great analytics start with a great question

7/30/2019 Great Analytics start with a Great Question

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Great Analytics startwith a Great Question



Analytics must deliverbusiness outcomes -otherwise it’s just reporting



Analytics needs a starting point

A common pitfall of analytics,especially when data sources are vastand varied, is to analyse them withoutthe eventual business outcomein mind. Instead, organisationsoften make the mistake of simplyretrofitting “scattergun” findings backto business issues.

While most organisations haveinvested in the right technologyplatforms to enable analytics, thelack of strategic intent behind theanalytics means that their return oninvestment can be slow.

The risk in approaching analyticswithout having a clear goal is that too

much time is spent analysing—and toolittle time spent answering criticalquestions.

The solution is to start your analyticswith a great question—a questiongenerated from a strong businesshypothesis, with the goal of solving anissue or satisfying a need.

Questions need to come from a varietyof sources beyond just your analyticsteam. A great question must be linked

to a business outcome, and there mustbe a compelling reason for asking thequestion—the “so what” factor. Don’tfocus on finding a perfect answer.Instead, focus on knowing more thanyou knew before, putting insights intoaction and producing value.

For example, when a large Australianclient wanted to develop new andinnovative offers for their customers,they did things the way they had alwaysdone them: “Let’s ask the analyticsteam to resegment the customer baseand identify target groups of customerswith similar needs and behaviours. Inaddition, we should ask them to build a

new propensity model so we know whois statistically more likely to take up anoffer, or who is about to leave us.”

The Accenture team saw potentialvalue for their client in challenging thestatus quo. Rather than spending 12weeks building the new segmentationand propensity models, the Accentureteam suggested value could beachieved faster by taking a business-led approach.

Working with the sales, product,marketing, segment and analyticsteams, the Accenture team developedmore than 20 ideas for new andinnovative offers that could be tested

using analytics. For example, one ideafocused on a loyal group of customersthat conducted regular transactionswith the large Australian client. Theclient wondered if these customers werereaching a point in their relationshipwhere they would begin to take smallpieces of their business to a competitor.A business question was established:

“Is a portion of our customer basegradually taking some of their businesselsewhere (‘silent attrition’), and if so,is this of greater concern to us thancomplete customer defection?”

Using analytics, the Accenture teamidentified a group of customers that hadbeen silently taking small pieces of theirbusiness elsewhere. Within three days(rather than 12 weeks), they determinedthat this gradual loss resulted in greaterrevenue loss than complete customerdefection. A recommendation was made,in collaboration with the wider teams of sales, product, marketing and segment,

to develop a proactive offer to stemgradual churn.

In this example, the analytics teamwas still employing the same modelingtechniques they normally would. Thedifference was that they struck abalance between letting the data decideand having a clear business objective.The clear business objective gave focus,helping them deliver value more quickly.

Analytics teams across Australia examine and dissect data everyday, sifting through past performance and examining customerprofiles in search of trends. Sound familiar? You’re not alone.Many organisations wait for their analytics team to come tothem with the answers, without first considering the question.

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Great analytics start with a great questionGenerating a question is about approaching your highest-priority business problems from a different angle so you cansolve them innovatively. This is why it is so important to bringtogether individuals from your analytics, marketing, segment,product and sales teams to help generate questions.

As shown in Figure 1, there are fourkey steps for successful analytics, andthe first one is all about defining theright question.

To define the right question,organisations should:

• Create new ways of doing things. In this model, analytics questions arenot generated in a vacuum by theanalytics team. Instead, it requiresbuy-in and participation from multipleteams, including product, analytics,marketing, segment and sales.

• Encourage maximum collaborationbetween all stakeholders to generatequestions. This requires individuals from

product, analytics, marketing, segmentand sales teams to collaborate, testand prioritise questions accordingly.Leverage existing forums whereappropriate, to enhance output andcultivate collaboration.

• Link to business outcomes. Adoptan approach that links your questionsand their ”so what” to how they wouldhelp drive the organisation’s strategicimperatives or existing tactical initiatives.

• Always confirm stakeholder buy-into a targeted question. Does the questionreally need to be answered? Will theanswer deliver critical business intelligencethat helps further a business goal?

• Think ahead to how you’ll answerthe question. When generating yourquestions, consider the obstacles youmight face in answering them—and startto address them.

These actions will help you get themost out of your team’s experience andcreate ownership for people’s questions.More importantly, it will help yourorganisation build a strong platformfrom which to perform your analytics,test and learn, and then scale theopportunity across the business.We’ll discuss these steps later.

1. Define the question: Bring your teamstogether to assess the most importantbusiness problems for analytics toanswer.

2. Perform analytics: Using the questionto guide your thinking, run analytics tofind your answers.

3. Test and learn: Validate your insightby applying the findings to a smallsample.

4. Scale: If you got it right, apply theinsights from your analytics across thefull extent of the initial business issue.

Figure 1. Four steps for successful analytics



How to come up with agreat question


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…the role of sales is to:Take a lead role in the development of the new questions and campaigns, withsupport from the marketing, segment,analytics and product teams. Sales are

integral in identifying opportunities forfurther analysis (based on direct contactwith customers) and helping providereal-time feedback on the products andoffers from the customer’s perspective.

…the role of segment andmarketing is to:Identify how the question will applyto the customer segments within thetarget market. When coming up with

questions, marketing and segmentrepresent the customer from theorganisation’s point of view. Segmentshould provide a perspective on theopportunity, competitor activity andareas for exploitation. Marketing willguide the question to answer howchannels, event-based triggers andoffers can be used to most effectivelyflag and target specific customers.Together, they must also addressany sales or service improvement

opportunities that come out of the analytics.

Once the question is defined, they willneed to develop the contact strategyand creative requirements of theanalytics-driven campaign, or tailorthe organisation’s channel and serviceexperience to address the findings.

…the role of product is to:Help to define the question, focusingon offers, features and pricing. Productis critical to providing a perspective onthe cost and benefit of any changes

to product dimensions. Product usesanalytical information to drive theirdecisions, and should be embedded inthe process from the beginning.

…the role of analytics is to:Assess the validity of new questions bylooking at the size of the opportunityand expected take-up rate, based onwhat has worked in the past. Analyticsbring their aptitude and experiencein developing insights from analysingdata in new ways. The focus for theanalytics team includes deliveringinsights that assist in forming newoffers (for example, increasing revenueor reducing cost), performing additionaldata analysis (as agreed upon by allstakeholders), and championing aculture of data-driven decision making.



Linking analytics to business outcomes makes a powerfulbusiness case

Have you ever tried to develop a business case for improving yourorganisation’s analytics capability? When we see organisationsstruggling to quantify the benefit associated with analytics, it isoften because they are not involving all parts of the business.

Picture the scenario where your data owner, usually IT, wants toput together a business case to improve analytics. Analytics isthe right thing to be doing, but from IT’s perspective, the businesscase will be more about cost savings than revenue generation. Toadd more of the “so what” and justify the time and expense of developing a robust analytics capability, the business case needsa revenue lens. This is what a customer and sales view can bring,making the business case truly compelling.

A combined IT and business perspective can help put together amuch stronger, more persuasive argument for building the reachof analytics within your organisation.

Figure 2. To be truly compelling, a business case for analytics requires both IT and business

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Analytics must support business initiatives.It is not an independent endeavour.



Customer ServiceWe offer a range of sales andservice channels. Are therepatterns in a customer’s recentservice interactions acrossthese channels that would helpus to predict a future purchaseor indicate an early warningsignal for possible churn?If we know this, then so what? This insight can help us target customerswho are likely to purchase, or thosethat are at risk of churn. For example,analytics may detect a significant

increase in customer enquiries thatcould go either way: they are lookingto purchase a new product, or they arelooking to leave us, as indicated by theircomparison shopping behaviour.

Sales Staff Performance within our salesteam varies significantly. Whatare the characteristics sharedby our top sales people that arekey to their success?If we know this, then so what? If we can understand the salescompetencies, personality traits andbehaviours of high performers, we cansee the path to shifting and reshapingthe performance curve of our sales force.

Examples of great questionsto be answered withanalytics(Tied to a business “so what”)

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ProductsWe believe groups of similarcustomers buy or use ourproducts in a similar way. Forexample, that they use thesame channels, purchase inthe same sequence and repeatpurchases at regular intervals.Is this true?If we know this, then so what? If we understand a customersegment’s purchase behaviour today,we can logically offer customerswith similar characteristics the most

obvious, next-best product, thusdriving increased sales.

ProcessesWhich common customer callcentre transactions take thelongest? For these transactions,which processes do employeesfollow, and which tools andresources do they use?If we know this, then so what?For our key processes, we can identifyerrors, bottlenecks and training gapsto improve productivity and customerexperience—or even reduce thelikelihood of a customer triggering aparticular process to begin with.

CompetitionIn the current aggressive andcompetitive market, it will behard to win new business. Howmuch scope is there to leverageour existing customers to stayahead of our competitors (forexample, with increased share-of-wallet or reward referrals fornew customers)?If we know this, then so what?Increasing sales within the currentcustomer base and relying on a referralstrategy to win new business may notdrive the levels of growth required. Wemay also need to remain competitive orbe aggressive to win new business.

New revenue streamsAre other companies interestedin our data, thereby creatinga possible additional revenuestream for us?If we know this, then so what?Our customer base and the informationwe hold on them (within privacylegislation) may represent a new,incremental revenue stream. Informationcan be packaged and sold to newand potential customers, resulting ingame-changing product innovation andpartnering. This approach is commonlyreferred to as data monetisation.



Answer the question –then act on the answer

The remaining steps in the model areas pivotal to the analytics function asdefining the great question, but arenot the focus of this point of view.Thus, we will examine them briefly.

Analytics will answer:

• “What is it today?”

• “What could it potentially be?”

• “What is the value of the difference?”

If analytics shows that an opportunityexists (that is, the potential value isgreater than the value today), then thenext step should be to take action.

In the case of the European bank ABNAmro, analytics were first employedto gain a better understanding of howcustomers were using their website. Thekey question would have been: “How canwe optimise the website to promote useand improve the customer experience?”

For ABN Amro, the steps highlighted onthe previous pages were key to distillingtheir findings into actionable insights.This meant using web metrics to identify

issues with the website, so as to improveits structure, layout and content. The “sowhat” was to initiate and measure testpages that highlighted a stronger andmore prominently placed call to action.

By making these changes, ABN Amrowas able to improve their web conversionrates by 290 percent—thirty times theuplift they had anticipated.

Once you have an answer, the next stepis to relate it back to the original corebusiness question. As in the case of the large Australian client highlightedpreviously (see page 3), the hypothesisis an important foundation for the nextsteps of building an analytics strategyand approach. In other words, to helpensure that your insights are valuable,you need to establish a scientificapproach for acting on the answer.

Example of acting on ananswer: A marketing campaignA test-and-learn approach, in mostcases, should be used if the actionon the answer is to target a group of customers via a marketing campaign.For example, if the organisationquestioned whether they had a sourceof untapped customer potential, andthe analytics validated the hypothesisthat a large opportunity did exist, theycould develop an offer to appeal to this

potential customer group.

Prior to delivering a fully scaledand multi-channel campaign, theorganisation should test the offerwith a small group of customers. Aftertesting the campaign, the learnings

are used to adjust the next iteration of the campaign. This cycle may repeat anumber of times. Only when there areno more adjustments required, and thetest has shown itself to be successful(that is, when test campaign responserates are above control or hurdle rates)should the campaign be scaled. Thisapproach requires descriptive progresstracking and review activities to ensurecontinual learning and improvement.

The goal here is to rapidly enhance the

quality and relevance of the analyticsinsight given to sales staff. A successfultest-and-learn approach will want toachieve the following outcomes:

• Refine and optimise the campaign,as well as iron out any problems priorto scaling.

• Create a more responsive process forrepeating successful campaigns.

• Focus the marketing effort towards

quality and applicability, ratherthan volume.

Answering your analytics question may seem like an obvious step,but it’s one that many organisations struggle with. The key isnot in perfecting all aspects of the question to derive an exactanswer. Rather, focus on finding an answer that’s more accurateor relevant than you had before. This avoids “analysis paralysis”.


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Measure the effectiveness of your answer

Things change over time, butfew organisations revisitpreviously establishedassumptions until they are wellpast their effective expirationdate. It’s important to ensurethat your answer is still valid—

and to adjust it if required.Here are three steps tomake sure:

1. Always link your question tomeasurable outcomes, suchas increased revenue, reducedcosts, improved customersatisfaction or betterstaff engagement.

2. Measure the impact of youranswer. For example, if yourorganisation generated anoffer to exploit a new customermarket, understand the successcriteria and measure thefinancial outcomes.

3. Finally, use return oninvestment (ROI) hurdle ratesto validate the financialfeasibility of acting onthe answer.

“Did we get it right”?

By following these three steps,you would continually learnmore about your question, youranswer, and most importantly,your customers and what works.

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High Performance.Delivered.

Gaining a competitive advantage with analyticsrequires more than the best analytics technologyand the smartest people staring at data all day. Itrequires a great question as a starting point: a strong,business-driven question to guide and focus theorganisation’s energy and resources. This questionshould be developed with a range of stakeholders thateach brings a valuable customer perspective to theorganisation’s key challenges.

The organisations that are the most effective andefficient at answering their great questions, thentaking and measuring action, will stay ahead of theircompetitors and lead the pack.

Remember, it’s not about finding theperfect answer. It’s about knowingmore than you knew before, andputting the insights into action to deliverbusiness outcomes.



ContactsPhilippe KonfinoManaging Director S&CS, Asia [email protected]

Alex BurrowsSenior Manager, S&CS Australia &New [email protected]

About AccentureAccenture is a global managementconsulting, technology services andoutsourcing company, with 257,000people serving clients in more than120 countries. Combining unparalleledexperience, comprehensive capabilitiesacross all industries and business

functions, and extensive research onthe world’s most successful companies,Accenture collaborates with clients tohelp them become high-performancebusinesses and governments. Thecompany generated net revenues of US$27.9 billion for the fiscal year endedAug. 31, 2012. Its home page iswww.accenture.com.

About Sales & Customer ServiceAccenture’s Sales & Customer Service(S&CS) service line helps organisationsachieve high performance by transformingtheir marketing, sales and customerservice functions to support acceleratedgrowth, increased profitability and greateroperating efficiency. Our research, insightand innovation, global reach and deliveryexperience have made us a worldwideleader, serving thousands of clientsevery year, including most Fortune® 100companies, across virtually all industries.

Disclaimer

This point of view is intended as a generalguide and not as a substitute for detailedadvice. Neither should it be taken asproviding technical or other professional

advice on any of the topics covered. Sofar as Accenture is aware the informationit contains is correct and accurate butno responsibility is accepted for anyinaccuracy and error or any actiontaken in reliance on this publication.This publication contains Accenturecopyrighted material and no part of itcan be copied or otherwise disseminatedwith Accenture’s prior written consent ineach case.

Copyright © 2012 AccentureAll rights reserved.

Accenture, its logo, andHigh Performance Deliveredare trademarks of Accenture. 12-1689

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