ONLINE MARKETING A CUSTOMER LED APPROACH PDF

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Review Number: /1; Review Subject: Online Marketing: A Customer‐led Approach Richard Gay, Alan Charlesworth and Rita Esen; Publisher Name: Oxford. [(Online Marketing: A Customer-Led Approach)] [ By (author) Richard Gay, By ( author) Alan Charlesworth, By (author. Book Download, PDF Download, Read. // Online Marketing: A Customer. Led. Approach provides an DOWNLOAD PDF smeltitherabpigs.cf Key Concepts in.


Online Marketing A Customer Led Approach Pdf

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Online Marketing: A Customer Led Approach provides an excellent and stimulating balance between theory and practice by recognising the. Online marketing: a customer-led approach /​ Richard Gay, Alan Charlesworth, Rita Esen. Author. Gay, Richard. Other Authors. Charlesworth, Alan, Online marketing: a customer-led approach by Richard Gay, lecturer. Online marketing: a customer-led approach. by Richard Gay, lecturer.; Alan Charlesworth;.

But, how important is being customer centric?

What does it mean to be customer-centric? Customer centricity is not just about offering great customer service, it means offering a great experience from the awareness stage, through the downloading process and finally through the post-download process.

The challenges of becoming a customer centric organization The power shift between brand and customer happened during the economic downturn. Customers became more selective in which brand they chose to spend their money with — The winning brands were the ones who treated their customers with respect, with great service, and built a relationship with them that still exists today.

And during the same time as the recession, social media marketing and social selling exploded onto the scene and mobile became a major part of the customer journey. Customers can now compare products and services in real time and across multiple devices, which has presented a huge challenge for many brands. Research has found that companies are struggling with this change and are unable to become a customer-centric organization — with the biggest challenge not being able to share customer information across departments.

Most companies do not have all of the components in place to claim they are customer centric, but the most important part to remember is this: You need to start with your customers, not your products and focus on what your customers want to do. Thus, the customer centric brand creates products, processes, policies and a culture that is designed to support customers with a great experience as they are working towards their goals.

The four best practices that stand out regarding customer-centricity are: Brands that are committed to customer centricity are passionate, and truly believe the customer comes first. Marketers inside customer-centric organizations understand what customers want, and use customer data to capture customer insights and share this across the organization. Brands that are committed to customer centricity focus on what the customer wants and needs, and develop products and services around that.

Brands that are committed to customer centricity analyze, plan and implement a carefully formulated customer strategy that focuses on creating and keeping profitable and loyal customer.

How to measure the success of a customer centric company? Not every organization will have the same customer metrics to measure customer centricity. However, the two most important customer centric metrics that should be carefully monitored are churn rate and customer lifetime value. Therefore, more companies are investing in keeping existing customers instead of trying to find new ones. To calculate the churn rate, measure the number of customers who left in the last 12 months divided by the average number of total customers during the same period.

Customer lifetime value CLV For a customer-centric business, the most valuable asset is the customer. The link between geodemographics and the postcode system added further sophistication to market segmentation. It became possible to process a file of postcoded customers and to directly infer from their addresses the types of neighbourhood that generated the highest level of business, whether in terms of the number of customers, the amount they spent or their propensity to download from particular product categories.

Profiling prospects by where they live The link between Census and postcode geography also played a major role in the upgrading of the services offered by door-to-door distributors. Before the late s, this form of distribution was used principally by retailers who needed to target customers within the vicinity of a particular new or existing store. With distribution now targetable by market segment, advertisers could achieve greater cost-effectiveness by targeting distribution at households known to be of a type that bought their products.

In due course, Direct Marketers also came to realize that the postcode field in the PAF could also be used to provide an effective index field for real-time access. This indexation was a key factor in enabling organizations to trace the history of previous transactions with customers who contacted them in real time over the telephone. Viewing the customer in the round In the early days of business automation, it was often the business processes of individual product divisions that were automated.

Even then, different processes in the customer cycle were often held independently on separate databases. Once these efficiencies had been achieved, there were many benefits to be gained — and not just by marketers — by linking together the information held on these different files in such a way as to form a single customer view. This integrated customer view often replaced the diverse set of views held by the divisions of the business that managed the promotion of individual products. Information was read, updated and stored offline in an electronic archive.

Towards a single view of the customer Initially, the decision to migrate towards a single customer view could be achieved using flat files stored on magnetic tapes. However, so long as one relied on this format, it was necessary to pre-accumulate event and transaction details and for these to be summarized at account level.

Summary data was typically recorded at points in time that corresponded to the billing cycle so that the value of spend, for example, would be calculated for each monthly cycle, and information on products bought would be stored at an aggregate level rather than at individual transaction level.

The s and s therefore saw the development of what came to be known as Customer Relationship Management CRM systems for storing, manipulating and accessing customer data in such a way so as to support a unified, integrated strategy for customer communications applied across all product divisions and all channels.

During the downturn of —, companies began to realize that their existing customer base held more value than their prospect pool. It was also realized that the use of cross- and up-selling to existing customers yielded a higher return on investment than seeking to achieve higher market share by aggressive new customer acquisition.

The cost of making a sale to a new customer was estimated to be between five and seven times as much as the cost of making a sale to an existing customer. Increasing customer retention rates by 5 per cent a year can increase customer value by between 25 and per cent, depending on the industry sector, according to Reichheld. The immediate impact of the financial pressures of the recession of the early s and these findings about customer value was the widespread introduction of customer loyalty marketing programmes that relied on creating a single view of the customer to aggregate their downloading activity and CRM systems to deliver loyalty benefits and rewards.

Direct Marketers forced to work with others The requirement for business to consolidate all information held about a customer within a single customer view was much more easily delivered using new relational database technologies that began to be promoted from onwards, not least by Oracle. However, this new technology and the fact that it linked together all different aspects of customer information often resulted in Direct Marketers losing control of operations through the centralization of computer processing, analytics capability and dedicated IT personnel.

Direct Marketers lose their independence As a result of this change in technology, the decisions that Direct Marketers were previously able to take independently now started to be taken within a more complex environment in which other factors had to be considered.

Decisions that were previously justified on the grounds of predicted mailing response now had to give due consideration to process handling capacity, stock levels, competitor activity, advertising, the creditworthiness of customers and so on.

Closer cooperation between DM and other corporate departments led to a re-evaluation of the criteria used to drive to optimization processes in computer modelling.

Thus, the use of simple RFM models was challenged by more complex predictive models. At a purely DM level, these were used to forecast the expected payback from individual campaigns, based on the likelihood to respond and predicted download value.

More sophisticated practitioners sought to predict the likely lifetime contribution to profits of recruiting a new customer, reflecting the discounted value of a new customer's future flow of transactions and resulting costs incurred over the lifetime of his or her account. This was an ambition that few marketers successfully realized.

Predictive modelling becomes more sophisticated RFM models were easy for managers to understand as they did not require statistical software or expertise and were straightforward to apply to customer data. Unlike the RFM approach, which describes past downloading behaviour and assumes that customers continue to behave in the same way, predictive techniques attempted to forecast future behaviour. By definition, RFM only incorporates three variables, whereas the inputs to predictive models are only limited by the number of variables recorded in the data and the processing power of the analytical software.

The increasing use of predictive models led to improved statistical techniques being introduced for modelling responses to DM campaigns. When practitioners started to apply predictive analytics widely, either to predict response propensities or the download values of responders, they realized that the complexities went beyond the choice of modelling technique. The advent of data mining A best practice process was required for developing and implementing models.

The activity became known as data mining, and the Cross Industry Standard Process guide 7 for data mining was launched in Nowadays, users have access to a wide range of systems and tools for data analysis and mining, including automated methods for building and deploying literally hundreds of models on a customer database. A review of the data mining process and the techniques currently available is provided by Leventhal.

This spawned much greater interest in the analysis of product download below the category level, for instance, using as predictive variables the colour, the country of origin and the grape variety of the wine that was downloadd rather than just the fact that it was wine that a customer bought.

Such analysis could only be undertaken using data mining tools that can search for patterns among a very large number of different variables and reduce a diverse set of customers to a limited set of intelligible market segments.

Clustering applied to transactional data Business analysts often found it beneficial, when tackling the analysis of a seemingly infinite number of product categories, to make use of tools for data reduction, in particular cluster analysis.

This technique, originally used to build geodemographic classifications, could in this context be used to organize customers into broad-brush behavioural groupings, according to the products that they downloadd.

This segmentation went beyond a simple set of groups — it was designed to segment consumers on their use of financial services and therefore was based on dimensions of known importance in this market: life stage, financial strength ie ability to download and product portfolio ie breadth of products used. Cluster analysis was applied in two different ways. At the lowest level of the segmentation, clustering was used to create groups of consumers with different patterns of financial product ownership — this was the product portfolio dimension of FRuitS and identified nine types of financial services holders.

The life stage and financial strength dimensions were specified by demographics such as age, marital status, presence of children, household income and total value of savings and investments. A fuller account of FRuitS development is given by Leventhal. As a result of these analyses, businesses began to appreciate the advantages of using specific events as triggers for direct communications to a customer rather than, as previously, organizing all communications on a monthly cycle of selections.

This shift from cyclical to event or trigger-based communications was facilitated by the move from maintaining data on flat files to the use of relational databases from which queries, as well as selections, could be conducted on an ad hoc basis. As the amount of data that Direct Marketers could access grew, the focus of segmentation shifted from the selection of the customers who should or should not be mailed to the optimization of the timing and content of customer communications.

As sales targets still applied at a product level, this resulted in the growing need for models for prioritizing and timetabling the different campaign mailings that should be sent to each individual customer during any particular period of time. This trend coincided with developments in printing technology that made it easier and much less expensive to create multiple selections of copy and imagery in a single run.

Previously prospects for different campaigns had to be separated out into separate files and the production of mailing material had to be undertaken segment by segment.

Changes in print technology and data management resulted in the development of what became known as mass customization or one-to-one marketing. Some believed that the shift from mass marketing to targeted marketing would eventually lead to the redundancy of segmentation and a regime in which every individual would be serviced in a manner reflecting his or her own unique characteristics. It may well be that there are some business areas where this prediction has come true — online marketing is theoretically capable of infinite variations — but achieving this goal has proved far harder than first imagined.

Recognizing the financial value of customer-contributed information The demand for ever greater personalization at this time spawned the development of a new class of business, such as CMT's National Shopper Survey and ICD's Consumer Lifestyle Survey, the purpose of which was to generate qualified names and addresses for list rental and customized couponing.

Some of these businesses acted as direct response media in their own right. Mailings to survey respondents would carry a variety of coupons, the value of which was driven by their earlier responses. For example, if you reported that you owned a large dog, not only would you receive a promotion for dog food, but the value on the voucher you received would be of a higher value than if you reported owning a small dog.

With increased consumer familiarity response rates declined to the point where the original business model was no longer profitable. However, the underlying concept of promotional coupons, personalized according to individual product preferences, continues to be used to great effect by companies selling a wide range of products to their consumers, as, for instance, with Tesco's Clubcard reward system.

An even more prescient insight of these businesses was the monetary value of information volunteered by individual consumers, a notion that underlies in different form the business models underpinning the success of Facebook, launched in , and Twitter, 2 years later.

How to Create a Customer Centric Strategy For Your Business

Before the advent of online communication, this content could not be collected and processed at a low enough cost for the business model to be effective. The shift from single to multi-channel operation During its early phase, DM was often perceived as a new channel enabling a new type of business to develop using a distinct form of distribution.

Some people, it was supposed, preferred to shop for certain products by mail order, some preferred telephone banking to high street banking, others preferred to download insurance door-to-door, others liked using mail order agents. These changing preferences reflected changing work and leisure patterns.

This thinking led to the launch in of Direct Line and First Direct, both providing a 24 hours telephone-only service. While some financial service providers set up their own telephone-based subsidiaries, others supplemented their high street presence by expanding their direct communications with customers from the mailing of monthly statements to that of product propositions.

Recipients were invited to respond either by telephone or via the branch. In the case of credit mailings, DM had the advantage over retail marketing in that applications for loans could be pre-approved. Segmentation could also be extended to the interest rate at which people could borrow or the upper limit of the value of the loan. The bank branch was restricted in the variety of tariffs that could be displayed visibly in the branch.

That made it difficult to price products on a customer-by-customer basis taking regard of likely differences in customer profitability. The opportunity to customize terms and prices based on information about individual customers has been greatly increased by the advent of online self-servicing systems. Therefore, banks were among the first to offer services on a multi-channel basis and increasingly sought to segment customers according to the channel preferred by the customer and the channel most likely to maximize profitability to the bank, reflecting the very different costs of servicing customers via different channels.

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The introduction of self-service During the s, the introduction of the Automated Telling Machine ATM provided an addition to the mix of channels offered, even if it was used for self-servicing rather than for direct selling. However, with interchangeability of cards and ATMs, and with the introduction of an increasing number of non-cash services eg mobile phone top-ups , the ATM has become one of the most important touch points for financial service customers.

This enabled businesses to reduce costs and prices, as low-cost airlines did, by introducing automation to the customer ordering process. Finding an appropriate role for the telephone With the growing use of telephones, particularly mobiles, and the declining cost of telephone charges relative to post, banks increasingly began to extend their use of telephone call centres to act as an additional channel for transactions, rather than just as a device for resolving customer service issues.

The growth of the call centre as a channel for transactions itself precipitated a series of technological innovations. These included systems for managing queues, the pre-recording of customer information and systems for directing calls to appropriate handlers using the telephone as a keypad.

Computer-assisted scripting systems enabled the way calls were handled to be more effectively and consistently controlled, scripting the different responses that could be given in different circumstances, enabling the recording of content for auditing purposes and providing real-time statistics on call centre performance.

Call centre management focuses on operations rather than segmentation The use of the call centre had added hugely to the volume of information that can be used for DM purposes. However, the analytics needed to make use of the data are very different from those with which DM analysts are familiar. It has not proved easy to integrate event-based data recorded at a call centre with information derived from other sources.

Applying customer segmentation in real time is also a major analytical challenge. As a general rule, the management of telephone call centres has focused less than other DM channels on improving business performance via better customer targeting and much more on optimizing the use of the operational staff or, in the case of outsourcing operations to other countries, reducing the average cost per call.

Call centres handle the most difficult-to-service enquiries In their early days, call centres were often used for handling direct response from TV advertisements.

The evolution of direct, data and digital marketing

The effectiveness of this strategy has been undermined by difficulties in managing fluctuations in peak volumes and their consequent impact on costs. Sharing the use of larger operations servicing multiple campaigns has proved logistically difficult. By contrast, fluctuations in peak volumes is not a problem for self-servicing channels, which is why an internet URL is taking over from a telephone number in the call to action.

As a result, these centres have become consigned either to outbound calling or to dealing with the residue of difficult-to-service enquiries that cannot be addressed using self-servicing channels. If they did, then commercial opportunities existed for different forms of direct organization based on direct mail, the telephone and the internet.

The prevailing view to the contrary was that that most consumers prefer to deal with organizations that offer a range of different channels for self-servicing, information and handling customer issues and that consumer preferences for different channels varies more according to circumstances than to any innate preference.

Most grocery retailers have recognized the necessity of offering an internet-sourced delivery service. Its role has evolved to become a source of expertise in multiple business processes spanning the processing of certain data types, the use of certain analytical techniques and in the application of certain types of segmentation strategy based on customer needs. Measurement — A traditional DM strength One consequence of this integration of DM into business as usual is that campaigns, whose effectiveness in a single-channel organization could be evaluated using a simple set of parameters, now need to be evaluated using more complicated metrics.

In the early days of DM, one of its advantages was that it was an ideal environment in which to build statistical evidence of what worked and what did not. Today, in order to evaluate the true impact of a retailer's marketing communications, it is necessary to measure not just the volume of direct response, but also the incremental value of additional downloads from the store, information that then needs to be linked back to the identity of the customers who were mailed.

This has resulted in the drive to capture information on the clickstream of existing customers and to relate their web visiting behaviour with information on their subsequent order downloads. Revisiting segmentation The introduction of the internet as a communications and sales channel was disruptive of established methods in that it offered a much lower cost communications channel than any previously used by Direct Marketers.

If it has virtually zero cost, one might suppose that there is no penalty for its wasteful use, in which case there is little need for the application of segmentation strategies required to ensure profitability from the use of higher-cost channels.

The internet — A zero-cost channel? Therefore, there is a role for segmentation in identifying and ensuring the relevance of communications to each target prospect or customer. Segmentation can, of course, be applied at a very fine level of detail in the download of keyword-based online display advertising and also in the manipulation of page-based search rankings, as well as to the use of social media. Although we can now treat each consumer as a digital island, unconnected to social and geographical neighbours, analytics is increasingly recognizing the predictive value of understanding the influence of both forms of network on attitudes and downloading decisions.

Thus, in addition to the content of social media being of predictive importance in its own right, social media provides an additional important source of analytic information in the form of the character of the contacts that a person communicates with.

Enriching customer records with locational information The internet also provides an alternative way of undertaking market research surveys. Of increasing importance in large organizations is the linkage of information from customer surveys with information held on a typical CRM database. Linking attitudinal information to behavioural data provides a much more rounded qualitative base for strategy development than the inference of attitudinal preferences based on transactional data or analysing survey data that is not linked to customer behaviour.

Examination of fast moving trends is also facilitated by the use of text mining to examine exchanges on Twitter. This has been used to understand public order issues see Indianapolis case study on pages — of this issue , forecast the outcome of elections and even to predict the outbreak of diseases.

Social networks can be mined for any data that is published publicly, that is where the information is available to all by default as the ten core variables of the Facebook Graph are or where the user has set privacy controls so that they are available to all.

To link other data elements to the CRM systems will involve the creation of APIs and often a commercial contract with the third party in question. The skills required to access, manipulate and interpret digital footprints from these new channels is a specialist activity currently largely disconnected from the experience of DM professionals.

The web as an enabler of direct business The web has transformed DM in numerous ways. The principal one of these is that it provides a much lower-cost channel both for the communication of content and for order processing than previous communications channels.

Compared with previous forms of outbound communication, the web appears to offer access to information at virtually zero cost — and email the opportunity to deliver messages almost free — and thus further intensifies the shift from marketing and selling through intermediaries to direct sales.Therefore, more companies are investing in keeping existing customers instead of trying to find new ones.

Customer lifetime value CLV For a customer-centric business, the most valuable asset is the customer. All those organizations that have asked me to monitor or participate in their digital mar- keting efforts — you learn more in an hour at the sharp end than you do in days of reading the theory. Includes bibliographical references and index. Previously prospects for different campaigns had to be separated out into separate files and the production of mailing material had to be undertaken segment by segment.