data gold to retail organizations.

6 HOW DOES RETAIL WORK? RETAIL FITS ON TOP OF TRADITIONAL BI & REPORTING STACKS LARGE SETS OF HISTORIC DATA ARE PULLED FROM BI & REPORTING SOURCES INTO RETAIL SYSTEMS DATA IS CLEANSED, PROCESSED, & SEMANTICALLY FORMATTED DATA IS PREPARED FOR MODELLING INSIGHTS ARE GENERATED & AUTOMATED Many retail intelligence systems work through machine learning techniques finding interesting events and communicating the results. Machine learning is a subfield of computer science that enables computers to learn from and make predictions on data (https:// en.wikipedia.org/wiki/machine_learning). Instead of a human looking at past reports and trying to make conclusions on limited information, retail intelligence systems mine the data and find actionable insights for the retailer. Many retail intelligence systems analyze data above the analytics and reporting layer. These systems learn from historical data sets aggregated at the reporting layer and try to predict customer behavior, price sensitivity, and market forces. This empowers the retailer to take action faster than the average human-based approach of analyzing historical reports. Retail intelligence systems are fueled by high quality and high volumes of data. Data going into these systems needs to be cleansed, processed, aggregated and semantically understood. Once this process takes place, the data is prepared for modeling either through feature engineering efforts and/or feature induction methods. Feature engineering is the process of finding and calculating important factors that impact an outcome for a predictive model. For example, a customer s income may be an important factor in predicting whether they will buy a luxury item. Feature induction attempts to automate the discovery of these factors. Both of these steps require expertise, planning and patience before true automation can occur. As new streams of data are introduced into the system, the machine learning-driven system needs to adapt and learn the makeup to intelligently report back strong correlations to existing streams. The value of a machine-driven approach is that it improves in accuracy over time if maintained properly. Many different machine learning techniques are used depending on the objective and use-case of the retail intelligence system. Techniques as basic as linear regression, to more complex models such as neural networks, can be used (http://www.ftpress.com/articles/article. aspx?p= ).

7 EXAMPLES OF RETAIL SYSTEMS RUBIKLOUD TM Rubikloud empowers retailers to transform into data-driven organizations. Rubikloud developed a product layer named RUDI that retailers use on a regular basis that delivers actionable recommendations. RUDI is used across the entire organization, from marketing directors to promotional planning teams, from e-commerce directors to executive members. RUDI instantly applies the Rubikloud models to your data to automatically identify and push valuable data-driven stories that are most important. Presenting complex data in a story form transforms the communication of data into a truly human experience. Providing a story around the data enables teams to internalize the impact and share it easily to the greater organization. RUDI accomplishes this through a smart analysis engine, linking the most relevant insights together to generate a meaningful story. Imagine a system that never sleeps, constantly finding retailers moneymaking opportunities and effortlessly communicating these opportunities back as relatable stories; this is RUDI. RETAILNEXT RetailNext integrates with the largest number of data sources inside and around physical stores, enabling retailers to measure all aspects of the business. RetailNext offers many ways to view store data from one centralized platform. RetailNext helps understand shopper behaviors and discover opportunities where you can improve strategies to increase store profitability (http://retailnext.net/). VISION CRITICAL Vision Critical s Retail Intelligence Suite is the industry-tailored solution. It allows you to connect directly with customers, contextualize transactional data, elevate the omnichannel experience, and gain deeper insight into purchase behavior and loyalty. Marketers, brand managers, market researchers, product managers and executives all use insight communities to gain a deeper understanding of customers and gather actionable insight to make better business decisions. (https://www.visioncritical.com/ solutions/retail/).

8 EXAMPLES OF LEGACY ANALYTICS SYSTEMS ADOBE MARKETING CLOUD (ANALYTICS) Adobe Marketing Cloud (Analytics), or formerly known as Omniture SiteCatalyst, provides web analytics to business websites looking to track and measure user interactions. It offers an exploratory reporting system, predictive marketing, mobile app analytics, and a real-time dashboard. If the business user wants to track, monitor and know visitor stats on their website, then Adobe offers a system to find answers to these basic questions. The business user must explore or create new reports in order to receive informational data points such as how many visitors clicked on my campaign and how many converted. Extensive setup and training are required by the business user in order to garner basic information. The system relies on the expertise and analytical skills of the business user to garner useful information. Clients also rely on professional services team for support and training. There remains an insurmountable gap between informational reports to true insight and let alone action, regardless of Adobe s marketing spin. COREMETRICS Coremetrics is an outdated web analytics system founded in 1999, purchased in 2010 by IBM (http://ibmcoremetrics.blogspot.ca/). Coremetrics is a stable web analytics solution for enterprise clientele and integrated tightly with IBM WebSphere platform. Coremetrics offers similar historical reporting capabilities of visitor interactions as other web analytics solutions but remains outdated. Coremetrics does not offer predictive modeling but does offer an industry benchmarking dashboard. Coremetric users rely heavily on professional service support and training from IBM to get the most out of the solution. Again this puts pressure on the business team to create and analyze reports in order to discover useful insights to help drive decisions. In addition, Coremetrics continues to cost significantly more than comparable solutions.

9 GOOGLE ANALYTICS Google Analytics is a widely used freemium web analytics service offered by Google that tracks and reports website traffic (https://en.wikipedia. org/wiki/google_analytics). Google Analytics is a software as a service and provides basic statistics on traffic, segmentation, custom reporting, event tracking, real-time dashboard much like other analytics systems in the market. Customers install javascript tracking code into their site and configure custom variables and conversion goals. The analysis is left up to the user and it remains difficult to correlate one data source to another to find actionable insights. WHAT IS THE FUTURE OF RETAIL? The future of retail is driving toward true personalized omnichannel experiences driven by modern data science. Retailers will offer relevant products to the right consumers via one to one experiences from mobile, virtual reality, video games, tablets, itvs, desktops, and laptops. The modern retailer will understand that to succeed and thrive in the future they must understand their customer and product relationship at a deeper level. In order to connect and adjust over time, the retailer must utilize data and software in a whole new way. Retailers will drown in data without automation, delivering the ultimate one to one relationship consumers expect with their favorite brands and products. Retail intelligence will continue to provide more and more accurate predictions and recommendations and the next step is to allow the retailer to push a button to provide the right products, to the right consumer, at the right price across every single channel possible in the future. About the Author: The author has over 10 years of experience helping retailers use analytics and conversion optimization tools to help them make the right data driven marketing decisions. He is now a co-founder of a Toronto based retail intelligence startup called Rubikloud. web: rubikloud.com

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