“Google, Facebook are really big data companies, not software companies. They collect data, process it and sell it back with value added extensions. They don’t have better algorithms. They simply have more data.” — Anonymous
The convergence of cloud, social, mobile and connected computing has sparked a data revolution. More than 90 percent of the world’s data has been generated over the last two years . And with a projected 50 billion connected “things” by 2020 , the volume of data available is expected to grow exponentially. This proliferation of data has created a vast ocean of potential insights for companies, allowing them to know their customers in a whole new way.
Data is valuable. Data is plentiful. Data is complex. Data is in flux. Data is fast moving. Capturing and managing data (Cloud, On-Premise, Hybrid IT) is challenging. It’s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight.
Help people uncover, see, understand and visualize data presents a broad and momentous market opportunity….call this user-driven discovery. Take for instance, Facebook (like Amazon.com) builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their postings, photos, likes, the songs they listen to, the products they like—and determines in milliseconds which items you might wish to see, and in what order. Is this the future for every firm…..
The opportunity is simply getting bigger by the day. Every customer interaction is generating a growing trail of data (“data exhaust”). Every machine that services the customer is generating data. Every conversation, transaction, engagement, touchpoint location, offer, response is a potential digital bread-crumb of opportunity.
Now let’s flip the context. A typical mobile user check their phone interface 150 times a day for updates. A Gen Y or Millenial user obviously much more than a Gen X user. The consumption patterns for information are changing continuously. Facebook style real-time updates which were revolutionary 5 years ago seem outdated in the mobile world. We live in an “attention deficit economy” where attention is the new basis for competition. The firms that create the evolving experience using data which can grab/hold your attention will attract marketing and ad $$.
As a result, the buzz and hype around data…small data, big data, machine data, social data, mobile data, wearables data….is relentless. As a result there are a lot of new initiatives and companies. I have been asked repeatedly by a lot of entrepreneurs and strategy teams about analytics market size and opportunity size. Product and services firms are also interested in opportunity sizing as they create new offerings in the data rich world.
I thought i would share a mashup of industry and market sizing data i have collected so far.
- How big is the overall market for Analytics, Big Data?
- How big is the market for Digital Customer Interaction or Engagement?
- How big is the market for Mobile and Social Intelligence?
- How big is the market for Wearables?
- What is growing fast, faster and fastest?
All good questions as services firms think about digital strategy, analytics and future state. You always want to be in the “hot” area… selling is easier, valuations are richer, revenue growth percentages exponential.
Every firm wants to bring the power of big data and data science to streamline healthcare encounters ~ member/consumer engagement, provider/PCP engagement or clinical/care engagement.
Health expenditures in the United States crossed $3.0 trillion in 2013 which is more than ten times the $256 billion spent in 1980.
Almost 15% of U.S GDP is spent on healthcare…a staggering number. As a mega-vertical, healthcare covers several major segments (the 7 Ps)
- Payers (Health Insurance and Health Plans),
- Providers (Hospital Systems, Labs and IDNs),
- Pharmacy (retail distribution networks), and
- Pharmaceutical and medical equipment manufacturers,
- Prescribers (Physicians, clinics and pharmacy minute clinics)
- Police (Regulators, FDA)
- Patients (consumers)
U.S. healthcare system is a complex beast and difficult to navigate – providers need to make it easier for patients. They are using people resources like care coordinators and patient navigators to help patients navigate the system.
The focus on the payor side is in digitizing health today is to reduce the amount of waste in the health care system via implementation of new forms of health IT and Analytics… that reduces inefficiencies, redundancies and administrative costs.
According the CEO of Aetna…”the health care system wastes more than $765 billion each year – that’s 30 percent of our health care spending.”
While spending on health care is dominating headlines, the health care industry (7Ps) is in a state of flux. Stakeholders across the health care sector are running hard to reduce costs. The drivers impacting cost of healthcare include:
- Aging population – Patient history and patterns of care impacting patient readmission rates
- Rise in Chronic Disease – 75% of cost – Prevention not reactive medicine
- Drug cost – escalating for certain therapies (Generics exchanged for biological drugs)
The healthcare ecosystem is being reshaped by two powerful counter economic forces at work: (1) Improve quality of care and (2) drive the cost of care down. Basically spend less and get more.
As a result, the entire healthcare ecosystem is changing into a “information-driven”, “evidence-based” and “outcome-driven” model.
The target healthcare transformation goals are:
- align economic incentives between payers and providers,
- digital engagement…create a simpler, more transparent consumer experience, and
- connected health….technologies that seamlessly connect our healthcare system.
In this posting we look at Digital Health Care use cases and how data and analytics are being slowly but sure being adopted in the form of informatics. All this change is being driven under the guise of Health Reform.
The bleeding edge of data and insight innovation is around next generation digital consumer experience. Consumer behaviors are rapidly evolving….always connected, always sharing, always aware. Obviously new technology like Big Data drives and transforms consumer behavior and empowerment.
With the influx of money, attention and entrepreneurial energy, there is a massive amount of innovation taking place to solve data centric problems (such as the high cost of collecting, cleaning, curating, analyzing, maintaining, predicting) in new ways.
There are two distinct patterns in data-centric innovation:
- Disruptive innovation like predictive search which brings a very different value proposition to tasks like discover, engage, explore and buy and/or creates new markets!!
- Sustaining innovation like mobile dashboards, visualization or data supply chain management which improves self service and performance of existing products and services.
With either pattern the managerial challenge is moving from big picture strategy to day-to-day execution. Execution of big data or data-driven decision making requires a multi-year evolving roadmap around toolset, skillset, dataset, and mindset.
Airline loyalty programs are a great example of multi-year evolving competitive roadmaps. Let’s look at BA’s Know Me project.
British Airways “Know Me” Project
British Airways (BA) has focused on competitiveness via customer insight. It has petabytes of customer information from its Executive Club loyalty program and its website. BA decided to put customer big data to work in its Know Me program. The goal of the program is to understand customers better than any other airline, and leverage customer insight accumulated across billions of touch points to work.
BA’s Know Me program is using data and applying it to customer decision points in following ways:
- Personal recognition—This involves recognizing customers for being loyal to BA, and expressing appreciation with targeted benefits and recognition activities
- Personalization — based on irregular disruptions like being stuck on a freeway due to an accident – A pre-emptive text message… We are sorry that you are missing your flight departure to Chicago. Would you like a seat on the next one at 5:15PM. Please reply Yes or No.
- Service excellence and recovery—BA will track the service it provides to its customers and aim to keep it at a high level. Given air travel constant problems and disruptions, BA wants to understand what problems its customers experience, and do its best to recover a positive overall result
- Offers that inspire and motivate—BA’s best customers are business travelers who don’t have time for irrelevant offers, so Know Me program analyzes customer data to construct relevant and targeted “next best offers” for their consideration.
The information to support these objectives is integrated across a variety of systems, and applied in real-time customer interactions at check-in locations and lounges. Even on BA planes, service personnel have iPads that display customer situations and authorized offers. Some aspects of the Know Me program have already been rolled out, while others are still under development.
The Need for New Data Roadmaps
New IT paradigms (cloud resident apps, mobile apps, multi-channel, always-on etc.) are creating more and more complex integration landscapes with live, “right-now” and real-time data. With data increasingly critical to business strategy, the problems of poor quality data, fragmentation, and lack of lineage are also taking center stage.
The big change taking place in the application landscape: application owners of the past expected to own their data. However, applications of the future will leverage data – a profound change that is driving the data-centric enterprise. The applications of the future need one “logical” place to go that provides the business view of the data to enable agile assembly.
Established and startup vendors are racing to fill this new information management void. The establish vendors are expanding on this current enterprise footprint by adding more features and capabilities. For example, the Oracle BI stack (hardware – databases – platform – prebuilt content) illustrates the data landscape changes taking place from hardware to mobile BI apps. Similar stack evolution is being followed by SAP AG, IBM, Teradata and others. The startup vendors typically are building around disruptive technology or niche point solutions.
To enable this future of information management, there are three clusters of “parallel” innovation waves: (1) technology/infrastructure centric; (2) business/problem centric; and (3) organizational innovation.
IBM summarize this wave of innovation in this Investor Day slide:
Data Infrastructure Innovation
- Data sources and integration — Where does the raw data come from?
- Data aggregation and virtualization- Where it stored and how is it retrieved?
- Clean high quality data — How does the raw data get processed in order to be useful?
Even in the technology/infrastructure centric side there are multiple paths of disruptive innovation that are taking along different technology stacks shown below.
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, 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
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
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.
Facebook understands personalization. Do you? Facebook builds a custom Web page every time you visit. It pores over all the actions your friends have taken—their photos, their friends, the songs they listen to, the products they like—and determines in two-hundredths of a second which items you might wish to see, and in what order.
[SOURCE: Bloomberg Businessweek, “Facebook: The Making of 1 Billion Users,” Ashlee Vance, October 4, 2012]
A common CMO issue… Digital marketing is not working. Visits are up but sales are down, Site conversion is trending down. E-mail open rates are ok but click thru rates are down. What do we do?
- Can you predict what customers want before they do?
- Can you formulate the “next best action”?
- Can offers be better targeted or timed to improve customer acquisition and conversion?
Growing the customer relationship is the perpetual challenge of all companies. To change status quo, EBay bought Hunch to help improve its recommendation services. EBay uses Hunch’s “taste graph” technology to provide its users with non-obvious recommendations for items based on their unique tastes. E-bay applied Hunch’s technology to other areas such as search, advertising and marketing, in order to better surface product information based on its customers’ tastes.
It’s becoming a data-driven world. We are awash in data, but the problem is figuring out what we are supposed to do with it.
Data Driven Commerce & Retailing
Recommendations and promotions are the most effective when you target them on customer behaviors.
Recommendation and decision engines, an area of predictive analytics and decision management, is quite active right now in the digital arena. The early online pioneer was Amazon.com which used collaborative filtering to generate “you might also want” or “next best offers” prompts for each product bought or page visited.
Next best action, next best offer, interaction optimization, and experience optimization all share similar structure. A typical targeted offer analytics model is shown in the figure (source: blog.strands.com).
The premise of data driven commerce & retailing is simple:
- Acquire the right customers
- Offer the right products
- Personalize relevant offers
- Focus on the Right timing & Channels
To understand the impact that recommendation engines can have on sales, let’s look at a traditional brick-and-mortar firm doing direct to home face-to-face selling…Schwan Food.
Schwan Food – The Business Problem
The Schwan Food Company is a multibillion-dollar, privately owned company with 17,000 employees in the United States. Based in Marshall, Minnesota, Schwan sells frozen foods from home-delivery trucks, in grocery-store freezers, by mail and to the food service industry. Schwan produces, markets, and distributes products developed under brands such as Schwan’s, Red Baron, Freschetta, Tony’s, Mrs. Smith’s,Edwards, Pagoda Express and many others.
Schwan’s Home Service, the company’s flagship business unit, is the largest direct-to-home food delivery provider in the United States. Sales are done door-to-door by 6,000 roving sales people who deliver frozen products to homes of three million customers across the country.
Schwan home sales were listless for four straight years, beset by high customer churn and inventory pileups. So the challenge was: How to spark sales? How to get an uplift of 3-4%?
At the point of customer contact…Schwan wanted to personalize the experience. The goal is to dig deep into customer data, generate insights and engage customers in innovative ways.
What are primary drivers of sales? Schwan realized that by recommending to the customer, products that fit their profile, purchase history and interests there is a higher revenue potential for cross-sell and up-sell.
The challenge was to overhaul the current crude recommendation program that existed. Most firms like Schwan provide to the sales team data from the SAP back-end. Most of this data is stale and not dynamic. For instance, sales people could look at six weeks of orders, and suggest purchases from that list.
To completely overhaul the recommendation engine. Schwan began an analytics project with Opera Solutions.
The analytics project took it into more sophisticated territory: Matching seemingly disparate customers with similar purchase patterns in their past. Opera calls them finding “genetic twins.” It added ways to track whether customers’ spending was fading from certain categories—say, breakfast foods—and offered product suggestions and discounts to keep the spending intact.
How does this work? At the core of a recommendation engine is predictive modeling. This identifies and mathematically represents underlying relationships in historical data in order to explain the data and make predictions or classifications about future events.
Predictive models analyze current and historical data on individuals to produce easily understood metrics such as scores. These scores rank-order individuals by likely future behavior, e.g., their likelihood of responding to a particular offer.
Schwan’s database is now pushing out more than 1.2 million dynamically-generated customer recommendations every day, sent directly to drivers’ handheld devices. Opera says Schwan’s revenues are up 3% to 4% because of it.
It would be interesting to see the correlation between Schwan’s customer satisfaction scores and shopping basket mix with recommendations versus non-recommendations.
Netflix Real-Time Recommendation
The Netflix movie recommendation contest (blending of different statistical and machine-learning techniques) has been widely followed because its crowdsourcing lessons could extend beyond improving movie picks. The outcome: CineMatch recommendation solution built around a huge data set — 100+ million movie ratings — and the challenges of large-scale predictive modeling.
Netflix’s overview of the competition:
We’re quite curious, really. To the tune of one million dollars.
Netflix is all about connecting people to the movies they love. To help customers find those movies, we’ve developed our world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. We use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better.
Now there are a lot of interesting alternative approaches to how Cinematch works that we haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business.
So, we thought we’d make a contest out of finding the answer. It’s “easy” really. We provide you with a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.) If you develop a system that we judge most beats that bar on the qualifying test set we provide, you get serious money and the bragging rights. But (and you knew there would be a catch, right?) only if you share your method with us and describe to the world how you did it and why it works.
Serious money demands a serious bar. We suspect the 10% improvement is pretty tough, but we also think there is a good chance it can be achieved. It may take months; it might take years. So to keep things interesting, in addition to the Grand Prize, we’re also offering a $50,000 Progress Prize each year the contest runs. It goes to the team whose system we judge shows the most improvement over the previous year’s best accuracy bar on the same qualifying test set. No improvement, no prize. And like the Grand Prize, to win you’ll need to share your method with us and describe it for the world.
Netflix announcement of winner:
It is our great honor to announce the $1M Grand Prize winner of the Netflix Prize contest as teamBellKor’s Pragmatic Chaos for their verified submission on July 26, 2009 at 18:18:28 UTC, achieving the winning RMSE of 0.8567 on the test subset. This represents a 10.06% improvement over Cinematch’s score on the test subset at the start of the contest.
Interestingly several people think that “what your friends thought” feature to be extremely accurate in predicting and suggesting movies…more than the recommendation feature.
Netflix announced a second recommendation contest that was later discontinued. Contestants were asked to model individuals’ “taste profiles,” leveraging demographic and behavioral data. The data set — 100 million entries will include information about renters’ ages, gender, ZIP codes, genre ratings and previously chosen movies. Unlike the first challenge, the contest will have no specific accuracy target. $500,000 will be awarded to the team in the lead after six months, and $500,000 to the leader after 18 months. This contest was cancelled in May 2010 after a legal challenge that it breached customer privacy with the first contest.
Building on Netflix model, California physicians group Heritage Provider Network Inc. is offering $3 million to any person or firm who develops the best model to predict how many days a patient is likely to spend in the hospital in a year’s time. Contestants will receive “anonymized” insurance-claims data to create their models. The goal is to reduce the number of hospital visits, by identifying patients who could benefit from services such as home nurse visits.
I expect to see a lot more activity around Predictive Recommendations as mobile technology makes it easier to influence buyers or convert prospects into customers. Also technology like Hadoop makes it easier to build predictive insights that can be leveraged in real-time.
E-mail Based Recommendations
In multichannel customer-facing business processes, marketers must continually and automatically optimize all offers and customer interactions through all channels, business processes,and touchpoints such as sales, marketing, and customer service. E-mail based recommendation models are pretty advanced.
The same push based recommendation model can be leveraged via e-mail (in addition to mobile handheld direct sales). Williams-Sonoma, all things kitchen and cooking, has a database of 60M households tracking variables like income, number of children, housing values, etc. They leverage these variables in e-mail targeting programs.
Offers embedded in e-mail are tailored to the recipient at the moment they’re opened. In less than 250 milliseconds, analytics software can assemble an offer based on real-time information: data including location, age, gender, and online activity both historical and immediately preceding, along with inventory data. These offers have lifted conversion rates by as much as 30%—dramatically more than similar but uncustomized ad campaigns.
Targeting customers with perfectly customized recommendations at the right moment across the right channel is sales and marketing’s holy grail. As the ability to capture and analyze highly granular data improves, such recommendations are possible.
Perfecting these “next best product recommendation” models involves four steps: defining sales and marketing objectives; gathering detailed primary or secondary data about your customers, your products, and the contextual prompts that influence customers to buy; and using data analytics and business rules to devise and execute offers.
As the amount of data that can be captured grows and the number of channels for interaction proliferates, companies that are not providing recommendations to influence buyers will only fall further behind.
Notes (and Interesting Factoids)
- A recommendation engine generates tailored, and context-sensitive recommendations to guide decisions and actions taken by humans, automated systems, or a combination thereof. For Recommendation Engines background: http://en.wikipedia.org/wiki/Recommender_system
- In the late 1990s, predictive recommendations were created by Amazon and other online companies that developed “people who bought this also bought that” offers based on relatively simple cross-purchase correlations; they didn’t depend on substantial knowledge of the customer or product attributes.
- See of Opera Solutions work at Schwan’s: Dennis Berman’s article in the Wall Street Journal, “So, What’s Your Algorithm?”
- Additional Insights that can improve Sales Effectiveness
• What are the characteristics of my most loyal customers? Least loyal?
• How do customers feel about our company and products?
• Which items drive sales? Which items are frequently purchased together?
• If I discount an item by X, what impact will it have on sales and revenue?
• How do my internet sales compare to brick and mortar in terms of revenue and cost?
• Which prospects should I target to convert into loyal customers? What products or offers would be most effective?
• Will my inventory levels meet sales forecast? When will we run out of stock?
- Every vendor recognizes the power of data. For instance, Salesforce wants to be the center of data-driven customer strategy. To that end, the company introduced the Internet of Things Cloud @ Dreamforce 2015, which is supposed to pull in data from devices, sensors and non-IoT sources like app behavior and social streams. In Salesforce’s view, it’s all in the service of the customer, grabbing data and wrapping a rules engine around it to drive automated Next Best Offers or Actions for the customer.
Obsolete KPIs can be Lethal
In the Aesopian fable of the one-eyed stag, a deer overcomes his visual handicap by grazing on a cliff near the sea with his good eye facing the land. Since all his known dangers were on land, this keeps him safe from predators for a very long time – until he is killed by a hunter in a boat.
The relevance of our KPIs can make or break our business. KPIs are often defined as static metrics for an enterprise and can easily become outdated. Economic uncertainty and competitive pressures are prompting questions on the validity of KPIs and performance management processes. To stay competitive requires a process of continually validating metrics with the business environment.
Another common challlenge with KPIs is that there are too many of them. Modern technology has gven us the ability to measure a very large number of parameters in the business. Some of these are more relevant than others. Jack Welch is known to have said, ”Too often we measure everything and understand nothing”. Monitoring some metrics and ignoring others are decisions we make based on our business perspective.
Relevance Enabled by Process
How do you decide on which KPI’s are most relevant to success? An often overlloked first step is to understand that primary business goals before looking at the technology solution. Avinash Kaushik defines KPIs simply as “Measures that help you understand how you are doing against your objectives”. This fundamental aproach is a good way of weeding out items which are not relevant to what we want as a business and avoid adverse surprises. At a more deeper level, building a robust Business Analytics solution requires answers to questions such as:
1. What events have the greatest impact on the busiens and how are they measured?
2. How often do you validate that you are measuring the right parameters ?
3. What instrumentation do you need to create the right dashbords for your KPI’s ? Can this instrumentation be updatd as teh KPIs change?
4. What is the process for collecting, synthesizing, manipulating and presenting the data to represent thsese metrics? How does the process change when if the metric change?
5. What technologies and architecture are necessary to support those decision-making patterns? Is there need for a “single source of truth” or a federated model possible?
Centers of Excellence
Needless to say, this approach requires a tight inegration between the business owners and IT acrchitects. A recent study by Gartner says that ”IT collaboration initiatives fail because IT leaders hold mistaken assumptions about basic issues…..rather than making technology the starting point, IT leaders should first identify real business problems and key performance indicators (KPIs) that link to business goals.”
Many business executives believe that IT is unable to deliver results where it counts. At the same time, IT organizations spend an incredible amount of time, money and resources simply reporting obvious data within their business process and workflows.
An organizational solution to this problem is the creation of a Competency Center or Center of Excellence (CoE) with representation from from both business and IT and shared objectives. The CoE defines the blueprint for implementing BI, Performance Management and Analytics aligend with KPIs. Some of the obvious benefits include:
- Cost savings from eliminating Silos
- Better collaboration between Business and IT
- Joint ownership of corporate objectives
There are other aspects of the CoE which make it a practical approach to creating an effective vehicle for deploying analytics solutions. The sheer volume and texture of busienss data is much more complicated than it has ever been in modern busienss history. The world’s data doubles every two years creating more opportunities for analyses. Understanding this data even at an aggregate level requires a business perspective combined with technological expertise. Furthernore, understanding technologies such as Big Data for unstrcutured data analysis requires business leaders and IT eimplementors to work together.
The CoE is the ideal structire to implement a Business Perspective Solution. A well implemented Business Perspective Solution takes into account the key objectives of the busienss, leverages sophisticated analytics technologies and focuses on sustainable processes to support decision making in an organization.
Superior decisions based on business perspective separate winners from losers.
Are your KPIs in sync with your business perspectives? Please share your comments below.
1. Six Web Metrics / Key Performance Indicators To Die For by Avinash Kaushik, Occam’s Razor
2. Practical BI – What CEOs want from BI and Analytics by Ravi Kalakota, Business Analytics 3.0
3. The Stupidity of KPIs in Business Analytics by Mark Smith, Ventana Reasearch
- How to convert Lookers to Bookers…
- How to create unique and effective Digital Experiences that impact probability of purchase or likelihood of return.
- What offers might result in higher “take rates”
The change in consumer behavior and expectations that e-commerce, mobile and social media are causing is hugely significant – big data and predictive analytics will separate brand/retail winners from losers. This won’t happen overnight but the transformation is for real.
Retail Industry makes up a sizable part of the world economy (6-7%) and covers a large ecosystem – E-commerce, Apparel, Department Stores, Discount Drugstores, Discount Retailers, Electronics, Home Improvement, Specialty Grocery, Specialty Retailers and Consumer Product Goods suppliers.
Retail is increasingly is looking like a barbell – a brand oriented cluster at the high-end, a very thin middle, and a price sensitive cluster at the low end. The consumerization of technology is putting more downward pricing pressure in an already competitive “middle” retail environment. The squeeze is coming from e-commerce and new “point, scan and analyze” technologies that give shoppers decision making tools — powerful pricing, promotion and product information, often in real-time. Applications in iPhones and Droid, like Red Laser can scan barcodes and provide immediate price, product and cross-retailer comparisons. They can even point you to the nearest retailer who can give you free shipping (total cost of purchase optimization). This will lead to further margin erosion for retailers that compete based on price (a sizable chunk of the market in the U.S, Europe and Asia).
Data analytics is not new for retailers. Point of sale transactional data obtained from bar-codes first appeared in 1970s. A pack of Wrigley’s chewing gum was the first item scanned using Universal Product Code (UPC) in a Marsh Supermarket in Troy, Ohio in 1974. Since then, retailers have been applying analytics to get even smarter and speedup the entire industry value chain.
More recent use cases of retail analytics include: Read more