You fix a particular performance  level as base, you can assign a metric as 0 or 100 or of your liking. All other performances are Indexed on base.

This will help you establish high/low movement of metric from base.

Therefore sales of apples and oranges, even though are different may be at same Index point. On the other hand grapes sales may be same as apples but its index may be 1.5 times that of apple.

Another use of index is offering points to sales guys for their performances. Every sales guy on field may have different targets to achieve. However they may get points depending on the ratio of their performance to their base level performance.

A particular state of performance is considered as BASE then the future performances are treated as ratio from base expressed in range of 0 – 100 or 1 – 5 or 1 – 10.

You can square the number before taking ratio. Alternatively take root, mod, absolute, log etc depending on how you want to represent numbers.

B) Conversion Rates / Probability: Whenever there are two levels of metrics one leading to another. Conversion rates are normally used. Most commonly used conversion rate is (Customers acquired / Prospects contacted).

This can be used as performance of channel used for identifying propects. In internet businesses Conversion rates are used for evaluating performance of landing pages, acquisition channels (SEO, SEM, Affiliate), product efficiency, managing advertising and many more.

Conversion Rates can also be used as probabilities of occurence of the event. For all practical scenarios you can use this as a metric to analyze effectiveness of campaigns.

Frequency distribution: In other words it is proportion of segment compared to whole. This can also be used as probaility.

In futre topics, lets talk about how we can use this to calculate lift, ROC etc.

For now it this will help point out Outliers in database, segments that take up large portion of the pie etc. This metric will also help you in segmenting database.

C) Ratios: When you divide one number with the corresponding number of a different metric its a ratio. Conversion rate is also a ratio.

Odds Ratio: This is defined by probability of occurence of an event over probability of non-occurence of that event. In some cases it is (Conversion rate / (1 – Conversion rate ) )

This is mostly used in logistic regression to estimate the probability of occurence from a set of predictor variables.

You could run a predictive model to predict the log of odds ratio as the outcome (dependent variable) and input independent variables to be customer predictors.

Predictive analytics is good at identifying, whether a event will occur or not, rather than when the event will take place.

Further in the series of understanding numbers, we will talk about concept of lift and other metrics.

The important aspect is how this revenue data is used to identify impact due to other metrics or data. One big mistake people make is looking each of the data independently. This robs of the wonderful insight that seen from impact analysis.

Few of the important ways in which data can be understood are.

A) Combine Relevant metrics. Look at data in relation to other relevant metrics to understand the impact. For example instead of looking at revenue separately and expenditure separately we need to look at data revenue per every unit of expenditure. Similarly for other metrics, you can calculate ARPU, average revenue per user. Likewise average revenue per visitor and metrics that is actionable.

For example looking at telecom data, where average height of students in a class is of little importance. Instead you can look at average height of boys, average height of girls. Similarly average height of boys/girls who are active in sports can be of some value.

Another example is let’s say a company has 17% attrition rate (churn rate), and company has 400 employees with Revenue of $2,800,000.

You can look at this data as average revenue / employee as $7000 and 68 employees resign every year with a total of $476K. A delay of 1 month in hiring will cost you $40K. Look at data in combination and not independently.

B) Secondly looking at numbers at an average level does not make sense. You need to segment the data into relevant categories to make business sense. For example how much sense does it make by looking at average marks of students in 10th std for all subjects.

You need to look at each subject, each division, breakup on metros, rural/urban breakup, break on central/state boards etc.

Similarly looking at any data, segmentation is the basic key to understand it. You need to segment data in all possible ways across n x n dimensions that is possibly available.

C) Next is looking at data from paretos angle. Paretos principle says there exists a 80-20 rule. 80% of the revenue comes from 20% of customers. You can try and apply this paretos principle to as many situations you want. Average numbers may not even make sense in most cases.

You may find that 20% of the students score above 90% marks and rest of them score less than 75% marks, bringing the average down to 80% marks which is an imperfect number to look at.

D) Absolute Numbers & Percentages: Looking at numbers both in terms of absolute numbers as well as percentages is good. For example a distribution across geography may result in higher % in some locations, like for instance let’s say Chennai may contribute to 5% sales, however absolute numbers may be 10 to 15 units in number which may be insignificant. Need to be careful looking at numbers/percentages.

The advantage of the % is it offers the slice of the pie.  Absolute numbers gives us idea of volume of data, Percentages hide the volume.

The article is originally found on http://www.praveenkodur.com/blog

Few of the key things that could hamper their decision making is not having a consistent definition of metrics across the company. Finding cheap and effective way to store business information with minimal inconsistencies is essential. Along with this is required a solution that is scalable for data explosion and variety of future business / analytical needs.

There are lot of BI tools in the market from Top Premium tools to low cost open source tools. Finding the right tool and people for maintenance of the tool and even more people for using the tool is essential for every company.

Analytical needs of the company requires data capturing at granular levels. This transactional data needs to be captured for business intelligence at higher levels. There are lot of new technologies that are currently available such as databases on schemaless Mapreduce architecture or Databases on Cloud computing etc..

These new technologies can make the Implementation of BI much more simpler and easier than before. At the client end however, you need tools for analyzing and manipulating data.

Even web analytics tools like Google Analytics provides tools and features to create your customized reports which can be viewed online or scheduled to your email address. The most important thing however is to identify the few key metrics that matter to your business.

Its common that most business at the start want to look at total pageviews, total visitors as metrics in the dashboard. However everyone knows these metrics do not change on day to day basis. The report ends up showing almost the same data every day.

Identifying key metrics can be difficult and needs feedback from business heads and top management. Lets  take an example of Online Marketing, instead of generic report on total acquistions, total cost, pageviews, clicks, CTR, Revenue, ROI.

You could make different dashboard which contains – Profit from Branded Keywords, Profit from Long tail keywords, Profit from Acquisition keywords. Similarly Bounce Rate of landing page for each of the keyword segments. Another metric could be ARPU (average revenue per user) from each of keyword segments.

Since the action points are at keyword level or banner level. The segmentation is required first at keyword / banner level. You have to segment keywords/ banners based on their value delivered.

In case value is not directly visible as in revenue or profit we need to define proxies to identify the value. Some keywords may attract users who are more engaging on the site. This engagement has to be identified.

Few Laws of BI:

First law of BI is anything that matters is detectable. If a metric does not matter, then you dont care if it is detectable. Second law is anything that is detectable is measurable.

Therefore if a user engagement is important and matters to business then it is detectable. Engagement is different for different sites. For blog may be it is adding comments, for a e-commerce site it is browsing more products, for a classifieds site, it may be number of searches. For a social networking site it is number of pokes, scraps, wall posts etc. You have to define for your engagement metric for your business. Now go back and segment your action points with your engagement metric.

Hope this is useful..Please share your thoughts .. send in comments..

This article is originally found on http://www.praveenkodur.com/blog/

Every process, business or company can be considered to contain a list of inputs, transformations / systems (working) and finally the output. Of course all of these operate in a set of external and internal conditions (called environment). This real world phenomena can possibly be represented in mathematical form in terms of flowchart & numbers.

Pictorial and mathematical representation will help in optimization and alignment to the business KPIs. Coming to KPIs, these are set of metrics which measure performance of the business. These are performance metrics that are tracked, measured & monitored on regular basis.

Mathematical models contains an additional loop (feedback loop) in normal flow chart, gets formed from output to system as a feedback of performance. This feedback contains information on what to improve and  other intelligence that is fed back into the system. The feedback can be however not necessarily limited to effectiveness and efficiency of the system. First is the ability of the system to meet the desired output/ objectives. Second is the relationship between input and output, measure of how good is the relationship.

More articles on analytics, online marketing is present on http://www.praveenkodur.com/blog/

Users must understand that all mathematical models will only help support a decision or help solve a problem. At the time of decision making a knowledge worker generally has one or more alternatives to solve the problem. A list of feasible solutions will need to be chalked out from all possible solutions. The best solution or optimum solution to be derived from the list of feasible solutions again based on criteria such as profitability, cost, response etc.

There is a danger here, the easy use of GUI tools on large amount of data available is tempting and which makes users tempt to use black box methodologies available in the tools to solve their business problems. They mistakingly assume data mining is all about using a tool and running the data on the tool, this can be very hazardous and dangerous as actions are taken on business decisions.

Little knowledge is dangerous while applying powerful models.

Since the underlying logic used for model building in automated tools are unknown (their internal assumptions are unclear). Therefore it is very easy to do it in the wrong way assuming it is the right solution. Such mistakes are still made in companies.

People sometimes might argue that there is always a simpler solution to problems. I am the person who belives in Occam’s Razor, which means simplest explanation is always the best way. This means the interpretation of the model and the final solution should be as simple as possible. However,  method of arriving at solution must be as detail as possible taking every factor, element into consideration. Analysts must not make the mistake of over simplyfying the method and make too many assumptions.

A lot of knowledge workers believe that predictive modeling process can be automated by using tools and statistical software. These software are certainly useful but cannot replace the intellectual  of the analyst.

The processof building the model must be as detailed as possible, it has to tried & tested on various data and measured by various parameters. All possible factors must be considered while building the model.

Therefore Analyst must understand the underlying algorithm , process of arriving score, model design etc.

More on such articles regarding online marketing, data mining, data modeling can be found at http://www.praveenkodur.com/blog

Even a using a tool as simple as a spreadsheet will be extremely helpful in providing a mental representation of business situation. Most commonly used statistical techniques can be implemented in a spreadsheet as simple as MS EXCEL.

There are some very important techniques to business intelligence analysis. Most importantly defining the objective and performance indicators, these are metrics that are used to estimate performance of an object (entity). The next is developing mathematical relationships between variables and metrics through finding patterns. The last is What-If analysis is by determining variations in the output metric by changing the input variables.

The advantage of using mathematical models is beyond increasing performance and ROI. It helps knowledge workers in deeper analysis of the business and underlying product/domain. This will increase awareness in the company, knowledge transfer within the company,  and higher desire to learn better things. It encourages intellectual thinking within the company and promote people with good analytical skills who can offer great value to the company.\

There are many techniques like regression and classification, which are some of the popular mathematical models,however predictive analytics are not limited to these methods.

Regression:      Linear Regression, kNN, CART, Neural Net

Classification: Logistic Regression, Bayesian Methods, Discriminant Analysis, Neural Net, kNN, CART.

There are some limitations and advantages of each of the methods. The right model and right mathematical technique to be choosen for each problem. The underlining business value that needs to be increased with each of the techniques.

Best techniques are formulates after and testing and evaluating each approach and measuring the impact of success or performance.  All mathematical models use simple statistical techniques, however the value is in mapping the business problem into a mathematical problem, this requires some intellectual talent.

Models developed can be extremely useful in business critical process like sales, marketing and product.

More on such topics will be published on http://www.praveenkodur.com/blog/

Decisions are taken by knowledge workers at all levels of the company. Most of the knowledge workers generally adopt decision making style based on experience, gut feel, instinctive & spontaneous techniques. Such methods soon become predictable & stagnant. In a long run it does not prove useful in rapidly changing economic, technology & business environment. In rapidly changing technological environment, process become increasingly complex,

To stay competitive in a dynamic business environment requires a decision making style which is supported by knowledge driven by information & data. Analytics can provide a strategic value in providing competitive advantage to the companies.

For example, Internet company observes a lot of customer attrition on their website. These users may switch to a competitive website. The company is looking to reduce this churn by offering a discount or a trial. To increase the effectiveness of campaign is to recognize those customers and estimate their probability to discontinue the service. From a given list of customers we can a implement a model to estimate the probabilities and arrive at a smaller subset of users to be targeted for the campaign. This smaller set of users will be the majority of the future discontinued users.

Such tools and methodologies support knowledge workers in making better decisions. Generally several options are available to users before making a decision. This actually helps them choose the best methodology.

Often there is a enormous amount of effort is required to extract knowledge from data. Data is present in most raw format almost incomprehensible. Data needs to be transferred to human readable format and then has to be processed (sometimes tortured) to extract information and then to knowledge to make it useful for knowledge workers.

More articles in this blog http://www.praveenkodur.com/blog will talk in detail about BI systems.

Extracting knowledge from data requires deployment of various systems, process and maintenance. Installing Transaction servers to maintaining data warehousing systems to creating decision support systems. The idea of a Decision support system is to promote a scientific and rational approach to management.

Business intelligence architecture must be designed to function efficiently with cost effective components. There are various components such as logging systems, data warehouse, BI methodologies.

Focus for many companies such as Banks, Insurance companies, Telecommunication companies is to track customer end to end, from the time money spent on the customer acquisition to the time revenue is generated from the consumer for product/service until the customer attrites from the firm. Every activity in the chain must be closely observed to evaluate the true value of customer.  Companies worldwide are creating process to deal with customers  individually.  This can help them devout more attention to customers who are more valuable to the business and let go customers not valuable to business.

Data Mining requires a lot of effort/techniques and focus to centre their business around the customer than a product. Companies have to constantly keep a watch on what their customers are doing, keep in mind their past actions, discover knowledge from their actions (gain knowledge)  and finally use the knowledge cleverly to make decisions to make profit.

However data mining is not always beneficial for the user, consider the fact a model recommends a service to a user instead of the product A. however if the business makes more profit on product and very negligible amount on service. Model recommendations may not be implemented. However it helps in understanding such customers.

Good question to ask is how can a consumer company with large base of customers can individually deal with each consumer. This can be accomplished intelligently by deploying effective technology solutions which is customisable based on data mining models and techniques. Nowadays the customer is the data entry operator who enters data into the system at various points and these are captured by the system.  Consider your bank, your touch points are ATM, Bank Branch, customer care who responds on phone, mail, hard mail & lastly your account /loan/credit card that you hold of the bank. Each transaction records your behaviour which can be an additional knowledge that bank is made aware of, this knowledge can be used by the bank to learn more about you and customise the next interaction or next touch point instance with you.

The transaction system records every instance of transaction of the user enabling the bank to analyse the nature of every transaction and update your profile with rich insights. The knowledge discovery doesnt end here. It needs support of data warehousing system along with extremely good data mining models to be able to take action / make decisions and deal with each customer. More on this will be written in more detail in coming articles.

The blog  http://www.praveenkodur.com/blog/ will be updated with more such analytics, online marketing articles.

Most website users have already used Google Analytics as online visitor reporting tool, however new features like Custom reports / Advance segmentation has upgraded it to an analysis tool. With Google Analytics you can achieve detailed analysis to make well informed decisions for online marketing & Internet business in general.

Some of the new features in Google Analytics are:

Advanced Segmentation: This is a most important feature to segment GA data as per your criteria.

a. Define segments based on any Site metrics, Traffic, Content, E-Commerce or Systems parameters available.
b. Apply these advanced segments on any report to view slice of data as per your criteria.
c. Apply more than one segment, along with overall visits for comparison.
d. Use predefined segments available by default.
e. Understand behavior of users for each segment and differentiate your action for each of them.
f. This feature along with regular expression can identify key pain points & opportunities that can make huge difference to your website.
g. E.g Segment1: Visitors landing on home page and bouncing from site.
h. E.g Segment2: Visitors landing on inner pages.

Custom Reports: This is a user defined report, where you can choose to identify elements in rows & columns in a data table.

a. Choose desired dimension parameters in rows and your desired metrics in columns.
b. Select other sub-dimensions for drilling down the reports. You can select drill down parameter to three levels.
c. Sub dimension chosen will be a slice of data from top level dimension. Since you have the ability to choose any dimensions for both levels, you can get absolutely wonderful segments.
d. E.g : First level dimension can be Geography, drill down to Top Landing pages & Network location.
e. E.g: First level can be Count of Visits, drill down to Traffic Sources, Top landing pages.
f. These reports can be saved, exported, sent to email and viewed anytime online.

Motion charts: Great Visualizing techniques like motion charts are available for users to pictorially view metrics over a time period. You can choose other site metrics as bubble sizes with different colors.

a. You need give Google motion chart API code access to your GA account.

Data Export API: Use Google Analytics API to import data from Google Servers. This is very useful as you can store GA information in your database or use it third party reporting software.

a. Export Data on Dimensions & metrics which can be used in combination for a custom report.
b. Common calculation such as formula for Bounce rate (bounces/entrances), Exit rate (exits/pageviews) and more can be found here http://code.google.com/apis/analytics/docs/gdata/gdataReferenceCommonCalculations.html

Flash & Dynamic Page tracking: Use ga.js new code for tracking dynamic pages with _trackpageview function.

a. Track Videos & various actions like pause, play, stop etc.
b. View Developer Docs present at http://code.google.com/apis/analytics/docs/

User Defined functions: You can configure Google Analytics to store user attributes like Age, Income, demographic, psychographic attributes. The website needs to capture these attributes in first place and using the setvar function in javascript code, you can pass these values to Google Servers. Setvar function overwrites new values for same cookie, to avoid this there is another function called supersetvar, which can be used to append future values to existing ones.

a. You could slice,dice, segment data on Age, Income & custom defined values.
b. View the list of functions which can be used to customized data representation in GA. http://code.google.com/apis/analytics/docs/gaJS/gaJSApi.html

Internal Site Search: Configuring your GA for site search can be done by enabling in main settings panel. You also have to give the parameter which stores the search keywords in URLs.

a. Search Exits, Bounce Rate, User behavior during search, post search and pre-search.

Ecommerce Tracking: You can use GA to store your e-commerce transactions. This is done by enabling e-commerce settings. You also need to place additional code in Thank You! Page to capture the elements of transaction such as price, product name, quantity etc.

a. You can find more info here about installing the Ecommerce tracking. You can even track 3rd party shopping transaction. (can be done by _linkByPost function) http://www.google.com/support/googleanalytics/bin/answer.py?hl=en&answer=55528 http://www.google.com/support/analytics/bin/topic.py?hl=en&topic=11001

Adwords Integration: Integrating Adwords keyword, impression, cost, cpc, clicks metrics on GA will be extremely useful as you can segment, slice, dice cost, cpc, impression via Geography, content, landing pages, visitor behavior, loyalty etc.

a. Adwords optimization is done based on conversion, however using GA we would be able to identify various segments of users such as Visitors doing research, Visitors browsing FAQs, Visitors commenting, browsing etc. Therefore we would be able to attach value to each segment and hence can better optimize Adwords campaigns based on different actions done by users rather than just conversion.

Coming articles will contain more information on GA regular expressions, using Goals, filters and advance implementation on pageviews etc.

This Article is originally available only in http://www.praveenkodur.com/blog/

Some of the common uses of analytical models are:

Acquiring new customers: Acquiring new customers is generally a big cost for the company, with increase in prices on various acquisition channels, companies find it hard to reduce cost in acquiring good profitable customers. Predictive modeling to certain extent becomes very useful in reducing costs in acquiring right customers and increase profitability. It also helps in designing marketing offers, special campaigns for customers to reach out to them.

Predictive models use historical data of customer attributes to understand relationships between attributes and their specific response or behavior. The output of predictive model is generally to predict a future response of the customer with their present data. These models are generally used to rank a list of prospective customers on the likelihood of their predicted response. This is very useful as it helps take present decisions. Other factors can also be fit into the response such as risk of acquiring customers like credit risk, or the cost of retaining customer. We can also predict response to each specific product, which helps in better targeting the customer and increases chances of acquiring them or even for cross selling products

(http://www.stochasticsolutions.com/pdf/CrossSell.pdf).

Additionally, there might be situations where it might not be easy to connect both customer and the response desired. Additionally there may be situations where various responses of customers are also valuable. For example, a customer might purchase a product after visiting the site 4 times, each time the customer performs a action like search, sending an enquiry, contacting user, reading knowledge material etc. Each action may be valuable to business therefore need to included in the model. In all such cases we need to use proxies to understand purchase behavior. Model building is a tedious task, but very worthy effort in increasing profitability.

In process there are additional outputs generated like customer profiling, customer segmentation, clustering and affinity pairs which is very valuable in developing products for customers.

http://en.wikipedia.org/wiki/Predictive_analytics

Customers Retention: The second problem is of retaining existing customers, companies are willing to provide offers to retain customers. The data mining problem will translate into finding the customers at risk (or customers who are looking to switch) and additionally identify those customers who are more likely to change behaviour due the marketing offer given by the company. (http://www.stochasticsolutions.com/pdf/FinanceRetention.pdf).

The data useful will be behavior of customers before attrition for certain time duration along with their demographics, attitudinal & behavioral patterns.

Predicting customer attrition rate is separate from predicting their behavioral change because of promotional offer. These models are extremely useful where markets are saturated and acquiring new customers becomes increasingly difficult.

Marketing techniques for retaining customer could backfire sometimes, resulting in loss of customer due to persuation from the company. There are also certain customers who would attrite irrespective of any marketing offered to them. Capturing this behavioral difference can be done through Control Groups, Test Groups & Hold Groups. This method is called as Differential Response modeling or Incremental impact model or Uplift model or Net model. Here is more info on the same: http://en.wikipedia.org/wiki/Uplift_modelling, and few white papers on the same http://www.stochasticsolutions.com/pdf/SavedAndDrivenAway.pdf. Customers in Control Group are randomly targeted, Customers in Test Groups are targeted based on model, Hold Group Customers are not considered for targeting of offer. Results of test and experiment is used in building the Net Model.

The industries where these techniques will be highly useful are Financial Services, Retail, Telecom, Internet Companies and Software Houses.

Do check back for more articles on this topic.

List of resources to find more information about Analytics

1) http://www.destinationcrm.com/Articles/CRM-News/Daily-News/Predictive-Analytics-Can-Pinpoint-Profitable-Customers-52164.aspx

2) http://scientificmarketer.com/search/label/response

3) http://www.redclaymedia.com/response_modeling.php

4) http://stochasticsolutions.com/retention.html

5) http://www.information-management.com/specialreports/2008_62/10000747-1.html?ET=dmreview:e323:1015879a:&st=email

6) http://www.information-management.com/issues/2007_52/10001990-1.html

7) http://www.predictiveanalyticsinsight.com/articles/callcenter.htm

http://www.predictiveanalyticsworld.com/predictive_analytics.php

9) http://www.marketingprofs.com/4/shearer1.asp

10) http://semphonic.blogs.com/semangel/2009/01/predictive-analytics-getting-a-legup-on-where-analytics-is-headed-.html

The Original article is present on http://www.praveenkodur.com/blog