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Customer segmentation, much as it has done in the past, continues to occupy a centre stage in marketing. Simply put, it is a critical element in marketing that helps connect customers to 4 Ps of marketing – Product, Placement, Price and Promotion.

Behavioral segmentation categorizes customers based on their shopping behaviors. The segments can be developed by a retailer based on how often customers shop, what they buy, how much they buy and when they buy, etc.

In a brick and mortar store, retailers would need to observe these characteristics for a few months through the data they generate in stores. In an online environment, the information is more readily available and often denser. Much of the insights generated online could also be leveraged in offline channels. McKinsey, in its report ‘All Visitors Are Not Created Equal’, identified 6 online segments some of which are mentioned below along with our views on how a retailer could act upon them:

Simplifiers - 29% of active users
Use the Internet for ease and convenience. They are most profitable but challenging to serve. They go online with a specific purpose in mind; spend less time while there, but purchase the most. In order to sell to Simplifiers, provide superior end-to-end convenience - ease of registration, availability of product information, reliable customer service and easy returns. Eg: Amazon’s ‘One-Click’ ordering process.

Surfers - 8% of active users
They spend a lot of time online. They quickly jump from one site to another, continually seeking new information and experiences. Convert Surfers into purchasers by grabbing their attention as they come to your site. Motivate them to buy. Offer them ‘Related items’ / ‘Add on Accessories’ / ’You may also like’ merchandise.
Bargainers - 8% of active users
Bargainers are those who look for rebates, coupons, promotions and freebies. Basically they hunt for best deals as they shop online. Provide price comparison, coupon/promos during the visit; convince them that they have the best deal. Do keep them posted with follow-up offers, promotions, clearance sale, etc. — this will encourage them to come back.

That is just an example. You can also build segments in various other ways, simple or complex depending on the data available, for online and offline customers. Behavioral segmentation is being increasingly used by various industries – financial services, telecom. retail, airlines, etc. This is in contrast to earlier segmentation schemes that relied primarily on demographic and psychographic data.

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In today’s world, Companies are spending thousands deploying customer relationship management (CRM) systems to identify, win and retain its most valuable customers. These CRM systems capture customer data from every possible source of touch points. The result is overwhelming and overlapping data.

Customer-One-View means that the individual customer data is integrated across all products and channels,. It is the sum total of a customer’s interactions with the enterprise – including past purchases, preferences, tastes, impressions, and opinions – based on integrated, up-to-date, real-time information from multiple sources.

The 360 degree view of customer offers a variety of advantages:

The prime goal is to radically change a company's view of its customers. It helps in identifying the most valuable customers. It assists in improving marketing communications, generate new cross-sell opportunities, and implement more effective customer retention programs.
It enables you to feed all applications using customer data to share a central data hub with a common view of the customers. This provides an integrated view of customers that can help create a better understanding of customer activity.

In addition, companies will be able to perform data mining, modeling and predictive analysis based upon holistic view of the customers rather than partial information about the customer. This will enable much more effective target-marketing.
It can save money by reducing the duplication of marketing efforts. The need to maintain overlapping information systems has a significant impact on internal costs as well.

Customer-One-View is a data integration technique that allows you to gain a better understanding of your customers and their relationships with your organisation. The increasing availability and complexity of customer data and the critical importance of standardized data makes it imperative for your company to develop a road map to initiate Customer-One-View.

“Organizations typically have customer data spread across many systems. It is fragmented and often inconsistent. Without a single view of those customers, organizations are in the dark. Some form of single customer view is, therefore, fundamental to managing customer relationships.”

– Gartner on The Single Customer View.

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Every minute of every day, customers dial call centers, send emails, and talk among themselves online in blogs, forums, and myriad social networks. Lots of unstructured data contained in e-mails from customers, call center representative notes and other sources contain critical nuggets of information about how customers feel about the products and services, their likes and dislikes, their hopes for future offerings, about product failures and opinions. All these information contain valuable insights for organizations.

It is possible to integrate the tabular data and text mining capabilities within the same environment. The analysis can help understand customer churn, identify cross-sell/up-sell opportunities and develop future offerings.

A broad approach to carry out this kind of analysis is as follows:

Definition of the Objective: The analysis starts with definition of the business objective. An example could be to predict customers likely to churn and detect the underlying churn drivers.
Data Preparation: This involves data audit, understanding of distribution of structured data, missing value treatment, capping and transformation of data. The structured data would primarily consists of demographic and performance data of the customer. For preparation of textual data, a combination of removal of stop words, stemming and lemmatization, etc. is followed.

Identification of Significant Variables: For the given business objective, identify the variables that are significant. The textual data is represented as 'bag-of-words". Each word or a combination of words is represented as a separate variable having numeric weight (importance). Words or combination of words with higher belief/probability are selected.
Predictive Analysis: Predictive models to predict churn or classify sentiments/opinions are built using Classification techniques. Inclusion of textual data provides superior predictive power.

The myth is that it is very laborious and extremely expensive to go through customer feedback to extract and convert into powerful business decisions. Over the last decade, rapidly maturing text processing technologies are making this process cost effective and efficient.

“80 percent of business is conducted on unstructured information and it doubles every three months”
– Gartner Group.

The bottom line: The interesting part of the hidden information in call centre notes and emails is that they often provide valuable insights that may not be reflected in structured data.

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"Think about a sensor that catches information of your eye contact with the newspaper articles and then infers areas of your interest. The next day, you get a personalized newspaper that delivers articles suiting your taste. This kind of sensor may still be far from development but it’s definitely a possibility in e-world. It’s not difficult for an online news site to track the articles that you read and then provide a customized menu of articles the very next time you login to the site."

The personalization process typically tracks customers' likes and dislikes and look for patterns similar to other customers. The process involves identifying affinity of products/services at transaction level, studying demographic profile of the customer and using past purchases and click-stream data (for online) to provide recommendations to the user.

Some of the business benefits of personalized recommendations:

Cross-sell/Up-sell: Incremental sales from recommendations at every stage of customer touchpoint (statements, inbound calls, POS, email/SMS campaigns, online purchase, order confirmation).
Customer Loyalty: In the era of click away competition, the customer loyalty is most tenuous. Recommendations improve loyalty by creating a value-added relationship between the business and the customer.
Shoppers to Buyers: The recommendation tools give a new meaning to the concept of impulse shopping – resulting in higher sales revenue for the business.
Personalized Campaigns: Win customers with personalized email/SMS campaigns with recommendations of next likely purchases.

In the last few years, the companies that have grown and dominated the market place have also been the early adopters of personalization. Dell, Amazon, Netflix, etc. are just a few names that come high on the mind. For example, Amazon would recommend books, apparels, electronic items, etc. based on your past purchases, clickstream data, etc.

You don’t need to be a big and powerful company to get started with personalized product recommendations. Recent improvements in processing power help in compiling and analyzing vast amounts of customer data. Some recommendation tools are available as SaaS (Software as a Service) that allow you to deploy recommendation engine into your business system in a cost-effective manner.

76% of consumers make additional purchases when presented with tailored product recommendations. Additionally, many consumers (43%) now expect retailers to provide recommendations that take into account personal tastes and shopping history
– A Consumer Survey Report in 2009

The bottom line: The data is captured by most of the organizations. With customer relationship management at an individual level, one can increase customer delight and loyalty.

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74% of Best-in-Class companies track past business history for improved segmentation and 56% track behavioral attributes for use in segmentation.
- Aberdeen Group

Behavioral segmentation has recently regained its ebbing popularity thanks mainly due to the resurgence of behavioral targeting in digital marketing. Web marketers have banked on the quick and easy availability of the behavioral information in web marketing to anticipate and fulfill customer choices.

Psycho-Demographic profiling is important but they tend to be incomplete because they do not leverage knowledge, attitudes, interests and actions of the customer, which in turn determine, how customers respond to actual products/services or their attributes. With the recent advances in technology, this critical information which was once difficult to acquire, is now easy to obtain. Behavioral segmentation, relying on past customer behavioral data, is used in identifying homogeneous groups of customers that reveal similar behavioral patterns in the future.

Behavioral segmentation divides the customer base into groups based on the way they respond to promotions, price changes, channels they use to communicate, etc. Based on behavioral segmentation, consumers can be grouped aligned with any of various business strategies such as:

Product Usage: Rather than offering one’s product as a direct replacement for a similar or competitive product, it may be useful to segment customers based on benefits that she seeks for in a product thereby intensifying its relevance.
	Buying Pattern: This includes recency, frequency and monetary (RFM) value of purchase, channel used, day/time of purchase, etc. Decision Makers: This involves understanding people behind decision making process - is the customer influenced by online reviews/opinions or does she rely on feedback from friends within social networks or act on opinions within the family?
Decision Attributes: The criteria can include price, preference to self service, quality of product, service quality, approachability, events in customer lifecycle, etc. Customer Attitude: This may include customer’s readiness to purchase, risk appetite (early adopter, early majority, late majority), brand loyalty, etc.

Some examples of behavioral segmentation in various industries are:

Retail: Using data from loyalty card program, one can understand purchase frequencies, transaction value, categories purchased, product affinities, etc. This information combined with demographic information can help in developing personalized campaigns.
Telecom: Based on customer’s usage pattern, the telecom companies can predict customer’s preference for value-added services, likelihood of attrition, etc.
Financial Services: As one of the most mature practitioners of behavioral segmentation, financial services industry make extensive use of financial behavioral data from credit bureaus to segment and manage its customer from both risk management and marketing perspective.

With real-time behavioral segmentation, one can better fulfill customer’s needs and dynamically align marketing strategies. The true aim of behavioral marketing is to deploy profitable marketing effort based on behavioral patterns exhibited by the customer.

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There has been a norm around for many years that somewhere around 75 per cent of customers who defect say they were "satisfied".
- Anne Mulcahy, CEO of Xerox

According to a survey, major companies lose 50% of their customers over 5 years. A significant portion of these are "Silent Attritors", customers who normally say they are satisfied much less betray their intent to leave.

How do you identify these "Silent Attritors"? How do you intervene in a timely fashion to prevent these seemingly satisfied customers from leaving?

The biggest challenge facing marketers is getting the timing of the attrition right and reaching out to the customer in time to incent them to stay. Companies often make significant investments to get back lost customers with win-back programs and re-acquisition efforts. These efforts are quite often wasted since they are too late in their intervention.

Recent advances in data mining and predictive analytics make it possible to not only predict quite accurately the likelihood of a customer attriting but also when they most likely to attrite. Knowing this in advance it is possible to stop customers from leaving by intervening immediately before they decide to leave. Reducing the elapsed time between potential attrition and customer outreach produces dramatically higher levels of customer receptivity and, as a result, significantly lower attrition rates.

The first step towards developing an analytical process for retention program is to identify the segments of most profitable customers or those with high Customer Lifetime Value (CLV). For the customer within segments that are profitable or high value, predict the likelihood of attrition and when they are likely to attrite using data mining techniques such as survival analysis.

The additional benefits of Churn or Attrition Analysis include:

A logical approach to churn management might include the following steps:

identification of factors influencing customer defection
determining the factors that influence when the customers are likely to attrite
assessing the effectiveness of available customer retention tools
selecting the optimal retention channel for a given customer profile and churn risk level

The remedy for churn is inducing loyalty. To encourage loyalty in their customers, firms implement strategies such as greater personalization of services, bundled services or differentiated customer service. Proactive churn management is extensively followed in telecom, insurance, financial services and healthcare industries.

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The ability to make quick and effective business decisions based on accurate and reliable information is essential to most business enterprises across all industry sectors. Implementing an effective Business Intelligence (BI) solution has been expensive so far.

With the growing acceptance of open source technologies, leading companies are able to reduce total cost of ownership (TCO). According to a study, the cost of deploying and maintaining Open Source BI solution are up to 90% less than the cost of traditional, proprietary BI offerings. Recently, MicroStrategy released a free version of Reporting Suite for use by up to 100 users.

A leading open source business intelligence solution Pentaho can be integrated with open source Data Warehousing from MySQL and ‘R’, a powerful open source data mining software. The open source tools can be optimized for Heterogeneous Technology Stacks and has reduced risk of obsolescence or future incompatability.

Pentaho can easily provide complete BI solutions at a fraction of the cost of the proprietary software products by:

Dramatically lowering up-front and ongoing costs: The application requires considerably less effort to integrate, manage and maintain.
Easy to deploy predictive platform: Pentaho can be integrated with ‘R’ to develop robust predictive models.
Access to data across the enterprise: Build relationships across metadata (structured, unstructured, text data, etc.) structures to provide key business insights across various departments.
Holistic BI Tool: Supports features like ETL (Extraction, Transformation and Loading), creation of data models, generation of adhoc as well as standard reports.

Pentaho is world's most popular open source BI suite and its products are used by leading organizations including U.S. Naval Air Systems Command, Lifetime Networks, Terra Industries, Sun Microsystems, Orbitz, Otto International, Mozilla, etc.

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Tables, Text, Audio & Video……Integration?

According to a recent study, the volume of corporate data doubles each year and the public web grows by over seven million pages a day. The challenge is in tapping the information from all the sources (customers, employees, suppliers and partners) and converting into actionable business intelligence.

Primarily data is available in two basic forms – Structured (tabular data) and Unstructured. The latter can be the combination of text, audio and images. As much as 80% of all potentially usable business information exists in unstructured (external and internal data) form.

Frameworks and related standards are laid to a great extent in sectors like insurance (ACORD), healthcare (HL7) for the exchange, integration, sharing and retrieval of electronic information. Other sectors are also gearing up for the integration.

Business Applications of Integrated Data:

In healthcare industry, integrated business intelligence can be developed based on transcription data (audio/text), patient’s medical record (text) and diagnostic reports (text/images).
With a combination of past transactions information, call center notes and customer conversation, personalized product recommendations can be made during the call.
Holistic view of customers: In telecom, churn can be better predicted using survival techniques. The data used can include call center grievances, usage patterns and demographic profile.
Gain the competitive edge in multi-channel retailing by better designing customer loyalty programs, campaign management systems and predictive models.

Data integration with a clear business orientation and collaborated by powerful analytic technologies gives quick and effective business insight—that you need. Data integration can take customer-service to the ultimate level, from attracting new prospects to retaining the blue-chip ones. It’s a proven fact that decisions based on sound business insight deliver consistent results that keep you ahead of the competition.

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Enhance the Level of Engagement

Expectations are high!

Customers expect you to know their preferences, purchase patterns and their preferred channels of communication. A predictive and intelligent campaign management system can enhance the customer delight by addressing their wants, needs and likes.

Following are the analytic practices in various stages of campaign life cycle:

Identify Target Customers
Ensure that only potential customers for the campaign are contacted. Cross-tabulation with dynamic filtering & profiling identifies the target customer base.
Design the Campaign

Personalize the offers based on customer profile, similar customers and past purchase behaviour. Recommendation engines and analytics filters help in crafting the most relevant and personalized marketing messages.

Execute
Timely deployment of your campaign is essential, especially, if co-ordinated with multiple channels. Cross-channel optimization strategies can help in integrating and synchronizing cross-channel shopping. The final strategy can also incorporate relative cost and benefits of multi-channel targeting.
Monitor
Measure real-time response and conversion. Get an accurate picture of the campaign along with basic attributes, relevant statistics and status history. The response from email campaigns can be integrated with customers surfing behaviour on the website easily

Test and Control
A continuous test and control approach can help learn the best practices related to creatives, promotions, discounts, etc. and refine targeting strategies.
Predictive Analytics
Usage of Market Basket Analysis, Customer Segmentation, Next-best Recommendation Models, Response Models, etc. can help in estimating likely response from a particular campaign. Overall, predictive analyses help in reducing the number of mails and increasing response rates.

Satisfying customers in today's highly competitive global marketplace has never been more challenging. Having a deeper insight into customer expectations and future behaviors is the key to successful marketing campaigns. Predictive analytics and good marketing instinct will always be helpful in determining which message will elicit the best response.

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Gaining Customer Loyalty is motivating the customer to come back to your business time and time again. A clear and structured loyalty program will increase retention by enhancing customer experience and satisfaction. It’s a proven fact that satisfied and loyal customers become fervent advocates for your business attracting new customers .

Predictive analytics provides powerful methods to devise and execute well designed loyalty programs.

Some of analytic underpinnings to loyalty management are:

Understanding the profitability of your customers through techniques such as RFM analysis
Identify likelihood and timing of attrition of high value customers through predictive modeling and encouraging customers at high risk of attrition to stay by providing meaningful incentives
Identify customers likelihood of responding to retention offers so as to design appropriate incentive programs
Cross Sell / Up Sell customers based on customized recommendations based on previous purchase patterns and current purchase intent

We now know that Marketing ROI of retention programs are twice that of customer acquisition programs. In a recessionary environment, when customer loyalty is most tenuous, it makes good judgment to invest significantly more in gaining customer loyalty.

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Online strategy has started taking pivotal role for most of the organizations. A well-developed web analytics strategy helps in understanding user activity on a website, predict customer’s needs and preferences and achieve the intended objective of the website.

Web analytics is the process of tracking, collection, measurement, analysis and reporting of online data for purposes of understanding and enhancing web usage.

What Do You Get with Web Analytics

Understanding entry/exit points of visitors, conversion rate of customers, etc.
Behavioral Analysis - Engaging users and enhancing their experience
Personalized Online Experiences using click-stream data analysis
Knowing which marketing efforts are paying off, etc.
Measuring and maximizing ROI
Recognizing repeat visitors and their interest levels
Integrating online customer insights with offline data can help in getting Customer One View.

For a long time Web Analytics is the most trusted solution used by companies like Royal Bank of Scotland, British Airways and Siemens to deliver a high quality customer service. Socrates, a legal solutions provider, increased to 15% growth in offline and 40% growth in online sales with the execution of Web Analytics. In the most successful deployment of predictive web analytics, 90% of companies saw a lift in ROI.

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Every marketer is reducing the marketing budgets but cost-effective targeting strategy is the differentiating factor. Take the advantage of this downturn to gain ground and outflank competitors.

Recession Marketing is ‘To do more with less’. Here are recommendations for marketers looking to reverse the recession.

Sell more by combo offers at less price (Market Basket Analysis, Price Discovery). Focus more on products without frills.
Interact more with customers (Customized E-mail/SMS Marketing, Real-time Analytics)
Integrated Campaign Management System with Web Analytics

Use cost-effective media for promotions (Social Media Marketing, Blogs, Online Videos, Banners, Mobile Messages)
Get Customer Feedback from Online Media(Automated customer opinions/reviews classified let’s say as positive/negative)
Build a smart and intelligent website (Web Analytics, Click-stream Analysis, Online Personalized Recommendations)

Customer Retention Strategy using Survival Analysis. Retaining existing customer is cheaper than acquiring new customers through ads, etc.
Improve service levels primarily for B2B and high-end customers (Pareto analysis)

As a response to the recession, 53% of interactive marketers expect to increase their budgets for social media marketing. HomeAway an online vacation-rental firm in Austin rose to $48 billion market in the USA and Europe through predictive analytics and efficient online strategies. Kellogg's capitalized a successful recession message "less than 50 cents a bowl." Wal-Mart offers discount exercise machines that will "trim your body and your budget."

Finally, it may not be a bad idea to link key performance indicators with macro economic indicators. It may be much easier to shift from one strategy to another whenever there’s a change in the external indicators.

Image source: Wall Street Journal

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For years the customer information is piling in information-wells such as datawarehouses, surveys, reports, feed-back forms, transactions, operational data points, etc. but the transformation of information into full fledged actionable insights is not realized fully. Marketing Analytics is the missing link between information-wells and Actionable Intelligence. It’s an opportunity loss if the data collected is not utilized for effective decision making.

Marketing Analytics is the scientific process for analyzing and identifying patterns and relationships within customer data. The information is gathered from transaction data, customer surveys, call center data, online activity (for online retailers) and so on. Marketing Analytics targets the right individual for the right offer or promotion to significantly increase the effectiveness of marketing programs.

Business applications along with techniques are mentioned below:

Customer Profiling and Portrait Development using Clustering Techniques
Improving Response Rates to Marketing Programs by Regression Models
Improving Activation and Acceptance Rates on Loyalty Programs
Identifying Product Associations for Combo Offers, Bundling, etc. using Market Basket Analysis
Personalized Recommendations for Cross-sell by Similarity Analysis
Customer Life-time Value for Lifecycle Management
Predicting Customer Churn using Survival Techniques

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Nowadays, business information is collected through multiple channels in variety of forms. The most pressing issue is in transforming the flux of information into manageable streams of business intelligence.

Crunching text (unstructured data) and tabular data (structured data) together allows organizations to convert accessible information to actionable intelligence. With integrated text mining, smart organizations leverage the power of information to reveal gaps in the market and take action long before their competitors are even aware of the opportunities.

Reuters (News), Chase (Financial Services), Fireman's Fund (Insurance), the City of Turin Tourism Office (Travel), the US Air Force (Govt.), Honda (Automobile), HP (Computer Technology), Dow (Chemical Mfg.) and PepsiCo (Food and Beverages) are just a few of the far-sighted organizations that realized the value of Analytical bridge between valuable text intelligence and enterprise data.

Business Applications of Text Analytics:

Sentiment Analysis
Competitive Intelligence
Churn Management
Behavior-based advertising
Identification of new product possibilities

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US online retail sales will rise from $172 billion in 2005 to $329 billion in 2010.

eCommerce will represent 13% of total US retail sales in 2010.
An increase in the number of women shoppers will contribute to 14% of jewelry sales moving online by 2010.
Online sales of health and beauty products will grow at an annual rate of 22%.
29% of small appliance sales will migrate online by the end of the decade.

Retailers are investing newfound profits in emerging technologies, such as sophisticated analytics and personalization tools that enhance the online experience for existing consumers.

As the number of people new to the Internet begins to wane, online retailers are constantly struggling between investing in strategies that retain current customers or those that attract new ones. According to the report, online retailers allocate 53% of their marketing budgets to online customer acquisition and 21% of marketing dollars to online customer retention. However, retailers are finding that traditional acquisition programs such as search engine or affiliate marketing may also serve as retention tools that attract existing customers as well as new shoppers.

Companies that compete on their online strategies to drive in store traffic include Target, which is using its website to promote and brand its retail stores; Nike and Timberland are exposing online shoppers to the idea of product customization. All are being forced to become more innovative online because pure plays such as Amazon.com, Drugstore.com, Blue Nile and Newegg.com have raised the customer experience bar.

Result: Businesses no longer view the Web as a low-cost sales channel but as a way of improving customer service and retention.

Courtesy: Forrester Research

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Are you getting any insights from the picture beside?

A new approach called social network analysis or SNA, has been gaining currency among business managers. Social Network Analysis examines the structure of social relationships in a group to uncover the informal connections between people.

SNA has become an important tool for organizational consultants seeking to understand the connection between patterns of interactions and business outcomes such as job performance, job satisfaction, adoption of new ideas or technologies, likelihood of information getting shared, and creation of new ideas. Several companies have had big success with social media. Starbucks managed to engage thousands of customers and collected many new ideas. Southwest Airlines provides several ways (such as on Facebook) for customers to post itineraries and comments.

SNAs can help track the adoption of new products, technologies and ideas. They can also suggest communication strategies.

Business applications of SNA

Knowledge Management and Collaboration
Team-building
Human Resources
Sales and Marketing

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In dynamic environment, businesses require real time solutions to gain significant edge over competitors. With integrated real-time analytics into business and operational processes makes a company more responsive, competitive and successful.

Real-time Analytics is a combination of data mining, integrating, dynamic intelligence, reporting and executing actions based on a series of inputs in real-time - a matter of seconds. Real-time analytics quickly turn insights into actions. Real-time analytics can be used in CRM (Customer Relationship Management) analytics, which includes all programming that analyzes data about an enterprise's customers and presents it so that better and quicker business decisions can be made.

This is already happening a fair amount in online space. Apple i-phone notifies deals customers would be interested in when they are physically within the proximity of a retailer. Manufacturers such as Toyota, become more proactive in approaching problems. The new product Kate (fragrance brand) from Coty, a perfume company, claims the success to up-to-date point-of-sale real-time analytics.

Business Applications using Real Time Analytics:

Retail: Real-Time Personalized Offers
Instant Cross-sell
Real-time Feedback Analysis
Real-time Web Tracking

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