Turn Every Moment into Opportunity with Psychographic SegmentationCleverTap
Every single one of your users is unique. Their behaviors and interests change frequently. For marketers, it is more critical than ever to identify micro-segments of users based on their changing needs and pivot their marketing efforts to bring timeliness, context and relevance.
See how CleverTap is staying at the cutting edge of innovation to help its customers reinvent segmentation strategies in ways that were unthinkable before with Psychographic Segmentation.
This is the ppt translation of the second part of 25 keys to sales & marketing, an audio portable MBA course, which has been developed by New York Times publishing,
with the contribution of some of the best known business academicians and practitioners of the contemporary world. This is only a reproduced graphical version of the same
with no commercial motive. It has been developed for better self learning and for assistance to the large community of several business practitioners & students, who are in
constant pursuit for quality stuff on-line.
In the highly specialised study of “BUSINESS MANAGEMENT”, today, the function of “MARKETING MANAGEMENT” plays a very critical role. This is because this functional area of management :
(1) “EARNS” the revenue, &
(2) “WORKS” in the close proximity with the public or persons outside the organisation.
Controlling these two attributes to have the desired benefits are the most difficult part of the management, because none of these two are within the direct control of the marketers.
This doesn’t mean that the other functional areas are not important, but they are not “DIRECTLY” involved in the activities mentioned above.
Similarly, within the study of Marketing Management, the “Consumers” or the “Customers” play a very critical role as these are the people who finally BUY the goods & services of the Organisation, and the firm is always on the move to make them buy so as to earn revenue.
Turn Every Moment into Opportunity with Psychographic SegmentationCleverTap
Every single one of your users is unique. Their behaviors and interests change frequently. For marketers, it is more critical than ever to identify micro-segments of users based on their changing needs and pivot their marketing efforts to bring timeliness, context and relevance.
See how CleverTap is staying at the cutting edge of innovation to help its customers reinvent segmentation strategies in ways that were unthinkable before with Psychographic Segmentation.
This is the ppt translation of the second part of 25 keys to sales & marketing, an audio portable MBA course, which has been developed by New York Times publishing,
with the contribution of some of the best known business academicians and practitioners of the contemporary world. This is only a reproduced graphical version of the same
with no commercial motive. It has been developed for better self learning and for assistance to the large community of several business practitioners & students, who are in
constant pursuit for quality stuff on-line.
In the highly specialised study of “BUSINESS MANAGEMENT”, today, the function of “MARKETING MANAGEMENT” plays a very critical role. This is because this functional area of management :
(1) “EARNS” the revenue, &
(2) “WORKS” in the close proximity with the public or persons outside the organisation.
Controlling these two attributes to have the desired benefits are the most difficult part of the management, because none of these two are within the direct control of the marketers.
This doesn’t mean that the other functional areas are not important, but they are not “DIRECTLY” involved in the activities mentioned above.
Similarly, within the study of Marketing Management, the “Consumers” or the “Customers” play a very critical role as these are the people who finally BUY the goods & services of the Organisation, and the firm is always on the move to make them buy so as to earn revenue.
Lead gen and demand creation in europe full report - sirius decisionsHervé Gonay
Ensemble BtoB (Think Tank in marketing BtoB) has invited Sirius Decisions to have a conference on Lead Generation and demand creation in europe. This is the final report with text and notes
Here is the brief overview of this cumulative Session Long Project (mealsdeidre
Here is the brief overview of this cumulative Session Long Project (SLP). In this research project, you would work as a marketing consultant to develop a feasible marketing plan for your client. You would conduct secondary research in SLP1 and SLP2 to glean the necessary information for your marketing plan in SLP3 and SLP4.
It is important to conduct quality market research on your focal product/company in order to develop realistic and workable marketing plans. Generally speaking, there are two types of research. One is secondary research, which refers to data collection using existing sources, and the other is primary research, which is your own data collection for the specific study at hand. The purpose of market research is to collect usable information to make more informed decisions on the business problem, thus increasing the chance of business success in the marketplace.
Please check the
outline of the
marketing plan
, which provides information on:
The final format for this cumulative Session Long Project;
A list of topics for the whole project;
The continuity and connections among SLPs 1-4.
In this module SLP1, identify a company and a charge (or task) for this marketing research project and conduct situation analysis related to your charge. This is the first step of this cumulative research project. You need to review all four SLPs first in order to better understand the requirements for this project.
Product Statement
In this section, describe the company and the product that is going to be the focus of interest for your marketing plan. For example, if your client is Apple, provide background information on the organization (e.g., what it is, what it does, history, success in what it is doing, etc.). If the charge is to market a new product (such as iPad 6), describe what iPad is, how long it has been around, how successful it has been, and who the target audience is. Be specific and detail-oriented, and do not assume that the reader is familiar with the company and product.
Identify a company and a charge for your research project at the very beginning, based on the detailed requirements for this cumulative Session Long Project. The new charge is a hypothetical task, which should be based on one of the company’s existing brands or products. It is also better to identify a new product charge for existing public firms so that you can find enough information for your task. For example, you may pick one of the following companies and charges.
Apple needs to increase its market share for iPhone 8.
Microsoft intends to increase sales of Xbox One S 10TB console.
Fitbit plans a successful release of Charge 3 wristband.
Ford needs to have a successful introduction of the 2018 Fusion.
Amazon wants to increase brand awareness for Echo digital media streamer in Japan.
Do not choose the product that is the basis of Case 1.
It is best to check with your instructor regarding the company and the charge you choose to analyz ...
2018 Edelman Trust Barometer - i dati italiani sulla fiducia
Crollo di fiducia, fake news, il ruolo dei CEO: i temi dell’Edelman Trust Barometer di quest’anno hanno suscitato un notevole interesse anche in Italia.
Read more: http://edl.mn/2HZ0gto
Strategic Market Research (Chapter 7): Analyzing Numeric Data to Determine W...Matthew A. Gilbert, MBA
What determines whether market research makes a difference for an organization? The difference is the approach. Strategic market research is an approach that makes a large impact on the companies that use it. In Strategic Market Research, author Anne Beall shares her unique approach for conducting market research. In addition to talking about qualitative as well as quantitative research, Strategic Market Research provides real-life examples of how these concepts have been applied in businesses and non-profit organizations. Implementing the strategic approach from the beginning to the end of a project provides information that inspires and changes organizations.
This article gives analysts some tips on how to formulate meaningful insights derived from careful planning, organizing, and contextualizing of available data from various social media channels.
This paper aims to help you understand the four main types of social media tools, their key features, main benefits, and the value they can bring to your social media marketing efforts.
Data Literacy in Public Relations by the PRCA Innovation Forum.pdfJames
As part of the PRCA Innovation Forum I have published a new paper tackling data literacy in PR.
Key themes in the new paper:
- Numbers that matter
- Designing a listening & measurement strategy
- Identifying a public and listening to conversations
- Tools to use
- Translating data into insights
- Building a culture of digital literacy
- Data storytelling & visualisation
Download the report and read reactions from Wadds Inc. Founder and Managing Partner Stephen Waddington, and AMEC Measurement and Evaluation Global Managing Director Johna Burke.
Thank you to Shayoni Lynn FCIPR FPRCA CMPRCA, Iretomiwa Akintunde-Johnson, Stella Bayles, 💡 Sophie Coley, James Crawford FPRCA (me), Orla Graham MPRCA Alex Judd, Steve Leigh, Andrew Bruce Smith, Allison Spray, Stephen Waddington for contributing to the paper.
A case study from the Stukent platform (https://www.stukent.com/). Presentation deck was created for the course, 203 Web Analytics, Postgraduate Social Media Program at Seneca College (February 2018).
Tool used: Microsoft Powerpoint
The analysis is only for academic learning purposes.
Customer Journey Insights using Structural Equation Modelling_BLA GLOBAL 2014Masood Akhtar
This analysis sheds light into the connectedness of one brands' insight ecosystem. Using Structural Equation Modelling we have been able to provide a multitude of insights from; some of the causal relationships that exists, the transmission of offline and digital media to sales, the role of social display versus true social consumer engagement and salient concepts that can help steer content generation
The report describes the results of a Discrete Choice Experiment (a type of Conjoint-Analysis) to explore the potential configuration of a tablet computer from a new entrant to the category.
In this paper, I develop a custom binary classifier of search queries for the makeup category using different Machine Learning techniques and models. An extensive exploration of shallow and Deep Learning models was performed using a cross-validation framework to identify the top three models, optimize them tuning their hyperparameters, and finally creating an ensemble of models with a custom decision threshold that outperforms all other models. The final classifier achieves an accuracy of 98.83% on a test set, making it ready for production.
A large appliance manufacturer was interested in using propensity models to better target consumers with direct mail campaigns. A data set containing transactional data from past purchases and enriched with all kinds of data about the consumer, the household or the zip code, from third party providers was used to develop a model to predict non-responders and avoid targeting them. Simulations varying the estimated revenue per customer and the cutoff point used to filter out potential consumers allowed me to identify different optimal point in the Reach-vs-Response-Rate tradeoff.
Modeling Sexual Selection with Agent-Based ModelsEsteban Ribero
The paper discusses a well-known principle in evolutionary biology called the handicap principle. Two agent-based-models were developed to illustrate the principle in an attempt to better understand its implications for the study of human behavior.
A focused practice aimed at using simulations from simple System Dynamics models to help us better understand the intended and unintended consequences of our actions.
Brand Communications Modeling: Developing and Using Econometric Models in Adv...Esteban Ribero
This report presents a description and a complete example of the modeling process required to build a comprehensive market response model that would account for the impact of previous marketing actions on sales.
Is looking at consumers' brain the ultimate solution?Esteban Ribero
The idea of using the latest techniques in the field of neuroscience to study consumer behavior has become a hot topic. The presentation delves into the debate over borrowing knowledge and techniques from neurosciences to bypass consumers’ rationalizations and get to the truth about their behavior. My point of view is that the answer is not in consumers’ brains but in ours: Strategists and Creatives, who are the endless students of human behavior.
This presentation was given at the 2007 AHAA Conference in NYC.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Consumer Segmentation with Bayesian Statistics
1. Market Segmentation with Bayesian Statistics
Esteban Ribero
The following report describes the process and results of a market segmentation analysis for
‘App Happy’ (fake name to mask the real company), a new entrant into the Consumer Entertainment
App Industry. A data set with 1800 observations from a survey conducted by the company was provided.
The survey included 40 attitudinal statements about technology, apps, shopping, leadership, and being
connected to the culture and the moment. The survey also contained a series of demographic and app
usage questions. It is believed that that sample is representative of the population of interest, although
no additional background was provided regarding the sampling methodology and data collection
method. App Happy is particularly interested in understanding the market from an attitudinal
perspective so it can better develop products and services aligned with those attitudes, as well as focus
on the most desired segments.
Attitudinal data is messy as it often is collected using a set of statement that consumers agree or
disagree with using a Likert scale. Consumers tend to skew towards one end or the other on the scale
and the ratings in one statement are often correlated with that of many other statements making it hard
to disentangle distinct dimensions underlying in the data. This is the best we can do since attitudes
cannot be directly observed but it is believed that they manifest in the agreement/disagreement with
those statements.
Exploratory Analysis
The raw data in the App Happy data set shows some diversity (dispersion) in consumer’s
attitudes but not a clearly discernable pattern can be observed to the naked eye. Figure 1 shows the
dispersion of the data across the 2 Principal Components of the variance. Notice that there is a high
concentration of points in the middle with more dispersion at the extremes of the two components. An
initial Exploratory Factor Analysis identified 3 to 4 main underlaying dimensions in the data but the
correlations among the attitudinal statements was too high (and in a single direction -positive) that is
was not useful as a technique for extracting features.
Manually creating dimensions by averaging the scores in similar statements and then running a
cluster analysis using different number of clusters using K-means algorithm was useful but there was still
not a clear and distinct pattern or solution emerging. Scree plots and silhouette scores, techniques to
identify the optimal number of clusters and quality of the groupings, indicated that the solution was
somewhere between 3 to 6 groups. However, it was not until we binarized each of 40 statements into
top agreement (bottom 2 boxes: Agree OR Agree Strongly) vs other agreement (Agree Somewhat,
Disagree Somewhat, Disagree, and Disagree Strongly) that a clear pattern and solution emerged. Figure
2 shows the resulting dispersion across the two first Principal Components. It is not unusual in marketing
research projects to binarize the data in this way after a solution is found. It provides a clearer
distinction between those that agree with some strength and those that do not, facilitating the
interpretation of results. In this case we did the binarization even before identifying a satisfactory
solution as part of the feature engineering process.
2. Figure 1. Figure 2.
With a recognizable pattern it was then easier to identify the appropriate solution. Although
binary data can be used as numeric variables it is more appropriate to use a clustering algorithm suited
for categorical data such as poLCA. Although a 3-clusters and a 5-clusters solution were explored, it was
clear from figure 2 that a 4-cluster solution existed and poLCA was able accurately classify the 1800
observations into those 4 groupings with precision.
Market Segmentation
The four-segment solution provides a useful segmentation from a marketing perspective. There
are two clear axes that divide the segments into four quadrants. Axis I is a combination of two sub-
dimensions that are highly and positively correlated between themselves. One dimension contains
attitudes towards technology (app/tech usage, amount of information out there, fear of being left
behind). We have called this dimension Tech influence/anxiety since it contains both statements
indicating enthusiasm about technology as well statements about feeling overwhelmed with the amount
of information and technological development and its influence in daily life. People scoring high in this
dimension are highly adept at using technology and are motivated to keep up with its developments.
This would be a highly desirable trait among a potential consumer of App Happy products. The other
sub-dimension included in this axis is Shopping enthusiasm. This dimension contains positive attitudes
towards shopping, impulse purchases, shopping for what is hot and trendy as well as appeal for luxury
and designer brands. People scoring high on this dimension would also be ideal for App Happy given
their tendency to be up to date with what is on trend, and the willingness and enjoyment to shop for
things that reflect who they are.
Axis II also contain 2 highly correlated subdimensions: Leadership and Being in-the-know.
Leadership is the clearest of all. It includes statements about being in control, being the first among
family and friends to try new things, being an opinion leader, optimistic and creative, and wanting to
stand out and take risks. Being in-the-know is more abstract including a diversity of statements that
reflect and active lifestyle and the desire to stay in touch with friends, music and TV shows. Both of
these dimensions are highly desirable for App Happy since people scoring high on Leadership will tend
to be early adopters, willing to take risks and try new things, as well as influencers of the larger market;
and people scoring high on Being in-the-know will enjoy products that keep them up to date with
culture and entertainment, areas of interest for App Happy.
3. Table 1, shows the list of attitudinal statements grouped by the four dimension described above
as well as the level of agreement for each of the four segments. The level of agreement is calculated as
an index by dividing the percentage of people in each segment that agree with the statement by the
percentage of agreement among all respondents and multiplying this by 100. The index then represents
the likelihood of a consumer in each segment to agree more (or less) with the statement than the
average. For instance, an index of 100 means that a consumer in segment x has the same probability to
agree with the statement than the average. An index of 160 means that a consumer in segment x is 60%
more likely to agree with the statement than the average, and an index of 75 means that a consumer in
segment x is 25% less likely to agree with the statement than the average. The overall level of
agreement with each statement is provided for reference. A high or low index for a statement with a
high level of agreement is even more indicative of an important difference between the groups.
Table 1. Attitudinal indexes for each statement and dimension by segment.
4. Notice the names of the segments in Figure 1. This is deliberate because the segments have a
logical appeal to App Happy. Although a more descriptive name can be provided, we wanted to make
the business implications take the lead. Figure 3 show the segments plotted in the quadrants created by
the crossing of the two main Axes and their sub-dimensions.
The NOW segment
As can be seen in Figure 1 and Table 1, the first segment “NOW”, it scores high on all dimensions
making it the ideal segment to start marketing to with a new product designed for technology
enthusiasts that enjoy shopping, being in-the-know, and are leaders. They make up about 19% of the
market. The difficulty marketing to them, relative to the other segments, is low and the potential
rewards can be high. Take a look at their demographic and app behaviors: Their estimated median age is
28.4 years old, slightly skewing towards females, more than half are single but a high number of them
(47%) have children 12 years old or younger. They have a decent median income and skew towards
iPhones (iOS) vs other operating systems. They have the second highest number of apps in their mobile
devices (21.4) and are the ones showing higher than average interest for a variety of apps related to
entertainment, shopping and news. Their most distinct statements are “I cannot get enough Apps”, ” I
am influenced by what is hot and what is not”, “I am very active and always on the go”, and “I love
showing off my new Apps to other”.
Figure 3. Segmentation Scheme for App Happy
The THEN segment
The THEN segment (18% of the market), shares most of the NOW enthusiasm for technology
and shopping -although to a lesser degree- but lacks the Leadership attitudes and Desire to being in-the-
know. It is the youngest off all the segments, more likely to be single, less likely to have kids under 12,
high multicultural index, lower income, and a lower average number of apps downloaded on their
mobiles (18.6). However, consumers in this segment show a variety of interests for apps about Tv
5. shows, Entertainment, Gaming and Social Networking. Given their high score on the Technology and
Shopping dimension they are still ideal prospects and may not require a lot of marketing efforts
specifically directed at them since they will follow the aspirational NOW segment and App Happy just
needs to make sure they are extending their marketing effort to include them. They won’t be early
adopter and may need reassurance from their more adventurous NOW counterparts, but once App
Happy has established itself among the NOW, THEN will likely follow.
The NEXT segment
The NEXT segment is highly desirable for a couple of reasons: They are the biggest group of all
(representing about 35% of the market), have the highest median income at $68,189, high education
level, skew toward iOS operating system, have the largest average amount of apps downloaded on their
devices at 23.4, and show a variety of interests for app about music, shopping, specific publications, and
other. They are a bit older with a median age of 33.9 and are less likely to be single although as likely as
the average to have children under 12 years old. They score high on the Being in-the-know dimension as
well as the leadership dimension. All ideal traits. What makes them less ideal than the other two
segments described above is that they score low on the Technology and Shopping dimensions and so
will require an extra effort to convinced them. They appear to be more discerning and cautious
consumers, not easily persuaded by the what is hot and less likely to make purchases out of impulse.
The extra difficulty convincing consumers in this segment may however be worth it since they have the
potential for the biggest reward among all segments. It may be advisable to start with the other
segments first without loosing sight of this bigger and more discerning segment that may be accessible
once enough experience and success have been collected from the easier NOW and THEN segments.
Convincing NEXT would be the ultimate goal.
The NOT segment
Good market segmentation provides prioritization as well as focus. That means sometime
ignoring certain groups. Consumers in the NOT segment lack all the characteristics that makes them
appealing to App Happy. They have the lowest interest in apps and Technology, lack enthusiasm for
Shopping, don’t care much about Being in-the-know, and won’t take the risk to lead and try new things.
They are the antithesis of the NOW segment scoring the lowest on all dimensions. They may share
similar demographic characteristics to the NEXT segment but lack all of the potential for a Consumer
Entertainment app. Although they represent a sizable portion of the market at 28%, It is advisable to
ignore this segment as it will be extremely difficult to inspire, and the potential reward may be low.
Focusing on the other three segments may increase the chances of success in this marketplace.
Typing Tool and Consumer Classification
The NOW THEN NEXT NOT framework described above can be a powerful tool for App Happy as is
develops and further explore the opportunity for products and services in the Consumer Entertainment
App industry. The key to its power is the easy classification of consumer into segments that have clear
business and marketing implications. Since App Happy is early in its effort to market to this industry is
expected, and recommended, to do further research to hone-in on the specifics of a product or service.
It would be key to be able to classify new research participants into each of the segments. To do this we
will develop a Typing Tool using one or more of the following approaches.
6. 1. We could train a Machine Learning classifier such as a Random Forrest to identify a subset of the 40
attitudinal statements (no more than 10, ideally between 4 to 8) and include some demographic and
other info such as the number of apps, and app interest to make a short battery of questions with
high predictive power that can be included in the screeners that are used to recruit participants. A
simple web app or Excel macro will host the algorithm so recruiters can easily type in the answer to
these questions and get a suggested segment and the probabilities of belonging to each of the
segments just in case of borderline cases.
2. A faster and easier approach would be to use the conditional-item-response-probabilities provided
already by poLCA, the model-based algorithm used for the segmentation, to select the most
predictive statements of each class and simply use those statements and multiply the probabilities
to get the posterior probabilities of class membership once we know the answer to the selected set
of statements (if we don’t want to use them all). This will have the same effect as the Random
Forrest classifier but there will be no need for additional training. The information is already in the
model used for the segmentation. This is one of the advantages of poLCA vs other methods such as
k-means.
3. A simplified approach can be to craft a decision tree with the top statements in each dimension or
developing new statements that better reflect the sentiment of the two Axes described here, and
create a heuristic (rule of thumb) that would give immediate segment assignment to the user.
4. In the future, when more behavioral patterns and media consumption is collected about consumers
in each segment, it may be possible to forgo the attitudinal statement altogether and develop a
classifier trained purely on behavioral and other inferred data. This could include 1st
party and 3rd
party data. This will be a more involved process and will require large amounts of data.
Conclusion
Market segmentation is as much of an art as it is a science. Alternative segmentation schemes
are possible, and we must balance statistical rigor with marketing and business sense. Fortunately, we
found a solution that achieved both objectives here. The NOW THEN NEXT NOT scheme provides
actionable segments, clear distinctions across easily to understand dimensions, a well-balanced size of
the segments, and direct and clear marketing and business implications. The poLCA algorithm used here
was able to fully capture the natural structure of the data once it was binarized without need to further
reduce the dimensionality of the data or perform additional transformations. An additional benefit of
poLCA is that it already provides the conditional-item-response-probabilities by statement for each class
and so a typing tool can be easily constructed from these making the bridge between clustering method
and future classification seamless. The segmentation scheme and insights uncovered here can be used
immediately and we look forward to continuing our understanding of the opportunity for App Happy in
the Consumer Entertainment App industry.