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Archive for the ‘Business Analytics’ Category

Business performance management consists of a set of management and analytic processes, supported by technology, that enable businesses to define strategic goals and then measure and manage performance against those goals. BPM processes include financial and operational planning, consolidation and reporting, business modeling, analysis, and monitoring of key performance indicators linked to strategy. BPM involves consolidation of data from various sources, querying, and analysis of the data, and putting the results into practice.

Difference between business intelligence and business performance management.

• BPM (Business performance management) contains the concept of a control or feedback loop that helps guide the business towards its goals.
• BI (Business intelligence) may provide the analytics to help the business set those goals and to monitor progress towards them.

BPM enhances processes by creating better feedback loops. Continuous and real-time reviews can help to identify and eliminate problems before they grow. BPM’s forecasting abilities help companies take corrective action in time to meet earning projections. Forecasting is characterized by a high degree of predictability which is put into good use to answer what-if scenarios.

BPM can help in risk analysis and in predicting outcomes of merger and acquisition scenarios and in planning to overcome potential problems.

Methodologies

Methodologies for implementing BPM.

• Six sigma
• Balanced scorecard
• Activity based costing (ABC)
• Total Quality management (TQM)
• Integrated strategic measurement

Areas where BPM is used

1. customer-related numbers:
   1. new customers acquired
   2. status of existing customers
   3. attrition of customers (including breakup by reason for attrition)
2. Turnover generated by segments of the customers – possibly using demographic filters
3. Outstanding balances held by segments of customers and terms of payment – possibly using demographic filters
4. Collection of bad debts within customer relationships
5. Demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers
6. Delinquency analysis of customers behind on payments
7. Profitability of customers by demographic segments and segmentation of customers by profitability.
8. Campaign management
9. Real-time dashboard on key operational metrics
   1. Overall equipment effectiveness
10. Click stream analysis on a website
11. key product portfolio trackers
12. Marketing channel analysis
13. Sales data analysis by product segments
14. Call center metrics

 Strategic Solutions used for effective implementation of BPM

• OLAP — online analytical processing,   
• Score card, dash board and data visualization
• Data warehouses
• Document warehouses
• Text mining
• DM — data mining
• BPO — business performance optimization
• EIS — executive information systems
• DSS — decision support systems
• MIS — management information systems

 Challenges when implementing a BPM program

• Goal-alignment queries: one must first determine the short- and medium-term purpose of the program. What strategic goal(s) of the organization will be addressed by the program? What organizational mission/vision does it relate to? A hypothesis needs to be crafted that details how this initiative will eventually improve results / performance.
• Baseline queries: current information-gathering competency needs assessing. Do we have the capability to monitor important sources of information? What data is being collected and how is it being stored? What are the statistical parameters of this data, e.g., how much random variation does it contain? Is this being measured?
• Cost and risk queries: someone should estimate the financial consequences of a new BI initiative. It is necessary to assess the cost of the present operations and the increase in costs associated with the BPM initiative. What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.
• Customer and stakeholder queries: determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers / stakeholders will benefit directly from this initiative? Who will benefit indirectly? What quantitative / qualitative benefits follow? Is the specified initiative the best way to increase satisfaction for all kinds of customers, or is there a better way? How will customer benefits be monitored? What about employees, shareholders, and distribution channel members?
• Metrics-related queries: information requirements need operationalization into clearly defined metrics. One must decide what metrics to use for each piece of information being gathered. Are these the best metrics? How do we know that? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?
• Measurement methodology-related queries: one should establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. What methods will be used, and how frequently will data be collected? Are there any industry standards for this? Is this the best way to do the measurements? How do we know that?
• Results-related queries: someone should monitor the BPM program to ensure that it meets objectives. Adjustments in the program may be necessary. The program should be tested for accuracy, reliability, and validity. How can it be demonstrated that the BI initiative, and not something else, contributed to a change in results? How much of the change was probably random?

www.ibm.com ibms data analysis expertise helps clients improve the speed and quality of decisions, while better understanding the consequences and business outcomes of those decisions.

Predictive analytics is a solution used by many businesses today to gain more value out of the large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it’s customer base, it’s products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

The benefits of implementing predictive analytics is undeniable. There are countless documented case studies and success stories where predictive analysis yielded a substantial return on investment, helped companies optimize existing processes, provided a better understanding of customer behavior, identified unexpected opportunities, and anticipated problems before they occurred. But with all of the benefits associated with predictive analytics, there are many challenges that accompany becoming an analytics-driven organization.

The perceived complexity is the largest challenge facing executives today. The cost of implementation is a close second. While these are legitimate fears, many tools are being developed to simplify the process and establish transparency from the complex formulas and statical modeling. It is, however, up to organizations to educate themselves on the basics and concepts of predictive analysis in order to fully utilize these tools.

Another challenge, which is more technical, is the traditional approach of having analyst explore data sets by saving data and manually applying relationships in order to make predictive assumptions. While this can work at a basic level of predictive analytics, predictive analytics at it’s most effective application requires extremely large amounts of data and thus is best suited for analytics platforms wih parallel processing, which support custom analytical applications that query data using SQL.

This brings us to another challenge with implementing predictive analytics in your organization, and that is managing the enormous data volumes associated with it. Some organizations known to apply leading edge analytical techniques, are gathering perabytes (that’s approximately 1000 terabytes, or 1 million gigabytes) of data. While these amounts of data require costly data warehouse upgrades, it enables organizations to form very comprehensive analytics and it enhances visitor/customer experience by providing targeted, customized marketing and services.

But with these large amounts of data and data storage comes the challenges of producing the platform for processing this data with complex formulas at fast rates. Because of this, analytic platforms often run off massively parallel processing (MPP) databases. MPP databases coordinate processing of a single program by more than one processor by dividing up parts of a program into several processors with their separate memory and operating systems. But many organizations that cannot afford MPP databases, instead implement analytical platforms as data marts to off-load complex processing.

While these challenges to indeed appear to be complex, the important thing to know is that if you have the architecture to support it, there are several tools out there that take out the complexities and applying predictive modeling.

TREND ANALYSIS AND TREND ESTIMATION

What Does Trend Analysis Mean?
An aspect of technical analysis that tries to predict the future movement of a stock based on past data. Trend analysis is based on the idea that what has happened in the past gives traders an idea of what will happen in the future. 

There are three main types of trends: short-TERM,intermediate- and long-term.

The term “trend analysis” refers to the concept of collecting information and attempting to spot a pattern, or trend, in the information. In some fields of study, the term “trend analysis” has more formally-defined meanings.

TREND ESTIMATION:

Trend estimation is a statistical technique to aid interpretation of data. When a series of measurements of a process are treated as a time series, then the application of trend estimation can be used to make and justify statements about trends in the data. Using trend estimation, it is possible to construct a model which is independent of anything known about the physics of a process of an incompletely understood physical system. This model can then be used to explain the behaviour of a measurement.

Process may refer to:Biology Process, a projection or outgrowth of tissue from a larger body. Biological processScience and technnology Process ,a computer program or an instance of a program running concurrently with other programs…
is treated as a time series

Time series

In statistics, signal processing, and many other fields, a time series is a sequence of data points, measured typically at successive times, spaced at time intervals trend estimation is the application of statistics Statistics

Statistics is a Mathematics pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It also provides tools for prediction and forecasting based on data techniques to make and justify statements about trend

Trend may refer to:In Business:* Market trends, a prolonged period of time when prices in a financial market are rising or falling faster than their historical average, also known as “bull” and “bear” markets, respectively.

Assuming the underlying process is a physical system that is incompletely understood, one may thereby construct a model, independent of anything known about the physics of the process, to explain the behaviour of the measurement. In particular, one may wish to know if the measurements exhibit an increasing or decreasing trend, that can be statistically distinguished from random behaviour. For example, take daily average temperatures at a given location, from winter to summer; or the global temperature series over the last 100 years.

Particularly in that latter case, issues of homogeneity (statistics).

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The software-as-a-service (SaaS) model is disrupting traditional approaches to business analytics. The long deployment cycles, high costs, complicated upgrade processes and IT infrastructure required of traditional on-premise business intelligence solutions are no longer acceptable in the era of on demand. Instead a new breed of analytic solutions has emerged that are simple to set-up and simple to use and deliver immediate business value.
The difficulty for salesforce.com customers is in knowing where to start. With well over 50 analytic applications to choose from on the Force.com AppExchange and native transactional reporting features constantly improving in the CRM application itself, it can sometime seem like the status quo of “Excel Hell” is the easiest and safest choice.
Unless you’re happy managing and maintaining those unwieldy spreadsheets, pivot tables, disconnected Access databases, and numbers that often don’t even add up, here is primer to help you move from sales force automation to salesforce.com acceleration with on-demand business analytics:
Understand Your Sales Analytics Requirements
If you’re a Salesforce administrator, you already know how important it is to become proficient with the built-in reporting and dashboard capabilities of the application. If you’re not already up to speed, be sure to sign up for a training course, watch a Dreamforce presentation on the success.salesforce.com community website, and try downloading a few of the free dashboard applications on the AppExchange. (Adoption Dashboards, for example, are a great introduction and jumpstart to salesforce.com dashboards and they’ll also get you familiar with the process of installing applications on the AppExchange.)
But this is only the beginning. Inevitably with Salesforce, as is typical of transactional reporting, you’re always 4 or 5 reports away from answering the question you really want to answer. To understand your Sales Analytics requirements, you need to consider the following:
• What information do sales managers, the CFO and the CEO need today to be successful? (Having a clear understanding of their objectives and success metrics is critical. How many of these questions can you answer today?)
• What business questions are the most difficult to answer today? Who is asking these questions? When and why?
• Would people prefer to answer their own business questions or are they content relying upon the sales operations, business analysts, and/or IT function for information?
• How do managers prefer to access and analyze business information–dynamic dashboards, spreadsheets, pdf, PowerPoint, email, mobile device, etc.?
• What other sources of information do people need to access and analyze in order to achieve sales success? (Note that critical sales data is often locked in financial systems – orders, bookings, billing information; or lives outside of the CRM system in spreadsheets–commissions, quotas, forecasts .)
Know Your AppExchange Analytics Options
In their paper, Sales Management 2.0: Metrics, Not Hunches, Barry Trailer and Jim Dickie from CSO Insights describe the key sales analytics criteria this way:
“You can decide whether you need to pull and analyze data from multiple data sources (e.g., accounting, inventory, sales, etc.) or just one (CRM). Solutions are available either way; what you want is an application that will allow you to defi ne business rules, historic trends and exception reporting with a minimum of administrative/set up effort.”
Here’s an overview of some of the AppExchange choices available to salesforce.com customers and their pros and cons:
Production Reporting
These are tools designed for advanced report developers to create virtually any report on transactional data. Also known as “enterprise reporting”, these tools typically provide built-in scheduling of pre-authored, highly formatted, “pixel perfect” reports that may include prebuilt prompts or filters to make them seem interactive. For salesforce.com customers these tools, can create virtually any join, but the data size must be small. Attempting to replicate all of your transactional data in a desktop reporting tool in order to get the queries you need will not work. If people want to be able to ask spontaneous, iterative, or trend-based questions of their business data, these tools are not a good fit. If you have someone in house who understands SQL (and SOQL), and you just need a couple of static reports delivered, start here. Just be sure to find out about customization, support, and advanced report-writing costs up front and keep an eye on the enhancements coming in the native salesforce.com transactional reporting features. Also keep in mind what Neil Raden noted in his salesforce.com paper called, Accelerating Analytics Success with On Demand:
“Porting a desktop application by removing its user interface and replacing it with a Web front-end masks the fact that its internal operations have not been migrated to an on-demand, multi-tenant architecture. The result is likely to perform poorly, to require time-consuming labor for upgrades and patches, and quite possibly to be discontinued when the vendor releases its “real” on-demand product at some point in the future, likely with no satisfactory conversion path.”
Native Dashboard Applications
There are many interactive, real-time charting and Adobe Flex-based analytic dashboard components on the AppExchange today. Some are easier to set up and use than others. Most are eye catching. The native dashboard applications often impress executives and non-analyst roles in the company, but because they are built on the underlying transactional Force.com platform, they typically do little for the people struggling with disparate Excel spreadsheets and historical reporting and analysis requirements. Keep in mind that a nice-looking, mashed-up dashboard widget may have “demo sizzle” and may even make sense for your business process, but all dashboard-focused applications on the AppExchange are not alike. Be sure to find out about the vendor’s vision to go beyond operational or embedded business analytics for one transactional system in order to determine if they’ll be able to meet both your short-term tactical and long-term strategic on-demand information access and analysis requirements.
As stated earlier, definitely download the relevant free native AppExchange dashboard applications to jumpstart your sales analytics initiatives and to get comfortable with the AppExchange experience.
True Analytic Applications
Also known as online analytical processing (OLAP), it’s important to look for analytic applications that are built on a separate data platform designed from the ground up with user interactivity and information analysis in mind. They will allow you to monitor and track historical trends and get answers to ad hoc questions, not just static reports. These applications must be simple to set up and simple to use. They should also be built on an underlying on-demand business intelligence platform that takes care of the “heavy lifting” by integrating, cleansing, and aggregating data from multiple sources into a single reporting and analysis interface.
But beware of tools approaches. Instead look for true applications that deliver prebuilt best-practices and are designed for specific industries and roles. When Henry Morris coined the term “analytic application” over 10 years ago, he defined three key criteria as being essential:
1) Process support
2) Separation of function, and
3) Time-oriented, integrated data from multiple sources
Also be sure to find out about the trial process and how easy it is to get up and running with an on-demand analytic application on your company’s data.
Whatever You Do, Don’t Wait for Sales Analytics!
Putting off sales analytics is like putting off winning. But for many organizations, getting started can be equated to getting fit and joining a gym. You know you need to do it, but there always seems to be a good excuse not to. In order to get more out of your CRM investment and drive sales performance with data, not opinions, here are a few suggestions to help make sales analytics a top business priority for your company:
• Make analytics a business initiative. Determine the metrics that matter and build a plan. Executive sponsorship is critical to analytics success.
• Think big, but start small. Starting in one department or even one region will allow you to get some quick wins and you’ll be amazed at how fast word of your success travels. Have a vision to go beyond one area of the business, but don’t let a broader vision slow you down. The most important thing is to get started.
• Make it about business process. Sales analytics and the lead-to-cash cycle is a great place to start. And given that this is an analytics initiative, be sure to set clear goals and measure your success against those goals at every step along the way.
In difficult economic times, more and more companies are relying on sales analytics to give them the competitive edge and win. Make sure you’re one of them. No more excuses. No more surprises.