|
|
Decision makers or knowledge workers are looking for information and knowledge at various kinds for making strategic or even mundane decisions. The process of extraction of information and knowledge from data is called data mining. This knowledge and information can be represented in various formats, this could be as simple as mean, median, mode, count or it could be in a graphical format such as histogram, line, pie chart, trend lines or moving averages. Advanced techniques like learning models, business optimizations are next level of knowledge requirment for the organization.
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/
Business Intelligence can be referred to a set of analysis methodologies & mathematical techniques that leverage the available data to derive knowledge / information that can be used to support/action business critical decisions. Call for a robust BI system comes from the need of making effective decisions, which has to be based on accurate information about business.
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.
Analytics plays a important role in customer relationship management. CRM is area which includes a whole host of activities which involves using the CRM software to campaign management tool to call tracking tool to database marketing tool etc. Each tool holds a customer information and in some way has a touch point with the customer. For building a good business it is important to deal with customer individually, rather than deal with competitor.
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.
Google Analytics is a very useful tool for Internet business especially online marketing. Google Analytics is probably the only free enterprise class tool available for website owners. It’s amazing the kind of features available for users at no cost. The amount of analysis, insights that can be accomplished is enormous considering the tool is available for free. The auxiliary benefits of Google analytics tool such Benchmarking & Google Trends is equally big.
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/
This is the most widely used area of data mining techniques, in sales and marketing to understand customer and their behavior on various products offered by the company. It helps companies in understanding and predicting customer behavior for each specific situation and therefore introduce sophistication in targeting. There are lot of information generally collected about the customer such as demographic, geographic, lifestyle, attitudinal, behavior and many more. These data can be efficiently used to model customer behavior under different circumstances (situations). Through effective models we can improve ROI of marketing efforts & campaigns to make an impact in the overall profitability of the business.
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
|
|
