Posts Tagged ‘Analytics

01
Sep
08

Predictive vs Descriptive modeling – Understanding the difference

Many organizations use historical analytics data as a basis for forecasting future growth, and establishing performance goals and budgets. This applicaton for analytics data can blur the distinction between predictive and descriptive data. Understanding this difference is critical to an effective analytics program.

Predicitive modeling refers to a mathematical model that can accurately predict future outcomes. For instance, I know that if I apply sufficient heat to water, the water will reaach 100 degrees celsius and begin to boil (barring slight variations for altitude which are also predictable). The rate at which this happens and the amount of energy required can be mathematically described.

Descriptive modeling refers to a mathematical model that describes historical events, and the presumed or real relationship between between elements that created them. For instance, yesterday when I went to the store to buy milk, it cost me $1.00 a litre, last month it was 95 cents, last year it was 80 cents.. Based on historical events, I assume it will cost me roughly $1.05 to buy a litre of milk next month.

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10
Aug
08

How Harrah’s Entertainment translates customer data points into the big picture.

At Cequity, we have always believed that “earning points” in a loyalty program is a means to an end and not the end itself. Many companies fail to see the data from such programs holistically and tend to treat it too tactically leading to managing the points rather than an effective customer management strategy intent with which they started these programs. One of the few companies that have made this transition happen is Harrah’s Entertainment. Here’s what David Norton, CMO of Harrah’s Entertainment had to say:

Pointillism is a painting style in which the artist dabs small dots of primary colors on the canvas. Viewing the artwork at nose-distance, you see nothing but adjacent points. But step back, and suddenly you see Georges-Pierre Seurat’s A Sunday Afternoon on the Island of La Grande Jatte.

Analysis of customer behavioral and transactional data can similarly suffer from such granular myopia. Individual data points viewed without an overall perspective not only masks the larger view, it also blinds you to opportunity.

When I joined the company in 1998, we designated customers who played $400 in a given visit to one of our casinos as VIPs who received preferential treatment. But a customer who played $402 during one visit, triggering VIP treatment, might play only $398 on a subsequent visit and receive no such treatment. More importantly, customers who played only $50 a day but visited 50 times a year received no differentiated service at all.

As our goal is to see the total picture of a customer’s play with Harrah’s casinos, members earn credits across any one of our 12 casino brands. This feature not only benefits our members; it also allows us to measure their cross-market play. A third of our revenue comes from members playing in a property other than their home property—whether home is Las Vegas, Atlantic City, Reno, New Orleans or Tunica.

Our Total Rewards database also gives us unique insight into our customers’ total relationship with Harrah’s—including their non-gaming spend. Historically, Harrah’s Entertainment earned 80 percent of its revenue from gaming; our initial data analysis revealed that we received less than a third of our customers’ non-gaming budget. Once we acquired the Caesars Entertainment family of properties, however, non-gaming revenue—from hotel stays to fine dining to entertainment to shopping—became a significant portion of our business. Caesars Palace provides a strong gaming revenue stream from a relatively small number of VIP players, but many Caesars customers come to Vegas for non-gaming entertainment. We can now encourage non-gamers to use their Total Rewards card, for stays at Caesars Palace, trips to the spa and on our shows and still receive all the benefits of our loyalty program. Today, $2.1 billion of our revenue comes from non-gaming activity—and we want the total picture of those customer relationships too so we can customize their marketing and service interactions.

02
Aug
08

Customer-facing analytics – building relevance to business

DM Direct has interesting article on how now it is important to take data, information & analytics from back-office to front-office if it has make business impact & drive ROI. At Cequity, we have been constantly talking to business leaders in different forums and also advising our clients that it is imperative to find methods to convert data into action in real-time. Else, given the huge data decay that happen over a period of time, one always tends to use data to analyze the past rather than influence the future or proactively manage the present. This is really where we believe enterprises must build competitive advantage with data analytics and data-driven marketing.

Take a look at  what the DM Direct article has to say about this:

We’ve all been through it. After a frustrating attempt to figure out a discrepancy in your phone bill online, you finally give up and call the company. After being transferred twice and speaking to three people who each had to validate your information and ask what the problem was the problem of customer-facing analytics takes on real meaning.

Improving Phone Interactions

Most people who call into customer service are already irritated. The best way to transform an unhappy customer into a happy one is to address his or her problems quickly, interact with the customer on a personal level, listen to (and document) their issues and provide information about what will happen next. A customer service representative armed with information can transform a bad situation into a memorable, positive experience.

For those instances when a customer calls into an organization for reasons other than a problem – for example to start a new service – customer-facing analytics provide the customer service representative the ability to capture information about a prospective client and tailor offerings to the individual’s needs. For example, a phone company can sign up a family for the most cost-effective plan. Analytics provide the ability to cross-sell and upsell based on consumer patterns, versus promoting a cookie-cutter offering for all customers and making them feel that they’re just another checkbook.

Personal Touch

In-person encounters with customers provide the opportunity to make a lasting impression. Any employee can be a company advocate in your campaign to create customer satisfaction, but one behind a computer screen can be a particularly valuable asset. In addition to providing good service, a customer service representative can ask better questions to serve a customer, provide insights into products that may interest the consumer and appear more knowledgeable.

As analytics improve, it’s astonishing how good computers have become at “guessing” what you’d like. Netflix predicts what movies a viewer will enjoy based how the viewer has rated other movies in the past. Through the simple application of customer analytics, the Netflix Web site provides a valuable recommendation service. Customers expect this level of service, and it’s becoming commonplace on successful businesses’ Web sites.

27
Jul
08

Marketing metrics in action – Case study

It’s always easy to think about marketing metrics but very difficult to implement on the ground. Here’s a great case study by US Bank presented at the recent ANA Marketing Conference last week:

19
Jul
08

Cross-Selling & Up-selling in a contact centre

When millions of customers contact your contact centre, there is always an opportunity to interact with the customers once their primary request or query is addressed. Very often, this  customer interaction may not be used as a  “point of  leveraging the relationship”. Best-in-class organizations use this opportunity to the best.

According to research estimates:

  • Best-in-class companies, share several common characterstics.
  1. 62% utilize analytics solutions
  2. 57% of the agents are empowered to decide when is the right time to sell
  3. 57% have access to sales experts and knowledge for informal training
  • Best-in-class companies achieved
  1. 86% of the companies achieved a sale of $ greater than $20
  2. 84% of the companies have customer retention of 50% or greater
  3. 59% have a cost per contact of less than $10

Interestingly, only 40% of the best-in-class companies leverage their cross-selling & up-selling measurements to support their cross-selling & up-selling initiatives!

19
Jul
08

Taming the data beast

In a world where the quantum of data that’s being captured is increasing leaps and bounds, how do companies make sense of these huge piles of data? It is imperative that data must be converted to simple, easy to interpret visual methods at speed.

In this article, Angela writes:

Moore’s Law has driven quantum leaps in the processing power of software and hardware systems. Organizations have become larger and more complex. Demands for up-to-the-minute access to data have intensified.

he most effective way to tame the data beast is through interactive visualization. Spreadsheets and tabular reports are at their limits. Utilizing visual metaphors allows multiple dimensions of the data to be understood at once. In context, it provides a “narrative” for the data. Interactivity allows the user to engage the data in his or her thinking process, which enables a dynamic dialogue with the data.

By empowering knowledge workers with visual tools and hands-on access to data, they can find patterns, distributions, correlations or anomalies across multiple data types. Users can select data elements, filters, highlighting and display options to change data perspectives – from high-level overviews down to the lowest levels of detail. The visual cues inherent in the software enable a deep exploration and understanding of the data set at hand.

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12
Jul
08

Business Analytics – 10 top moments in history

It’s always interesting to check back in history to track how things evolved and grew. Here’s an interesting piece of news that tracks the history of how business analytics started since 5000 B.C. and has grown over years.  Thanks SAS for this info:

5000 BC: Grog uses two sticks and four rocks to graph the upward trend in sales of his new invention, the wheel.

3200 BC: Sumerian analysts predict the world’s use of letters will be greater than Mesopotamia’s supply of clay tablets by 3,000BC. Analysts suggest something called “papyrus” may solve the problem.

44BC: Roman leader Caesar receives analysts’ prediction that March will be a “down month,” but disregards the data.

1508: Michelangelo uses an advanced abacus to estimate the amount of paint needed to cover the Sistine Chapel.

1590: The Globe Theatre of London text mines peasants’ comments after a play by a fellow named “Shakespeare” and decides to ask him to write more plays like the last one.

1908:
Henry Ford conducts a What-If analysis that makes clear that limiting the Model-T to one color, black, is the best way to maximize profits.

1962: The Beatles manager uses early marketing automation software to reveal that Ringo should not sing lead on “I Want to Hold Your Hand.” John and Paul take over on the microphones.

1969: Woodstock ends in financial disaster after organizers rely on spreadsheets to estimate attendance. Hippies dance anyway.

1976: Analysts’ predictions that this will be the bicentennial of the United States are fulfilled. World gains sudden interest in the power of predictive analytics.

1976: SAS is formed and begins to give businesses The Power to Know.




At Cequity, we believe customer intelligence will be the biggest competitive advantage enterprises will have in the next decade or two. Successful enterprises of tomorrow will be the ones who can organize and leverage this information at speed to optimize their marketing performance, increase accountability, improve profit and deliver growth. Cequity insights will bring to you trends and insights in this area and it’s our way of sharing best practices so as to help you accelerate this culture and thinking in your organization.
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