turning customer knowledge into business growth
TRANSCRIPT
Turning Customer Knowledge into Business Growth By embracing big data and predictive analytics to create multidimensional customer profiles, companies can make more informed business decisions that better anticipate consumer needs, wants and desires.
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Executive SummaryCustomers today can access an unprecedented volume of
information via varied channels before making an informed
purchase. For organizations, this means continuously learning
from customer behavior to stay relevant. But while there is no
dearth of customer data available, organizations often grapple
with the challenge of developing clear, complete and fully
updated profiles of their customers.
In a 2012 study, conducted by Columbia Business School and
New York American Marketing Association,1 39% of corporate
marketers said their company’s customer data was collected
too infrequently and was not up to date. Meanwhile, a January
2013 study by Aberdeen Group2 found that top-performing
companies are more likely than others to use a rich set of data
sources to feed their predictive analytics models, including
internal transaction data and unstructured or real-time data, to
provide actionable guidance for decision-makers (see Figure 1).
Data Source Leaders Followers
Internal transactional records 93% 74%
Internal customer records 75% 80%
Customer sentiment data 57% 29%
External customer information 56% 36%
Customer interaction data 56% 36%
Clickstream data 40% 18%
Unstructured data 38% 29%
Base: 157Source: Aberdeen Group report, January 2013 Figure 1
Creating Rich Customer Profiles
The use of big data and analytics can be extended to customer
relationship management (CRM), as companies need to
combine structured and unstructured data with powerful
analytics tools to create a multidimensional customer profile.
This white paper describes a solution concept and
implementation approach to developing a multidimensional
customer profile and deriving actionable insights with the help
of big data and analytics.
TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 3
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The Data ChallengeOrganizations have traditionally used structured customer data stored in their enterprise systems to develop customer profiles. Additionally, a few have attempted to incorporate external data purchased from third-party agencies, by converting it into structured formats that can then be stored in their enterprise systems.
However, this approach results in customer profiles that, at best, are incomplete in the following ways:
• Data stored in enterprise systems is dated and restricted to past interactions. Many times, data integrity is questionable; for instance, a promotional mailer may use a customer address from the CRM system, but if the customer has relocated, the promotion campaign is rendered ineffective.
• Agency data is based on extrapolated customer surveys, which can never replace actual data insights on individual customers.
• Customers no longer use only company-operated channels. Consumers have a much broader footprint through social media to broadcast their experience with the company’s products or services or even their intent to switch to a com-petitor’s offerings.
Because of these factors — and with the fast uptake of social, mobile, analytics and cloud technologies (the SMAC Stack™), creating customer profiles without semi- or unstructured data can render an organization uncompetitive and even irrelevant.
The Customer’s Multiple DimensionsFor any sales and marketing team, it is vital to keep current with the pulse of the customer, and this cannot be accomplished by relying solely on internal enterprise data. Information avenues that can provide crucial insights include social media activity, browsing behavior, mobile app downloads, games played, past purchases, photos shared, music/video preferences and vacation choices. We call the accumu-lation of all these activities a Code Halo™, which is essentially the digital footprint created by enterprises, customers, employees and processes from their online behavior. Business leaders such as Amazon and Google have quickly risen to the top of their industries by deriving meaning from the intersections of Code Halos and building their strategies around these insights. (For more on this topic, read our white paper, “Code Rules: A Playbook for Managing at the Crossroads.”)
A true view of the customer, then, needs to link the details stored in enterprise systems with Code Halos, or external customer information. This consolidated or augmented view presents a near-real-time and complete picture of the customer (or potential customer) with which the business is interacting. Because the tradi-tional view completely ignores the social aspects of the customer, it can best be described as a dormant description that is waiting to be brought to life by social information and the customer’s Code Halo.
For any sales and marketing team, it is vital to keep current with the pulse of the customer, and this cannot be accomplished by relying
solely on internal enterprise data.
TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 5
However, this does not happen automatically; semi- and unstructured data that supplies information on customer activity in the external world needs to be analyzed and indexed before it can be melded with structured data from enterprise systems and delivered in the form of a multi-dimensional customer profile. We call this process AIM (or analyze, index and meld) & Deliver.
The multidimensional customer profile is like a coin with two sides; the face of the coin depicts the structured data elements of the customer, and the back depicts the unstructured data elements. When both of these aspects are melded and delivered together, the true customer profile can be derived.
The multidimensional customer profile can also be visually represented by a sphere (see Figure 2). Note that when you slice this sphere, you can look at various aspects of the customer and company from both structured and unstructured perspectives.
Once the multidimensional customer profile is available, it opens up multiple use cases that drive real-time actionable insights. The insights can be made available
Figure 2
Creating the Multidimensional Customer Profile
Creating the Multidimensional Customer Profile
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Unstructured
Structured
Customer
Photo Audio Video Docs
Life events
Searches Downloads Sharing
Comments Favorites
Events
Web/mobile clickstream
Product pages visited
Frequently used Web site
Search keywords
Device preferences
Location Intelligence
Frequent visits
Travel/vacation
Contact center
Likes
Bookmarks Circle Sharing
Social Professional
Customer influencers
Professional
Mobile
GamesApps
Blogs
Boards Forums
Grievances
Product failures
ChatE-mail
Direct mail
Skill set
Customer surveys Demographics
Age Gender OtherProduct/brand interest
Credit history
Credit-worthiness
Credit terms
Service history
Cases
Product interest
Accept/ignore
historyChannel
Product groups/
hierarchy
Product groups/
affinity
PAS
Social Professional Influence
Podcast Videos
Store
Environment
Economy Weather
Footfalls
Product interest
Product/brand sentiment
Social
Product influencers
Company
Offers database
Store locations
Partner networks
Inventory
availability
Loyalty
Tiers
Data Elements
Attributes
Interaction historyCompetitor purchase interest
Influence
Job profile networks
Company Web site
Browsing behavior
Current residence
Points of interest
Micro-blogs
Social networks Professional networks
Product commentsDislikes
Allied product interest
Payment history
Past offers
Offer responses
Purchase history
Preferred mode
Contact preference
program
Benefits
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and customized for different stakeholders in the form of decision matrices/maps that can be leveraged for real-time data-driven decision-making. The effective-ness of decisions using this approach drives continuous closed-loop feedback (see Figure 3).
An example of this is real-time cross-sell offers, in which the decision matrices/maps can vary for different stakeholders (see Figures 4 and 5). Using the multi-dimensional customer profile derived from big data and analytics, the contact center agents, sales representatives and any other customer-facing personnel have access to the exact real-time offers they need to entice customers or prospects. This kind of decision-making is more operational in nature and targeted to the timing of the customer trigger.
At the same time, the multidimensional customer profile can deliver the much-needed fuel to power analytics for executive decisions. In order to understand which offers performed well and the changes needed to improve the offer management process, executives would need a dashboard providing planning insights such as purchases made to date, potential pairing across products and categories, and customer profile acceptance levels to boost success rates.
Figure 3
The CRM Analytics Continuum
2
Customer trigger
Analytics insights lead to decision maps
for executives.
Analytics-based insights lead to decision matrix for field service reps.
Connect with real-time customer profile.*
*Powered by the AIM & Deliver process.
5
Product Affinity
High
High
Low
Low
A1
C3
Brand C
Brand B
Brand A
Brand D Brand B
Brand E
Cu
sto
mer
Pro
file
Acc
epta
nce
Figure 4
Cross-Sell Decision Matrix for the Customer Operations Team
Customer ID Customer Profile
Product Purchased
Cross-Sell Offer
Cross-Sell Success
ABC A1 Brand A Brand C Y
XYZ B2 Brand B Brand C Y
123 A1 Brand C Brand A Y
DEF C3 Brand D Brand B Y
Customer ID
Customer Profile
Product Purchased
Cross-Sell Offer
Cross-Sell
Success
ABC A1 Brand A Brand C Y
XYZ B2 Brand B Brand C Y
123 A1 Brand C Brand A Y
DEF C3 Brand D Brand B Y
Creating the Multidimensional Customer Profile
3
Unstructured
Structured
Customer
Photo Audio Video Docs
Life events
Searches Downloads Sharing
Comments Favorites
Events
Web/mobile clickstream
Product pages visited
Frequently used Web site
Search keywords
Device preferences
Location Intelligence
Frequent visits
Travel/vacation
Contact center
Likes
Bookmarks Circle Sharing
Social Professional
Customer influencers
Professional
Mobile
GamesApps
Blogs
Boards Forums
Grievances
Product failures
ChatE-mail
Direct mail
Skill set
Customer surveys Demographics
Age Gender OtherProduct/brand interest
Credit history
Credit-worthiness
Credit terms
Service history
Cases
Product interest
Accept/ignore
historyChannel
Product groups/
hierarchy
Product groups/
affinity
PAS
Social Professional Influence
Podcast Videos
Store
Environment
Economy Weather
Footfalls
Product interest
Product/brand sentiment
Social
Product influencers
CompanyOffers database
Store locations
Partner networks
Inventory
availability
Loyalty
Tiers
Interaction historyCompetitor purchase interest
Influence
Job profile networks
Company Web site
Browsing behavior
Current residence
Points of interest
Micro-blogs
Social networks Professional networks
Product commentsDislikes
Allied product interest
Payment history
Past offers
Offer responses
Purchase history
Preferred mode
Contact preference
program
Benefits
TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 7
Implementation ApproachTo implement the solution, we recommend a four-phased approach (see Figure 6).
Phase 1: AIM & Deliver To initiate the first phase of the AIM & Deliver process, the underlying data elements must be identified. This entails merging the customer details available within and outside the enterprise (see Figure 7, next page).
Disparities across the data sources need to be ironed out to associate customer data within the enterprise with the right data sources in the external world. This can be done with advanced analytics. By combining automatic entity extraction with name matching, users can automatically identify entity mentions in unstructured data and link them with structured information. This linkage simplifies the process and combines data about an entity into a complete customer profile.
Figure 5
Figure 6
Cross-Sell Decision Map for Executives
Four-Step Implementation Process
5
Product Affinity
High
High
Low
Low
A1
C3
Brand C
Brand B
Brand A
Brand D Brand B
Brand E
Cu
sto
mer
Pro
file
Acc
epta
nce
P
hase
1
Pha
se 2
P
hase
3
Pha
se 4
AIM & Deliver
Analytics engine
Assess business relevance, technology and economic hurdles.
Define stakeholders and detail the use case.
Configure analytics engine for actionable insights.
Design the big data architecture after use case crystallization.
Analyze Index Meld Deliver
Evaluate business case and
stakeholders
Big data architecture
Derive real-time multidimensional customer profile.
Deliver augmented real-time multidimensional
customer profile
• Entity extraction• Document clustering
• Attribute matching• Customer name
matching
• Link structured data• Link customer to
enterprise applications
• Real-time customer profile • Location intelligence
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The key steps involved with combining these two different genres include:
• Analyze the different types of data, clustering them based on specified parameters and extracting entities, such as customer name, organization, product name, location, etc.
• Index the clustered data sets and create structured metadata for each entity, enabling fast filtering and searching by people, places, company names or other entities.
Figure 7
Merging Two Worlds of Data
Transactions
Influence
E-commerce
M-commerce
Blogs/Micro-blogs
Direct Mail
Events
QuoteContracts
Orders/ Pipeline
Loyalty Program
Data
Contact Preferences
Campaign Data
Contact History
Payment History
Channel History
Credits/Terms Structured
Surveys
Service History
Purchase History
Structured Data
Agency Data Social Curation Scan
Documents
Quantified Self
Professional Network
Activities
Social Bookmarking
Photo Sharing
Voice Portal/IVR
Video Sharing
Boards/ Forums/Activities Boards/
Forums/ Activities Online
Searches
E-mails/Chat
Store Surveillance
POSTransactions
Unstructured Surveys
MobileActivity
Social Activity
Web Clickstream
ContactCenter Data
Location Intelligence Agency/
Semi-/Unstructured Data
Enterprise System
s
Analyze
BI /
Ana
lytic
s
Inde
x
TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 9
• Meld the extracted entities with near-perfect attribute matches (i.e., accurate customer names with existing customer data in the CRM system).
• Deliver the augmented customer profile, enhanced with location intelligence for easy consumption by CRM systems, BI/analytics or any other point solutions.
Phase 2: Evaluate the initiative’s business case and stakeholders.This crucial step can make or break the overall initiative. We offer a proven approach to creating and finalizing the business case for big data analytics that is specifically relevant from a CRM perspective.
This cost-benefit analysis-based approach can help define the stakeholders and detail the use case while also assessing ROI. It helps answer questions such as:
• How do you approach your first big data implementation?
• Do you have the information necessary to determine the approach?
• How can you ensure you receive the business value of the big data journey?
• What metrics and cost factors affect the success of your big data program?
The output of this step provides the company with a business case and an ROI calculation to ensure management will fund the initiative. More than a proof of concept, this process results in a proof of value and helps customers understand the business relevance, technology challenges and economic hurdles of a typical big data/analytics engagement.
Phase 3: Design the big data architecture and configure the analytics engine.Once the business use cases have been crystalized, the big data architecture and analytics engine needs to be designed for focused analysis and to derive actionable insights for different stakeholders. This significantly reduces the time to value and also brings a sharp focus to the expected business outcomes.
The use case-driven approach can help map the business requirements tightly with the big data technology design considerations, such as relational storage and query, distributed storage and processing, and low latency/in-memory. This, in turn, leads to a sustainable and scalable architecture.
The analytics engine must then be configured for linking datasets around an entity (e.g., what do I know about this customer?) or around a relationship (e.g., how is this customer related to others?) Successfully configured, such analytics can produce qualitatively new insights that result in business value, such as reduced customer churn rate, next best action and better predictions of risk and failure.
The use case-driven approach can help map the business requirements tightly with the big data technology design considerations, such as relational storage and query, distributed storage and processing, and low latency/in-memory.
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Phase 4: Create real-time, multidimensional customer profiles.Once the multidimensional customer profile is established, the possibilities are endless. The profile provides access to customer data residing not only in the enterprise but also from every other area in the external world with which the customer has interacted. In essence, the profile captures every digital trace that the customer creates. This invaluable data can now be exploited for driving several applications (see Figure 8).
Challenges Along the Way Companies can expect to be faced with several challenges when developing multidi-mensional customer profiles, including:
• Data explosion: Customers are increasingly interconnected, instrumented and intelligent. Accordingly, an unprecedented velocity, volume and variety of data is being created. As the amount of data created about consumers grows, the percentage of data that businesses can process quickly decreases, because tradi-tional systems cannot store, process and analyze massive amounts of structured and unstructured data. Business systems are not designed for today’s unstruc-tured data, rapidly changing schema and elastic scaling of storage.
• Privacy and regulatory issues: Another issue is regulatory and privacy issues. Data collectors bear a tremendous responsibility to provide full disclosure of what they plan to do with customer data. But an even greater challenge is the sharing of data.
For instance, if a consumer grants one company permission to use his or her data, what rules (if any) will regulate how that information is shared across multiple companies? Such questions will become one of the biggest sticking points in terms of trying to navigate the right policies.
Figure 8
Making Meaning from Digital Fingerprints
Applications
Improved Upsell/Cross-sell
Real-Time Offers
Enhanced Marketing Effectiveness
Proactive Servicing
Personalized Campaigns
Objectives
Delight customers and cross-sell/upsell by making intelligent, real-time recommendations.
Create offers and next best offers on the fly based on updates to real-time customer profiles.
Fine-tune offers and channel effectiveness during campaign planning and creation.
Service customers proactively using social listening.
Use the multidimensional profile to personalize campaigns.
Success Metrics
• Increase revenue generated from cross-sell/upsell offers.
• Increase share of wallet.
• Increase customer satisfaction scores.
• Increase number of real-time offers sent.
• Improve offer acceptance rate.
• Reduce customer offer-related spending.
• Reduce turnaround time for offer.
• Reduce marketing campaign costs.
• Increase lead conversion.
• Positive sentiment service level.
• Customer loyalty.
• Campaign acceptance rate.
TURNING CUSTOMER KNOWLEDGE INTO BUSINESS GROWTH 11
Data collectors also need to make it easier for customers to opt in or out of having their information used, similar to opting into mailing lists or using an “unsub-scribe” option to opt out. When consumers feel they’re getting a tangible benefit for their personal information, their resistance to data collection starts to fade. Loyalty and rewards programs are a good example of how companies can persuade customers to reveal more details about behaviors such as shopping habits.
Looking ForwardLeading organizations are already gearing up to create multidimensional customer profiles using both structured and unstructured data sources. Complete and continuously up-to-date customer profiles enabled by big data and analytics are increasingly an essential tool in the arsenals of organizations across industries and geographies. `
Footnotes 1 “Marketing ROI in the Era of Big Data: The 2012 BRITE-NYAMA Marketing in
Transition Study,” Columbia Business School and NYAMA, 2012, http://www4.gsb.columbia.edu/null/2012-BRITE-NYAMA-Marketing-ROI-Study?exclusive=filemgr.download&file_id=7310697&showthumb=0.
2 “Maximizing Customer Lifetime Value with Predictive Analytics for Marketing,” Aberdeen Group, February 2013, http://www.aberdeen.com/_aberdeen/public/view-lookinside-pdf.aspx?cid=8362.
About the AuthorsSairam Iyer is a Senior Information Management and Analytics Consultant with Cognizant Business Consulting’s Enterprise Information Management Practice. His core responsibilities include providing thought leadership in the areas of business intelligence and analytics, and consulting with clients across industry verticals. Sairam has nine years of rich experience with Fortune 100 companies, specializing in CXO and business leader-level workshops to understand business processes and concerns and convert them into business intelligence and analytics solutions. As a multidisciplinary BI strategy expert, he has hands-on experience in executing information management and analytics engagements from concept to delivery. Sairam obtained his M.B.A. from the Xavier Labor Relations Institute (XLRI), Jamshedpur, specializing in marketing and strategy. He can be reached at [email protected].
Vikas Singhvi is a Senior CRM Consultant with CBC’s Enterprise Applications Services (EAS) Practice. Vikas’s core responsibilities include working on consulting projects in the sales, marketing and customer service domains across industry verticals. He has four-plus years of progressive experience in business strategy, customer relationship management consulting, digital marketing consulting, sales and marketing process consulting and business development. His consulting experience includes extensive multicountry project exposure across the high-technology, retail, manufacturing-logistics, information services and transporta-tion domains. Before joining Cognizant, Vikas worked with Microsoft India as an APEX (Accelerated Professional Experiences) member, which is a program for high-potential entry-level employees. Vikas received his M.B.A. from the prestigious Indian Institute of Management at Indore, specializing in marketing and strategy. He can be reached at [email protected].
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About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world’s leading companies build stronger busi-nesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, col-laborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 166,400 employees as of September 30, 2013, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant.