defining the value of big data customer analytics€¦ · gaining deeper insights about customers,...

Defining the Value of Big Data Customer AnalyticsA Guide to Building a Business Case and Alignment

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EBOOKDEFINING THE VALUE OF BIG DATA CUSTOMER ANALY TICS

For many companies, customer relationships are the new competitive front line. Increased wallet share, better customer experiences and higher loyalty are all revenue gains for your organization and losses for your competitors.

Customer analytics driven by big data can transform the buyer-seller relationship. It’s not simply about gaining deeper insights about customers, but using that data to drive more effective personalized marketing, increasing sales productivity and retaining customers for a higher lifetime value.

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Now more than ever, building expertise in deep, diagnostic customer analytics translates directly to business value. The combination of modern BI platforms and big data helps you achieve this value. Here are some prime examples:

• A large credit card company increased conversion rates by 25 percent and lowered customer acquisition costs by 30 percent.

• Aleadingfinancialservicesfirmreducedchurnby50percent,increasingthelifetimevalue of their customers and regaining lost revenue.

• Surfdome, a leading European specialty retailer, boosted their customer acquisition rates, raised the average purchase size and increased the average lifetime value of customers.

Customeranalyticsencompassesanumberofdisciplines,impactingmanydifferentpartsof the organization. Successfully driving a big data customer analytics initiative requires two key ingredients:

• Clear articulation of the business value that deeper customer insights will bring to the entire organization and individual units

• Alignment with the business group stakeholders who use customer insights, and the teams that will implement the analytics (analysts and IT)

This paper will explain how big data customer analytics adds business value to a range of areas. In turn, it will provide the foundation to build your business case that will motivate stakeholders from across the organization to create deeper customer analytics based on big data.

Introduction

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Whiletraditionaldescriptiveanalyticsofferhigh-levelanswersaboutwhathappened,thesecrettobuildingmoreprofitablecustomerrelationshipsliesinharnessingthisdatatocreatedeeper diagnostic analytics that provide insights into why and how customers took action. Thisdatacanbeusedtodefinewaystobuildstrongerrelationshipsandimpactbehavior.

Gaining value from customer analytics requires deep insights driven by two key items: the experience a customer encounters and the behavior customers exhibit in reaction to those experiences. The complication comes from:

• themanydifferenttouchpoints• the multiple types of behavior• the multitude of characteristics that can describe customers.

It’s easy to underestimate the breadth and depth of customer analytics. It contains a numberofdisciplinesthatspandifferentdepartments—sales,marketing,serviceandoperations.Here’sanoverviewofthedifferentgroups,andhowacomprehensivesetofcustomer analytics using big data can help them create excellence within their disciplines.

MarketingMarketing teams require a deep understanding of customers, their activity and their behavior. They need to ensure their outreach is valuable and relevant, as well as measure theireffectiveness.

Big data analytics will help marketing identify detailed segmentation and behavioral attributes that in turn will drive additional analytics with actionable insights, including:

• Theabilitytocreateandmeasurepersonalizedmarketingoffersthatgeneratenewsalesrevenue

• Insightsintotheimpactandsuccessofmarketingcampaignsacrossdifferentchannelstoacquiremorecustomersatthemosteffectivecost

• Acomprehensiveviewintotheoverallomni-channelcustomerjourneytooptimizethejourneyandguidecustomersdownthemosteffectivepathstopurchase

The Impact of Customer Analytics

Every day, customers generate 2.5 quintillion bytes of data. This massive amount of data is generated in a large number of areas: online ads, email campaigns, mobile apps, transactions and numerous other sources.

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SalesSales teams and similar groups such as retail store operations, need detailed customer analytics to achieve their revenue goals and do so in the most productive manner.

Sales teams use a set of analytics about how customers interact, purchase and behave across various channels to drive business value such as:

• Creatingcompellingup-sell,cross-sellandnext-bestoffersthatincentcustomerstoacceleratepurchasesandincreasewallet-share

• Craftingpersonalizedexperiencesaspartofpersuasivesalesjourneysthatincreasesales conversion rates

• Optimizingsalesproductivitythroughmoreeffectivetargetingandactionsthatgeneratesales revenue

ServicesServicesteamsneedmorecomprehensivecustomeranalyticstorapidlyandeffectivelyhelp customers with their problems to increase loyalty and retention.

Service teams will use customer analytics and data around experience, behavior and outcomes to drive business value by:

• Creating superior service experiences that rapidly solve customer issues and increase loyalty

• Identifying the patterns that lead to customer churn and drive proactive outreach that increases customer retention

• Optimizing how services are delivered for faster time to resolution and a reduction in customer service spend

OperationsOperationsteamswillusecustomeranalyticsspecifictocustomerpurchasingbehaviortoplace inventory in the right locations, and optimize product delivery to customers.

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Marketing

Operations

Sales Service

PersonalizedOffers Customer Acquisition

Customer Journey

Service Experience Customer Retention Service Optimization

CrossandUp-SellPersonalized Experience

Sales Productivity

Logistics Product Placement

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Datameer’sself-serviceplatformallowsyourorganizationtodigdeeperintoyourcustomersandwhatmakesthemtick,withthreeimportantbenefits:

• Answer new questions—Datameerhelpsyouintegrateandusemoredata,whetherstructuredorunstructured,andmakesiteasytoapplyadvancedanalyticstofindundiscovered patterns and trends. Through this, Datameer allows your team to answer the deeper diagnostic questions that lead to highly actionable customer insights.

• Deliver more results—Customeranalyticsisadeepdiscipline,coveringmanydifferentdepartmentsandareasofthebusiness.Datameerdramaticallyincreasestheproductivity of your business analysts so they can deliver results for the many customer analytics use cases.

• Put your insights to work—Insightsaren’tvaluabletothebusinessunlesstheyreachthe business teams that need them on a regular basis. Datameer makes it easy to operationalize your analytics, execute them regularly, deliver results to the business teams and continuously improve the processes.

Datameertakesyourcustomeranalyticstoanentirelydifferentlevel.Theanalyticalresultscanrevealtotallynewpatternsandinsightsyouneverknewexisted—andaren’tevenconceivable with traditional analytics. The possibilities are endless.

Big Data Customer Analytics, Powered by Modern BI Platforms

Modern BI platforms like Datameer are designed to help you understand your customers and their journey more precisely. By adding more data to your analysis and using more sophisticated techniques to analyze it, modern BI gives you a more diagnostic examination of customer attributes and behavior so you can better align your actions to customer needs.

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Basedontheearlierexaminationofthewayscustomeranalyticsaddvalueindifferentdepartments, here’s a list of value metrics where big data customer analytics can deliver tangible results:

Marketing:

• Higher customer conversion rates• Increased customer acquisition rates• Lower cost per customer acquisition

Sales:

• Higher sales conversion rates• Increasedordersizesandwallet-share• Lower cost per sale

Services:

• Higher customer retention/Reduced customer churn• Increased service productivity and faster problem resolution time• Reduced customer service costs

Operations:

• Reduced inventory costs• Reduced shipping costs

Business Value Drivers of Customer Analytics

Big data customer analytics can deliver tangible results for any company, and essentially every department within a company. When properly operationalized into a company’s customer-facing processes, big data customer analytics provides a wide range of measurable benefits that lead to increased performance.

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What Questions Do I Need to Ask My Company?Almost certainly, your company is collecting large volumes of customer data. The next questions are:

• What analytic use cases can our data drive?• What is the priority of these use cases?• How can the business use the analytic results?• What barriers are there to implementing the use cases?• What technical, data or process impediments are there, and what are they? When considering the impact of big data customer analytics on your company and how tosellittoyourcompany’sc-levelmanagementteam,it’simportanttounderstandyourcompany’s situation and to examine how big data customer analytics can improve it.

Use Case Questions• What key indicators is our company focusing on right now?

• Whatisourcurrentcustomerconversionrate,andwhatimpactwouldaone-pointincrease in the conversion rate have on revenue?

• Whatisourcurrentnewcustomeracquisitionrateandwhatimpactwouldaone-pointincrease in the acquisition rate have on revenue?

• What is the current cost to acquire each new customer?

• How many orders are lost with customers abandoning products in their shopping cart?

• How many orders fail that could be completed?

• What would be the impact of a 10, 20 or 30 percent increase in sales conversion on revenue?

• Whatdoourcurrentupsellandcross-selleffortslooklike?Whatisourcurrentaverageorder size?

• What is the current cost per sale – sales costs/sales revenue?

• What is our current customer retention rate and what is the annual revenue impact for every point increase in this rate?

• What is the current customer churn rate and what is the annual revenue impact for each point decrease in churn?

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• How much time is spent on the average customer service call or conversation? What do the 25th and 75th percentiles look like?

• What is the average cost per service call and what are the cost components? Truck rolls? Service agents? Other?

• What is the current cost of inventory?

Potential Barrier Questions• Where is our customer data coming from right now? What state is it in? Where is it

stored? What format is it in?

• Whattouch-pointsorareasofthecustomerexperiencedoesthedatacover?Cantheresultsbeusedinthosetouch-points?

• Do we have enough demographic data and attributes on our customers?

• Can we create a more detailed model of customer behavior and actions?

• How long does the average analytic cycle take today? Why does it take this amount of time?Canself-servicebigdataanalyticsreducethiscycletime?

• Ifoutfittedwithself-servicebigdataanalytictools,couldanalystsaccessthedatatheyneed?

• How can the results be sent to the business teams to use it? What form do the results need to take so it is usable by the business teams?

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Use Case Discovery

Conduct a use case discovery workshop that will uncover the attributes of key use cases (data sources, potential business value and ROI, barriers to implementation) and help prioritize the the implementation of the use cases.

Proof of Value

Usedatadiscoveryforthetoppriorityusecasetofindnewpatterns,unexpectedoutcomes,andopportunitieshiddenwithinthedata.UsethistodemonstratethefitandvalueoftheanalyticsandbuildaprojectedROImodel.

Identify the Business Plan

Working with the business teams that will use the analytics and resulting data, formulate an action plan to use the data to improve the performance indicators.

Gather the Data

Identify all the various data sources, where the data is currently being generated, the format of the datasets, and how much data there is.

Produce the Analytics

Bring the data from all sources into the modern BI platform and conduct the complete analysis,intheprocessdefiningtheexactformatoftheresults.

Show the Results to the Business

Compilethefindings,buildapresentation,anddeliverthefindingstobusinessteams.Workwiththebusinessteamstofinalizeprocessesandpersonnelthatwillusethedata.

Operationalize the Process and Results

Defineandimplementtheattributesoftherepeatedprocesstothatwillproducetheresults.Thiswillincludejobexecution,dataretentionpolicies,securitymodels,andintegration with downstream applications or tools used by the business users.

Steps to Produce and Operationalize Customer Analytics Using Big Data

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Continuously Improve

As the analytics and business plan is implemented, you should continuously measure progressinwell-definedtimeframes.Inaddition,newdatawillbegeneratedcreatingtheneedtore-visittheanalyticstodetermineifadditionaldata,resultsorviewsarerequired.

Repeat

Perform the same process for new, related use cases. The new use cases should be able to leverage the data and models used in the initial use case.

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Building Your Value Calculations

All business value calculations will produce answers in terms of revenue (gained or retained), costs (lowered) or both. For example, customer acquisition optimization can produce both incremental revenue from an increase in new customers and lower costs frommoreefficientmarketingcampaignspending.

Depending upon the type of business you are, revenue gains (or retention) may be calculateddifferently:

• Ifyouareatransactionalcompany–industriessuchasretail–yourgainswillbedefinedby the average sale amount.

• If you are services company – industries such as banking or telecommunications – your gainswillbedefinedbythelifetimevalueofacustomer.

By way of example, here is how the business value of two use cases with potential revenue gainswouldbecalculateddifferentlybasedonthetypeofbusiness.

The business value calculations are one of the most important measurements you can compile for your customer analytics use cases. They are used in the Proof-Of-Value stage of implementing the customer analytics use case, and will determine if there will be enough resulting business impact to continue the process.

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Customer Acquisition Optimization

Customer Offer Optimization(up-sell/cross-sell)

Transactional Company

(Percent Conversion Rate Increase x

No. of Transactions x

Average Transaction Amt.) +

Marketing Spend Decrease Amt.

PercentOfferConversionRateIncrease

x No. of Transactions

x Increase in Average Transaction

Amount

Services Company

(Percent Conversion Rate Increase x

Annual New Customers Acquired x

Average Customer Lifetime Value) +

Marketing Spend Decrease Amt.

PercentOfferConversionRateIncrease

x No. of Customers

x Increase in Average Customer Lifetime

Value

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Case Studies

Optimizing Customer AcquisitionAfinancialservicesfirmwasfacingrapidlyincreasingcustomeracquisitioncosts.Previously, it relied primarily on basic demographic data (age, gender, etc.) gleaned from sales transactions. The company wanted to delve deeper by looking at a wider range of interactions,suchassocialmediaposts,profileinformationandpastpurchases.

UsingDatameer,thefinancialfirmfoundinterestinginsightsintotheirhigh-valuecustomers’preferences.Forexample,high-valuecustomerstendedtoliketheFoodChannelandweremorelikelytoshopatWholeFoods.Armedwiththisinformation,thefinancialfirmshifteditsmarketing strategies to target people who shared these interests and habits.

Results:

Conversion rates increased by 25 percent and customer acquisition costs decreased by 30 percent.

Reducing Customer Churn to Retain RevenueAleadingfinancialservicesretirementplanningfirmwantedtoreducecustomerchurn,particularlyamongclientswhowereapproachingretirementage.Inordertodoso,thefirmwanted to better understand their customers’ behavior to identify warning signs so they could then launch customer retention programs.

The company had a lot of data, but it was fragmented and spread across their CRM platform,website,callcenter,customerprofiles,andotherplaces.UsingDatameer,thefirmcompiledallofthisdataandthenidentifiedbehaviorsthatindicatedwhichparticularcustomersweremorelikelytoleave.Thefirmfoundthatclientswhocalledinwithafinancialadvisor or other party on the line, changed their addresses, changed employment, or browsed the company website looking for forms were more likely to leave.

Results:

Thefirmreducedchurnby50percent,increasingthelifetimevalueoftheircustomersandregaining lost revenue.

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Increasing the Lifetime Value of CustomersSurfdome, a leading European specialty retailer, needed to fuel growth through targeted marketing,customercross-sellingandhigherrepeatpurchases.Theyhadalargevolumeofdataabouttheirproducts,customers,transactionsandpurchasesindifferentsilos,andneeded to bring this data together to gain deeper insights.

The company used Datameer to integrate their data, and then analyze it to identify deeper customer segmentation and customer behavior. Surfdome used the analytic results to drivehighlytargetedmarketingtoacquirenewcustomers,offercross-selloffersinthepurchasefunnel,andusecustomermarketingofferstospuradditionalpurchases.

Results:

Surfdome boosted their customer acquisition rates by targeting the most valuable segments,raisedtheaveragepurchasesizethroughbettercross-selloffers,andincreasedthe average lifetime value of customers through greater customer loyalty and repeat purchases.

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Conclusion

Gettingyourbigdatacustomeranalyticsinitiativeoffthegroundrequiresbuy-infrommanypartsoftheorganizationandalignmentfromthefourkeystakeholders—management, the business teams, analysts and IT. Each needs to accurately understand what’s at stake, the value the new analytics will bring and what barriers exist to creating and using the new analytics.

To learn more about creating value with big data customer analytics, and how modern BI and Datameer can help you achieve this goal, please visit our website at www.datameer.com.

Customer analytics using big data can help drive business value to many parts of your organization, helping improve existing processes and drive new strategies. Big data analytics can discover key insights and opportunities, as well as opportunities you might not fully be tapping.

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Worksheet: Metrics and Questions to Ask the Organization

Metrics ¨ Marketing:

¨ Higher customer conversion rates ¨ Increased customer acquisition rates ¨ Lower cost per customer acquisition

¨ Sales: ¨ Higher sales conversion rates ¨ Increasedordersizesandwallet-share ¨ Lower cost per sale

¨ Services: ¨ Higher customer retention/Reduced customer churn ¨ Increased service productivity and faster problem resolution time ¨ Reduced customer service costs

¨ Operations: ¨ Reduced inventory costs ¨ Reduced shipping costs

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What key indicators is our company focusing on right now?

What is our current customer conversion rate, and what impactwouldaone-pointincreaseintheconversionratehave on revenue?

What is our current new customer acquisition rate and what impactwouldaone-pointincreaseintheacquisitionratehaveon revenue?

What is the current cost to acquire each new customer?

How many orders are lost with customers abandoning products in their shopping cart? How many orders fail that could be completed?

What would be the impact of a 10, 20 or 30 percent increase in sales conversion on revenue?

Whatdoourcurrentupsellandcross-selleffortslooklike?What is our current average order size?

What is the current cost per sale – sales costs/sales revenue?

What is our current customer retention rate and what is the annual revenue impact for every point increase in this rate?

How much time is spent on the average customer service call or conversation? What do the 25th and 75th percentiles look like?

What is the average cost per service call and what are the cost components? Truck rolls? Service agents? Other?

What is the current cost of inventory? What is the cost impact to improve inventory turns?

Business Metric Questions

QUESTION ANSWERS

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Where is our customer data coming from right now? What state is it in? Where is it stored? What format is it in?

Whattouch-pointsorareasofthecustomerexperiencedoesitcover?Cantheresultsbeusedinthosetouch-points?

Do we have enough demographic data and attributes on our customers? Can we create a more detailed model of their behavior and actions?

How long does the average analytic cycle take today? Why doesittakethisamountoftime?Canself-servicebigdataanalytics reduce this cycle time?

Ifoutfittedwithself-servicebigdataanalytictools,couldanalysts access the data they need?

How can the results be sent to the business teams to use it? What form do the results need to take so it is usable by the business teams?

Data Questions

QUESTION ANSWERS

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defining the value of big data customer analytics€¦ · gaining deeper insights about customers,...

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