55,355 research outputs found
Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store
Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined
To boardrooms and sustainability: the changing nature of segmentation
Market segmentation is the process by which customers in markets with some heterogeneity
are grouped into smaller homogeneous segments of more âsimilarâ customers. A market
segment is a group of individuals, groups or organisations sharing similar characteristics and
buying behaviour that cause them to have relatively similar needs and purchasing behaviour.
Segmentation is not a new concept: for six decades marketers have, in various guises, sought to
break-down a market into sub-groups of users, each sharing common needs, buying behavior
and marketing requirements. However, this approach to target market strategy development
has been rejuvenated in the past few years. Various reasons account for this upsurge in the
usage of segmentation, examination of which forms the focus of this white paper.
Ready access to data enables faster creation of a segmentation and the testing of propositions to
take to market. âBig dataâ has made the re-thinking of target market segments and value
propositions inevitable, desirable, faster and more flexible. The resulting information has
presented companies with more topical and consumer-generated insights than ever before.
However, many marketers, analytics directors and leadership teams feel over-whelmed by the
sheer quantity and immediacy of such data.
Analytical prowess in consultants and inside client organisations has benefited from a stepchange,
using new heuristics and faster computing power, more topical data and stronger
market insights. The approach to segmentation today is much smarter and has stretched well
away from the days of limited data explored only with cluster analysis. The coverage and wealth
of the solutions are unimaginable when compared to the practices of a few years ago. Then,
typically between only six to ten segments were forced into segmentation solutions, so that an
organisation could cater for these macro segments operationally as well as understand them
intellectually. Now there is the advent of what is commonly recognised as micro segmentation,
where the complexity of business operations and customer management requires highly
granular thinking. In support of this development, traditional agency/consultancy roles have
transitioned into in-house business teams led by data, campaign and business change planners.
The challenge has shifted from developing a granular segmentation solution that describes all
customers and prospects, into one of enabling an organisation to react to the granularity of the
solution, deploying its resources to permit controlled and consistent one-to-one interaction
within segments. So whilst the cost of delivering and maintaining the solution has reduced with
technology advances, a new set of systems, costs and skills in channel and execution
management is required to deliver on this promise. These new capabilities range from rich
feature creative and content management solutions, tailored copy design and deployment tools,
through to instant messaging middleware solutions that initiate multi-streams of activity in a
variety of analytical engines and operational systems.
Companies have recruited analytics and insight teams, often headed by senior personnel, such as
an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such
personnel out-weigh posts for brand and marketing managers. Far more companies possess the
in-house expertise necessary to help with segmentation analysis. Some organisations are also
seeking to monetise one of the most regularly under-used latent business assets⊠data.
Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a
Chief Data Officer. This emerging role has also driven the professionalism of data exploration,
using more varied and sophisticated statistical techniques.
CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users
of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of
value propositions and the need to look after core customers; CFOs because segmentation leads
to better and more prudent allocation of resources â especially NPD and marketing â around the
most important sub-sets of a market; COOs because they need to better look after key
customers and improve their satisfaction in service delivery. More and more it is recognised that
with a new segmentation comes organisational realignment and change, so most business
functions now have an interest in a segmentation project, not only the marketers.
Largely as a result of the digital era and the growth of analytics, directors and company
leadership teams are becoming used to receiving more extensive market intelligence and
quickly updated customer insight, so leading to faster responses to market changes, customer
issues, competitor moves and their own performance. This refreshing of insight and a leadership
teamâs reaction to this intelligence often result in there being more frequent modification of a
target market strategy and segmentation decisions.
So many projects set up to consider multi-channel strategy and offerings; digital marketing;
customer relationship management; brand strategies; new product and service development;
the re-thinking of value propositions, and so forth, now routinely commence with a
segmentation piece in order to frame the ongoing work. Most organisations have deployed
CRM systems and harnessed associated customer data. CRM first requires clarity in segment
priorities. The insights from a CRM system help inform the segmentation agenda and steer how
they engage with their important customers or prospects. The growth of CRM and its ensuing
data have assisted the ongoing deployment of segmentation.
One of the biggest changes for segmentation is the extent to which it is now deployed by
practitioners in the public and not-for-profit sectors, who are harnessing what is termed social
marketing, in order to develop and to execute more shrewdly their targeting, campaigns and
messaging. For Marketing per se, the interest in the marketing toolkit from non-profit
organisations, has been big news in recent years. At the very heart of the concept of social
marketing is the market segmentation process.
The extreme rise in the threat to security from global unrest, terrorism and crime has focused
the minds of governments, security chiefs and their advisors. As a result, significant resources,
intellectual capability, computing and data management have been brought to bear on the
problem. The core of this work is the importance of identifying and profiling threats and so
mitigating risk. In practice, much of this security and surveillance work harnesses the tools
developed for market segmentation and the profiling of different consumer behaviours.
This white paper presents the findings from interviews with leading exponents of segmentation
and also the insights from a recent study of marketing practitioners relating to their current
imperatives and foci. More extensive views of some of these âleading lightsâ have been sought
and are included here in order to showcase the latest developments and to help explain both
the ongoing surge of segmentation and the issues under-pinning its practice. The principal
trends and developments are thereby presented and discussed in this paper
Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments
Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.fuzzy clustering;direct marketing;client segmentation;fuzzy systems
Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda
Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online
eEnabled internet distribution for small and medium sized hotels: the case of hospitality SMEs in Athens
Advances in information and communications technologies (ICTs) have strategic implications for a wide range of industries.
Tourism and hospitality have dramatically changed by the ICTs and the Internet and gradually emerge as the leading industry on online expenditure. The Internet revolutionised traditional distribution models, enabled new entries propelled both disintermediation and reintermediation and altered the sources of competitive advantage. This paper explores the strategic implications of ICTs and the perceived advantages and disadvantages of Internet distribution for small and medium-sized hospitality enterprises (SMEs). Primary research in Athens hotels demonstrates the effects of the Internet and ICTs for secondary markets, where there is lower penetration and ICT adoption. Interviews and questionnaires identified a number of strategies in order to optimise distribution. The analysis illustrates the strategic role of ICTs and the Internet for hospitality organisations and Small and Medium-sized organisations in general. Most hotels employ a distribution mix that determines the level and employment of the Internet. The paper demonstrates that only organisations that use ICTs strategically will be able to develop their electronic distribution and achieve competitive advantages in the future
Off-Policy Evaluation of Probabilistic Identity Data in Lookalike Modeling
We evaluate the impact of probabilistically-constructed digital identity data
collected from Sep. to Dec. 2017 (approx.), in the context of
Lookalike-targeted campaigns. The backbone of this study is a large set of
probabilistically-constructed "identities", represented as small bags of
cookies and mobile ad identifiers with associated metadata, that are likely all
owned by the same underlying user. The identity data allows to generate
"identity-based", rather than "identifier-based", user models, giving a fuller
picture of the interests of the users underlying the identifiers. We employ
off-policy techniques to evaluate the potential of identity-powered lookalike
models without incurring the risk of allowing untested models to direct large
amounts of ad spend or the large cost of performing A/B tests. We add to
historical work on off-policy evaluation by noting a significant type of
"finite-sample bias" that occurs for studies combining modestly-sized datasets
and evaluation metrics involving rare events (e.g., conversions). We illustrate
this bias using a simulation study that later informs the handling of inverse
propensity weights in our analyses on real data. We demonstrate significant
lift in identity-powered lookalikes versus an identity-ignorant baseline: on
average ~70% lift in conversion rate. This rises to factors of ~(4-32)x for
identifiers having little data themselves, but that can be inferred to belong
to users with substantial data to aggregate across identifiers. This implies
that identity-powered user modeling is especially important in the context of
identifiers having very short lifespans (i.e., frequently churned cookies). Our
work motivates and informs the use of probabilistically-constructed identities
in marketing. It also deepens the canon of examples in which off-policy
learning has been employed to evaluate the complex systems of the internet
economy.Comment: Accepted by WSDM 201
Pioneers of Influence Propagation in Social Networks
With the growing importance of corporate viral marketing campaigns on online
social networks, the interest in studies of influence propagation through
networks is higher than ever. In a viral marketing campaign, a firm initially
targets a small set of pioneers and hopes that they would influence a sizeable
fraction of the population by diffusion of influence through the network. In
general, any marketing campaign might fail to go viral in the first try. As
such, it would be useful to have some guide to evaluate the effectiveness of
the campaign and judge whether it is worthy of further resources, and in case
the campaign has potential, how to hit upon a good pioneer who can make the
campaign go viral. In this paper, we present a diffusion model developed by
enriching the generalized random graph (a.k.a. configuration model) to provide
insight into these questions. We offer the intuition behind the results on this
model, rigorously proved in Blaszczyszyn & Gaurav(2013), and illustrate them
here by taking examples of random networks having prototypical degree
distributions - Poisson degree distribution, which is commonly used as a kind
of benchmark, and Power Law degree distribution, which is normally used to
approximate the real-world networks. On these networks, the members are assumed
to have varying attitudes towards propagating the information. We analyze three
cases, in particular - (1) Bernoulli transmissions, when a member influences
each of its friend with probability p; (2) Node percolation, when a member
influences all its friends with probability p and none with probability 1-p;
(3) Coupon-collector transmissions, when a member randomly selects one of his
friends K times with replacement. We assume that the configuration model is the
closest approximation of a large online social network, when the information
available about the network is very limited. The key insight offered by this
study from a firm's perspective is regarding how to evaluate the effectiveness
of a marketing campaign and do cost-benefit analysis by collecting relevant
statistical data from the pioneers it selects. The campaign evaluation
criterion is informed by the observation that if the parameters of the
underlying network and the campaign effectiveness are such that the campaign
can indeed reach a significant fraction of the population, then the set of good
pioneers also forms a significant fraction of the population. Therefore, in
such a case, the firms can even adopt the naive strategy of repeatedly picking
and targeting some number of pioneers at random from the population. With this
strategy, the probability of them picking a good pioneer will increase
geometrically fast with the number of tries
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