Analytics Strategic Insight Analytics and the Customer Journey

8/13/2019 Analytics Strategic Insight Analytics and the Customer Journey

1/12

MWD Advisorsis a specialist IT advisory firm which provides practical, independent industry insightsthat show how leaders create tangible business improvements from IT investments. We use oursignificant industry experience, acknowledged expertise, and a flexible approach to advise businesses on

IT architecture, integration, management, organisation and culture.

www.mwdadvisors.com

MWD Advisors 2011

Strategic InsightAnalytics and the customer journey:

driving greater loyalty and profitabilityHelena Schwenk

Premium Advisory ReportJanuary 2011

Delivering an exceptional customer experience isnt just about becoming more customer centric it also

involves maximising the opportunities that come from every interaction a customer has with yourbusiness over the duration of their relationship, throughout their customer journey. This StrategicInsight report examines why enhancing the customer experience is so vital to improving profitability andloyalty and how analytics can support critical junctures during a customers journey. It also outlinessome of common technical challenges faced by organisations implementing analytics and how keytechnological advancements and innovations are helping overcome some of the barriers to successfulcustomer experience management projects.

This report has been made available free of charge as an example of our premiumresearch reports

This report is published as part of the MWD Advisors Analytics and Information Management research program.It is an example of the wide range of reports available for download as part of the MWD Advisors personal,

team or enterprise membership plans, or can be purchased as an individual report download fromwww.mwdadvisors.com.

For further information about membership plans visithttp://www.mwdadvisors.com/ec/membership.php

MWD Advisors membershi lans: invest in smarter decisions

a d v i s o r s

mwd
http://www.mwdadvisors.com/http://www.mwdadvisors.com/http://www.mwdadvisors.com/ec/membership.phphttp://www.mwdadvisors.com/ec/membership.phphttp://www.mwdadvisors.com/ec/membership.phphttp://www.mwdadvisors.com/ec/membership.phphttp://www.mwdadvisors.com/


2/12

Analytics and the customer journey: driving greater loyalty andprofitability

2

MWD Advisors 2011

Summary

The link between a positive

customer experience, customerloyalty and ultimately a companysbottom line is growing stronger

Improved customer experience management is an imperative

today as the link between positive experiences and customerloyalty becomes more evident. It is generally agreed thatloyal customers are more cost effective to retain thanacquiring new ones, they are likely to purchase moreproducts and services from your company, are less prone toleaving and are more likely to refer your company to others.Enhancing the customer experience and the resultantimprovements in customer loyalty are seen as key to helpingdrive long term profitability for a company.

Organisations need to understandand optimise the customer journey

to deliver a truly excellentcustomer experience

Improving the customer experience is not only about beingcustomer centric, its about maximising the opportunitiesthat come from every interaction a customer has with your

business over the duration of the relationship. This concept,commonly referred to as a customer journey, involvesmapping the route customers take as they interact with yourcompany and highlighting where along the way improvementscan be made. Analytics play a key role in helpingorganisations optimise and support key junctures throughoutthis journey where the goal is to ensure that whatever typeof interaction or channel used, the customer receives aconsistent, personalised and compelling experience.

Leveraging BI and analytics within

an operational and analytic

environments is key

BI, analytic and information management technologiessupport key element of a customer experience management

solution. They provide an environment for deliveringactionable insights to employees (or applications) that guideor automate the decision making process throughout thecustomer journey. Each insight in turn provides anopportunity to enhance and improve individual customerinteractions in that way that drives greater loyalty andsatisfaction. BI and analytics help in two ways: firstly,conventional analytic stacks provide an environment forintegrating, aggregating and analysing large volumes ofhistorical customer information to support the planning andexecution of key customer experience decisions; secondly,operational analytics stacks make it possible to use analyticinsights to deliver personalised and relevant actions at the

point of action, which means they have to be integratedtightly with business processes and applications .

Advancements in technology and

deployment models are making BIand analytics easier and cheaper to

use, consume and deploy

Several technology advancements and innovations areimproving the potential of BI and analytics within customerexperience management projects. Pre-packaged analyticapplications and greater support for guided and automateddecision making are helping improve ease-of-use and supportfor operational analytics. In-memory, in-database analyticsand columnar database are helping reduce data latency inoperational analytic environments, whereas open source,appliances and cloud computing platforms are lowering some

of the traditionally high upfront costs of BI and analyticsoftware and hardware. Finally, unstructured data analytics isopening up rich sources of information that can be mined forgreater competitive advantage.


3/12


3

MWD Advisors 2011

Why the customer experience matters

Market changes and growing customer demands create new challengesIn the lead up to Christmas 2010, Europes snow and freezing temperatures keenly demonstratedhow the airline industry in particular has a lot to learn about ensuring a compelling customerexperience. During the severe weather many passengers across Europes airports experienced longdelays, cancellations or rescheduled flights during the busy holiday period. At Heathrowone ofEuropesbusiest airportsBritish Airways (BA) and BAA (the airline operator) appeared to come outof it particularly badly. With hardly any planes managing to take off from Heathrow during the peak ofthe weather chaos BA's share price slumped while BAA boss Colin Matthews was forced to publiclyapologise and face off accusations that he had under invested in anti-snow measures.

While the weather experienced in parts of Europe may be more of a one-off rather than a commonoccurrence it underlines the link between the customer experience and a companys reputation and

ultimately its bottom line. This link is becoming more evident especially in the context of the growinginfluence and sophistication of customers. A superior customer experience can not only drive sales,and improve customer satisfaction but is core to helping companies retain high value customers andimprove customer loyalty, all of which have an impact on the bottom line. Equally for thoseorganisations operating in mature and competitive markets, excelling at the customer experience canbecome a powerful way to build competitive advantage especially where their ability to differentiateon the basis of products and services is becoming increasingly unsustainable. Consequentlyorganisations find that to compete effectively across the board they have to consider broadercustomer experience issues.

Delivering a consistent and exceptional customer experience however is not only about putting thecustomer first; its also about maximising the opportunities that come from every interaction acustomer has with your company over the duration of the relationship, whether this interaction

happens prior to or during the sales process or equally during product delivery or customer support.Not only are todays customers more discerning and demanding than ever before, they are alsoutilising a greater number of touch points to communicate with a company whether this is in-store,online or via a mobile device. At the same time, smart connected customers using social media suchas micro blogging, online forums, chat, and video-sharing sites are able to spread news about theirexperiencesgood and badfar and wide in seconds.

Perhaps not surprisingly this proliferation of customer channels and social media increases anorganisations risk of not being able to deliver acompelling and joined up customer experience acrossall touch points and interactions. The challenge for organisations is ensuring they meet a customersneeds during each interaction whilst leveraging each channel (some of which are outside their control)to enhance the customer experience, fuel competitive advantage and deliver bottom line benefits. Inthe case of BA (as outlined in the introduction) one of the ways a better customer experience could

have been achieved was to communicate flight conditions to passengers more effectively and ensureits most loyal customers got the assistance they needed, whether this was via an online channel (suchas the companys website, Twitter or via a mobile device) or in person, by empowering front lineworkers with actionable information that enabled them to be more responsive to passenger demands.

The connection between customer experience, customer loyalty andincreased profitabilityGetting the customer experience right can of course reap significant rewards, especially when it leadsto greater loyalty and customer advocacy that in turn generates long term profits for a company. Inorder to be a market leader companies need customers who are ardent 'advocates'customers whoare highly loyal and drive new business to the company. Rather ironically the airline industry was oneof the early pioneers of loyalty marketing programmes when it introduced its frequent flyer schemes,designed to reward high value passengers with air miles that could be collected and redeemed for freetravel.


4/12


4

MWD Advisors 2011

The benefits of loyal customers are well understood. It is generally agreed that it is more costeffective to retain customers than to acquire them. Loyal customers are also likely to purchase moreproducts and services, cost less to serve, are less likely to switch to a competitor and often referyour company to others. In fact, loyalty guru Frederick Reichheld asserts that loyalty leaders grow, on

average, more than twice as fast as the industry average across a wide variety of industries1. In the UKfor example Tesco, the worlds third biggest retailer by sales, attributes its phenomenal growth andsuccess to its Clubcard customer loyalty scheme. Apart from the benefits its brings in incentivisingand creating loyalty amongst customers, the data collected by the scheme has been fundamental todeepening the companys understanding of its customer base, and for segmenting customers andtailoring products and services to specific groups or markets.

However there is a caveat to all of this: not all loyal customers are necessarily profitable ones. Toensure efforts are directed at the right customers, organisations need to be able to pinpoint whatmakes their customers loyal, and understand the profitability of each customer segment so they canimprove or cease relationships that are not proving profitable. Customer Lifetime Value (CLV) is acommonly used metric that helps provide a measure of how each customer is valued and can be usedto determine exactly how much an organisation should be willing to invest in acquiring or retaining

that customer. In short, companies can achieve greater profitability by increasing the lifetime value ofprofitable customers, all of which can be significantly improved upon through a compelling andrewarding customer experience.

1Loyalty Rules: How Todays Leaders Build Lasting Relationshipsby Frederick F. Reicheld (ISBN-10:1578512050)


5/12


5

MWD Advisors 2011

The customer journey and opportunities for

applying analyticsSo what exactly is the customer experience? A good way of thinking about the idea of customerexperience is:

The sum of all experiences a customer has with a supplier of goods or services,over the duration of their relationship.

The argument inherent in this viewpoint is that looking at customers experiences from theperspective of one-off transactions is only going to take you so far. What can appear to be a series ofone-off transactions from a suppliers perspective is actually part of a journey from the customersperspectiveand that journey can be intensely frustrating for customers, even if individualinteractions within that journey pass off without problems. In other words virtually every customertouch point or interactionwhether directly or indirectly contributes in some way to customer

perception, satisfaction, loyalty, and ultimately profitability.

The concept of a customer journey is a great place to start if you want to understand how anorganisation can leverage analytics to improve its customer experience. A customer journey, asshown in figure 1, can be seen as the series of touchpoints that a customer goes through as they seekto do business with your organisationbut its important to focus beyond a simp le sales cycle, and toput the customer at the centre. The customer journey is a well understood concept used bymarketing professionals. The idea is to map out the journey that different kinds of people will take asthey seek to do business with you, and look for points in that journey where the experience breaksdown from the customers perspective.

Figure 1: A simplistic view of a customer journey

Applying analytics as part of the customer journey

There is a close correlation between analytics and being customer centric as the former can play akey role in each phase of the customer journey as well as in the planning and implementation behindcustomer journey initiatives. In the context of the customer journey, analytics and BI can be used tounderpin core business functions, processes, interactions and touch points that impact on thecustomers journey. These include:

Investigate.This is when a customer or prospect is looking for a product or service that meetstheir needs. Analytics can become a key component for supporting this stage of the customerjourney by underpinning key activities such as:

Marketing campaign management.Supports the planning, testing, execution andmonitoring of highly tailored multi-media marketing campaigns to promote anorganisations product or service

Marketing optimisation.Uses forecasting and optimisation techniques to assess theright mix and level of investment afforded to advertising and promotions activities.

Investigate Purchase Deliver Use

Get support

Tell friends

Continue

or stop


6/12


6

MWD Advisors 2011

Acquiring customers.Using advanced analytics to help organisations accurately targetthe best and most profitable prospects for acquisition.

Survey and marketing analysis. Using text mining tools to understand about people,their thoughts, opinions and behaviours in order to better understand their productneeds and requirements

Purchase and deliver. This is when the customer or prospect purchases the goods orservices and takes delivery; analytics can support key areas of this capability such as:

Pricing optimisation. Use analytics to optimise the price offered at the right time inview of sales performance, competitor actions and new market opportunities.

Profiling and segmenting customers. Use advanced analytics to create customerprofiles or personas to better understand customerspreferences and identify thosecustomers who might be suitable for cross sell and upsell opportunities.

Market basket or product affinity analysis. Identifies products that sell together bylooking for trends in customer and product purchase data.

Sales analysis. Use BI and analytics organisations to understand the demand, cost andprofitability of products and services.

Calculating profitability. For customers, products and channels at the level wherecosts are incurred.

Continue or stop. This is when a customer decides to either continue using the product orservice or finish their association with that product or service. Analytics supports key elementsof the customer journey by enabling organisations for example to:

Retain customers. Historical data and data mining and predictive projections can beleveraged to assess the motivational and behavioural factors associated with customerchurn or defection and to provide insights into what can be done to prevent them fromleaving.

Calculate the next best offer. By mixing decision models and business rulesorganisations can determine how best to approach or service a customer prior to andduring an inbound interaction such as a request, complaint or an inquiry.

Get support. Customers and prospects can need help at any time, and expect to be able to getit even before theyve bought anything. Their ability to get the right supporting information

quickly may make an average experience into a stellar one, even if they dont buy in thatparticular instance. Analytics can support many aspects of this process by:

Utilising Customer Lifetime Value (CLV) calculations to dictate the appropriateprioritisation and level of service to apply to a customer interaction.

Tracking customer service and the analysis of unstructured data such as support logs andemails to improve the understanding of customersfrequently occurring question, issuesand complaints.

Tell friends. It might not be an integral part of the interactions a customer has with yourorganisation, but smart organisations are finding ways to take proactive roles in conversationsthat customers have with their peers online so they can influence the overall experience thecustomer has. Analytics supports this capability through:

Social media analytics.Using the analysis of unstructured text such as public forums,webchat, social network content and blogs to stay abreast of sentiment, put out fires anddemonstrate to customers that they care about their wants and needs.


7/12


7

MWD Advisors 2011

Social network analysis. Using advanced analytics to understand the value of acustomersinfluence in the context of your social community.

Understanding the past, the here and now, and the futureAs mentioned previously, BI and analytics can be used to model, measure, predict or provideactionable insights that enable organisations to deliver a superior customer experience. In order tounderstand how to deliver these insights, its worth differentiating between the various componentsof BI and analytics technology. Perhaps not surprisingly, CRM applications are a natural home foranalytics as they provide packaged capabilities for out-of-the box analytic models, template, reportsand dashboards. In this regard they provide a good entry point for applying analytics to the customerjourney as they are focused around specific customer processes and real time interactions. Howeverthey often lack analytic and data management depth and flexibility and this can prevent them frombecoming a one-stop-shop for customer experience analytics that offers a differentiated andpersonalised customer experience. If anything, CRM analytic applications should be complemented bythe conventional BI and analytic stack which consists of:

Foundational BI tools. Tools such as standard and adhoc reports and OLAP for querying,reporting, and comparative analysis capabilities.

Advanced analytics tools. Tools such as text, predictive and data mining tools are used touncover previously unknown trends and patterns in large volumes of structured andunstructured data. Equally recommendation and optimisation engines, business rules andsimulation techniques can be used to determine a set of alternative actions or automate partsof the decision making process.

Analytic applications. These combine domain expertise, business logic, and predefinedcontent (such as analytic models, templates, and reports) to address a particular businessissue. Examples are market basket analysis, marketing optimisation and churn reduction.

Data integration tools and data management platforms. Tools such as ETL, masterdata management, metadata management and data quality tools together with datawarehouses and data marts are responsible for structuring, cleansing and integrating data in aformat that is accessible for analysis.

Expanding focus to higher-value decision-making

Although lots of companies already use BI and basic analytics to help improve the customerexperience, organisations are increasingly looking beyond these basic capabilities to derive deeperinsight into their businesses to help create and sustain competitive advantage. Whereas BI hastraditionally been associated with rear-view analysissuch as analysing historical data and eventscompanies increasingly recognise the value of a more predictive and forward-looking approach.

Analytics differs from conventional BI because it is oriented towards knowledge discovery in whichthe relationships and patterns between different data points are unknown or not understood. Thefocus is on producing a solution that can generate useful insights, trends or predictions. In turn,analytics can help organisations ask more complex questions from their data such as what customerswill drive future growth?, who is likely to churn and why? and what types of customers are likely to

be interested in what products? This requires more sophisticated analysis methods such as datamining, predictive modelling and text analytics and in turn requires organisations to expand theirtraditional focus to identify new ways to target new or existing customers, anticipate, predict andunderstand customer behaviour.


8/12


8

MWD Advisors 2011

Bringing analytics into an operational environment at the point of impact

Both CRM applications and conventional BI and analytics architectures have an important role to playin supporting the customer experience as shown in figure 2. The analytics stack provides an

environment where a full range of unstructured and structured data analytics can be supportedtypically on an integrated set of data (such as a data warehouse). However the environment is nottypically geared for execution in operational situations where the data is volatile and real timeresponses are required. The need to bring deeper customer insights into the customer journey at thepoint of need is driving more analytics techniques to be embedded within the operationalenvironment. Embedding pre-built analytic models into business processes makes analytics moreaccessible as they can also be used to guide or automate decision-making in more consistent andrepeatable ways.

This is being made possible through a blended approach that typically uses the analytic environmentfor historical and trend analysis for building an analytic model and using customer interaction data tore-analyse and recommend the best strategy based on the course of a conversation. The output fromthe model is usually a recommendation for action based on a score and is used to guide the decision

making process. In this scenario the analysis needed to create the model is not performed in realtime, however when the model is deployed into the operational environment it runs against a singleset of customer data, which is often carried out in real time.

Figure 2: Meshing together operational and analytic environments

While some analytic models simply augment a human decision such as in the example above,sometimes the output can be used as part of an automated system that executes a decision.This issometimes commonly referred to as automated decision managementwhereby the results ofanalytic models are combined with business rules and operational data to deliver targeted decisions toother services or systems. The benefit of running automated decision logic in the background is thatbusinesses can speed up operational customer facing decisions and lower the requirements for(costly) human intervention. The rules-based nature of automated decision making means thetechnology is best suited to high volume operational decisions where both the problem and therelevant decision criteria are clearly defined and structured.

Data integration and information management

BI and analytic modelling

Customerfacing processes, applications and systems

Scoring and

recommendations

Automated

decision making

Analytic

applications

Web Mobile Social mediaIn-store

Analytical

Operational


9/12


9

MWD Advisors 2011

Beyond the theory: what does reality look like?

Common technical challenges and pitfalls to implementinganalytics as part of the customer journey

As mentioned previously BI and analytics has an important role in each phase of the customer journeyas well as in the planning and implementation behind each phase. While companies are becomingmore proficient and mature in their use of BI and analytics, they still struggle with the ideal ofdelivering a consistent, personalised and joined up customer experience throughout the customerjourney. Apart from the strategic and organisational choices that inevitably impact on the success ofsuch a project, there are wider technology issues such as data quality, complexity and lack ofspecialised skills that make the process of differentiating on the customer experience a challenge formany companies. In spite of the compelling reasons and potential value for investing in BI and analytictechnologies, many organisations find there are barriers to implementation that prevent them from

maximising its true potential and deploying analytics throughout the whole organisation. Some of themost common pitfalls to successful customer experience analytic projects include:

Cost. The high cost of buying, implementing and maintaining a BI or analytics tools has so farprevented adoption of these technologies on a mass scale. The most immediate obstacle tothis mass adoption comes from the high upfront cost of implementing the technology andsome of the pricing and licensing models employed by many of the vendors.

Lack of specialised analytic skills. As organisations continue to leverage analytics forhigher value customer interactions they often find that the skills required for preparing thedata, knowing what to look for in the data, making sense of that data and recognising relevantpatterns and trends within it are in short supply. In spite of the high demand for analyticcapabilities, the experience and skills need to make a customer experience project a successare often scarce and expensive. This is placing a premium on acquiring, developing, managingand deploying the right analytic talent within end user organisations.

Data integration and quality. Issues such as poor data quality, isolated data silos andmultiple versions of the truth contribute to lack of trust and confidence in customer datawhich can prevent many customer experience deployments from maximising their truepotential and ROI. Moreover, getting a single analytical view of the customer often becomesthe Holy Grail for large enterprises, especially those that sell products and services throughmultiple channels. The problem is by leveraging multiple channels (such as online, phone or instore), this often means multiple customer touchpoints which, from an IT perspective, meansdata is fragmented and strewn across multiple systems, applications and databases. Thismakes it much harder for organisations to service their customers and gain an understandingof their preferences, interactions and behaviour.

Complexity. Another potential pitfall is BI and analytic tools themselves. Though the toolsare more scalable and user-friendly than they used to be, they have still failed to penetratethe mainstream user base, many of whom still find it hard to use, navigate, and interpret theinformation they need to do their job successfully. Increasing both ease of use and improvingthe capability for self service is a core requirement for improving the adoption of BI andanalytic technologies.


10/12


10

MWD Advisors 2011

Hope for the future: lower technology costs, new innovations

Although the process of applying analytics throughout the customer journey can be complex andexpensive, advancements in technology and deployment models are making BI and analytics easier and

cheaper to use, consume and deploy. Similarly the requirement for improving the analysis of large andcomplex data sets as dictated by the high volumes of customer data processed by analytic solutions isdriving demand for better performing data management or data warehousing infrastructures. Severalnew and existing technologies are emerging that can lower the barrier to entry for those embarkingon a customer experience analytics project. In the following sections we look at examples of these.

Improving ease of use

Today organisations have a choice between buying a pre-packaged analytic application or building onefrom scratch using tools for statistical analysis and modelling. Today the general trend is veeringtowards buying pre-built packaged solutions that aim to reduce the complexity of building predictiveanalytic models and pre-packaged models, reports, and processes gleaned from predictive analyticbest practice or industry-proven practices. This trend is driven by the general trend in corporate IT

to reduce deployment time and costs and drive faster time to value from IT investments. Similarlyvendors are incorporating capabilities into their analytic workbenches which apply best practices andhelp automate parts of the model building process to lessen the dependency on analytic professionalssuch as statisticians and data miners.

Equally, embedding pre-built analytic models into customer-based business processes makes analyticsaccessible in the operational environment, where they can be used to inform and guide a call centreagent during a customer interaction. For example, certain words or responses routinely captured byagents could automatically trigger a cross-sell modelusing information derived from previous andthe current interactionsfor a more personalised recommendation. Moreover the capability toautomate elements of the decision making process by leveraging business rules and predictive analyticsbased on mathematical and statistical algorithms can be a useful technology to drive greater precisionand consistency for real time operational decisions.

Mining non-traditional data sources

New text and social media analytic applications are emerging to provide deeper customer insights inthe rich stream of unstructured data that is captured by companies in call logs, comments, email, webchats or social media discussions. These data sources are proving to be invaluable as a source ofintelligence and competitive advantage. Social media data in particular is a hot area of interest,especially as organisations look to tap new sources of online information such as Twitter feeds andblogs to understand how successful they have been in engaging with their customers, to understandtheir attitude and intent and to help understand how best to serve these customers in the future.However, social media analytics also raises new issues such as what social media sources to monitor,how to integrate these with other marketing efforts, as well as issues around privacy, data protection,governance and regulatory compliance.

Reducing data latency

The need to make decisions in faster timeframes in response to a customer event or shifting businesspriorities is creating demand for faster analytics processing at near real time speeds. The traditionalbatch-oriented analytics model can introduce latency as data is moved between transactional andanalytic databases and platforms. Subsequently this mode of operation can result in delays in thedecision making process or even worse cause decisions to be made on outdated or decayedinformation. New technologies are emerging to reduce delays inherent in this processing modelincluding:

In-memory computing. This provides business users with a fast and streamlined way toaccess and analyse information loads by making data available in-memory rather than theslower method of processing it on disk. This performance advantage allows users to look at

data on the fly, without the need to access pre-aggregated data, pre-built OLAP cubes or forIT to undertake specialist database tuning.


11/12


11

MWD Advisors 2011

Columnar analytic databases. These are becoming a fashionable way of storing data asthey process data by columns rather than by row which can improve response time and saveon disk space (due to its capability for data compressions). However the drawbacks ofcolumnar databases are that they are slow for write operations or updates, particularly when

new data has to be inserted into the columns. As such, they are particularly suitable foroperational/real-time environments, where there is a need for near real time updates.

In-database analytics. Thisrefers to embedding or pushing analytic functions, proceduresand algorithms within the database as opposed to having to analyse, process and model datain a separate staging area or analytic environment. This not only saves on storage costs butreduces the amount of data movement required and allows companies to take advantage ofthe availability of parallel processing capabilities of the source database to speed upprocessing times.

Reducing software and deployment costs

The rise of the open source languages R and Weka and the commercial open source offerings based

on them, are providing a lower cost alternative to proprietary offerings for modelling and statisticalanalysis, allowing companies to do predictive analytics on a lower budget. Similarly, appliances (pre-packaged software and hardware bundles) promise to shorten implementation times and lower costsas they require less tuning and configuration than traditional approaches so less time and money iswasted on assembling, configuring and optimising the hardware and software infrastructure.

Additionally, cloud computing is starting to offer a risk-free way to prototype and engage in analyticaldata warehousing and datamarts without having to invest and install IT infrastructure. The ability toget a high-performance analytic application up quickly (without waiting for hardware procurement)and to provision resources on-demand will continue to appeal to companies who want to start ortrial customer analytic technology as a way of lowering the risk and cost of a project. EquallySoftware-as-Service (SaaS) CRM analytic applications can provide a good entry point for the mostcommonly used customer analytics capabilities. However, although they are good at foundational BI

capabilities such as reporting and query they do tend to lack the depth and coverage of moreadvanced analytics such as predictive modelling and data mining.

Improving performance and scalability

Massive parallel processing (MPP) architectures can boost the performance of analytic systems bysplitting the processing of operations across a number of parallel-processing nodes running onclusters of commodity hardware where each node works on its own set of data. This is alsocommonly referred to as a share nothingarchitecture. MPP architectures and databases arebecoming increasingly common as a means to improve the processing performance and scalability ofanalytic systems on growing volumes of customer data at a lower cost compared with traditionalapproaches.

A newer development in parallel processing is the uptake of distributed computing frameworks such

as Hadoop and MapReduce. These distributed software frameworks offer significant benefits foranalysing and processing large amounts of data in parallel across large clusters of compute nodes in acost effective way. As such it has become a popular development area for vendors in the large scaleanalytic platform space.


12/12


12

MWD Advisors 2011

What should you do?

The organisational and process perspective

Organisations embarking on a customer experience initiative need to start by getting a complete viewof their existing customer interactions and touch points, together with the people and processes thatsupport them. This can then be used as a basis for identifying, measuring and understanding whichtouch points are essential for delivering a compelling customer experience and where any gaps orbroken parts exist. In turn companies may also need to rethink and optimise existing legacy systemsand processes that have until now been focused on the needs of the company rather than therequirements and needs of the customer. Above all its important to remember that acustomerexperience strategy needs to work across multiple business functions and silos from sales andmarketing, to customer service, finance and R&D. This inevitably means the success of any customerexperience initiative depends on senior executive leadership, solid management, a clear understandingby all of the benefits (and risks) of such a project and an organisationsreadiness for change.

The skills perspective

In addition those who are new to analytics or have thus far had a limited exposure need to pay carefulconsideration to how they source, retain and promote the highly skilled and specialist analyticprofessionals needed to make your customer experience initiative a success. These analytic skillsextend beyond traditional BI capabilities and involve being proficient in data selection and preparation,analytical algorithms, modelling, and data interpretation. In many cases sourcing the right people andskills can be problematic, which consequently makes recruitment expensive. Building an analyticalcompetency using in-house or professional services will be needed. Organisations that are new toanalytics or have limited experience should use consultants or the vendors professional services in

the first instance, using the opportunity to learn from the experts and undertake skills transfer andinternal staff training to promote analytical capabilities in house. Equally many companies can

complement in-house expertise by outsourcing analytic work to a vendor or system integrator. In agrowing number of case organisations are considering offshoring expertise to India and China wherethere are highly skilled and cheaper analytical resources.

The technology and deployment perspective

To succeed in offering a more satisfying and profitable customer experience, we believe there mustbe a strong focus on information management that provides an up-to-date, comprehensive view ofcustomers, covering all touch points, interactions, transactions and experiences. In this regard, thedevelopment of a data integration layer that provides clean, consistent, timely and complete customerdata is fast becoming a strategic imperative for organisations who want to pursue customerexperience initiatives. Arguably the greatest value of having a single view of a customer is delivering arobust foundation for leveraging analytics (for example predictive modelling or text mining) that

provides a deeper understanding of the preferences, attitudes and behaviour of the customer base soa company can better serve its customers. Organisations that are struggling with the performance oftheir analytics information management infrastructure should consider alternative architectures andprocessing models. In-database analytics, MPP architectures, in-memory and columnar databases canimprove the performance and reduce data latency within analytic deployments for resource andcomputational intensive functions like predictive analytics and data mining.

Similarly, packaged analytic applications, run on premise or as a managed service, do help addresssome of the complexities of analytic projects by providing packaged content and models that allowsorganisations to fast track some of the development process without huge investments in skills andtechnology infrastructure. Equally, alternative deployment models such as cloud computing, and opensource analytic packages offer a way for companies to lower software license, maintenance, andservice costs. However, while the lure of faster implementation times and lower total cost of

ownership may provide instant appeal, organisations should be aware that these modes of deploymentare still at a relatively early stage of maturity and are likely to complement rather than replace existingdata and tool infrastructures.

Documents

Analytics Strategic Insight Analytics and the Customer Journey