Next best offer, next best action, interaction optimization, and experience optimization typically have similar architecture. Machine learning and multivariate statistical analysis are at the heart of these cutting edge Behavioral Analytics strategies. Typically firms use statistical tools for segmentation models, behavioral propensity modeling, and market basket analysis.
The bleeding edge in next best offer is increasingly around:
- Applying machine learning to find connections between product tastes and different affinity statements
- Developing low-latency algorithms that help show the right product at the right time to a customer
- Developing rich customer affinity profiles through a variety of feedback loops as well as third-party data source (e.g. Facebook user demos and taste graph)
Targeted Offer Solutions
Procurement organizations tend to swim in data. One of the most important strategies for any best-in class procurement organization is spend analytics. In conjunction with sourcing, category, contract management and purchasing, spend analytics provides a window into spend behavior to drive cost reduction and cost avoidance efforts.
As a result, we are seeing a lot of interest around Spend BI and Analytics projects. Chief Procurement Officers and other Sourcing/Procurement leaders of Global, large and even mid-market firms are increasingly focusing on spend data analytics as part of a new wave of spend rationalization projects. Read more
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 blog 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
MULTI-CHANNEL is simply having multiple channels through which you buy, market, sell, and fulfill.
CROSS-CHANNEL has the ability to see all of a customer’s information across all channels enables more personalized offers based on their brand relationship.
OMNICHANNEL weaves all the touchpoints of the products and services of the brand into a seamless fabric of all phases of the customer’s brand experience.
Which one are you?
Let’s face it – The old uni-channel retail model is dying in some cases and changing in others. E-commerce is driving nearly all retail growth. Digital customers want simple, consistent, and relevant experiences across all channels, touchpoints, mobile screens and devices.
Gen Y and Millenial segments increasingly call the shots and success will be based on how well companies, and every frontline employee, decipher the growing disparity between “what they say” vs. “what they actually do.”
Focus on the user and all else will (or should) follow.
No brainer strategy right.. but cohesive multi and omni-channel experiences that are satisfying, fulfilling, and engaging are proving to be non-trivial to engineer or execute. Just watch Walmart as it plays catchup with Amazon.com. Or ask Sears, Best Buy, B&N, and others as they attempt to transform. Every retailer today struggles to prioritize omni-channel and multi-screen initiatives in order to meet customer needs and drive maximum $ impact given scarce organizational resources.
Omni-channel digital platforms are not easy solutions to build or get right. Amazon.com took almost 15 years and $15+ billion in app+infrastructure investment to get to a low cost-to-serve digital model. Competing with them and trying to build an equivalent in 18 months, with even $500M investment, offshore low-cost developers etc. is an impossible task even for the likes of Walmart. They can do me-too things but experience innovation is next to impossible.
Change is continuous. Just when firms think they have the omni-channel experience figured out they have to evolve to address the next mobile channel or social channels. There are more mobile-connected devices than there are people on earth. Typical mobile users check their screens more than 150 times a day. There are more than 1.25 billion Facebook accounts across Facebook, Instagram and now WhatsApp. The half-life of a piece of content shared on social networks Twitter and Facebook is 3 hours.
The new customer experience battle is being fought around same-day delivery. Global companies are implementing local shopping: Google Shopping Express, AmazonFresh, eBay Now. Amazon is spending $13.9 billion on new warehouses and offering same-day and Sunday delivery.
Next-generation omni-channel fulfillment is transforming execution and changing customer expectations. Some companies like Google are debuting same-day delivery service starting at $5.99 to cross-sell their Google Wallet. Other new offerings include WunWun, Instacart and TaskRabbit. Are retailers ready to compete? Can their systems support this across corporate, product, channel, and customer data ?
Execution rather than strategy is the management challenge. The challenge for large retailers is not just having a strategy but efficient digital execution. The large retailers have an architectural IT and analytics challenge that more nimble startups don’t. They often try to build comprehensive platforms a part of a wider strategic initiative which aims to support, provide and deliver digital services in the multi-channel, multi-device, multi-format, multi-sku, multi-language, multi-country and multi-culture world that they operate in.
Digital Transformation of Retail Underway
In 1999, physical retailer Circuit City pioneered the option to buy a product online and pick it up in-store.
Today, as digital & mobile channels transform the customer experience and engagement, retailers like Best Buy, Lowes, Barnes and Noble, Saks, Macys, Nordstrom are very worried about becoming showrooms for online retailers.
This “showrooming” trend is a steady seismic shift and poses a strategic problem in consumer electronics, books, shoes, appliances where a growing number of consumers are going to retailers to test drive products and then go online with mobile phones to transact at a cheaper price elsewhere. Best Buy and others are in danger of turning into Amazon.com’s “showroom”, which has the advantage of low-touch self-service, and other lower overhead costs (e.g, automated warehouses).
To create shopper stickiness retailers are trying parallel engagement/fulfillment strategies (1) offering the buy online, pick up in-store or ship-to-store options; (2) price match; (3) same-day delivery options; (4) more “consulting” and shopping assistants.
Amazon.com, on the other side, is playing offense by increasing same day fulfillment, increasing distribution warehouse footprint and automation with the Kiva purchase (robotic fulfillment), and also partnering with brick-and-mortar footprint companies including Staples, RadioShack, and 7-Eleven, to place Amazon delivery lockers in stores.
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
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
BI is key to enabling companies to turn oceans of data into predictive models and actionable decisions. However, a survey of 353 executives in large companies, reported that their chief BI concern was the performance of various BI solutions.
Development, support and enhancement teams are typically deployed to address BI performance challenges with varied success. But most companies don’t have a dedicated focus on performance.
A BI Center of Excellence (BI CoE) measured by performance KPIs and service metrics is one solution to this problem. This is not an area that traditionally draws high-level attention or is featured in a dedicated CoE initiative, yet in the right circumstances it offers unique value. Read more