Guest post written by Quentin Gallivan
Quentin Gallivan is CEO of Pentaho Corp., an Orlando, Florida-based provider of business analytics software.
Online retailers are seeing record holiday sales this year. ComScore reports that 57 million Americans shopped online on Black Friday, a 26% increase over 2011.
As retailers settle in for this, their biggest selling season of the year, the use of big data has become a critical force in growing sales. Big data analytics is helping retailers stay in front of a new breed of consumer, the omni-channel shopper, and the avalanche of data they are generating.
This transformation is in large part driven by advances in mobile, digital, social media and location-based technology. Consumers are shopping across multiple channels from brick-and-mortar stores to catalogs, online websites and mobile devices. The omni-channel shopping revolution has put the customer in charge, and retailers are scrambling to adopt a single, seamless approach that lets them interact with their customers anytime and anywhere, across any and all channels.
With its ability to foster a sales experience that melds the advantages of physical stores with the information-rich experience of online shopping, it’s not surprising that consumers and retailers alike are eager to embrace omni-channeling retailing.
Savvy retailers and e-commerce companies have turned to big data analytics during the crucial holiday shopping season to increase sales, better target customers, improve reach and keep a competitive advantage. They are using big data to analyze tweets, reviews and Facebook likes, and matching this data against customer lists, transactions, and loyalty club memberships to determine on-line promotional campaigns, and specifically where they are likely to have the most success.
Here are some industry examples that illustrate the future of retailing and big data:
- Tracking the Omni-Channel Supply Chain
Big data analytics enables retailers to take advantage of multi-channel/omni-channel retailing, and ensures that consumers get the product they want, when they want it through any medium.
For example, a prominent outdoor recreation retailer has developed an omni-channel customer analytics initiative to give merchants and marketing teams unprecedented insight into customers’ needs and behavior using integrated customer, shopping and behavioral data from every touch point and channel. As a result the company has visibility into store traffic patterns, user demographics, conversion and buying behavior, traffic by category and SKU, mobile device patterns and mobile application downloads.
One of the largest U.S. retailers, an early leader in analyzing on-line customer behavior, is another 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.
- Distilling Better Intelligence From Customer Data
On-line and in-store activity adds up to billions of transactions and customer interactions that retailers can leverage to better target customers and drive loyalty. Big data makes it more cost-effective for retailers to capture, keep and analyze all their shopper data, regardless of channel.
Another major retailer has deployed the big data store Hadoop 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 records.
- Generating Real-Time Pricing From Big Data Algorithms
The National Retail Federation Thanksgiving weekend shopping survey reported that shoppers took advantage of retailers’ promotions to the full extent with the average holiday shopper spending $423 over the Thanksgiving holiday weekend, up from $398 last year. Retailers are dialing into big data to monitor and adjust promotions and campaigns in near real-time to incent consumers to buy when browsing. The power of big data technology to analyze ecommerce transactions is making it possible for retailers to monitor their rivals' pricing strategies and react in seconds, sometimes with computer algorithms making the decisions.
- Connecting One-on-One With Shoppers
According to research cited in a Harvard Business Review blog, personalization can deliver five to eight times the ROI on marketing spend and lift sales 10% or more. So it’s no surprise that retailers are turning to big data analytics to pair more personalized customer communication with real-time pricing.
A good example is a global on-line fashion site with access to top designers, $500 million in sales, and almost five years of customer data. The company is using big data and advanced analytics to drive personalized e-marketing, too. The collection of web sites targets shoppers with flash sales on merchandise – roughly 30 different sales per day -- offering members exclusive discounts on high-end clothing, shoes and other apparel.
Within a minute at noon every day, the company sends more than 3,000 versions of messages to customers; each one tailored based on what the customer shops for, what they like, and the sizes they wear. It also personalizes each customer’s on-line experience with prioritized sales items based on shopping preferences.
- Countering Smart Phone ‘Showrooming’
Retailers also are using big data to counter “showrooming” behavior where shoppers check out an item in a brick-and-mortar store, then use their smart phones to go online to find the same item at a better price. They are offering full-featured mobile websites and smart phone shopping apps so that customers can easily compare prices via QR codes.
In short, big data analytics have arrived just in time to help retailers adjust and thrive amidst the omni-channel shopper revolution. It’s giving them the tools and intelligence to communicate directly with their customers, sell smarter and take retailing to the next level. This trend isn’t one that will go away anytime soon; in fact, it’s being adopted by many other industries and organizations as a way to maintain a competitive advantage.
