Whirlpool - Improving Customer Experience

WHIRLPOOL CORPORATION ▪ CONFIDENTIAL

Improving Customer Experience Using Smarter Analytics


Built upon a history of strong values

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Headquartered in Benton Harbor, MI

104 year old company …

#1 major appliance manufacturer in the world

About $23 billion in annual sales

100,000 employees

70 manufacturing and technology research centers throughout the world ■ 3


7 GLOBAL $1B BRANDS

NAR

ASIA

LAR

EMEA

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Whirlpool Brand

Every meal you cook, every dish you wash, every shirt you clean – that’s caring. And every act of care you give helps the people you love become people who love.

That’s why at Whirlpool®, we believe that helping you care for the ones you love is the most important thing we can do.

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ICONIC DESIGN

THOUGHTFUL CRAFTSMANSHIP

SUPERIOR PERFORMANCE THAT FUELS CULINARY PASSION

KitchenAid® brand fuels cooking passion through great

cooking tools that bring out the best in inspired cooks.

®

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Brand success begins with consumer understanding …

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First consider the kitchen environment..

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Then consider the food we prepare there..

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The connections food carries in our lives ..

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All of which requires the right tools to make her successful

To deliver outstanding experiences, appliances must offer: The desired features Leading quality Intuitive, innovative designs

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The connected future

We are transitioning to Smarter Homes

Accelerated analytics

Streaming data on usage

Patterns, consumer demand

Early detection

Suggestive customer instructions to aid

their experience

Proactive alerts

Preventive Maintenance

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Understanding the consumer helps design and deliver the right product and experience

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Deliver Branded Consumer Experience

Our goal is to improve consumer experience.

How we assess that experience must be:

1. Objective2. Measurable3. Timely

For this we are using IBM SPSS for predictive analytics.

This sets the foundation for connected data analysis.

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Case Study 1: Understanding root cause for customers filing warranty claims for a leaking dishwasher

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Issue 1:

Consider a customer who files a warranty claim for a leaking dishwasher.

Issue Implications Response

Leaking is bad!

Can destroy the kitchen floor.

Harms customer experience and loyalty.

Very bad for brand reputation.

Dispatch a service technician and fix it

Examine suppliers, parts and sub-assemblies

Contact engineering

Revisit house of quality

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Dispatch a service technician and fix it

Examine suppliers, parts and sub-assemblies

Contact engineering

Revisit house of quality


Issue 1:

Consider a customer who files a warranty claim for a leaking dishwasher.

Issue Implications Response

Leaking is bad!

Can destroy the kitchen floor.

Harms customer experience.

Very bad for brand reputation.

Did we hear the issue correctly?

Have we accounted for true

root cause?

Have we aligned our solution to

root cause?

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Dishwasher Leaking

What’s happening here?

Hint: it may not be what it seems! A service tech can spot root cause straight away.

Let’s read their notes. ■ 18


Customer Report: Leaking

Leaks - No Parts Replaced - Group BDIAGNOSED UNIT OVERSUDSING. ADVISED ON PRODUCT USAGE AND SOAP USAGE.FOUND WATER TEMPS OK.ADJUSTED RINSE AGENT CUP.ALSO ADVISED CUST ON DETERGENT USAGE._ JOB CMPLTDCUSTOMER INSTRUCT - ADVISED CUSTOMER ON SOAP USAGE AS WELL AS THE DIFFERENT CYCLES. LEAK WAS DUE TO A SOAP USAGE ISSUE. UNIT TESTED OKCHECK INSTALLATION-OKAY FOUND ERROR CODE F6E4 ADVISED ON USING THE PROPER DETERGENT AND LOADING TECHNIQUES.TEST ALL FUNCTIONS OF UNIT_ COULD NOT REPRODUCE FAILURE AT THIS TIME. POSSIBLY WAS CONTAMINATED WITH DISH SOAP. CUSTOM ER INSTRUCT.LEVELED AND TESTED ALL OK. ADVISED CUSTOMER ON SOAP._ JOB CMPLTD_ SNAPPED 1 QUICKPICS***FCC***LOTS OF SUDS IN DW .USING _ORGANIC_ DETERGENT .RECOMMENDED PODS OR OTHER DETERGENT FOR DW ._ JOB CMPLTD_ SNAPPED 2 QUICKPICSFOUND THAT VENT HAD NOT BEEN SECURED FROM FACTORY_ SO WATER WAS LEAKING FROM VENT. TIGHTENED AND TESTED_ UNIT CK_ED OK._JOB CMPLTD_ SNAPPED 1 QUICKPICSINSTRUCT CUSTOMER FOR PROPER DETERGENT USE WORK FINE_ JOB CMPLTDINSPECTED UNIT _ TESTED COMPONENTS -- INSTRUCTED CUSTOMER ON CYCLE SELECTIONS TO MAXIMIZE CLEANING ABILITY OF PRODUCT

Without text analytics, we would have to manually read all claims. Today, we pick ‘random’ samples.

What do you see?

Do you see any trends?

Major recurring themes?

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Correct!

A number of customers are mistakenly using the wrong detergent in the dishwasher.

Using regular dishsoap instead of dishwasher detergent will create excessive sudsing which bursts out of the seal.

The root cause here is improper usage, not a product design issue.

Our response must therefore be for better customer instruction, not product redesign.

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‘By educating the customer we fixed the problem’

However:

We had to pay for a technician to visit the home (labor)

Technicians were deployed on these cases instead of other issues with true mechanical situations

We have a skewed understanding of true root cause Paretos

We may not be investing in the proper way to improve customer experience

So it keeps happening

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So how do we objectively measure and assess?

(without manually reading all the notes!)

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Leaks - No Parts Replaced - Group BDIAGNOSED UNIT OVERSUDSING. ADVISED ON PRODUCT USAGE AND SOAP USAGE.FOUND WATER TEMPS OK.ADJUSTED RINSE AGENT CUP.ALSO ADVISED CUST ON DETERGENT USAGE._ JOB CMPLTDCUSTOMER INSTRUCT - ADVISED CUSTOMER ON SOAP USAGE AS WELL AS THE DIFFERENT CYCLES. LEAK WAS DUE TO A SOAP USAGE ISSUE. UNIT TESTED OKCHECK INSTALLATION-OKAY FOUND ERROR CODE F6E4 ADVISED ON USING THE PROPER DETERGENT AND LOADING TECHNIQUES.TEST ALL FUNCTIONS OF UNIT_ COULD NOT REPRODUCE FAILURE AT THIS TIME. POSSIBLY WAS CONTAMINATED WITH DISH SOAP. CUSTOM ER INSTRUCT.LEVELED AND TESTED ALL OK. ADVISED CUSTOMER ON SOAP._ JOB CMPLTD_ SNAPPED 1 QUICKPICS***FCC***LOTS OF SUDS IN DW .USING _ORGANIC_ DETERGENT .RECOMMENDED PODS OR OTHER DETERGENT FOR DW ._ JOB CMPLTD_ SNAPPED 2 QUICKPICSFOUND THAT VENT HAD NOT BEEN SECURED FROM FACTORY_ SO WATER WAS LEAKING FROM VENT. TIGHTENED AND TESTED_ UNIT CK_ED OK._JOB CMPLTD_ SNAPPED 1 QUICKPICSINSTRUCT CUSTOMER FOR PROPER DETERGENT USE WORK FINE_ JOB CMPLTDINSPECTED UNIT _ TESTED COMPONENTS -- INSTRUCTED CUSTOMER ON CYCLE SELECTIONS TO MAXIMIZE CLEANING ABILITY OF PRODUCT

We can use SPSS Modeler to mine the text data and objectively

measure root cause scenarios

Raw Text

SPSS Modeler

Root cause Pareto (non-repair group)

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We had the data to identify root cause of these sorts of claims.

We just needed a better way to put that data into the proper context.

This helps us better connect to the customer issue and devise a targeted response.

What soap should you use?

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This is an extendable method.

Case study 2: Let us consider another example where a customer reports their dishwasher is not draining. This can cause backups.

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Installation problems

Despite written instructions and labels, Do-It-Yourself (DIY) appliance installers sometimes forget to properly connect their new dishwasher to garbage disposer.

Customer Warranty Claim Symptom = Not Draining

Analysis of technician notes reveal that in many cases the job is as simple as knocking out a dummy display plug that goes to the disposal.

Design of Experiments for Instructional videos tested well.

The analytical models helps quantify the business case for engaging video production.

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We can assess which parts and sub-assemblies are affected..

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Customer Report: Dishwasher LeakingWe can also identify which parts or assemblies may be of concern.

First we divide claims into those where parts were replaced in service visits and where no parts were replaced:

By directing the research down the appropriate pathway, we can further identify the rank-order of problematic parts and respond accordingly.

Drill-downs here help identify range and profile of customer instruction scenarios

Exploration in this subpopulation reveals potential design or product defect scenarios for resolution

No parts replaced

Parts replaced

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Improve Reporting – from symptoms to root causes

Not perfect! But gets us closer to the true root cause

We can now add value to our existing data by better quantifying root cause:

Customer Symptom Service Technician Resolution Service Tech CategorizationAnalytical Forecast Root Causeleaking LEVEL UNIT TO STOP THE LEAKING rack stop Levelingnot closing/latching UNIT WAS NOT LEVEL_ ADJUSTED DOOR SPRINGS AND LEVELED UNIT_

WORKING AND CLOSING PROPERLY_ JOB CMPLTDdoor balance Leveling

miscellaneous CHECKED UNIT FOUND UNIT OUT OF LEVEL_ LEVELED UNIT CYCLED UNIT TESTED OK_ JOB CMPLTD.

rack Leveling

not cleaning UNSTUCK WATER LEVEL FLOAT IN W/P D/W float Levelingnot draining RAN DIAG AND FOUND NO EROR CODES. RAN THROUGH AUTOMATIC

TESTFINE. NOTICED TAT UNIT ISNT INSTALLED LEVEL UNIT IS LEANING FWD 1/4-1/2 A BUBBLE CAUSING THE UNIT TO LEAK A LITTLE OUT THE FRONT. DRAIN HOS

tub Leveling

not cleaning UNIT WAY OUT OF LEVEL CAUSING POOR WASHING AS WATER WOULD TRIP FLOAT SWITCH PREMATURELY. LEVELED AND TESTS GOOD._ JOB CMPLTD

float Leveling

noise 32170 MOTOR DRAIN NOTES: CHECKED DRAIN MOTOR. ALL CK OK. COMPLAINT OF NOISE. S EEMS OK. NORMAL SOUND LEVEL. NOTES: CHECKED DRAIN MOTOR. ALL CK OK. COMPLAINT OF NOISE. S EEMS OK.NORMAL SOUND LEVEL.

drain pump Leveling

miscellaneous LOWERED REAR WHEELS TO THE LOWEST POSITION TO ALLOW THE FRONT OF THE UNIT TO COME UP LEVEL. STILL NOT ENOUGH ROOM TO RAISE THE FRONT. INSTALLATION ALTERATIONS MUST BE MADE TO PROVIDE ROOM. CUSTOMER WI

roller Leveling

1. Symptom 2. Service Tech Resolution 3. Tech Categ. 4. SPSS Forecast

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We can improve our understanding of issues: RACK

Get the data to create more insights and faster …

Before we had only a high-level, generic understanding of RACK issues.

With SPSS text mining we now have a more descriptive issue of problematic parts that have impacted our customer experience.

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Case study 3: Identify areas of opportunity within our factories..

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Changing Processes to Improve Product Quality

Our dishwasher factory in Findlay, Ohio.

These are big places. We have over 70 manufacturing and research facilities world wide.

Lots of complex processes. Moving parts. 24 hour shifts. Tens of thousands of workers.

Static or partial analyses with interesting findings are not good enough.

Results must be able to integrate across the various people, process and equipment systems.

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Locating points of improvement:

We measure a lot in these factories, but are we looking in the right area?

Example:In one model we identified an existing process (loop “X”, right) and quantified elevated risk for product-related warranty claims of 23% with this particular sub-routine.

Bottom LineAnalytical models can help spot sub-optimal processes, profile their impacts, and help guide interventions.

To predict operational issues, thoroughly model the processes with input from Subject Matter Experts. Challenge status quo where needed.

Step a

Step b

Step c

Step d

Process Loop X

Associated with 23% elevated risk for field failure

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Can we learn more from data? Create focus

We applied predictive analytics to the product test data (so-called ‘End-of-the-Line’ data).

A classic black belt approach is to tighten all variance (dotted lines).

However this may not be feasible in all scenarios:• May result in too many false positives.• May be too costly to implement• May not solve the problem• Too broad

Analytical modeling shows that out of 17 tests, just three are the main predictors for failures in the field for a given SKU.

Now we can focus. This helps reduce false alarms, ensure production targets are met, and more effectively deal with capacity constraints in the factory.

TAKEAWAY: integrate site tours to your development. Walk the plant. Talk to the line operators. Incorporate insights into the analytical modeling design.

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IoT allows us to better understand customer needs

Analytic foundations and best practices are required!

Anticipate the situation

Help our customers maximize the value they get from our products

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Lessons learned from our analytical journey

Data mining for success is an iterative process.

Allow time for each of these phases to be implemented.

Empower your people to investigate the issues.

Set milestones for the research project

Engage experts, analytical or subject matter experts (SME) as much as you can

Don’t assume you need to collect more data; start with what you have.

Allow time for data preparation. Proper curation is essential for good models.

Analyze smarter.

CRISP-DM: Cross-Industry Standard Process for Data Mining

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Value Drivers

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Cognitive

Prescriptive

Predictive

Descriptive

Batch Analytics can be used to correlate data and find patterns and pathways that lead to on-demand, real-time actionable insights to create connectivity value.

• Create knowledge about our products and businesses: We can better quantify the issue and identify the main drivers. We are able to correlate and make sense of complex variables like consumer comments, technicians reports, and parts.

• Make optimum real time decisions. Models created with batch data analysis allow statistical decision making based on connected data about the product and its environment.

DEVELOP ANALYTICS CAPABILITIES

Bat

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nnec

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IDENTIFY ROOT CAUSES, PREDICT


Improving customer experience using smarter analytics

At the end of the day our customer experience mission remains the same.

We have more to do, but we are becoming better equipped to hear our customers and take the appropriate action.

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Thank you.

Emanuele Mora James BettlesWhirlpool Corporation IBM Global Business ServicesGlobal Product Strategy Director Business Sales & Delivery [email protected] [email protected]

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mailto:[email protected]

mailto:[email protected]

Documents

Whirlpool - Improving Customer Experience