5 obstacles to marketing intelligence - part i

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First part of the 5 Obstacles to Marketing Intelligence in organizations

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5 Obstacles to Marketing IntelligenceWhat Stops Us From Achieving Marketing Greatness

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In the first 2 lectures, I gave you an i d i h k iintroduction to what marketing intelligence is and how it can change the way marketing is done in our companies.

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Now we will start to look at what keepNow, we will start to look at what keep organizations from achieving greatness through marketing intelligencethrough marketing intelligence

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I am sure you are starting to wonder h h ’ d fi d k iwhy I haven’t defined marketing 

intelligence, metrics or any other terms related to the topics we are discussing?

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Examples and understanding is always better than a definition.a ays be e a a de o

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A small test for you working f i lprofessionals:

Talk to your marketing department about how they do their marketing…

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Then ask them about marketingThen ask them about marketing intelligence and analytic marketing and watch their eyes glaze overwatch their eyes glaze over.

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While most marketers can easily explain what they are doing in terms ofwhat they are doing in terms of marketing activities, very few can explain what intelligent marketing orexplain what intelligent marketing or analytic marketing would look like.

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In the process, 5 key roadblocks or obstacles were identified that keepobstacles were identified that keep marketers and organizations from adopting a marketing intelligenceadopting a marketing intelligence position.

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What Are The 5 Obstacles?

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Obstacle 1Obstacle 1: Getting Startedg

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We don’t know how.

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We don’t have the right metrics.

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Th bl i t t littlThe problem is not too little data, but too much that isn’t ,useful.

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We don’t know where to start.

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Ob t l 2Obstacle 2: Causalityy

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There are too many confounding f t i th t tfactors – meaning that too many campaigns overlap making cause p g p gand effect impossible.

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There is a time delay between ythe marketing campaign and a 

icustomer action.

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Awareness campaigns do not p gdirectly results in sales, but our CFO t t fi i l ROICFO wants to see a financial ROI.

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Ob l 3Obstacle 3: Lack of DataLack of Data 

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We are a business‐to‐business (B2B) d ll(B2B) company and sell indirectly. As a result, we don’tindirectly. As a result, we don t know who our customers are.

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W ’t ll t t d tWe can’t collect customer data due to privacy issues.p y

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Ob t l 4Obstacle 4: Resources and Tools

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W d ’t h th ti d/ itWe don’t have the time and/or it costs too much.

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We don’t have the tools and/or /systems to support analytic 

k timarketing.

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We are marketers and can’t communicate with information t h l (IT) ltechnology (IT) people.

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IT builds systems, but they are y , ynot the resources and tools we 

dneed.

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Ob t l 5Obstacle 5: People and Changep g

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W d ’t bWe don’t measure because we don’t want accountability.y

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O i ti ll fOur incentives are all for marketing activity, not results.g y,

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W d ’ h l fWe don’t have a culture of measurement.measurement.

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W d ’t h th kill fWe don’t have the skills for analytic marketing.y g

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Our organization is resistant to gnew ideas, such as analytic 

k timarketing.

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Marketing is creative; imposing g ; p gmetrics and process will kill 

ti it d i ticreativity and innovation.

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Breaking Down the 5 Obstacles

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Obstacle 1: G tti St t d FGetting Started—Focus on 

Collecting the Right Data and g gCreate Momentum

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Royal Bank of Canada started th i j t d k titheir journey toward marketing intelligence by working with their g y gown resources.

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“You have to look at what you do t d d thi k “H I dtoday and think… “How can I do this better, cheaper, faster andthis better, cheaper, faster and smarter?”

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Every year around IRA y ycontribution time, the sales force t RBC i li t t llat RBC was given a list to call.

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The average yield per sales 1 2 l t fperson was 1 – 2 people out of 

10 accepting the offer accepting10 accepting the offer accepting the offer being made by RBC.

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Th RBC k ti t b iltThe RBC marketing team built a model to rank and score the list based on the potential to 

ib h US$ 5 000contribute more than US$ 5,000 to an IRA.to an IRA.

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The model involved analyzing y gmore than 1 million customers, f 12 th f d t dfor 12 months of data, and score them to find the top 250,000 t e to d t e top 50,000that would potentially 

ibcontribute.

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While the size of the dataset i ti id ti iblseems intimidating, possibly 

solving the problem for 10, yousolving the problem for 10, you can solve the problem for 1,000.

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At RBC ll ti th l tAt RBC, collecting the relevant data to do the scoring required a g qgreat deal of manual legwork, 6 

h f ff dmonths of effort and approximately US$ 100,000.approximately US$ 100,000.

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The goal was to generate a g gtargeted list of the top 25 

llcustomers to call.

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The results were impressive.

8 out of 10 customers called accepted the offer.

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But….

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I k i f h l fIt took time for the sales force to realize the value of the new list.realize the value of the new list.

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I th fi t l 25% f thIn the first year, only 25% of the sales force participated.p p

I h hi d 75%In the third year, 75% were participating.participating.

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D t th f thi llDue to the success of this small experiment, RBC was able to p ,build a business case that b h h US$ 4 illibrought them a US$ 4 million marketing initiative.marketing initiative.

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Wh t did l ?What did we learn?

You don’t need 100% data and a l i illi d ll i fmultimillion dollar infrastructure 

to get started.to get started.

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The key question:

What 20% of data will give youWhat 20% of data will give you 80% impact?

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Walgreen’s Pharmacy

Walgreen’s Pharmacy is a US$ 59 billion annual $revenue pharmacy company with 6,850 stores across the United States

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Walgreen’s Geo‐SpatialThe geo‐spatial picture shows dots that are customers and where they live and are coded by shapelive and are coded by shape depending on which of the three Walgreen’s stores they shop.The “Diamond” customers shop atThe  Diamond  customers shop at Store 1The “Square” customers shop at Store 2The “Star” customers shop at Store 3

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Walgreen’s Marketing StrategyWalgreen’s has always marketed using flyers in newspapersThey pay based on zip codes (theThey pay based on zip codes (the dashed line in the map)The marketing manager noticed something very interesting – thesomething very interesting  the circle is two miles in radius and after looking at the pictures, he noticed that there are no dots for a store more than two miles from any store.The conclusion – if you live two miles from a pharmacy store, you probably don’t shop there.

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At this point, Walgreen’s treated h l l ll l d lleach locale equally – equal dollar 

amounts in each zip code acrossamounts in each zip code across the United States.

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But the data shows that if there i t ithi t il fis no store within two miles of the zip code, customers do notthe zip code, customers do not shop at the store

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B d thi d t diBased on this understanding, Walgreens stopped spending g pp p gadvertising dollars in all zip codes i h i hi ilwithout a store within two miles 

of the zip codeof the zip code

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Total cost of collecting the data gand creating the plots –US$ 200 000US$ 200,000.

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Impact to sales revenues – 0

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Impact to marketing was a cost p gsavings of more than US$ 5 illimillion

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“We started with simple Excel pspreadsheets that contained the d i i i l iadvertising circulation distributions by zip code. It wasdistributions by zip code. It was not hard to get the advertising data into the software.”

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The technology – a PC, ESRI map gy , pand graphing software and a d t t f E l d h tdata set of Excel spreadsheets

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The bigger challenge was h i W l ’ b ichanging Walgreens’ business process to use analytic marketing p y ginsights

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The mistake they made was too ymany changes too quickly that 

d l f blmade people uncomfortable. Within a few weeks, things hadWithin a few weeks, things had returned to the old way of doing things.

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So they started small….

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They found one Store Operations VP d di t i t th tVP and a district manager that were willing to try doing thingswere willing to try doing things different.

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They showed them the pictures y pand explained that it didn’t make 

t d US$ 80 000 isense to spend US$ 80,000 in advertising in a zip code five ad e t s g a p code emiles from a store to generate 

$ i lUS$ 20,000 in sales.

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After 5 experiments, Walgreens’ t US$ 300 000 ff th b t icut US$ 300,000 off the bat in 

advertising cost and showed that gthe approach really worked.

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With the win, they set up a process to review the marketingprocess to review the marketing spending with each district manager throughout the United StatesStates. 

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Overcome Obstacle 2: Causality—Conduct Small Experiments 

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There are too many possible causesThere are too many possible causes of anything you measure to identify a single cause and effect; overlapping marketing campaigns o e app g a e g ca pa g smakes it impossible to tell what’s working and what’s not working!working and what’s not working!

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The “answer” is one of taking a i d di i li dsystematic and disciplined 

approach to marketing campaignapproach to marketing campaign execution.

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Conduct small experiments, i l ti i blisolating as many variable as possible to see what works andpossible to see what works and what does not.

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While we know that most marketers are aware of this 

h ill b h k d tapproach, you will be shocked to learn that almost 70% do not use ea t at a ost 0% do ot useexperiments to pilot test 

k i imarketing campaigns

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Why?

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Most marketing organizations’ g greward systems are based on ti it t ltactivity, not results.

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Harrah’s Entertainment regularly g ydesigns experiments to quantify th i t f it k tithe impact of its marketing.

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It designed an experiment t ti t f f ttargeting two groups of frequent slot machine players in Jackson,slot machine players in Jackson, Mississippi.

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Th i t h d tThe experiment had two groups – a control group with a standard g pmarketing offer and a test 

k i ff ll d “Thmarketing offer called “The Challenger”Challenger

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Harrah’s Offers

Control Group “The Challenger”Control Group The Challenger

A $125 package of a free room, two steak dinners and $30 in

$60 in free casino chips, no hotel room and no steaktwo steak dinners, and $30 in 

free chips at the casinohotel room, and no steak dinner

Gaming Activity SignificantlyGaming Activity unchanged

Gaming Activity Significantly Higher

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Harrah’s Entertainment tested the experiment across other US 

higeographies.

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A lt H h’ bl tAs a result, Harrah’s was able to cut the budget for this type of g ypmarketing by more than 50% and i h f f hincrease the performance of the marketing.marketing.

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Wh d thiWhy does thisexperiment work?p

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By limiting who gets the i t l ff dd thexperimental offer addresses the 

causality issue and there is a yminimal time delay between 

ti th i dexecuting the campaign and obtaining the results of theobtaining the results of the campaign.

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