The core business problem that every retailer including Target is attempting to solve:
“Your loyalty cards and web application logs have captured all the activity in your stores, your Website and Mobile application. This data is priceless; for example, it not only contains the fact that a purchase has been made but also captures the thought process that went into making that purchasing decision. This session describes how you you can capitalize on this raw data to gain better insights into your customers, enhance their user experience, and make targeted recommendations.”
To provide insight into an approach…I am reposting this well written Best-in-Class Behavioral Analytics Case Study by Charles Duhigg on how Target is targeting customers using Predictive Analytics to anticipate shopper behavior.
Target was founded in 1902 and is headquartered in Minneapolis, Minnesota. Target operates over 1,750 stores in 49 states under Target and SuperTarget names. It offers general merchandise products through its Website, Target.com. The company distributes its merchandise through a network of distribution centers, as well as third parties and direct shipping. Additionally, it offers credit to guests through its branded proprietary credit cards.
Data Analytics and Influencing Pregnant Shoppers
Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that? ”
As the marketers explained to Pole new parents are a retailer’s holy grail. Most shoppers don’t buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target — cleaning supplies, say, or new socks or a six-month supply of toilet paper. But Target sells everything from milk to stuffed animals to lawn furniture to electronics, so one of the company’s primary goals is convincing customers that the only store they need is Target. But it’s a tough message to get across, even with the most ingenious ad campaigns, because once consumers’ shopping habits are ingrained, it’s incredibly difficult to change them. Read more
Data-driven DNA is about having the right toolset, mindset, skillset and dataset to evolve a major brand and seize today’s omni-channel opportunities. Whether it’s retooling and retraining for the multiscreen attention economy, or introducing digital innovations that transform both retail and healthcare, P&G is bringing data into every part of its core strategies to fight for the customer.
Striving for market leadership in consumer products is a non-stop managerial quest. In the struggle for survival, the fittest win out at the expense of their rivals because they succeed in adapting themselves best to their environment.
CMOs and CIOs everywhere agree that analytics is essential to sales & marketing and that its primary purpose is to gain access to customer insight and intelligence along the market funnel – awareness, consideration, preference, purchase and loyalty.
In this posting we illustrate a best-in-class “run-the-business” with Data/Analytics Case Study at P&G. The case study demonstrates four key characteristics of data market leaders:
- A shared belief that data is a core asset that can be used to enhance operations, customer service, marketing and strategy
- More effective leverage of more data – corporate, product, channel, and customer – for faster results
Technology is only a tool, it is not the answer..!
- Support for analytics by senior managers who embrace new ideas and are willing to shift power and resources to those who make data-driven decisions
This case study of a novel construct called Business Cockpit (also called LaunchTower in the Biotech and Pharmaceutical Industry) illustrates the way Business Analytics is becoming more central in retail and CPG decision making.
Here is a quick summary of P&G Analytics program:
- Primary focus on improving management decisions at scale – did the analysis to identify time gap between information and application to decision making
- “Information and Decision Solutions” (IT) embeds over 300 analysts in leadership teams
- Over 50 “Business Suites” for executive information viewing and decision-making
- “Decision cockpits” on 50,000 desktops
- 35% of marketing budget on digital
- Real-time social media sentiment analysis for “Consumer Pulse”
- Focused on how to best apply and visualize information instead of discussion/debate about validity of data
Fidelity Investments put out an interesting analysis on Big Data as a Macro Investment Themes for clients. Since everyone has an underperforming investment portfolio in this current market, I reproduced the article here to generate some ideas.
- New types of large data sets have emerged because of advances in technology, including mobile computing, and these data are being examined to generate new revenue streams.
- More traditional types of business data have also expanded exponentially, and companies increasingly want and need to analyze this information visually and in real time.
- Big data will be driven by providers of Internet media platforms, data amalgamation applications, and integrated business software and hardware systems.
Investment Theme – Big Data
The concept of “big data” generally refers to two concurrent developments. First, the pace of data accumulation has accelerated as a wider array of devices collect a variety of information about more activities: website clicks, online transactions, social media posts, and even high-definition surveillance videos.
A key driver of this flood of information has been the proliferation of mobile computing devices, such as smartphones and tablets. Mobile data alone are expected to grow at a cumulative annualized rate of 92% between 2010 and 2015 (see Exhibit 1, below). Read more
Our AMEX credit card was recently compromised. Someone got hold of the card information and Petro Canada charges started to rack up. Amex spotted this suspicious pattern and immediately initiated a fraud alert thru multiple touch points.
What does your credit card company know about you? A lot…maybe more than your spouse. A study of how customers of Canadian Tire were using the company’s credit cards found that 2200 of 100,000 cardholders who used their card at drinking places missed four payments within the next 12 months. By contrast, only 530 of the cardholders who used their card at the dentist missed four payments within the next 12 months. So drinking is a predictor of credit risk.
Predictive analytics is not a fad. It’s not a trend. In a real-time world, Analytics is a core business requirement/capability. However, many organizations flounder in their efforts not because they lack analytics capability but because they lack clear objectives. So the first question is, What do you want to achieve?
Analytics so far has largely been a departmental ad hoc activity. Even at the most sophisticated corporations, data analytics is a cumbersome affair. Information accumulates in “data warehouses,” and if a user had a question about some trend, they request “data priests/analysts” to tease the answers out of their costly, fragile systems. This resulted in a situation where the analytics are done looking in the rearview mirror, hypothesis testing to find out what happened six months ago.
Today it’s possible to gather huge volumes of data and analyze it in near real-time speed. A retailer such as Macy’s that once pored over last season’s sales information could shift to looking instantly at how an e-mail coupon impacts sales in different regions. Moving to a realtime model and also building an enterprise level “shared services” model is going to be the next big wave of activity.
“Running a company is an endless quest to find out things you don’t know“
– Jeff Immelt, CEO GE
What will 2012 bring? Recently, I attended the CIO Executive Leadership Summit in Greenwich, Connecticut. I was particularly intrigued by the presentation by the new CIO of IBM, Jeanette Horan where she presented the projects she was tackling and how IBM is thinking about business analytics.
IBM is making a bet that “true leaders” will develop the capabilities required for making good and timely decisions in unpredictable and stressful environments.
IBM is adapting to this new data analytics reality by a rapid-fire acquisition strategy: Cognos, Netezza, SPSS, ILog, CoreMetrics, Algorithmics, OpenPages, Clarity Systems, Emptoris, DemandTec (for retail). IBM also has other information management assets like Watson, DB2 etc. They are building a formidable capability around the value chain: “Raw Data -> Aggregate Data -> Intelligence ->Insight -> Decisions” . They see this as a $20Bln opportunity. Read more
The goal of these appliances (engineered systems) is to help IT groups further shrink data center costs, increase system utilization and enable better application integration. All goals that CIOs everywhere continue to struggle with. CIOs now face an interesting decision matrix: Exalytics/Logic/Data systems versus traditional build from components versus hosted.
With ExaSystems, Oracle has a tremendous market advantage. Oracle owns most of the software that enterprises need today. Via acquisitions, Oracle owns the whole stack! Web tier, Middleware, Database software, Database tier, Storage tier. With Sun Microsystems it’s ideally positioned to maximize the platform capabilities. It’s easy for Oracle make its own software play nice on the Exalytics, Exalogic and Exadata platforms.
Everyone is beginning to look beyond the status quo in BI, analytics, Big Data, Cloud Computing etc to fundamentally change how they discover fresh insights, how they can make smarter decisions, profit from customer intelligence and social media, and optimize performance management.
The headache for corporations is not the technology aspects but the leadership side. Who is going to lead this effort, corral the vendors and formalize and execute a more structured program.
Who is going to lead the effort to create the right toolset, dataset, skillset and mindset necessary for success?
As BI and Analytics moves from “experiment and test” lab projects to commercial deployments, companies are going to need more leadership and program management capabilities. They need leadership that can provide strategic, expert guidance for using powerful new technologies to find patterns and correlations in data transactions, event streams, and social media.
Some firms are making moves. In insurance, AIG – Chartis Inc. unit appointed Murli Buluswar to the new post of chief science officer. This aims to enhance Chartis’ focus on analytics… he “will be responsible for establishing a world-class R&D function to help improve Chartis’ global commercial and consumer business strategies and to deliver more value for customers.” This focus on analytics involves “asking the right questions and making science-driven decisions about strategies—whether it’s related to underwriting decisions, product innovation, pricing, distribution, marketing, claims or customer experience—with the end result of improving the scope of what Chartis delivers for customers”.
As a result of where we are in the maturity cycle and to support the business units better, we are seeing a new emerging role “CIO – BI” that is dotted lined to the global CIO or a shared services leader. Let’s look at a representative job posting from GE Capital, which always seems to be a step ahead of most companies. Read more
“More firms will adopt Amazon EC2 or EMR or Google App Engine platforms for data analytics. Put in a credit card, by an hour or months worth of compute and storage data. Charge for what you use. No sign up period or fee. Ability to fire up complex analytic systems. Can be a small or large player” Ravi Kalakota’s forecast
Big data Analytics = Technologies and techniques for working productively with data, at any scale.
Analytics-as-a-Service is cloud based… Elastic and highly scalable, No upfront capital expense. Only pay for what you use, Available on-demand
The combination of the two is the emerging new trend. Why? Many organizations are starting to think about “analytics-as-a-service” as they struggle to cope with the problem of analyzing massive amounts of data to find patterns, extract signals from background noise and make predictions. In our discussions with CIOs and others, we are increasingly talking about leveraging the private or public cloud computing to build an analytics-as-a-service model.
Analytics-as-a-Service is an umbrella term I am using to encapsulate “Data-as-a-Service” and “Hadoop-as-a-Service” strategies. It is more sexy 🙂
The strategic goal is to harness data to drive insights and better decisions faster than competition as a core competency. Executing this goal requires developing state-of-the-art capabilities around three facets: algorithms, platform building blocks, and infrastructure.
Analytics is moving out of the IT function and into business — marketing, research and development, into strategy. As result of this shift, the focus is greater on speed-to-insight than on common or low-cost platforms. In most IT organizations it takes anywhere from 6 weeks to 6 months to procure and configure servers. Then another several months to load, configure and test software. Not very fast for a business user who needs to churn data and test hypothesis. Hence cloud-as-a-analytics alternative is gaining traction with business users.