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TechRadar™: Customer Analytics Methods, Q1 2014by Srividya Sridharan, February 25, 2014

For: Customer Insights Professionals

Key TaKeaways

Myriad Methods Mystify Customer Insights ProfessionalsFrom descriptive to predictive methods, customer insights professionals can apply a wide array of established and emerging analytics methods to behavioral customer data. Customer insights professionals must carefully select the right portfolio of methods to elevate customer understanding.

Big Data supercharges established analytics MethodsEstablished methods such as behavioral customer segmentation and churn analysis have long informed acquisition, retention, and loyalty strategies. Now, big data analytics technologies and diverse data streams are extending their application, making it faster and easier to produce, and thus extending their adoption.

analytics Methods That Drive Contextual Understanding will Gain ImportanceAnalytics methods such as location analytics and device usage analysis that derive insight about the context around customer behavior will become increasingly important to provide personalized and relevant experiences to customers.

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© 2014, Forrester Research, Inc. All rights reserved. Unauthorized reproduction is strictly prohibited. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. Forrester®, Technographics®, Forrester Wave, RoleView, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. To purchase reprints of this document, please email [email protected]. For additional information, go to www.forrester.com.

For Customer InsIghts ProFessIonals

why ReaD ThIs RePoRT

Forrester has identified 15 key customer analytics methods that customer insights professionals must master. Customer insights professionals are faced with tough decisions about which combination of methods will have the biggest impact on marketing and customer experience goals. And new techniques continue to emerge as the complexity of customer data increases. This report of the customer analytics playbook uses Forrester’s TechRadar™ methodology to identify and analyze the current and future prospects of a broad range of statistical methods and predictive analytics techniques.

table of Contents

Customer analytics Is Critical In The age of The Customer

overview: TechRadar For Customer analytics Methods

Customer analytics aspires To Provide Contextual Insights

reCommendatIons

experiment with New Methods Before your Competitors Do

supplemental Material

notes & resources

Forrester interviewed several experts, customers, and vendors involved in customer analytics.

related research documents

the state of Customer analytics 2012august 8, 2012

how analytics drives Customer life-Cycle managementnovember 19, 2012

TechRadar™: Customer analytics Methods, Q1 2014road map: the Customer analytics Playbookby srividya sridharanwith shar VanBoskirk, tony Costa, Boris evelson, mike gualtieri, maxie schmidt-subramanian, allison smith, and samantha ngo

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techradar™: Customer analytics methods, Q1 2014 2

© 2014, Forrester Research, Inc. Reproduction Prohibited February 25, 2014

CUsToMeR aNalyTICs Is CRITICal IN The aGe oF The CUsToMeR

Customer obsession is nothing but a dream if you don’t have the customer analytics to drive it. No one knows this better than customer insights professionals. Many methods (15, to be exact) can be used by customer insights pros to understand why customers do what they do and predict future customer behavior, in real-time. By doing so, firms will differentiate themselves in the age of the customer.1 But the array of established and emerging analytics methods available makes it difficult for customer insights professionals to determine which analytics methods are most apt to help them maximize business value.

Blend a Cocktail of Versatile Methods To Gain Better Insight

Forrester believes that:

Customer analytics allows firms to analyze customer data to optimize customer decisions and use the analytical insight to design customer-focused programs and initiatives that drive acquisition, retention, cross-sell/upsell, loyalty, personalization, and contextual marketing.2

We have identified 15 customer analytics methods that fit this description. But these analytics methods shouldn’t be used in isolation. Customer insights professionals who want to use customer analytics to help their firms compete in the age of the customer should:

■ Map dependencies between customer analytics methods. The process for developing an advanced customer analytics function is as complex as the customer journey itself. Analytics methods are closely related and often feed off each other (see Figure 1). For example, customer churn analysis is an important ingredient of customer lifetime value analysis, which in turn is a mechanism to segment customers by value.

■ Recognize the power of blending customer analytics methods. No one method provides complete analysis of customer behavior. Applying multiple methods in concert can improve the accuracy of predictions. In statistics and machine learning, this is known as ensemble modeling, where multiple models are used to obtain better predictive performance than could be achieved with the individual models that the ensemble model is made from. Between 2006 and 2009, various contenders and eventual leaders of the $1 million Netflix prize used ensemble learning to improve the performance of the movie recommendation algorithm by more than 10%.3




Figure 1 Key Dependencies Exist Between Customer Analytics Methods

Source: Forrester Research, Inc.106141

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oVeRVIew: TeChRaDaR FoR CUsToMeR aNalyTICs MeThoDs

Forrester created a multistage research process to understand when and how customer insights professionals should apply 15 key customer analytics methods.4 We examined past research and interviewed several experts in the customer analytics ecosystem. We also conducted detailed research with multiple users of each method. We used this research to assess four factors: 1) the




current state of each customer analytics method; 2) each method’s potential impact on customers’ businesses; 3) how much time each method will need to reach the next stage of maturity; and 4) each method’s overall trajectory — from minimal success to significant success.5

how To Interpret The TechRadar Methodology For This Category

Forrester’s TechRadar methodology, originally created to assess the current and future prospects of technologies like business intelligence (BI) analytics technologies and customer relationship management (CRM) technologies, has been adapted in this report to analyze customer analysis methods. We recommend that as you use this TechRadar framework to plan investments, you acknowledge that:

■ Placement in an ecosystem phase does not indicate the method’s strength or weakness. Forrester’s TechRadar methodology outlines five major phases — Creation, Survival, Growth, Equilibrium, and Decline — in the development of an ecosystem that includes customers, end users, and vendors. Because the TechRadar methodology was created to assess the future of technology products, its five phases indicate how a technology progresses from creation in labs and early pilot projects to an increased installed base, and then declines as new technologies take its place. In this report, however, we use the phases to indicate the progression of a method’s development from early experimentation to gradual introduction, followed by growing adoption.6 Placement in a phase indicates the level of adoption and maturity of an analytics method, not which method is better than another.

■ Customer analytics methods do not decline completely. The TechRadar methodology was developed to indicate that technologies decline into obsolescence as other technologies take their place. But don’t presume that analytics methods will decline completely. Analytics methods will continue to be valuable even as they mature. For example, behavioral customer segmentation will not diminish, but rather evolve to support myriad data types and more sophisticated algorithms.7

■ Business value-add depends on the method’s applicability. This TechRadar analysis considers both inherent analysis capability of a method and how easy it is to make the method work at a company. Implementing and applying insights that an analytics method produces to improve customer treatments is a true test of the method’s adoption. For example, creating a customer propensity model predicting the propensity to buy a product as a second-time purchaser is fairly uncomplicated, but using the scores to target second-time buyers specifically when they walk into a store or anonymously visit the company website is an indication of how the analytics method is implemented.8




■ Specific industries use some methods more than others. The adoption of a method is a function of the data available for use. And data availability often varies by industry. For example, social network analysis is more applicable in the telecom vertical today because customer network data is available from call detail records (CDRs) that give information about which customers call each other frequently.

why Do These 15 Customer analytics Methods appear In The TechRadar?

The customer analytics methods included in this report were selected because they allow customer insights professionals to understand and predict customer behavior. In particular, each of these methods (see Figure 2):

■ Primarily uses behavioral customer data for analysis. This report focuses on descriptive and predictive customer analytics methods that rely on “customer-attributable,” behavioral data as the basis of analysis. We also included methods that enhance the context around behavioral data, such as customer satisfaction analysis and sentiment analysis.

■ Informs insights about customer relationships as opposed to marketing performance. We included methods that provide insights about customer relationships and what a customer does with respect to marketing offers, products, and experiences. We did not include methods that provide operational insights, like marketing mix modeling or marketing return on investment (ROI) analysis.

■ Receives attention from customer analytics practitioners. The methods addressed in this report also evoke interest from customer analytics practitioners; they are the ones our clients ask about most often in inquiries.

■ Uses statistical and data-mining techniques. To produce the methods featured in this report, customer insights professionals have to rely on techniques such as logistic regression, clustering, forecasting and optimization, survival modeling, and classification tree methods.




Figure 2 TechRadar™: Customer Analytics Methods, Q1 ’14 Methods Evaluated


Behavioral customer segmentation

Behavioral customer segmentation is the process of using behavioral customer data (such as product usage data, transactional data, web activity, marketing response data, and loyalty data) to create discrete, homogeneous subsets of customers. Typically, factor and cluster analysis is used to create the segments. Behavioral segmentation can be descriptive or predictive.

Behavioral customer segmentation has multiple applications across the customer life cycle. It is used in acquisition marketing to design targeted marketing campaigns across channels and also in retention marketing to drive pro�tability among existing customers.

Many types of vendors provide behavioral segmentation capabilities, including analytics software vendors, analytics software-as-a-service (SaaS) platforms, marketing service providers, and agencies. Marketing technology vendors specializing in marketing execution such as email service providers, and campaign management solutions also offer segmentation features.

Sample analytics software/SaaS vendors: Actuate, Acxiom, AgilOne, IBM, Pitney Bowes, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), SAS

Sample services vendors: Acxiom, Beyond the Arc, Epsilon, iKnowtion, Merkle, Rosetta, Targetbase

Behavioral segmentation can be produced by analytics software or through service providers. From a software perspective, the cost to produce is bundled into the pricing of analytics packaged solutions and data-mining software; segmentation is not priced separately. Cost of analytics software can vary widely, from a few thousand dollars to hundreds of thousands of dollars, depending on the deployment method (cloud licenses, on-premises) and pricing structure. From a services perspective, the cost to produce can be medium to high depending on the services partner, the scope and scale of behavioral data, the intended applications, the segment enterprise socialization plan, and the measurement and execution plan.

De�nition

Usage scenario

Vendors

Estimated cost to implement




Figure 2 TechRadar™: Customer Analytics Methods, Q1 ’14 Methods Evaluated (Cont.)


Cross-sell and upsell analysis

Cross-sell and upsell analysis is a method to evaluate which products should be offered in combination with others either as complementary or suggested products. The purpose of cross-sell and upsell is to drive additional revenue from current customer relationships. Cross-sell analysis is related to market-basket and product af�nity analysis in that it uses the association data-mining technique to understand relationships between product and offer entities. Cross-sell and upsell analysis is typically a descriptive analytics method; that is, it describes the relationship between products and product groups and the impact on pro�tability and revenue.

The output of cross-sell and upsell analysis is typically used to drive adoption across a portfolio of products or to develop new product bundles. Cross-sell and upsell can occur in customer inbound channels such as the contact center, website, bank branch, and kiosks or in direct outbound marketing channels such as email and direct mail.

Vendors offering cross-sell and upsell analysis range from software vendors to marketing service providers and analytics pure-plays.

Sample software vendors: Actuate, AgilOne, IBM, Oracle, Pitney Bowes, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), SAS, Tibco Spot�re

Sample services vendors: Acxiom, Epsilon, Merkle, Mu Sigma, Opera Solutions, Targetbase

Cross-sell and upsell analysis can be produced by analytics software or through service providers. From a software perspective, the cost to produce is bundled into the pricing of analytics packaged solutions and data-mining software; it is not priced separately. Cost of analytics software can vary widely from a few thousand dollars to hundreds of thousands of dollars depending on the deployment method (cloud licenses, on-premises) and pricing structure. From a services perspective, the cost to produce can be low to medium depending on the services partner, and the volume and complexity of the data sets.

De�nition

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Figure 2 TechRadar™: Customer Analytics Methods, Q1 ‘14 Methods Evaluated (Cont.)


Customer churn and attrition analysis

Customer churn and attrition analysis identi�es customers most likely to defect. In addition, this method helps pinpoint the timing of defection. Churn analysis can be performed using logistic regression, survival analysis, or hazard analysis and is inherently predictive in nature.

Industries with subscription-based models such as telecom, TV, Internet widely use churn and attrition analysis to understand customer defection. Churn is easy to spot when a customer is in a time-bound contract or billing cycle. But proxies such as reduction in account usage or low customer service engagement can be used in cases where expected customer tenure is not de�ned. One way to activate churn analysis through a marketing campaign is to use uplift modeling, which is an emerging technique to present offers and communications to those customers most likely to respond positively to a retention campaign, avoiding those customers likely to respond negatively or not react at all.

Vendors offering churn and attrition analysis range from software vendors to marketing service providers and analytics pure-plays.

Sample software vendors: Actuate, AgilOne, DataSong, FICO, IBM, Oracle, Pitney Bowes, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), SAS

Sample services vendors: Acxiom, Epsilon, Merkle, Mu Sigma, Opera Solutions, Targetbase

Customer churn and attrition analysis can be produced by analytics software and through service providers. From a software perspective, the cost to produce is bundled into the pricing of analytics packaged solutions and data-mining sof tware; churn modeling is not priced separately. Cost of analytics software can vary widely from a few thousand dollars to hundreds of thousands of dollars depending on the deployment method (licenses cloud, on-premises) and pricing structure. From a services perspective, the cost to produce can be medium to high depending on the implementation and activation scenarios, frequency of model maintenance, and customer data available.

De�nition

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Customer device usage analysis

This method uses customer data captured explicitly from devices (machine-generated data) that customers use (e.g., smartphones, wearables, telematic devices) to better understand product usage patterns and customer behavior. Sensor-laden devices, sometimes called the Internet of Things, have the ability to capture granular customer preference data.

For utility companies, smart meter data provides insights into energy consumption trends and can inform targeting of various customer segments with speci�c energy ef�ciency campaigns. For insurance companies, telematics data can inform personalized insurance rates based on motorists’ driving habits.

Few vendors specialize in device usage analytics today. Most analytics technology vendors like SAS or IBM can incorporate data from customer devices for analytical purposes.

The cost to produce this type of analysis can be high depending on the infrastructure needed to manage the data from the devices. Handling the sheer volume and variety of device data requires investments in big data technologies across data management, storage, computing, and analytic processing.

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Customer engagement analysis

This method focuses on how customers engage both actively and passively through various interaction points. This method is primarily a descriptive analysis method that describes the level, type, and drivers of customer engagement. Typical engagement metrics include website engagement, service interactions, content consumption, and registrations.

Customer engagement analysis can be used in multiple scenarios to drive understanding about how customers engage with channels, brands, and marketing interventions. For example, in a service context, customer engagement analysis is understanding transactional metrics such as cart abandonment or wait times. In a marketing context, customer engagement analysis involves understanding the level of involvement, interaction, intimacy, and in�uence an individual has with a brand over time. It can be used as a leading indicator of future engagement or a historical analysis of past engagement.

Many software and services vendors offer customer engagement analysis as part of their offerings. They either build custom algorithms, metrics, or indices on behalf of clients or use their proprietary methodologies to calculate customer engagement.Sample software vendors: IBM, SAP (Predictive Analysis), SASCustomer engagement agencies: Ansira, Epsilon, Rosetta, Targetbase, Wunderman

The cost to produce customer engagement analysis depends on how engagement is de�ned and what data points about the customers are considered as engagement triggers. For example, the cost can be low if engagement is restricted to online interactions on owned properties such as social pages, blogs, and websites. The cost can be high if customer engagement involves including all service transactions and other customer experience indicators.

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Customer journey and path analysis

This method combines advanced data-mining techniques, such as sequence mining, and time-series analysis on event-based customer data to identify patterns and sequence of customer behavior. A critical input to this analysis is customer journey mapping, which describes the customer’s path toward a certain action such as acquisition or attrition.

Customer journey and path analytics is currently used in a multichannel attribution context or understanding path to conversion on digital properties (traditional funnel analysis).

Cross-channel attribution vendors provide some capability to understand the customer journey, but more from a channel or touchpoint perspective. They are currently more focused on optimizing marketing return on investment (ROI) as opposed to strengthening customer relationships.

Sample cross-channel attribution vendors: Adobe, ClearSaleing, DataSong, Visual IQ

The cost to produce this analysis will be high because it requires the combination of multiple analytical methodologies to understand the overall customer journey (not just the path to purchase).

De�nition

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Customer lifetime value analysis

Customer lifetime value (CLV) analysis uncovers a customer’s potential and future monetary worth through the course of his or her relationship with a business. This analysis can be a historical measure as well as a predictive indicator of the health of a customer relationship.

Contribution-based or margin-based models like CLV help marketers create value across the customer life cycle to target customers based on value potential, not just propensity to buy or revenue-generating potential. For example, CLV can help marketers decide to increase investments in channels that consistently deliver them customers with high CLV. CLV also informs creating a differentiated approach for retaining different customer segments. That is, marketers may spend more to cultivate loyalty in high CLV customers through loyalty programs.

Vendors offering customer lifetime value analysis range from software vendors to marketing service providers and analytics pure-plays.

Sample software vendors: Actuate, AgilOne, FICO, IBM, Pitney Bowes, Oracle, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), SAS

Sample services vendors: Acxiom, Epsilon, iKnowtion, Merkle, Mu Sigma, Opera Solutions, Targetbase

The cost to conduct customer lifetime value analysis is high because it is an iterative process where challenges — such as attributing pro�t to a customer over his or her entire lifetime or forecasting lifetime value based on past behavior — often occur at each step.

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Customer location analysis

Location analysis uses visual mapping of location and customer behavioral data — derived from data sources like global positioning systems (GPS), iBeacon indoor positioning data or social check-ins — to illustrate when and where �rms should present an offer or experience to a customer both outside and inside a venue.

Customer location analysis �nds use in location-based marketing where marketers use location-sensitive data from mobile devices to present contextual offers in or near the location of interest (e.g., retail store, mall, etc.). Customer location analysis also �nds use in indoor tracking of customer movement, such as within a retail store to track shopping trip activity. Location analysis can use aggregated data on groups of customers or attributable data to a customer depending on the availability of privacy-compliant data.

Analytics technology vendors that speci�cally provide location analytics include Alteryx, Esri, Euclid Analytics, Path Intelligence, Pitney Bowes, SAP (In�niteInsight)

The cost to perform customer location analysis is medium to high depending on the availability of privacy-compliant location data, mapping software, and mobile apps needed to activate location marketing.

De�nition

Usage scenario


Customer look-alike targeting

Customer look-alike targeting is a method to identify prospects that exhibit characteristics similar to existing valuable customers with the aim of effectively targeting the prospects most likely to convert as customers.

Customer look-alike targeting is primarily used in acquisition campaigns to assign scores to prospects according to their likelihood to purchase a product. In the context of acquisition, the propensity to buy signals a likely conversion of a prospect to a customer. Look-alike targeting is also used in online advertising and ad targeting to identify relevant new audiences that match current customers.

Vendors offering customer look-alike targeting range from software vendors to marketing service providers and analytics pure-plays.

Sample software vendors: Acxiom, IBM, Revolution Analytics, SAP (In�niteInsight), SAS

Sample services vendors: Acxiom, Epsilon, Merkle, Mu Sigma, Targetbase

The cost to produce customer look-alike targeting models is low to medium — similar to that for producing propensity models.

De�nition

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Vendors






Customer propensity analysis

Customer propensity analysis derives individual customer probabilities — the likelihood that a customer will behave in a certain way, such as respond to an offer, purchase a product, or exit a relationship — from a range of behavioral and campaign data. In its simplest forms, propensity analysis uses logistic regression to derive the probabilities.

Propensity models are used for improving the performance of marketing campaigns and also in acquisition campaigns to assign scores to prospects according to their likelihood to take a desired action. These models can also identify the most likely additional product that a customer would purchase out of a set of products offered. One variation of propensity modeling is uplift (also known as true lift or net lift) modeling, which is an emerging technique where instead of identifying who will respond, the technique identi�es which customers will positively respond to a campaign or offer.

Vendors offering customer propensity analysis range from software vendors to marketing service providers and analytics pure-plays.

Sample software vendors: Actuate, Acxiom, IBM, FICO, Pitney Bowes, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), SAS, Tibco Spot�re

Sample services vendors: Acxiom, Beyond the Arc, DataSong, Epsilon, Merkle, Targetbase

Depending on the complexity of variables that will be predicted using a propensity model, the cost to produce customer propensity models can be reasonably low, especially if a simple logistic regression approach is used.

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Customer satisfaction analysis

Customer satisfaction analysis uses data from surveys, feedback forms, and customer service interactions to identify customer satisfaction with a particular experience. Any voice of customer data can be analyzed to understand satisfaction.

Customer satisfaction analysis gives �rms a pulse on how satis�ed customers are with various interactions, which is typically based on a comparison between customer expectations and perceptions. Many �rms use satisfaction scores and metrics to re�ne the service experience and improve overall customer experience.

Different types of vendors support customer satisfaction analysis, from enterprise feedback management systems to social media platforms.

Sample vendors: Allegiance Software, Beyond the Arc, Medallia, Satmetrix Systems, Vision Critical

The cost to perform customer satisfaction analysis is in the low to medium range. Costs include �elding the customer survey, collating results, and providing results over a period of time. The cost is also heavily dependent on the data integration efforts involved in bringing together survey data, customer service interaction data from various channels, and integrating into a CRM system.

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Next-best-action analysis

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Also known as next logical product or next best action, next-best-action analysis uses historical data to predict the product or offer that will drive a customer to take a desired subsequent action. Next-best-action methods combine the power of predictive models with recommendation engines to present context-sensitive offers across multiple customer relationship and interaction channels.

Implementing next best action often, but not always, involves deploying predictive models within recommendation engines. These engines automatically generate agile, tailored, and context-sensitive recommendations, including next best offers, across multiple interaction channels to guide decisions and actions taken by humans and/or automated systems.

Vendors offering next-best-action capabilities include software vendors and marketing and analytics service providers.

Sample software vendors: IBM, FICO, Pegasystems, Pitney Bowes, Oracle, SAP (In�niteInsights), SAP (Predictive Analysis), SAS

Sample services vendors: Acxiom, Epsilon, Merkle

The cost to implement a next-best-action program is high given the signi�cant implementation and integration needed between analytics production systems and marketing execution technologies.






Product affinity and recommendation analysis

Product af�nity and recommendation analysis leverages product purchase data to uncover hidden patterns and correlations between products in order to identify those that are often purchased together. This method typically guides next-best-action recommendations and is also the foundation for cross-sell and upsell analysis.

Popularized by Net�ix and Amazon.com-type experiences, this method is used both online (in eCommerce sites) as well as of�ine (in retail stores) to understand what products customers buy together. Traditionally known as market basket analysis, it has evolved to include online product recommendations for applications in the eCommerce context.

Vendors offering product af�nity and recommendation analysis include analytics software vendors, recommendation technologies, and marketing and analytics service providers.

Sample technology vendors: Baynote, IBM, Pitney Bowes, RichRelevance, SAP (In�niteInsight), SAS

Sample services vendors: Acxiom, Epsilon, Merkle

The cost to produce this analysis is low to medium, as it utilize algorithms that are standard and therefore lower cost.

De�nition

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Sentiment analysis

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Sentiment analysis uses unstructured data (typically text) from internal and external sources such as call center transcripts, feedback forms, social data, and surveys to extract sentiment behind customer comments. This method uses text analytics and natural language processing techniques to derive positive, neutral, and negative sentiment.

Sentiment analysis can be used in multiple scenarios from brand tracking and social media customer service monitoring to voice of customer analysis.

Sentiment analysis can be provided by text analytics technology vendors, analytics and data-mining software with text analysis capabilities, and social listening platforms.

Analytics and business intelligence vendors: Actuate, Alteryx, IBM, SAP (Predictive Analysis), SAS

Text analytics vendors: Attensity, Clarabridge, Lexalytics

Social listening platforms: Converseon, Synthesio

The cost to produce sentiment analysis is in the low to medium range. With a text analytics tool, users can derive basic sentiment about conversations and text quite easily.







CUsToMeR aNalyTICs asPIRes To PRoVIDe CoNTexTUal INsIGhTs

The 15 customer analytics methods we evaluated differ widely with respect to current adoption and the speed at which they will be adopted. In mapping the futures of customer analytics methods, we found that (see Figure 3):

■ Established methods fall into the Growth phase. More well-known analytics methods like behavioral customer segmentation and customer churn and attrition analysis will gain more adoption because of their potential to use enhanced customer data — like cross-channel interaction and engagement data. For example, as a brand-focused mass marketer, a global beauty care company struggled to establish a one-to-one relationship with its customers. To overcome this, the company implemented a personalization engine and next-best-product program that leveraged a multidimensional segmentation model from both online and offline data. This type of segmentation was not possible earlier due to the lack of granular interaction-level customer data from its website and email programs.

■ Methods that drive contextual insights are in early stages. Emerging methods such as sentiment analysis, location analysis, and device usage analysis are in early stages of development, but they have the potential to provide valuable context around behavior and other customer analytics methods. We expect firms that need insights like “why” or “where,” beyond


Social network analysis

This method is a branch of statistical modeling that mines behaviorally expressed connections, relationships, and af�nities. Traditional customer analytics models the behaviors of individual customers, but this method uses statistical graph analysis to search for complex patterns of in�uence and expertise among networks of customers.

This method is ideal for organizations where customer-to-customer interactions have a signi�cant impact on overall brand relationships (such as telecom providers, where customers fall naturally into a network).

Software vendors offering social network analysis capabilities include Actuate, IBM, FICO, Revolution Analytics, SAP (In�niteInsight), SAP (Predictive Analysis), and SAS.

Social network analysis can be produced by analytics software where the cost to produce is bundled into the pricing of analytics packaged solutions and data-mining software. Cost of analytics software can vary widely from a few thousand dollars to hundreds of thousands of dollars depending on the deployment method (cloud licenses, on-premises) and pricing structure. The cost to produce social network analysis can be medium to high depending on the complexity of algorithms used, the granularity of the data sets, and model production parameters.

De�nition

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just “what,” to experiment with these methods.9 This type of contextual understanding is needed to uncover nonobvious insights about customers. For instance, a mobile app can detect location near a store and serve a contextual offer for a sale that is ongoing in the store. This scenario would use geolocation analysis and the customer’s precalculated propensity to shop in-store to derive the customer’s in-the-moment likelihood of walking into the store.

■ Methods that drive personalization will enjoy significant success. Analytical methods that drive personalization, such as next-best-offer analytics, will enjoy more success because they fuel implementation technologies like recommendation engines, testing, and optimization tools or real-time interaction management technologies that power real-time, one-to-one interactions.

Figure 3 TechRadar™: Customer Analytics Methods, Q1 ’14


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Creation: Uncovering Purchase Drivers Is Not The only Goal of analytics

Creation phase customer analytics methods are not fully developed from a methodology standpoint, as data availability hinders widespread use. Customer analytics methods that provide insights about what drives product purchase are readily available, but as the lines between products, devices, and experiences blur, analytics methods such as device usage analysis and customer path analysis that uncover implicit consumer consumption patterns and experiences will emerge. Managing this type of granular data at scale needs big data approaches to handle both volume and low latency requirements. Forrester placed the following two customer analysis methods in this phase (see Figure 4):

■ Customer device usage analysis. Behavioral data emanating from sensor-laden devices like fitness-tracking devices and smart meters is not yet valued for marketing purposes.10 So the maturity and adoption of analytics methods that rely on it are still their infancy. We see some early examples of companies like Disney using device data from radio frequency identification (RFID)-enabled MagicBands to influence and predict the guest experience.

■ Customer journey and path analysis. Using path analysis and sequence-mining techniques to understand the whole customer journey is still a work-in-progress, even though understanding the path-to-purchase across digital and nondigital touchpoints is a popular goal today. For instance, credit card companies use complex event processing and streaming analytics to instantly uncover fraudulent behavior when customers deviate from a known transaction path. Similarly, in a marketing and customer experience context, companies could use customer journey and path analysis to understand desired customer paths and then trigger interactions in real-time when the customer falls off this path. Today, multichannel attribution understands channel paths and informs marketing investment choices, but tomorrow adapting this measurement approach to make it more customer journey oriented is a real possibility.11






Customer journey and path analytics is an ensemble method that combines the best from multiple analytics methods like sequence mining, time-series analysis, and path to conversion. But executing on the results requires a real-time streaming infrastructure such as what’s available in a complex-event processing technology.

Negative. At this stage, the business value add of this emerging method is unknown, although it has the potential to provide valuable insights about why customers follow a certain sequence of events in their relationship with a brand.

1 to 3 years. Using analytics to understand and predict the customer journey will become a critical capability to build for customer-obsessed companies in the next three years.

Moderate success. Customer journey and path analytics requires signi�cant development across multiple disciplines and capabilities such as attribution, path to conversion, and customer journey mapping to become a standalone method. Right now, �rms can access parts of customer journey and path analysis but cannot apply this method as a holistic measurement and analytics approach.

Why the creation phase?

Business value-add, adjusted for uncertainty

Time to reach next phase

Trajectory (known or prospective)


Customer device usage analysis is still at an early stage of maturity. Currently, it is only applicable in a few verticals that have the ability to collect customer data through varied devices such as wearables, telematics devices, and smart meters. And best practices for data usability, management, storage, and consumer privacy have not yet been established.

Negative. This method is largely descriptive; it describes historical behavior through data collected from a connected device. As a result, it is not valuable as a standalone customer analytics method.

3 to 5 years. This method is in its infancy and will take three to �ve years to bear fruit as a valued customer analytics method and reach the survival stage. This is because organizations dealing with any kind of device data �rst need to become mature in governing the data and safeguarding consumer privacy before using the data for analytics purposes.

Minimal success. This method is largely unproven even though it has the potential to provide contextual customer data such as location data, preference data, or af�nity data.

Why the Creation phase?




Figure 4 TechRadar™: Creation Phase Technologies




survival: Piggybacking with established Methods will Create Momentum

Early adopters already apply survival phase customer analytics methods. But these methods will gain momentum only if combined with more established methods. Three methods placed in this phase (see Figure 5):

■ Customer engagement analysis. Customer engagement analysis identifies the volume, quality, and type of multichannel interactions that would result in a highly engaged customer. To move this method from a nice-to-have to a needed outcome-based method, it must be combined with other analysis methods that track the impact of interactions on indicators such as purchase or response. Demand for customer engagement products and services is hot. But mainstream adoption of customer engagement analysis will only happen when companies can reconcile a universal definition of customer engagement for their firm.

■ Customer location analysis. Adding the dimension of “where” to customer analytics automatically opens up possibilities for marketers to become more contextual and personalized with offers and products. With data collected from location-aware devices, companies can present contextual offers in or near a location of interest, such as a store or mall.12 Location analysis also has wide applications to transform customer experience and will become a must-have to design, manage, and measure offline experiences.13 Broader adoption of location analysis will depend on the ability to process and analyze customer location data in a privacy-compliant manner as well as use attributable location data with other predictive analytic methods, such as next-best-offer analysis and recommendation analysis.

■ Sentiment analysis. Though popular today, sentiment analysis is still hard to do accurately. Its adoption is hobbled by two forces. First, customer insights professionals should apply it not just to social media data but also to text-heavy customer data sources such as voice of customer data, attributable customer conversation in social media sources, and call center transcripts. This will provide an assessment of the nature of customer comments and feedback. Second, sentiment analysis is an aggregate, descriptive analysis method that makes it challenging to integrate into customer- or transaction-level predictive modeling. Examples of transaction-level integration into traditional customer analysis methods such as churn analysis are few and far between.




Figure 5 TechRadar™: Survival Phase Technologies


Customer engagement analysis

Customer engagement analysis is in its early stages of maturity and usage because of the challenges organizations face to de�ne and agree on what drives customer engagement. Our research indicates that customer engagement is one of the least adopted performance measurement metrics among customer analytics professionals.

Low. Customer engagement analysis provides insights to other customer analytics methods such as segmentation, but as a standalone method it does not add much predictive value. It is more a marketing measurement and effectiveness method than a customer analysis method, as it observes changes in customer engagement as a result of marketing activities.

3 to 5 years. It will take customer engagement analysis three to �ve years to become a viable customer analytics method and reach the growth phase. This is because organizations now have access to more granular customer data as a starting point to uncover triggers and drivers of engagement.

Moderate success. Reconciling the de�nition of customer engagement across the organization will hinder the development of this analytics method.

Why the Survival phase?




Customer location analysis

The maturity of this analytics method is still in its infancy. But Forrester expects it will develop quickly as location-sensitive mobile devices proliferate and data processing power increases. Analysis of aggregate location data is mature and is already widely used within BI platforms and for in-venue analysis for retail, but using customer-based location analysis is untapped.

Low. Customer location analysis is not as valuable in isolation as it is when combined with other analytics techniques such as recommendation analysis and next-best-offer analysis.

1 to 3 years. Customer location analysis adds the dimension of location to traditional customer behavioral analysis. It will rapidly evolve to become a mainstream analytics method in the next one to three years because it provides valuable insights into the customer context and will mirror the success of web analytics as a means of analyzing digital data.

Significant success. Customer location analysis will enjoy signi�cant success if performed in conjunction with other customer analytics methods that reveal individuals’ in-the-moment propensities.








Figure 5 TechRadar™: Survival Phase Technologies (Cont.)

Growth: Big Data will spur Core Methods To Reach Their Potential

Half of the methods in the Growth phase — behavioral customer segmentation, churn analysis, and lifetime value analysis — are more established customer analytics methods. The other half are newer methods that are developing into critical ones. Forrester placed the following six customer analysis methods in the Growth phase (see Figure 6):

■ Behavioral customer segmentation. This method — one of the oldest in the customer analytics toolkit — is widely adopted but poorly implemented.14 It remains a firm’s first foray into the analytics journey and can be viewed as the bellwether method for future analytics maturity. It has a fairly mature ecosystem of providers helping firms with their segmentation needs. As organizations deal more with in-motion data, static segmentation schemes will become less useful and more on-the-fly segmentation algorithms will emerge. This will infuse new life into a well-adopted but poorly leveraged method.

■ Customer churn and attrition analysis. Churn and attrition analysis is a core component of many retention marketing programs. Enhancements in understanding when customers leave and why they leave will keep this method relevant and required, especially in subscription-based verticals like cable and telecom. We also expect customer churn and attrition analysis to become more relevant over time because it influences retention rates.


Sentiment analysis

Sentiment analysis as a customer analytics method is still largely untapped. While it is widely used for brand tracking and social listening, sentiment analysis is not yet applied to internal customer data sources such as call center transcripts or live customer interactions in a branch or store.

Low. Sentiment analysis is a descriptive and aggregate analysis method where its value is limited to general �ndings about the sentiment from customer comments and posts that may not be applicable at the individual customer level.

3 to 5 years. Sentiment analysis will take several years to evolve to the next maturity stage because the accuracy of analysis methods varies dramatically today.

Moderate success. This method is directional in providing customer-level insights and not immensely useful for activating individual customers or driving relevant interactions. It will enjoy only moderate success upon maturity because it cannot tie back to business results or metrics.








■ Customer lifetime value analysis. Your chief financial officer loves this analytics method because it helps prove the value of marketing in financial terms. Customer lifetime value analysis is complicated to develop and apply, as customer attributes constantly change. But with the growth of predictive analytics, it has the potential to become a forward-looking indicator of the health of customer relationships because of the ability to forecast future revenue streams (both tangible and intangible) from an individual customer.

■ Next-best-action analysis. This method has become increasingly important to activate customer recommendations in key interaction channels such as the website and call center, even though it originated as an enabler of offer optimization. We expect next best action to become better adopted as firms mature their big data capabilities and as vendors develop packaged solutions that specifically help marketers deliver effective recommendations across touchpoints.

■ Product affinity and recommendation analysis. With its roots in market-basket analysis, product affinity and recommendation analysis has become the poster-child for predictive analytics. It is widely used in multiple verticals to drive average revenue and product adoption per customer. We expect that this method will be the glue that stitches together various personalization-focused methods — such as next-best-action and cross-sell analysis — because it transforms backward-looking association analysis into more predictive insights about what to recommend to a customer.

■ Social network analysis. Most customer analytics methods focus on understanding and predicting individual customer behavior in isolation from the customer’s network of influence. But social relationships influence individual actions. While social network analysis is a significant step in analytics sophistication to understand group behavior and the role of influencers, it is currently only applicable in scenarios where natural networks of customers exist, such as in the telecom or media industries. This will limit its widespread adoption, but it will gain momentum quickly as emerging database technologies now allow processing of complex customer graph and network data.15




Figure 6 TechRadar™: Growth Phase Technologies



Behavioral segmentation will continue to be a foundational customer analytics methodology for understanding and predicting customer behavior.

Medium. The combination of behavioral segmentation with attitudinal, value-based, and psychographic segmentation has a bigger impact from a business-value-add and insights perspective than if behavioral segmentation is used in isolation.

5 to 10 years. To a large extent, the statistical sophistication of segmentation will not change dramatically, but the availability of granular behavioral customer data will. As a result, behavioral segmentation will continue to grow in adoption within �rms, and it will take several years to reach the equilibrium phase.

Moderate success. Enterprise adoption and the ability to execute remain a challenge to the widespread adoption of behavioral segmentation. While the output of the segmentation analysis is insightful, the success of the effort is determined by its adoption by various internal business stakeholders and how well these stakeholders can take action on segmentation efforts.

Why the Growth phase?







Figure 6 TechRadar™: Growth Phase Technologies (Cont.)


Customer churn and attrition analysis

Customer churn and attrition analysis can be produced by analytics software and through service providers. From a software perspective, the cost to produce is bundled into the pricing of analytics packaged solutions and data-mining software; churn modeling is not priced separately. Cost of analytics software can vary widely from a few thousand dollars to hundreds of thousands of dollars depending on the deployment method (licenses cloud, on-premises) and pricing structure. From a services perspective, the cost to produce can be medium to high depending on the implementation and activation scenarios, frequency of model maintenance, and customer data available.

High. Churn and attrition analysis is highly valuable for organizations committed to pro�table customer retention. This analytics method helps marketers determine what level of effort will be required to keep which customers. For instance, using customer lifetime value in conjunction with churn analysis can address whether it is worth spending marketing dollars on retaining certain customers or not.

3 to 5 years. Churn and attrition analysis, although a fairly adopted method in certain subscription-based verticals, still only provides insight into “who” will churn and not “why.” The “why” of customer churn is determined through other methods such as sentiment analysis on voice of customer data. By combining the “who” with the “why,” organizations will mature churn and attrition analysis within the next three to �ve years.

Significant success. Churn and attrition analysis is an important tool to tackle customer attrition and increase loyalty because a small percentage of increase in retention can drive a signi�cant rise in revenues or pro�ts. And churn analysis can be directly linked to business metrics such as retention rate and revenue protection. For example, when empowered with information about the customer’s churn probability score, a call center agent goaled on driving “customer saves” has the ability to present a relevant retention-based offer to the customer.











While the method itself has been around for many years, it continues to be relevant today to understand how customers can generate value in the present and future. The widespread adoption of predictive analytics will accelerate the development of actionable CLV models over the next few years.

High. This method is highly valuable because it is one of the only �nancial metrics in marketing that takes into consideration costs to serve customers as well as the resulting value and revenue that customers generate.

3 to 5 years. The meaning of customer value is constantly changing. While value traditionally meant revenue, sales, and pro�ts, the notion of value now includes softer dimensions like in�uence or af�nity. As a result, this method will evolve to include intangible value as part of overall customer value that the customer generates for the company.

Significant success. The meaning of customer value is constantly changing. While value traditionally meant revenue, sales, and pro�ts, the notion of value now includes softer dimensions like in�uence or af�nity. As a result, this method will evolve to include intangible value as part of overall customer value that the customer generates for the company.






Next best action requires the orchestration of multichannel, customer-facing business processes through offers and interactions and is an emerging as a way to deliver relevant, contextual experiences to customers.

High. Next-best-action analysis is a mechanism where all the predictive customer analytics methods come to life at the various points of customer interaction such as point of sale, contact center, websites, etc. With next best action, each of these interactions is enhanced from a predictive standpoint, and the opportunity to provide highly relevant experiences to customers is enhanced. This results in high business value, as it makes analytics useful when it’s needed the most.

1 to 3 years. Market adoption of next-best-action analysis is growing rapidly as companies focus efforts on anticipating customer actions through relevant products, offers, and experiences.

Significant success. Next-best-action analysis will start to play a signi�cant role in customer analytics as the focus shifts from producing analytical insights to activating relevant insights at the point of customer interaction.










Product affinity and recommendation analysis

Product af�nity in combination with recommendation analysis is making its way into multiple usage scenarios that serve the needs of personalized marketing in multiple channels. This will spur further adoption of this method to drive depth of customer engagement and relevance.

High. Product af�nity and recommendation analysis provides high value to companies looking to provide customers with personalized and relevant products and offers. Combining this method with more customer-facing methods such as next best action gives companies the ability to deliver relevant and accurate predictions of what customers will buy next based on prior purchases.

3 to 5 years. Product af�nity and recommendation analysis will continue to drive business value for years to come because it drives tangible metrics such as average order value, wallet share, and product revenue.

Significant success. Product af�nity and recommendation analysis will play an important role in delivering more personalized product offers. Although deeply rooted in retail promotions and eCommerce, over time it will apply to broader use cases like social recommendations.






The evolution of customer networks (including social media networks) will make understanding customer relationships and their in�uence over purchase decisions critical.

Medium. This method is applicable in situations where networks of customers exist, which are limited today to select verticals such as telecom and media.

3 to 5 years. Even though this method is in its infancy, it will gain ecosystem momentum quickly when �rms start experimenting with their traditional customer retention programs and start incorporating social network analysis to understand the role of in�uencers in customer retention.

Moderate success. This emerging method will enjoy moderate success, as its application is restricted to verticals where natural networks of customers form. It will �nd use only in those types of situations as opposed to gaining widespread adoption.








equilibrium: steady-state Methods Retain a Central Role

Methods in the Equilibrium phase have broad adoption and hold their own. Forrester placed the following four customer analysis methods in this phase (see Figure 7):

■ Cross-sell and upsell analysis. Widely used especially in the financial services vertical, cross-sell and upsell analysis is a way to drive incremental revenue from existing customers. Despite the development of more sophisticated and predictive methods such as recommendation analysis, this method will be in high demand for many years because of its ability to tie back to concrete business results.

■ Customer look-alike targeting. Direct marketers first initiated acquisition models to target prospects that look like their best customers in the mid-1990s. As behavioral or audience targeting matured in the early 2000s, online advertisers revitalized the approach to improve the effectiveness of their display advertising buys. Although not a mainstream customer analytics method, the concept of look-alike targeting will remain a mainstay in acquisition marketing because it drives efficiencies in audience and target selection and improves accuracy of targeting.

■ Customer propensity analysis. This method is a foundational customer analytics method and is widely adopted by direct marketers to understand propensities of customers to respond, buy, or engage. It will sustain its position and remain the backbone of predictive customer analytics because it is mature in terms of both its production and implementation.

■ Customer satisfaction analysis. This type of analysis helps illuminate the overall satisfaction that a customer experiences through various interactions with the company as captured by voice of customer data. It involves the comparison of customer expectations and perceptions of an experience. While this type of analysis is fairly mature, using insights from customer satisfaction to fuel customer analytics methods is yet largely untapped.




Figure 7 TechRadar™: Equilibrium Phase Technologies



This method is a mature customer analytics method and is widely used in many verticals to understand product associations. However, many advanced methods such as next best offer and product recommendations provide enhanced usage scenarios and are not yet mainstream.

High. This method is effective when there is a critical mass of customer, product, and transactional data available for analysis. Cross-sell and upsell analysis applies to customer retention, account penetration, and share of wallet penetration efforts.

5 to 10 years. This method is widely adopted and mature in certain verticals such as �nancial services. It will remain so for several years before it reaches the next ecosystem stage.

Significant success. Cross-sell and upsell analysis allows �rms to directly observe the impact of the application of the analysis method on customer and product revenue metrics and as a result has shown signi�cant success in product-centric verticals.

Why the Equilibrium phase?





Customer look-alike targeting has its roots in direct and database marketing and is a fairly mature method.

Medium. The value of customer look-alike targeting is realized in customer acquisition in limited usage scenarios.

3 to 5 years. With the development of customer recognition and identity stitching technologies, it will become easier to observe behaviors of prospects versus customers; consequently, this method will be replaced by more sophisticated analysis that drives better and deeper insights about prospects.

Moderate success. Customer look-alike targeting will continue to expand in online advertising, where behavioral targeting can accurately identify audiences based on behaviors of existing customers. But it will enjoy only moderate success in other applications.








Figure 7 TechRadar™: Equilibrium Phase Technologies (Cont.)



Propensity models are the staple of any customer analytics capability and are foundational for other advanced models of customer behavior.

High. Propensity models are highly valuable because they can calculate customer propensities of a wide range of actions — buy, respond, engage, attrite, convert, unsubscribe, or any other customer action. And they warm companies toward the adoption of predictive analytics.

5 to 10 years. Customer propensity models will continue to be a core analytics method for many years, because for many organizations it will be their �rst foray into predictive analytics and for others it will remain the foundation for advanced techniques.

Significant success. Customer propensity analysis plays a key role in understanding customers’ likelihood to take various actions and is poised to remain so, because customer propensity scores can be used widely from acquisition, retention, and churn to cross-sell and upsell.





Customer satisfaction analysis

This method is a very mature practice in any organization that prioritizes understanding and measuring its customer experience. Our research shows that more than two-thirds of organizations have voice of customer programs in place today and conduct some form of satisfaction analysis.

Medium. This method is more valuable when combined with loyalty, retention, and attrition analysis to understand the triggers and drivers of retention or churn through the lens of satisfaction. Customer satisfaction analysis also relies on perception-based metrics, as opposed to outcome-based or behavioral metrics, limiting the value it adds to customer analysis. Voice of customer data that signals satisfaction (both solicited and unsolicited/unstructured) is not as valuable when it is analyzed in isolation from other customer data sources. But it adds value to the derived insights when combined with other behavioral customer data.

3 to 5 years. While this method is an established method to measure customer satisfaction, it will become increasingly important as a customer analytics method in the next three to �ve years because more �rms are realizing that by analyzing purely behavioral data from transaction sources, they are getting only limited insights about customers.

Moderate success. From a customer analytics perspective, voice of customer and satisfaction data is valuable to provide the context around customer behavior, but matching the data collection methodologies between customer feedback sources and customer behavioral sources is challenging. As a result, this method will be moderately successful for use in customer analytics.








Decline: Methods will Continuously Improve, Not Completely Decline

Many customer analytics methods will be perpetually relevant, only the degree of importance and adoption will keep shifting depending on: 1) the outcomes that firms will drive using customer analytics; 2) the availability of data; and 3) the vertical or domain fit for the analytics method. As a result, Forrester did not place any customer analytics methods in the Decline phase.

R e c o m m e n d at i o n s

exPeRIMeNT wITh New MeThoDs BeFoRe yoUR CoMPeTIToRs Do

As you evaluate the customer analytics methods available, it is easy to become overwhelmed by the complexity of analytical choices, irrespective of whether you are building these capabilities in-house or relying on a technology or services partner for help. Many companies delay adopting customer analytics, arguing that the decision will be easier once the algorithms driving these methods are perfected. But with the backing of big data and the data science approaches, there is no excuse not to get good at customer analytics. We advise investigating early-stage methods now to push the limits of your existing customer analytics methods and uncover nonobvious and contextual customer insights. To do this:

■ Outline the chain of causality to business outcomes. Customer analytics methods and the algorithms and data-mining techniques that drive them are only a means to the end. By not understanding the linkage between how, when, and where to apply an analytics method, customer insights professionals run the risk of constantly justifying time spent on analytics production. Use our analytics method dependency model, outlined above, to chart out these linkages to business outcomes. For instance, cross-sell analysis and churn analysis have been successful analytics methods because they directly drive key business metrics such as retention rate and average order value.

■ Adopt new approaches to overcome implementation constraints. Even if methods are effectively mapped to outcomes, last-mile implementation of analytic methods is still difficult to do at the pace required for customer obsession. To speed up the application of analytics methods, we recommend two approaches: 1) explore technologies that allow for analytics processing within the customer database, such as in-database technologies; and 2) create analytic methods and models in formats such as predictive model markup language (PMML) or emerging data science languages such as R in order to port analytics output within and between analytic systems and marketing execution technologies.16

■ Leapfrog analytic maturity with high-impact methods. Most marketing analysts adopt new analytics methods through a phased approach, implementing descriptive, then predictive, then prescriptive customer analytics methods. But this linear approach isn’t always necessary, because sometimes a single method can generate multiple types of value. For example, predictive methods like next best action or customer life time value should be




adopted earlier than some descriptive methods because they affect acquisition, retention, and personalization efforts, extend the value of other analytics methods, and can provide consistent and time-based insights about individuals across various interaction points.

sUPPleMeNTal MaTeRIal

online Resource

The underlying spreadsheet that exposes all of Forrester’s analysis of each of the 15 customer analytics methods in the TechRadar (Figure 3) is available online.

Data sources Used In This TechRadar

Forrester used a combination of data sources to analyze each method’s current ecosystem phase, business value adjusted for uncertainty, time to reach next phase, and trajectory:

■ Expert interviews. Forrester interviewed experts on each method, including academics and vendors.

■ Current and prospective customer and user interviews. Forrester interviewed current and potential customers and users for each method to understand current and prospective uses for the methods and their impact on the customers’ businesses and the users’ work.

■ Online survey. Forrester invited experts to complete an online survey about each of the methods.

The Forrester TechRadar Methodology

Forrester uses the TechRadar methodology to make projections for 10-plus years into the future of the use of technologies in a given category. We make these predictions based on the best information available at a given point in time. Forrester intends to update its TechRadar assessments on a regular schedule to assess the impact of future technical innovation, changing customer and end user demand, and the emergence of new complementary organizations and business models. Here’s the detailed explanation of how the TechRadar works:

■ The x-axis: We divide technology ecosystem maturity into five sequential phases. Technologies move naturally through five distinct stages: 1) Creation in labs and early pilot projects; 2) Survival in the market; 3) Growth as adoption starts to take off; 4) Equilibrium from the installed base; and 5) Decline into obsolescence as other technologies take their place. Forrester placed each of the 15 customer analytics methods in the appropriate phase based on the level of development of its ecosystem, which includes customers, end users and vendors, complementary services organizations, and evangelists.17




■ The y-axis: We measure customer success with business value-add, adjusted for uncertainty. Seven factors define a technology’s business value-add: 1) evidence and feedback from implementations; 2) the investment required; 3) the potential to deliver business transformation; 4) criticality to business operations; 5) change management or integration problems; 6) network effects; and 7) market reputation. Forrester then discounts potential customer business value-add for uncertainty. If the technology and its ecosystem are at an early stage of development, we have to assume that its potential for damage and disruption is higher than that of a better-known technology.18

■ The z-axis: We predict the time the technology’s ecosystem will take to reach the next phase. Customer insights professionals need to know when a method and its supporting constellation of investors, developers, vendors, and services firms will be ready to move to the next phase; this allows them to plan not just for the next year but for the next decade. Of course, hardware moves more slowly than software because of its physical production requirements, but all technologies will fall into one of five windows for the time to reach the next technology ecosystem phase: 1) less than one year; 2) between one and three years; 3) between three and five years; 4) between five and 10 years; and 5) more than 10 years.19

■ The curves: We plot technologies along one of three possible trajectories. All technologies will broadly follow one of three paths as they progress from creation in the labs through to decline: 1) significant success and a long lifespan; 2) moderate success and a medium to long lifespan; and 3) minimal success and a medium to long lifespan. We plot each of the 15 most important technologies for customer analytics on one of the three trajectories to help customer insights professionals allocate their budgets and technology research time more efficiently.20 The highest point of all three of the curves occurs in the middle of the Equilibrium phase; this is the peak of business value-add for each of the trajectories — and at this point, the adjustment for uncertainty is relatively minimal because the technology is mature and well-understood.

■ Position on curve: Where possible, we use this to fine-tune the z-axis. We represent the time a technology and its ecosystem will take to reach the next phase of ecosystem development with the five windows above. Thus, technologies with more than 10 years until they reach the next phase will appear close to the beginning of their ecosystem phase; those with less than one year will appear close to the end. However, let’s say we have two technologies that will both follow the moderate success trajectory, are both in the Survival phase, and will both take between one and three years to reach the next phase. If technology A is likely to only take 1.5 years and technology B is likely to take 2.5 years, technology A will appear further along on the curve in the Survival phase. In contrast, if technologies A and B are truly at equal positions along the x, y, and z axes, we’ll represent them side by side.




Companies and experts Interviewed For This Report

We interviewed several companies for this report, some of which elected to remain anonymous.

Actuate

Acxiom

AgilOne

Beyond the Arc

DataSong

Dr. Peter Fader, The Wharton School, University of Pennsylvania

Dr. V. Kumar, J. Mack Robinson School of Business, Georgia State University

Epsilon

FICO

IBM

iKnowtion

Merkle

Mu Sigma

Opera Solutions

Pitney Bowes

SAP (KXEN)

SAS

Smith

eNDNoTes1 Forrester defines the age of the customer as a 20-year business cycle in which the most successful

enterprises will reinvent themselves to systematically understand and serve increasingly powerful customers. Customer-obsessed companies will prioritize investments in real-time data for actionable customer intelligence. See the October 10, 2013, “Competitive Strategy In The Age Of The Customer” report.

2 For more on how the customer analytics category is defined, see the November 30, 2012, “Deciphering A Fragmented Customer Analytics Ecosystem” report.

3 Source: Eliot Van Buskirk, “How the Netflix Prize Was Won,” Wired.com, September 22, 2009 (http://www.wired.com/business/2009/09/how-the-netflix-prize-was-won/).

4 The original TechRadar analysis for this report was completed in Q1 2014.

5 For further details on the TechRadar methodology, see the supplemental material section of this document and our report introducing this new type of research. See the August 1, 2007, “Introducing Forrester’s TechRadar™ Research” report.

6 For further details on each of the phases, see the August 1, 2007, “Introducing Forrester’s TechRadar™ Research” report.

7 For more on how to analyze the incremental benefit that a method brings to customer insights, see the November 1, 2013, “Five Emerging Methods Advance Customer Analytics” report.












8 Additionally, the cost to implement considered in this analysis is only the cost of producing the analytics methods, not the costs involved in data preparation, data management, and data integration, which precedes the analytics process.

9 Wearables, or sensor-laden devices, will be the vehicles that drive the world into the realization of the Internet of Things dream. See the October 17, 2012, “Smart Body, Smart World” report

As a result, wearables, sensors, and embedded devices can add even more context. See the June 25, 2013, “Predictive Apps Are The Next Big Thing In Customer Engagement” report.

10 Source: North American Technographics™ Consumer Technology Survey, 2013.

Sensor-embedded objects — the endpoints that compose the so-called Internet of Things — present marketers with the opportunity to learn more about their customers and the way they use products in the real world. They also present the opportunity to engage with customers in new contexts, deliver more value, and deepen customer relationships. The problem is, there really is no Internet of Things today — it’s a fragmented landscape of devices with small installed bases that produce siloed data to which marketers have limited access. During the next three to five years, however, technology improvements will give marketers more to work with. See the October 17, 2013, “There Is No Internet Of Things — Yet” report.

11 Source: Tina Moffett, “Q3 2013 Takeaways: Advanced Measurement Continues To Be A Key Initiative,” Forrester Blogs, October 8, 2013 (http://blogs.forrester.com/tina_moffett/13-10-08-q3_2013_takeaways_advanced_measurement_continues_to_be_a_key_initiative).

12 For more on location-predictive marketing, see the June 27, 2013, “Location-Predictive Marketing” report.

13 For more on location analytics and its impact on customer experience, see the February 11, 2014, “You Are Here: Location Analytics And The Rebirth Of Customer Experience” report.

14 The No. 1 challenge that customer insights professionals face with segmentation is the poor adoption of results within the organization. See the June 9, 2011, “Segmentation: New Approaches To An Old Problem” report.

15 Graph databases simplify and speed access to data containing many relationships. Graph structures consist of nodes (things), edges (relationships), and properties (key values) to store and access complex data relationships. See the June 7, 2013, “The Steadily Growing Database Market Is Increasing Enterprises’ Choices” report.

16 For more on in-database analytics, see the November 12, 2009, “In-Database Analytics: The Heart Of The Predictive Enterprise” report.

PMML is an XML-based file format that allows for different statistical and data mining tools to speak the same language. In this way, a predictive solution can be easily moved among different tools and applications without the need for custom coding.















17 Note that the five phases are not of any prescribed length of time. For the typical technology ecosystem profiles for each of the five phases, check out Figure 3 in the introductory report. See the August 1, 2007,

“Introducing Forrester’s TechRadar™ Research” report.

18 We outline the detailed questions we ask to determine business value adjusted for uncertainty in Figure 4 of the introductory report. See the August 1, 2007, “Introducing Forrester’s TechRadar™ Research” report.

19 Forrester will include relatively few technologies that we predict will take more than 10 years to reach the next ecosystem phase. Expect to see these 10-year-plus technologies only in the Creation phase for fundamental hardware innovations and in the Equilibrium and Decline phases for hardware and software on the “great success” trajectory. We provide details on how we predict the amount of time that a given technology will take to reach the next phase of technology ecosystem evolution in the introductory report. See the August 1, 2007, “Introducing Forrester’s TechRadar™ Research” report.

20 We provide detailed information and examples of how we predict the amount of time that a technology will take to reach the next phase of ecosystem development (alternatively called “velocity” or “velocity rating”) in the introductory report. See the August 1, 2007, “Introducing Forrester’s TechRadar™ Research” report.





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TechRadar™: Customer Analytics - ProStrategy€¦ · Figure 2 TechRadar™: Customer Analytics Methods, Q1 ’14 Methods Evaluated 106141 Source: Forrester Research, Inc. Behavioral