1. The document discusses segmentation analysis and its importance for understanding customer differences and targeting products and services effectively.
2. Segmentation analysis uses statistical clustering techniques to organize customers into distinct groups based on similarities in attributes, needs, and preferences.
3. An example analysis of potential internet telecommunications customers identified four distinct segments prioritizing different attributes like cost, convenience, security, and international access.
An exclusive presentation by Mr. Mayank Sahai, AVP - Corporate Marketing - Tata Teleservices Ltd. on ‘Enhancing Marketing Performance to drive Business Objectives.’ The presentation was made at SAS Forum India 2013.
An exclusive presentation by Mr. Mayank Sahai, AVP - Corporate Marketing - Tata Teleservices Ltd. on ‘Enhancing Marketing Performance to drive Business Objectives.’ The presentation was made at SAS Forum India 2013.
Self Service Customer Care For Next Generation NetworksGreen Packet
Smartphone devices have dramatically changed the way consumers consume data. The need to be connected anywhere and anytime is driving service providers to put focus into customer experience to grow customers, build loyalty and drive profits through new services. As a result, the expectations of service experience from the end-user are heightened and cannot be underestimated.
Service providers are under pressure to deliver customer experience that separates them from the competition, while at the same time trying to delicately balance their revenues and managing costs for near term gain and longer term success. In this paper, we outline the scale of issues faced in mobile device and service care that impacts both service providers and end-users beyond connectivity. With a broad range of services and wide variety of applications, the smartphone requires more complexity to handle a multitude of firmware and software configurations which legacy CRM systems are inadequate to deal with today’s customers.
Remote device support and service care is a key component to tackle the demands of escalating costs in customer care. Service providers are seeking integration onto backend systems without incurring heavy infrastructure changes and ensure the best fit into the existing operations through automated and proactive customer care mechanisms to ensure service excellence and thus prevent service quality degradation.
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Everest Group experts will highlight how the end-of-term stage can force organizations to assess the value achieved from their current outsourcing efforts and build next-generation outsourcing engagements focused on driving broader sets of value for both buyer and supplier.
Learn about Voice-of-the-Customer business practices:
* This report defines, explores the potential benefits, and illustrates a planning methodology for VOC
* Presents findings from a Best Practices in VOC study
* Explores some emerging VOC technologies
Combining their experience, SAP and Dassian, a certified SAP software and development partner, identified five critical areas of transparency for government contractors to help them in the following ways.
Slide deck from the Webinar with KANA Software to discuss the rise of knowledge infused processes, evolution of knowledge in the organization, and providing better self-service solutions.
http://inarocket.com
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In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
Self Service Customer Care For Next Generation NetworksGreen Packet
Smartphone devices have dramatically changed the way consumers consume data. The need to be connected anywhere and anytime is driving service providers to put focus into customer experience to grow customers, build loyalty and drive profits through new services. As a result, the expectations of service experience from the end-user are heightened and cannot be underestimated.
Service providers are under pressure to deliver customer experience that separates them from the competition, while at the same time trying to delicately balance their revenues and managing costs for near term gain and longer term success. In this paper, we outline the scale of issues faced in mobile device and service care that impacts both service providers and end-users beyond connectivity. With a broad range of services and wide variety of applications, the smartphone requires more complexity to handle a multitude of firmware and software configurations which legacy CRM systems are inadequate to deal with today’s customers.
Remote device support and service care is a key component to tackle the demands of escalating costs in customer care. Service providers are seeking integration onto backend systems without incurring heavy infrastructure changes and ensure the best fit into the existing operations through automated and proactive customer care mechanisms to ensure service excellence and thus prevent service quality degradation.
End-of-Term Strategy: Unlocking Hidden Deal ValueEverest Group
Everest Group experts will highlight how the end-of-term stage can force organizations to assess the value achieved from their current outsourcing efforts and build next-generation outsourcing engagements focused on driving broader sets of value for both buyer and supplier.
Learn about Voice-of-the-Customer business practices:
* This report defines, explores the potential benefits, and illustrates a planning methodology for VOC
* Presents findings from a Best Practices in VOC study
* Explores some emerging VOC technologies
Combining their experience, SAP and Dassian, a certified SAP software and development partner, identified five critical areas of transparency for government contractors to help them in the following ways.
Slide deck from the Webinar with KANA Software to discuss the rise of knowledge infused processes, evolution of knowledge in the organization, and providing better self-service solutions.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
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A global airline leader had enjoyed steady growth until the recession. They needed to shift their strategy to focus on efficiency while maintaining credibility and retain loyal customers. Specifically: driving marketing efficiency, effectiveness, and targeting solutions suitable for global roll-out.
Customer churn classification using machine learning techniquesSindhujanDhayalan
Advanced data mining project on classifying customer churn by
using machine learning algorithms such as random forest,
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Data Mining on Customer Churn ClassificationKaushik Rajan
Implemented multiple classifiers to classify if a customer will leave or stay with the company based on multiple independent variables.
Tools used:
> RStudio for Exploratory data analysis, Data Pre-processing and building the models
> Tableau and RStudio for Visualization
> LATEX for documentation
Machine learning models used:
> Random Forest
> C5.0
> Decision tree
> Neural Network
> K-Nearest Neighbour
> Naive Bayes
> Support Vector Machine
Methodology: CRISP-DM
VINVOX’s telematics applications help automotive industry clients secure customer loyalty, win new business, and make informed strategic decisions based on accurate real-time data.
INTEGRATION OF MACHINE LEARNING TECHNIQUES TO EVALUATE DYNAMIC CUSTOMER SEGME...IJDKP
The telecommunications industry is highly competitive, which means that the mobile providers need a
business intelligence model that can be used to achieve an optimal level of churners, as well as a minimal
level of cost in marketing activities. Machine learning applications can be used to provide guidance on
marketing strategies. Furthermore, data mining techniques can be used in the process of customer
segmentation. The purpose of this paper is to provide a detailed analysis of the C.5 algorithm, within naive
Bayesian modelling for the task of segmenting telecommunication customers behavioural profiling
according to their billing and socio-demographic aspects. Results have been experimentally implemented.
Prepaid customer segmentation in telecommunications: An overview of common pr...Exacaster
There are number of frustrating factors for marketers who work with prepaid customers in
telecommunications. This Exacaster white paper summarizes the pros and cons of common segmentation strategies in prepaid markets.
Beyond Omnichannel: Determining the Right Channel MixCognizant
Many companies believe that simply adding more customer channels or reducing the time it takes to handle customer queries will boost customer satisfaction and enhance the customer experience. Yet the proliferation of digital technologies and touchpoints have made it more difficult to track customer preferences and purchasing traits. By identifying customers’ preferred contact channels, companies can more effectively engage, serve, and retain them while driving profitable growth.
Making Analytics Actionable for Financial Institutions (Part I of III)Cognizant
To maximize ROI from their analytics platforms, financial institutions must build solutions that explicitly, visibly and sustainably enable real-time translation of data into meaningful and continuous improvements in their products, services, operating models and supporting infrastructures.
Mr. Mayank Sahai presented at SAS Forum 2011 - one of the largest Analytics conference in India. He enlightened the audience on the role Analytics plays in Customer Management and organizations can maximize the value
Is Your Customer-Centric Transformation Living Up to its Promise
Segmentation white paper_final_111505
1. An Overview of Segmentation:
Why You Should Consider It
And a Thumbnail of Its Dynamics
Prepared by:
Edward J. Hass, Ph.D.
Vice President – Advanced Research Methods
2. The Motivation to Segment
The ultimate outcome of We all understand that consumers are not all alike. This provides a
segmentation is challenge for the development and marketing of profitable products and
superior satisfaction services. Not every offering will be right for every customer, nor will
with what you every customer be equally responsive to your efforts to bring your
provide.
offering to their awareness. Success in these regards requires a
targeted approach to ensure bottom-line efficient use of product
development and promotional resources.
Segmentation is an informed means to organize customers into groups
that allow such targeting. The ultimate goal of segmentation for you is
the pragmatism of superior deployment and utilization of corporate
performance capabilities in meeting the needs and expectations of the
customer population. The ultimate outcome of segmentation for the
customer is superior satisfaction with what you provide.
Segmentation touches on matters that directly impact, and derive from,
your business strategy. On what type of customers should you focus
your efforts? How broad or narrow should your focus be? In which
competitive space is it most profitable for you to operate? Getting the
answers to these types of questions by means of a segmentation
analysis increases the chance for you to establish, maintain, and grow
a loyal customer base.
What Segmentation Does
Segmentation partitions a general population with the goal of tailoring
actions toward specific subgroups. It is a way of organizing customers
into groups with similar traits, performance characteristics or
expectations. These similarities can be obvious and simple at one
extreme or subtle and complex at the other. Information requirements
and the “organizing tools” used to create the segments vary greatly as
the nature and purpose of segmentation changes.
Consider a very simple segmentation exercise, one in which segments
were predetermined. Such an a priori scheme might begin with dividing
a customer base by zip code, with the eventual goal of regionally-based
promotions. Such data could then be crossed with sales data to see
which zip codes are heavier or lighter users of the product or service.
The output from that analysis would then be weighed against a
particular business strategy, such as working on maintaining loyalty
versus winning new customers.
While this approach may serve well as an exploratory attempt at
understanding how customers differ, it is a rather blunt instrument in
several regards. First, the partitioning takes place at a fairly gross
3. level, employing only the two variables of zip code and level of
product use. And then, it describes a situation without prescribing
the nature of an action that might be taken. Furthermore,
segmentation by its very nature assumes that all customers within a
segment are more alike than unalike – and that they are more
different from customers in other segments than similar to them.
Hence, using this very simple segmentation scheme, all customers
in a zip code would be considered alike. So any action taken
toward that zip code would be assumed to have the same response
from all customers in that zip code, clearly a very chancy
assumption.
Now consider a more complex example. Instead of starting with
completely predetermined segments, we actually search for a
segmenting rationale to infer to a population. And we do that with
variables that will inform us of customer motivations, trying to
understand the Why? behind purchase behavior. We might include
a bank of attitude statements surrounding purchase decisions in our
survey, or ask the importance of select product/service features,
lifestyle questions and demographic questions. With this we would
not only have the potential for a much more finely-tuned partitioning
of customers, but also a much more actionable partitioning.
An Example
We have a client who is planning to enter the Internet
telecommunications marketplace and needs to understand what
customers view as important from their phone service. Having
determined whether there are differences between customers in
that regard, and the nature of those differences, our client will be
able to devise a strategy to target customers most in line with their
currently planned service offerings. For future development, such a
segmentation can also point directions for new services.
We ask respondents to rate each of the following in importance
when considering the type of Internet service they would use:
• Conveniently available
• Inexpensive
• Is portable
• High speed band width
• Free connection
• Excellent security
• Reliable service
• Phone interface
• International access
4. In addition, we ask our sample to rate how well they perceive each
of several providers to perform in each of these areas.
Unless each of the 1000 respondents we survey gives identical
ratings, our data will contain variations that we can use to cluster or
group respondents together, and such clusters are the segments.
The mathematical algorithms we employ produce segments such
that:
a) variation within the segments is minimized
b) variation across the segments is maximized
This yields groupings of customers that are most similar to each
other if they are part of the same segment and most different from
each other if they are part of different segments. By inference,
then, actions taken toward customers in the same segment should
lead to similar responses, and actions taken toward customers in
different segments should lead to different responses.
Another way of saying this, in our telecommunications example, is
that the aspects of Internet telecommunications that are important
to any given customer in one segment will also be important to
other customers in that same segment. Furthermore, those aspects
that are important to that customer will be different from what is
important to a customer in a different segment. Here is what the
analysis in this example showed:
5. Segmentation
Convenient AT&T SPRINT
Free Connections
Segment- Segment-
A Low Cost
B
(52%) (24%)
High Speed
Security
Portable choice
MCI
Reliable
VERIZON Segment-
Ego Segment- COMCAST
International
D C
PacTel
Access (10%) (14%)
Phone Interface
Our analysis shows four distinct segments. The majority of
customers
(Segment A, 52%) want mainly convenience from their service;
AT&T is perceived as convenient. Another quarter of customers
(Segment B, 24%) seek low cost and free connections, which
seems to be a Sprint characteristic. Two smaller segments seek,
respectively, a phone interface from their Internet
telecommunications (Segment C, 14%) or international access
(Segment D, 10%); Comcast and PacTel apparently meet the first
need, and Verizon the second.
In this case, there are a number of strategic options for the client to
consider. There seem to be open competitive spaces for customers
who might want portability, or reliability. Can such a need be
created, drawing customers from existing segments, or drawing
entirely new customers who might desire such qualities? None of
the current suppliers tested seem to satisfy jointly the need for a
phone interface with international access, nor to be both convenient
and low cost. Could our client make a claim to either combination,
or to develop a service that would make such joint claims? There
might be opportunities there.
6. How The Analysis Is Accomplished
Segmentations are generally performed via cluster analysis. There
are numerous specific techniques used for cluster analysis. Some
operate by treating all respondents initially as part of the same
cluster, then splitting the sample along “natural” fracture lines based
on survey responses, much like cutting a diamond (divisive
techniques). Others start by assuming all respondents belong in
different clusters, but then build larger and larger groupings based
on comparing survey responses (agglomerative techniques).
All methods share in common the computation of a measure of
similarity among respondents. Some techniques define similarity
on the basis of feature comparison, others employ the physical
metaphor of distance. In any case, the analysis takes the data
we’ve collected (behaviors, geography, attitudes, beliefs, benefits
sought, etc), scores respondents in a multivariate fashion, and
using a criterion we’ve selected, clusters respondents based on
whether they meet the similarity criterion level for inclusion with
other respondents.
There are many tools at the disposal of the segmentation analyst.
You may hear such diverse techniques as single linkage, complete
linkage, Ward’s method, polythetic, k-means interative partitioning,
CHAID, latent class cluster analysis, etc, etc. In fact the choice of
technique can determine the nature of the solution, so it is
legitimate, standard practice to use multiple methods and search for
convergence. Such convergence lends assurance that a valid
solution has been found.
But even with that, the utility of a clustering solution requires an
interpretation of substance within the context of “reality” based on
the applied discipline of the research. How does the
telecommunications industry work? Does this answer make sense
in the context of my knowledge of the pharmaceutical industry?
What are the constraints of financial services that impart credibility
to this result? The statistical solution must always be filtered
through intelligence about the industry environment in order for the
result to be accepted and acted upon.
Conclusion
Marketers accept the construct that what consumers seek out in
products and services is not uniform across all potential users – we
are no longer in the age of Henry Ford, who said, “The customer
can have any color car he wants, so long as it is black.”
7. Segmentation analysis shares this same construct – to conduct a
segmentation is to seek out differences in customers to capitalize
upon for ensuring maximum product uptake and brand loyalty. The
sophisticated multivariate techniques we employ for segmentation
are simply the tools to reduce the massive amount of data on
customer wants and needs to a manageable form so that we can
make educated targeting decisions. Hence, strategic customer
segmentation exists in service of the goal of striving to place the
right product or service with the right customer, to the benefit of all
involved.
Next Steps
Think about the business challenge confronting you. If the
questions you ask include such words as “targeting”, “customer
differences”, or “where to focus”, the chances are very strong that
segmentation analysis will be crucial for informing your decision
making. Discuss the problem with your research consultant in light
of the background you’ve gained from this white paper.