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5 Obstacles to Marketing Intelligence What Stops Us From Achieving Marketing Greatness

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In the first 2 lectures, I gave you an  i introduction to what marketing  d i h k i 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 keep  Now we will start to look at what keep organizations from achieving greatness  through marketing intelligence through marketing intelligence

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I am sure you are starting to wonder  why I haven’t defined marketing  h h ’ d fi d k i intelligence, metrics or any other terms  related to the topics we are discussing?

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

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A small test for you working  professionals: f i l Talk to your marketing department  about how they do their marketing…

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

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

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

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What Are The 5 Obstacles?

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Obstacle 1:  Obstacle 1 Getting Started g

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We don’t know how.

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We don’t have the right metrics.

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

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We don’t know where to start.

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Obstacle 2:  Ob t l 2 Causalityy

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

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There is a time delay between  y the marketing campaign and a  customer action. i

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

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Obstacle 3:  Ob l 3 Lack of Data Lack of Data 

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We are a business‐to‐business  (B2B) (B2B) company and sell  d ll indirectly. As a result, we don’tt  indirectly. As a result, we don know who our customers are.

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

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Obstacle 4:  Ob t l 4 Resources and Tools

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

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We don’t have the tools and/or  / systems to support analytic  marketing. k ti

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

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IT builds systems, but they are  y , y not the resources and tools we  need. d

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Obstacle 5:  Ob t l 5 People and Change p g

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

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

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

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

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Our organization is resistant to  g new ideas, such as analytic  marketing. k ti

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Marketing is creative; imposing  g ; p g metrics and process will kill  creativity and innovation. ti it di ti

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Breaking Down the 5 Obstacles

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Obstacle 1:  G tti St t d F Getting Started—Focus on  Collecting the Right Data and  g g Create Momentum

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

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

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Every year around IRA  yy contribution time, the sales force  at RBC was given a list to call. t RBC i li t t ll

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The average yield per sales  person was 1 – 1 2 people out of  2 l t f 10 accepting the offer accepting 10 accepting the offer accepting  the offer being made by RBC.

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The RBC marketing team built a  Th RBC k ti t b ilt model to rank and score the list  based on the potential to  contribute more than US$ 5,000  ib h US$ 5 000 to an IRA. to an IRA. http://emagine‐group.com

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The model involved analyzing  y g more than 1 million customers,  f 12 for 12 months of data, and score  th f d t d t e to d t e top 50,000 them to find the top 250,000  that would potentially  contribute. ib http://emagine‐group.com

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While the size of the dataset  seems intimidating, possibly  i ti id ti ibl solving the problem for 10, you solving the problem for 10, you  can solve the problem for 1,000.

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At RBC, collecting the relevant  At RBC ll ti th l t data to do the scoring required a  g q great deal of manual legwork, 6  months of effort and  h f ff d approximately US$ 100,000. approximately US$ 100,000. http://emagine‐group.com

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The goal was to generate a  g g targeted list of the top 25  customers to call. ll

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

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IIn the first year, only 25% of the  th fi t l 25% f th sales force participated. p p IIn the third year, 75% were  h hi d 75% participating. http://emagine‐group.com

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Due to the success of this small  D t th f thi ll experiment, RBC was able to  p , build a business case that  b brought them a US$ 4 million  h h US$ 4 illi marketing initiative. marketing initiative. http://emagine‐group.com

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Wh t did What did we learn? l ? You don’t need 100% data and a  multimillion dollar infrastructure  l i illi d ll i f to get started. to get started. http://emagine‐group.com

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The key question: What 20% of data will give you  What 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‐Spatial The geo‐spatial picture shows dots  that are customers and where they  live and are coded by shape live and are coded by shape  depending on which of the three  Walgreen’s stores they shop. ¾ The  The “Diamond” Diamond  customers shop at  customers shop at Store 1 ¾ The “Square” customers shop at  Store 2 ¾ The “Star” customers shop at Store  3 ¾

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Walgreen’s Marketing Strategy Walgreen’s has always marketed  using flyers in newspapers ¾ They pay based on zip codes (the  They pay based on zip codes (the dashed line in the map) ¾ The marketing manager noticed  something very interesting – the  something 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. ¾ The conclusion – if you live two  miles from a pharmacy store, you  probably don’t shop there. ¾

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At this point, Walgreen’s treated  each locale equally – hl l ll equal dollar  l d ll amounts in each zip code across amounts in each zip code across  the United States.

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

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Based on this understanding,  B d thi d t di Walgreens stopped spending  g pp p g advertising dollars in all zip codes  without a store within two miles  ih i hi il of the zip code of the zip code http://emagine‐group.com

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

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Impact to sales revenues – 0

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Impact to marketing was a cost  p g savings of more than US$ 5  million illi

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“We started with simple Excel  p spreadsheets that contained the  advertising circulation  d ii i l i distributions by zip code. It was distributions by zip code. It was  not hard to get the advertising  data into the software.” http://emagine‐group.com

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The technology – gy a PC, ESRI map  , p and graphing software and a  d t data set of Excel spreadsheets t fE l dh t

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The bigger challenge was  changing Walgreens’ business  h i W l ’b i p process to use analytic marketing  y g insights

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The mistake they made was too  y many changes too quickly that  made people uncomfortable.  d l f bl Within a few weeks, things had Within a few weeks, things had  returned to the old way of doing  things. http://emagine‐group.com

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So they started small….

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

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They showed them the pictures  y p and explained that it didn’t make  sense to spend US$ 80,000 in  t d US$ 80 000 i ad e t s g a p code e advertising in a zip code five  miles from a store to generate  US$ 20,000 in sales. $ i l http://emagine‐group.com

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After 5 experiments, Walgreens’  cut US$ 300,000 off the bat in  t US$ 300 000 ff th b t i advertising cost and showed that  g the approach really worked.

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With the win, they set up a  process to review the marketing process to review the marketing  spending with each district  manager throughout the United  States.  States http://emagine‐group.com

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Overcome Obstacle 2: Causality— Conduct Small Experiments 

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There are too many possible causes  There are too many possible causes of anything you measure to identify  a single cause and effect;  o e app g a e g ca pa g s overlapping marketing campaigns  makes it impossible to tell what’s  working and what’s not working! working and what’s not working! http://emagine‐group.com

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The “answer” is one of taking a  systematic and disciplined  i d di i li d approach to marketing campaign approach to marketing campaign  execution.

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

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While we know that most  marketers are aware of this  approach, you will be shocked to  h ill b h k d t learn that almost 70% do not use  ea t at a ost 0% do ot use experiments to pilot test  marketing campaigns k i i http://emagine‐group.com

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Why?

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Most marketing organizations’  g g reward systems are based on  activity, not results. ti it t lt

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Harrah’s Entertainment regularly  g y designs experiments to quantify  th i the impact of its marketing. t f it k ti

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

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The experiment had two groups  Th i th dt – a control group with a standard  g p marketing offer and a test  marketing offer called “The  k i ff ll d “Th Challenger Challenger” http://emagine‐group.com

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Harrah’s Offers Control Group

“The The Challenger Challenger”

A $125 package of a free room,  $60 in free casino chips, no  two steak dinners and $30 in two steak dinners, and $30 in  hotel room and no steak hotel room, and no steak  free chips at the casino dinner Gaming Activity unchanged

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Gaming Activity Significantly  Gaming Activity Significantly Higher

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Harrah’s Entertainment tested  the experiment across other US  geographies. hi

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As a result, Harrah’s was able to  A lt H h’ bl t cut the budget for this type of  g yp marketing by more than 50% and  i increase the performance of the  h f f h marketing. http://emagine‐group.com

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Why does this Wh d thi experiment work? p

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By limiting who gets the  experimental offer addresses the  i t l ff dd th causality issue and there is a  y minimal time delay between  executing the campaign and  ti th i d obtaining the results of the obtaining the results of the  campaign. http://emagine‐group.com

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5 Obstacles to Marketing Intelligence