Data Monetization is the End Goal

The billion dollar question facing executives everywhere: How do I monetize my data? What small data or big data monetization strategies should I adopt?  Which analytical investments and strategies really increase revenue? What pilots should I run to test data monetization ideas out?

Data Monetization is the process of converting data (raw data or aggregate data) into something useful and valuable – help make decisions (such as predictive maintenance) based on multiple sources of insight.  Data monetization creates opportunities for organizations with significant data volume to leverage untapped or under-tapped information and create new sources of revenue (e.g., cross-sell and upsell lift;   or prevention of equipment breakdowns).

But, data monetization requires a new IT clock-speed that most firms are struggling with. Aberdeen Research found that the average time it takes for IT to complete BI support requests, with traditional BI software, is 8 days to add a column to a report and 30 days to build a new dashboard.  For an individual information worker trying to find an answer, make a decision, or solve a problem, this is simply untenable. For an organization that is trying to differentiate itself on information innovation or data-driven decision making, it is a major barrier to strategy execution.

To speed up insight generation and decision making (all elements of data monetization) business users are bypassing IT and investing in data visualization (Tableau) or data discovery platforms (Qlikview). These platforms help users ask and answer their own stream of questions and follow their own path to insight. Unlike traditional BI that provides dashboards, heatmaps and canned reports, these tools provide a discovery platform rather than a pre-determined path.

Also companies like Marketo which create marketing automation software are getting into the customer engagement and data monetization game. Their focus is to enable marketing professionals  find more future customers; to build, sustain, and grow relationships with those buyers over time; and to cope with the sheer pace and complexity of engaging with customers in real time across the web, email, social media, online and offline events, video, e-commerce storefronts, mobile devices and a variety of other channels. And in many companies, marketing knits these digital interactions together across multiple disconnected systems. The ability to interact seamlessly with customers across multiple fast-moving digital channels requires an engagement strategy enabled by data and analytic insights. 

Data Management, AML, and KYC Analytics

To roadmap Wall Street priorities for 2013, we have been having an interesting set of meetings recently with MDs and leading architects in various banks and investment services firms.

Got the scoop on analytics projects they are investing in — Anti-Money Laundering (AML) monitoring, trade surveillance and Know Your Customer (KYC) analytics.  To enable AML and KYC initiatives…the big foundational investments in 2013 are around:

1)     Strengthening the Golden Sources – Security Master, Account Master and Customer Master.

2)     Various enterprise data management initiatives – Data Quality, Data Lineage, Data Lifecycle Management, Data Maturity and Enterprise Architecture procedures.

3) Reporting improvements via next generation Enterprise Datawarehouses (EDW) — Reporting on top of EDW addresses the core problems faced by Finance, Risk and Compliance when these functions extract their own feeds of data from the product systems through which the business is conducted and use differing platforms of associated reference data in support of their reporting processes. Lot of current investments are in the areas of Finance EDW which delivers common pool of contracts, positions and balances, organized on an enterprise wide basis and completed by anointed “gold” sources of reference data which ensure consistency and integration of information.

Crawl, walk, Run seems to be the execution game-plan as the data complexity is pretty horrendous. Take for instance, Citi alone….has approximately 200 million accounts and business in 160+ countries and jurisdictions.

The type of data challenges banks like Citi are wrestling with include:

Organizing for BI, Analytics and Big Data: CoE, Federated or Departmental

Consider this…eBay’s “Singularity” Teradata warehouse exceeds 40 petabytes. According to eBay, the company’s data volumes are 50+ terabytes per day in new incremental data, processing 50+ petabytes
and tens of millions of queries per day, with 99.98% availability and more than 50 petabytes of online storage.

Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data is challenging.

So, if you are a senior leader in a Fortune 2000 company.  How do you structure your group to deliver effective BI, Analytics or Big Data projects? Do you have the right structure,  toolset, dataset, skillset and mindset for analytics and Big Data?

Organizing for effective BI, Analytics and Big Data is becoming a hot topic in corporations.  In 2012, business users are exerting  significant influence over BI, Analytics and Big Data decisions, often choosing analytics and visualization platforms and products in addition to/as alternatives to traditional BI platform (reporting and visualization tools).

Bloomberg on Business Analytics

Interested in slicing, dicing, measuring, and analyzing data for customer and business insights?

According to a recent survey by Bloomberg, 97% of companies with revenues of more than $100 million are using some form of business analytics, up from 90% just two years ago.

While businesses have embraced the idea of fact-based decision-making, a steep learning curve remains. Only one in four organizations believes its use of business analytics has been “very effective” in helping to make decisions. Data is not just ignored but often discarded in many organizations as the business users can’t figure out how to extract signal from data noise.

Next Best Offer Design: Solution Architecture

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)

In this blog posting we examine a more traditional next best offer solution architecture.

Targeted Offer Solutions

Spend BI and Analytics

What is your spend IQ or data maturity?

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.

Target Your Shoppers – Retail Predictive Analytics

Best-in-Class Behavioral Analytics Case Study…I am reposting this well written article 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.

Proctor & Gamble – Business Sphere and Decision Cockpits

“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.” With this quote,  Charles Darwin clearly defined today’s business analytics imperative.

 

In this posting we illustrate a best-in-class Analytics Innovation Case Study at P&G. The case study illustrates the way Business Analytics is becoming more central as retail and CPG decision making speeds up. 

 

P&G Overview

P&G’s has 127,000 employees and 300 brands sold in 180 countries.  P&G averages about 4 billion transactions daily. P&G CEO Bob McDonald has staked out a strategy to “digitize” the company’s processes from end to end, and Business Sufficiency, Business Sphere and Decision Cockpits is enabler of that agenda. 

 

P&G is building analytics expertise at a time when P&G is cutting costs in other areas, including eliminating 1,600 nonmanufacturing jobs. The company’s IT organization itself has cut $900 million in total spending over the past nine years.

 

P&G is investing in analytics talent, even as the company cuts in other areas, to speed up business decision making.  True leaders develop the capabilities required for making good and timely decisions in unpredictable and stressful environments. 

Multi-channel to Omni-channel Retail Analytics: A Big Data Use Case

In 1999, multichannel retailer Circuit City pioneered the option to buy a product online and pick it up in-store.

Today, as digital and mobile channels accelerate in terms of adoption 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 seismic shift and already posing 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 appear to be in danger of turning into Amazon.com’s “showroom”, which has the advantage of lower overhead costs and mostly can avoid sales tax collection.

To create shopper stickiness they are trying parallel strategies (1) offering the buy online, pick up in-store or ship-to-store options; (2) price match; (3) same-day delivery options. Amazon.com, on the other side, is playing offense by   partnering with high brick-and-mortar footprint companies including Staples, RadioShack, and 7-Eleven, to place Amazon delivery lockers in hundreds of stores across the country.

Predictive Analytics – A Project or a Program?

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.