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.

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.

1) When the dependent variable contains missing values, simply eliminate the records.

2) Correctly Identify slices of data and Substitute with measure of central tendency like Median, Mean & Mode. Identifying the right slice is also important. You can group by various parameters and take a central tendency. Choose the one with highest bias (chi-square)

3) If the missing value forms a Normal distribution pattern, find the missing value by normal inverse function.

4) Treating the missing values as a dependent variable in a regression equation. Use the multiple linear regression function to impute the missing variable. You can try other methods instead of regression like classification, decision tree etc.

5) Use business logic to understand the missing values.

6) Check the data capturing process, there could a error present at source of data entry. Also it helps identify if the missing data points are at random or non-random. If it is random missing error then you can use simple imputations, however if it is non-random then you need advanced techniques to impute values. Also look at bias in the particular column, if the bias is significant then you need advanced techniques. If bias is minimal then you can proceed with simple imputation.

7) Identify the list of possible values for the missing data set. Try and replace each possible value and create different data sets and build the model. Calculate differences in accuracies and consistency based on different substitutes. This way you can even add variation of the values into the missing element and remove bias.

Use regression to determine the distribution of the values in place of missing values. Create a What-If scenario by imputing every range of value.

9) Do nothing remove missing values and duplicate records of sample data set to increase the size of the data set.

10) Measure similarlity of records like vectors. The similarity is the cosine function between records, and find similar records to the missing data values.

11) Use logistic regression to measure likelihood of observed or likelihood of missing. If value missing the output is 0, else 1. The rest of the variables (non-missing) act as independent variables. This does not predict anything but only a likelihood of finding the variable missing. Records with same probability or closest probability is considered similar and missing data is donated.

Multiple imputation generally yields better results but it requires high-end statistical software for computation. It becomes necessary to use the help of statistical software.

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

Traffic from Social media optimization can be tracked

1. Google Analytics Referral Traffic

2. Number of Friends who have added you in their friends list in each of your profile in each Social Networks.

3. Frequency of updates and quality of content posted.

4. Number of votes on your content and number of people who regularly vote on your content.

5. The Amount of content in each of your profile pages and community knowledge sharing page.

6. The Total Number of Links to and from these profile pages.

7. Number of comments and frequency. How many of them are not in your friends list.

The amount of effort that is needed to market on social networks is tracked

A Facebook or an Orkut application is like an small widget which is used by the Users of social network. Since this application interacts with lot of people, it also has the ability to interact with the external server. Google Analytics for applications can be embedded into the application giving all data w.r.t the users using the application. Developers will also get an idea about the extent to which the application was spammed by users.

Developer Analytics (http://www.developeranalytics.com/analytics.php) and http://adonomics.com/ are service which is offered to developers of application to keep in touch with the traffic, demographic data, visitor data and conversion data.

These analytical tools in combination with customized tracking which you can set at application level which can send data to external server allows developers to track a lot of information about the user profiles and get useful information such as Traffic, Demographic Data, Profile Data etc.

This data has to be used to remodel and remake applications on Social Networks to make it more useful for users and make it more spam proof. A successful application can be built overtime by building more features to app based on the data, response derived from it.

Google gives details about creating a App;

http://www.google.com/webmasters/gadgets/about/developer.html

There are 3 phases for Gadget promotion:

Create: Develop a gadget based on idea. Hire a developer for this or try out yourself. Thinking of an successful idea is Gold, however it happens by chance. Make sure it works on all kind of sites and all Browsers including remote ones like Safari.

Promote: This is the easiest and lest effort part, just add these in various webpages, sites. Give options to Add to iGoogle, My Yahoo etc.

Track and Optimize: This requires analytics and time. You must use Log file analysis tracking for analyzing the performance of visitor data, traffic data, demographic data, user quality data. Etc. For example your gadget is running on Orkut and Facebook. The number of users in facebook may be more than Orkut, but traffic from Facebook may not be of much use to your business. Then you must customize you App to favour more Orkut community users based on quality of profiles on Orkut. A popular web app can have an impact on Reputation Management and Brand on social networks apart from monetizable traffic.

This Article was originally found on http://www.praveenkodur.com/

The idea of this blog posts and future blog posts is to identify problems in web based business and how analytics can help solve it?

Blogging and Blogs

You have a small blog and spend considerable amount of time updating it, also have spent some money in creating and putting it up live. Measuring the success of a blog is not just pageviews, subscribers and unique visitors count.

ROI of a popular blog can be huge, if you have rich content and have active RSS subscribers. You don’t need a resume. Every popular blogger gets job offers regularly flowing into his email. Above this you have created a brand for yourself. This is a huge economical benefit considering the money and time you spend on make your blog active. If you have a great blog then you have separate brand and identity for yourself.

Small Scale Business Online

Concentrating on search engine marketing, seo, affiliate marketing to generate profitable visitors to site is fruitful. This is the cheapest form of advertising online. You have control over spends and will be able to monitor your ROI. Set up Google Analytics or any other analytics tool which will help you look at the visitor information.

Conversion University from google is very helpful in optimizing the website for higher conversions and sales (http://www.google.com/support/conversionuniversity/bin/answer.py?answer=77123)

Small scale business must focus their marketing efforts cleverly around geo-locations to get maximum return from marketing & advertising. The site must clearly offer maximum benefit & response to their end users as promised in their USP. Most sites initially work on small set of customer base and keep them satisfied at all circumstances.

These companies are ecommerce sites, shopping sites, travel agent sites that are looking to sell products to their websites’ visitors. They should target at long term steady and consistent growth not looking to achieve instant traffic hoping to dramatically increase page-views and visitor count hoping make revenue quickly and grow faster.

Large Businesses Online

There are various large online website who advertise heavily online & offline for the purpose of promotion. The spend a big amount of money with an objective to increase visitors, page views hoping to increase brand value. It is unwise to spend money on marketing, advertising without looking at the actual value it adds to their users (customers).

Increase in page views and visitors may not proportionally be related to increase in revenue and profit. There are definitely smarter ways to reduce cost of marketing & advertising without impacting the bottomline.

I read about many large advertisers in US & worldwide who have decreased spends on search marketing as it has become unprofitable after a certain limit. They have found search engine optimization, social media marketing ways more effective to gain relevant traffic to their websites for increasing their sales.

Measuring ROI through every channel and making them profitable is very essential and must be the key factor

EXAMPLE:

The traffic to my blog has constant traffic from social media sites like stumbleupon and other social network sites. Few days it has huge spikes of about 2000 hits in a day from stumbleupon alone, totally free traffic.

Blog attracts about 16% of overall traffic from social media sites of which 48% contribution is from Stumbleupon alone.

The cost of promotion on stumbleupon is consuming few hours of time in writing rich useful content and building a network of bloggers, friends & general users. Comparing this with search marketing campaigns, same amount of traffic might have costed me few thousand dollars.

Never be afraid to use new format of media, content for promotions such as Videos, Apps, Images, podcast, audio files, flash files etc. It is just a new form of content by the underlining principle of promotion remains the same.

Reading:

Measuring ROI from SEO : http://searchenginewatch.com/showPage.html?page=3625973

Measuring ROI from Social Media Marketing: http://www.pronetadvertising.com/articles/measuring-the-roi-from-social-media-marketing.html
This Article is originally found on http://www.praveenkodur.com/blog/