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Posts from the ‘BI CoE’ Category

9
Apr

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.

Read more »

20
Mar

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

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8
Mar

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. Read more »

29
Feb

Target Your Shoppers – Retail Predictive Analytics


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 »

28
Feb

Proctor & Gamble – Business Sphere and Decision Cockpits


English: Logo for Procter & Gamble. Source of ...

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.

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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:

  1. A shared belief that data is a core asset that can be used to enhance operations, customer ser­vice, marketing and strategy
  2. More effective leverage of more data – corporate, product, channel, and customer –  for faster results
  3. Technology is only a tool, it is not the answer..!

  4. 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
DatatoAnalyticsModel
mycockpit-pg
 

P&G Overview

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19
Jan

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


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, smart watches and other devices.

Read more »

16
Nov

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.

Read more »

6
Nov

What is a “Hadoop”? Explaining Big Data to the C-Suite


Keep hearing about Big Data and Hadoop? Having a hard time explaining what is behind the curtain?

The term “big data” comes from computational sciences to describe scenarios where the volume of the data outstrips the tools to store it or process it.

Three reasons why we are generating data faster than ever: (1) Processes are increasingly automated; (2) Systems are increasingly interconnected; (3) People are increasingly “living” online.

DataEvolutionAs huge data sets invaded the corporate world there are new tools to help process big data. Corporations have to run analysis on massive data sets to separate the signal from the noisy data.  Hadoop is an emerging  framework for Web 2.0 and enterprise businesses who are dealing with data deluge challenges – store, process, index,  and analyze large amounts of data as part of their business requirements.

So what’s the big deal? The first phase of e-commerce was primarily about cost and enabling transactions.  So everyone got really good at this. Then we saw differentiation around convenience… fulfillment excellence (e.g., Amazon Prime) , or relevant recommendations (if you bought this and then you may like this – next best offer).

Then the game shifted as new data mashups became possible based on… seeing who is talking to who in your social network, seeing who you are transacting with via credit-card data, looking at what you are visiting via clickstreams, influenced by ad clickthru, ability to leverage where you are standing via mobile GPS location data and so on.

The differentiation is shifting to turning volumes of data into useful insights to sell more effectively. For instance, E-bay apparently has 9 petabytes of data in their Hadoop and Teradata cluster. With 97 million active buyers and sellers they have 2 Billion page view and 75 billion database calls each day.  E-bay like others is racing to put in the analytics infrastructure to (1) collect real-time data; (2) process data as it flows; (3) explore and visualize. Read more »

2
Nov

IBM CIO Study: BI and Analytics are #1 Priority for 2012/2013


“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 »

3
Oct

Wanted: CIO – BI/Analytics


In a tough economy, a new tech-fueled BI and analytics arms race is on to create the next competitive advantage.

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 »

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