The retail industry is one of the largest industries globally. Measured solely by revenue numbers, the U.S. is the undisputed leader of retail. Wal-Mart is not only the largest global retailer, it is also one of the largest companies of any kind in the world. According to Fortune Magazine's 2010 "Global 2000" list, 54 of the largest companies of any type in the world are U.S.-based companies that are solely retail companies or have significant retail operations. Of the world’s 10 largest retail companies in the world, five of them are from the States and five are from Europe. These top 10 had combined sales of $1.15 trillion in 2009, according to international consulting group, Deloitte.
It is well known that retailers who know their customers — and apply what they know about their customers’ preferences — are finding a competitive advantage in the marketplace. If you’ve done any shopping online recently – you’ve probably seen big data in action. We’ve all experienced it: You go shopping for a pair of shoes online, put them in your virtual shopping cart, but then for some reason change our mind. Afterwards, seemingly every site you visit features an ad for that very pair of shoes at that same online store. The reason? Online retailers can give you a virtual identification number and track you as you go from site to site, and purchase targeted ads for products they already know you’re strongly interested in1.
Recently in my engagement at an ad-network client, I was reviewing the information architecture and helping develop the set of business analytics to drive optimal online ad placement and targeting. Millions of records from online cookies were being sourced and captured daily by this ad-network company. This data was then processed, segmented and fed into a rules engine that used the information to target future placement of ads using demographic, ad size, location, pixels and ad placement on web page.
Typical consumers wait year-round for the best shopping deals on large items, especially during "Black Friday" and "Cyber Monday." The reason it’s termed as “Black Friday” that many people may not realize is that this period signifies the approximate date on the calendar when many retail businesses move from operating in the red and start to actually make a profit for the entire year. Online retailers saw record holiday sales at the end of 2012. ComScore reports that 57 million Americans shopped online on Black Friday, a 26% increase over 2011.
Understanding and winning customers is complicated in today’s ultracompetitive retailing environment. But the problem isn’t a lack of data about who your consumers are and what they’re buying. Data pours in from multiple systems, channels, and regions around the clock. The challenge, rather, is how to extract meaning from the data to inform decision making and enable productivity and agility in the face of multi-faceted market demands. Part of that challenge is consolidating the large data sets your organization amasses from a variety of sources. That’s especially difficult given the many tools to analyze and report on the data, creating islands of information that may not offer the big picture or best decision-making insights. Today’s customers use social and mobile technologies to make more informed decisions.
Big data analytics make it possible for retailers to directly correlate consumer web activity with promotions and marketing campaigns, and track resulting sales transactions. And as a result, retailers can monitor and tweak promotions and campaigns in near real-time to maximize spend, increase profitability and generate revenue during this short, but critical period of time. They do this by quickly slicing and dicing terabytes of data, including millions of daily emails, every click on web sites, and every ecommerce and brick and mortar transaction.
These advanced analytics enable retailers to perform deep, precise customer segmentation by demographics, such as age and income, and psychographics such as interest and lifestyle profiles – segments which are then used to drive highly optimized and personalized offers and campaigns 2. Every day, retailers are taking steps to increase their efficiency, improve their customer experiences, and develop smarter retail. This analytical approach to customer decisions is not limited to the web; some retailers are now using technologies to analyze foot traffic throughout their physical stores. These maps, combined with sales data, make way for new applications focused on optimizing store layout and product placement.
Based on recent news, retail giant Debenhams has launched a new big data initiative to create a more personalized, multi-channeled marketing strategy. The UK-based company – founded in 1778 – had 167 stores as of October this year, and this heritage has seen it build a complex landscape of data. It now plans to use big data to analyze its 40 different databases – which include email lists, mobile users, and customers of its wedding services – in order to understand the individual preferences of its customers and shape its marketing communications accordingly. To provide an outstanding shopping experience while increasing sales and protecting profits is always a balancing act for retailers. Business analytics takes into account data streams from various areas of the retail operation to help decision makers improve the customer shopping experience.
Here are few types of big data analytics that can performed in the retail industry:
Customer Analytics and KPIs - Understand your most valuable customers and target them to maximize profits and loyalty
- Discover who your customers are
- Expectation and sentiment tracking
- Track impact of promotions on basket and provide a holistic view of behavior
- Tap into the transactional data to connect the dots between customers, stores, products and promotions
- Move beyond basic segmentation, personalities and list pulls to create targeted micro-segments
Merchandising KPIs - Significantly reduce costs, eliminate the expense of stockouts and overstocks, and make powerful, rapid decisions
- Quickly accelerate shipments by evaluating top-selling products
- Make markdown decisions based on seasonal sell-through
- Cancel shipments for bottom-selling products
- Communicate more effectively with vendors
Store Operation Analytics and KPIs - Keep store managers on the selling floor, not behind a desk. Give store operations the right information at the right time to make the right decisions. Addresses the challenges of sales assistance, queues, merchandising/promotions, and stock out.
- Increase profitability
- Gain visibility into service levels, operational performance, and customer preferences
- Optimize staffing, improve service levels, and enhance customer experiences
- Reduce out-of-stock situations
- Improve efficiency by facilitating management of compliance across hundreds or thousands of stores
Vendor and SKU Management Scorecards and KPIs - Analyze vendor performance, drive improvement, and strengthen negotiations. Improve performance across the supply chain.
- Increase sales as products reach sales floor faster
- Increase data accuracy for inventory management and replenishment
- Reduce costs through elimination of data entry and manual processes
- Marketing Analytics and KPIs
- Returns, Fraud and Loss Prevention Analytics
One of the largest U.S. retailers, an early leader in analyzing on-line customer behavior, is a good example of a retailer that is experiencing the blending of e-commerce, mobile apps, and in-store shopping. To stay ahead of the omni-channel shopping revolution, this retailer is capturing and analyzing enormous volumes of customer behavior information gathered across its stores, websites and mobile applications. The company uses this data to manage its entire demand chain. As a result, it is able to anticipate shopper behavior in a way that minimizes out-of-stocks while reducing overall inventory. This retailer also offers a smartphone check-in feature to allow in-store consumers to access and use coupons while in the store. Another major retailer has deployed a Hadoop-based big data store to more cost effectively capture, store and analyze an exploding volume of customer data. The new structure is allowing the company to personalize marketing campaigns, coupons and offers to the individual customer, with a solution that is cost effective and has timely turnaround. The retailer’s big data store holds more than two petabytes of data about consumer behavior – from point of sales devices, e-commerce web sites, GPS-enabled tablet devices and smart phones, and embedded sensors. With Hadoop’s massively parallel processing power, the company sees little more than one minute’s difference between processing 100 million records and 2 billion records3.
To stay competitive, retailers must understand not only current consumer behavior, but must also be able to predict future consumer behavior. Accurate prediction and an understanding of customer behavior can help retailers keep customers, improve sales, and extend the relationship with their customers. In addition to standard business analytics, retailers need to perform churn analysis to estimate the number of customers in danger of being lost, market analysis to show how customers are distributed between high and low value segments, and market basket analysis to determine those products that customers are more likely to buy together. Data mining within the retail industry can be used for many business objectives. For instance, data mining can be used to better understand the purchasing behaviors of your customers, to help you understand your high- and low-margin customers, to help you understand which customers are most likely to respond to a marketing campaign, or to help you identify which customers are likely to leave. Data mining can enhance and amplify the knowledge of all of your assets, from customers to suppliers to employees, and even the presentation of merchandise within the store.
Advanced analytical applications leverage a range of techniques to enable deeper dives into customer data, as well as layering this customer data with sales and product information to help retailers segment and market to customers in the ways they find most compelling and relevant. Historically, retailers have only scratched the surface when it comes to making use of the piles of customer data they already possess. Add social media sentiment to the mix, and they can access a virtual treasure trove of insights into customer behaviors and intentions. The timing couldn’t be better, because these days’ consumers award their tightly held dollars to retailers that best cater to their need for customized offers and better value. The ability to offer just what customers want, when they want it, in the way they want to buy it requires robust customer analytics. The opportunity is now: It’s critical that retailers step up their customer analytics capabilities as they transition to an all-channel approach to business.
A recent Cognizant study, in association with Forbes Insights, “Innovation Beyond the Four Walls: Breaking Down Innovation Barriers,” shows that 60% of companies surveyed encourage customer input to gather information and ideas for innovation, and another 22% are considering it. To this end, companies have been quick to adopt new structures that involve their customers in their innovation efforts by establishing internal company teams combined with customers. Forty-one percent of companies surveyed for the report already have such teams in place. When their customers come first, companies can thrive even as consumer critiques explode on social media and as the economy stagnates.
The proof lives at corporate culture pioneers Zappos.com, Southwest Airlines and The Walt Disney Co. These highly successful enterprises offer different philosophies, management strategies and lessons that can be adapted and applied to smaller businesses. But the underlying keys are happy customers and, just as important, happy employees. The customer-driven movement is getting stronger and stronger, and it’s about more than service—it’s about the experience you provide. The customer has indeed become the king for retailers, and smart retailers are willing to listen to them and go out of their way to offer customized and personalized services and product.
References & Credits
- Future of Retail: How Companies Can Employ Big Data to Create a Better Shopping Experience
- Retailers Hope Big Data Drives Big Holiday Sales
- Why Big Data Is All Retailers Want for Christmas