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

It is very essential for a financial company to know, which user to target for what product and when. The goal of data analytics and data mining department is to arrive at accurate answers to these 3 questions, (Which User, What Product, When), trust me it is a rewarding exercise for the bank, users and definitely the analytics manager.

The problem is very simple. A customer may have purchased a single product or multiple products from a bank. The company would ideally like if all its customers bought all of its products. This possibility however does not exist, therefore without any targeting model; the company would be wasting money, time and resources by randomly targeting customers for its products.

Using a little sophistication of data mining techniques, we would be able to build a targeting model or a response model on customer base. A response model is a predictive model which estimates the future behavior of a given customer even before it occurs. Even with a fair amount of accuracy it can be very useful for company in saving majority of costs in various departments/channels. There are various types of data available for analysis like demographic, behavioral, psychographic / attitudinal. The above data can be very useful in data mining methods, however the cost, usefulness and expertise to store / retrieve data has to be assessed and judged.

Giving an example: A customer has savings account in a bank, based on the information given to the bank along with account status and history, we could target her as a prospect for a personal loan product based on a logic / algorithm.

The logic / algorithm could be as simple as if a customer from metro with account balance of Rs 1,00,000 or more for over 6 months should be a prospect for home loans Rs 20,00,000 or personal loans over Rs 3,00,000. This is a simple example; however it could be a complicated neural network or logistic regression model.

Cross Sell Models: A customer of a product A, is a likely prospect for product B if his account activity positively predicts likelihood of purchase. Target model has to predict purchase of product B.

Up Sell Models: A customer can be targeted for higher segment product in the same category based on his account status and demographics. Normal saving accounts customers can be sold for Premium segments customers, if his account balance is over a period of time.

Deep Sell Models: A customer can be sold more of same product based on his need. Target model has to predict his need.

These target models are also called response models that only predict the purchase behavior of a customer for a given product. This model is ineffective as the cost of randomly targeting a customer for product is LESS THAN cost of targeting using predictive response model.

The main reason is because of below limitations of traditional response model

A) All customers behave in two ways: Purchase or DO NOT Purchase

B) Behavior of customers during absence of any marketing activity.

C) Maximum lifetime value to be derived from customer is unkown, this will result in underselling or selling lower value products when higher products could have been sold.

IF these issues are taken care of by strong statistical modeling then loss making response model can be made profitable.

The original article was found on http://www.praveenkodur.com/, Copy rights are reserved.

Disclaimer: These are my personal views and not a view of any organization or body.

1) High traffic/Popularity on Video Sites such as Youtube.com, Google Videos, Daily Motion, AOL-cut, Yahoo Videos and other video search engines,

2) Availability & increase in usage of broadband internet in India.

3) Search Engines moving towards Universal Search.

4) Social Sites enable sharing and voting best videos.

5) Increase in traffic to Torrent sites and forums where people share videos

Ways to Market Video Content

Marketing & Optimization are 2 different terms, Marketing is creating new and optimization is refining the existing content. Marketing is mainly push, Optimization is pull.

Social Media including videos is a place where there is a very thin line between the two, both exist simultaneously. For example: Viral Marketing is both Push and Pull. Videos is also both Push and Pull. Advertisers need to push only to an extent beyond which pull has to take it forward.

1) Include Your Website Name inside your Video

2) Keep posting Videos Frequently

a) Post Videos about your Company, Investor Information.

a) Post videos on Product Usage

b) Post videos on Customer Testimonials

c) Post Videos on Training and Development Program

d) Informational B2B programs in your Videos

e) Awareness about your Industry

3) Upload Videos in all possible video search engines such as youtube etc.

4) Submit your Videos to Digg, Stumbleupon and other such content sites.

5) Embed Videos in your Blogs, Websites etc.

6) Give Links to all your videos in your sitemap

7) Give links to Videos from inside the content or article.

Include Digg this Button on webpages which embed videos

9) Include Keyword rich content, brand names in Titles, Descriptions of Videos around the Videos.

10) Include ror.xml Sitemap or podcast RSS feed and submit to google, yahoo and other search engines.

11) Ask Users to Post comments, this will add the content on the page.

12) Create a Textual version (SubTitles) of the video in all languages of your TA and embed in your blog

13) Give links of your sites which link to Youtube video, inside Youtube Video page.

14) The Page which embeds the Video in blog, website must have keyword in H1 tag as Heading.

Copyright of this article is reserved by praveenkodur.com

Google is already a leader in Targeted Advertising both Text Ad/Image Ad display. They have great products such as Adwords & Adsense for Advertisers & Publishers respectively; this technology is for anybody and requires little or no human involvement from Google’s end.

The main question is why did Google Acquire DoubleClick? Some of the reasons I can think for are:

1) Google took inspiration from Overture to build an Ad system to display relevant text ads based on search terms. They built a system and revolutionized it in a big way. Now they might be thinking of repeating the history again with DoubleClick. DoubleClick has DART, which Google might want to make it enormously scalable for anybody to just plug and play. They might want to keep the human intervention to the minimum level and let technology work the maximum, mainly to reduce cost which ultimately benefits the end user.

2) The Ad business is growing at a rapid rate, with internet overtaking Radio and competing with Print. Ad Agency business is really on the rise. Google wants to have a share of that pie too. It not just wants to be a medium to offer and manage inventory, they would also want to actively work with advertisers and publishers to meet their end goals & take major share of the pie.

3) Most Agencies managing Ad business apart from few good ones do not have long term satisfied customers. Most companies are looking for more accountability and reliability from Agencies. Google is more trusted than anyone in the online industry.

4) Google being the leader in the Online Ad selling; its move towards vertical integration is very natural.

Google would be using its technological expertise in combination with DoubleClick’s business acumen & client servicing capabilities to reach it goals.

Yahoo has in acquired Right Media to be able to compete evenly with Google.com. To state another interesting acquisition is Google acquiring Performics, since Performics is a part of DoubleClick. Performics is a company that sells SEO services to its clients. So, formally Google has acquired a SEO Company, I am sure its business would increase whether Google actually supports it or not.

What will Google do with Performics is yet to be seen….

Copyright of  this article is reserved by praveenkodur.com

Here are some of my suggestions on how Youtube can win both companies’ as well as users popularity.

1) Discriminate the content on Youtube as User generated, Copyrighted.

2) Make deals with TV channels to publish Youtube’s user generated videos on TV program’s such as Top videos of the week. Top funny videos etc.. This would encourage people to post personal videos as viewer ship increases.

3) Ask big brands to purchase users videos which become popular. For example Nike could copyright popular user videos on football etc…

4) Reward the users whose videos get selected for TV broadcast or get Branded. These videos will be of course original videos

5) Distribute & publish popular Youtube videos across various other sites on Internet

From Advertisers Perspective:

1) Display Image Ads & Text Ads beside videos of similar theme.

2) Advertising costs are higher for Ads showing alongside videos that generate high volume of traffic

3) Upload advertiser’s videos which are displayed alongside popular videos of similar content.

Copyright of this article is reserved by praveenkodur.com

This is a tool using which you can generate customized content by mixing RSS feeds in any order. You could send the output of one feed as input source into another.

For example you could create customized Pipes to suit your users needs and generate content that can be very useful to your audience. Hence can be a great value addition to your auidence.

Create a new section in your website where you can extract data from various related websites and create mashups and throw results to users. This data can be published on new web pages. This will help generate new unique fresh content not available anywhere on Internet and getting refreshed regularly.

Another way is to send these mashups again as RSS feeds which can be subscribed by users. These can be tracked and monitored to know its performance.

This is a wonderful tool for bloggers, webmasters who are constantly looking for fresh content to be published on their sites.

If you own a huge sites having traffic in millions you would definitely have an idea of the exact needs of your end users. These can met by creating mashups to generate most popular data users are looking for on your site. Hence you could increase the stickiness of the website.

This would be of major help to Technology NEWS sites, Real Estate Sites & all major Web 2.0 sites.

Copyright of this article is reserved by praveenkodur.com

Online businesses succeed only with a strong revenue/business model that creates a genuine value to the consumer/user by making a strong impact in reducing the demand and supply gap in the industry.

Online businesses cannot grow just on advertising revenue that relies on worthiness of their website content. Neither it can grow by generating traffic and selling their webspace.

I am a regular reader of the latest happenings on online world, both in India and world wide. ContentSutra and Alootechie are some of the sources that provide upto date information on the latest news on Indias Online Industry.

Recently you could read about companies getting funds from various VCs for their businesses. This is the time where everyone and their mom and pop are looking to start a online businesses. We need to wait and see how many sustain and how many fall back.

Copyright of this article is reserved by praveenkodur.com