Brands and Politics, can the mix lead to healthy business growth?

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Successful brands have always found influencers and celebrities helpful in selling their product. Sometimes brands embed messages hoping to ride the waves of cultural change all the way to the bank.  But when the winds change suddenly, how a brand navigates the wave impacts its survival.  Continue reading Brands and Politics, can the mix lead to healthy business growth?


For JC Penney and Ron Johnson experience counts, but which one will deliver growth?

JCPenney in Frisco, TX
JCPenney in Frisco, TX (Photo credit: Wikipedia)

When I noticed the battering Ron Johnson received for attempting to reposition and re-brandthe  stalwart American department store JC Penney, I recognized a great case for peer learning among strategy as well as design thinking innovation professionals.  Johnson seems to have had the best experience for the job and  an attitude that placed the customer experience at the forefront of his proposed changes.  Falling stock price and fleeing customers tell a different story. Chart forJ. C. Penney Company, Inc. (JCP)

The Stock price when he took over as CEO on November 1, 2011 was $31.71.  Less than 14 months later, the day the discussion group met, the stock closed at $18.87.  In the last few days, the stock appears to be improving, most likely with the announcement that Johnson has backtracked on his strategy.  But I’m getting ahead of myself.

Two weeks ago, after reading a few background articles ( links and titles follow this posting), the discussion group met to review  JCP and Ron Johnson’s strategy.  Several questions raised in the course of the discussion prompted me to dig up additional history about this company and the changing conditions heating up the competition in this market sector.

Increased information, increased complexity

The days in which stores stood between buyers and consumer good manufacturers are dwindling. Location or proximity to the consumer may still have an edge but your competition’s ability  to insert themselves into the face to face transaction has dramatically altered the sales dynamic. Mobile communication devices  make it easy for sellers to find buyers anywhere anytime; and yet, the playbook  for many stores , from department stores to specialty retailers,  fail to keep pace with the change in buyer behavior, perception and thus fail to live up to  increased expectations.

Multi-channel interaction technologies perform double if not triple duty. Enabling information access by consumers for product details can direct attention to sales opportunities and enrich transaction data adding details and insights on behavior related to choice, in-store placement and preference. Investments  to enhance the customer experience easily generate additional sales but can also generate greater operating efficiency. In store sensors  make it possible to track consumer behavior similar to online consumer data collection software.  Once connected to specific consumer transactions the algorithms to generate value based pricing logically follow.  That is, if  pricing leverages the multiple data sources  which is typically the domain of  merchandisers and not a strategic function. RFID technologies and bar codes now  increase  supply chain efficiency all the way up to the checkout counter. (Note, JC Penney benefited from the tenure of VanessaCastagne ,a former SVP from Walmart, and  led online platform development efforts and integrated supply chain controls from 1999-2004. )  This data is just as valuable to suppliers, many experiment with QR codes  that allow them to forge direct relationships to customers via social media channels that can compete or play compatible with the store by directing consumers to specific purchase outlets either online or in store.

The arrival of direct consumer access, anywhere and anytime raises the stakes for all store owners. Setting priorities and synchronizing these technology introductions challenges  management in every sector.  For department stores and retailers alike, they have little time to adapt old school merchandising skills that support the  brand image and staff to client interactions while maintaining the cashflow necessary  to make it all work.  Oh, and figuring out the pricing thing in real time…that too!

Well that’s a tall order for any leader, let alone one who also needs to placate a trigger happy board and investors with high expectations.  It’s not a surprise that within one year of assuming the CEO spot at JC Penney, Ron Johnson  has backtracked on his strategy.  Year over year sales declines of 26%  are bitter pills for any business and the verdict on Johnson’s leadership choices are premature at best.

Additional context specific to JC Penney

A little more background may help. Just as the 2011 holiday sales season  commenced, Ron Johnson took the reigns and immediately set to the task of engineering a massive strategic overhaul of the JC Penney business.  Johnson in his first few months had opportunity to learn  the level of in-house capabilities and competencies of his team  from operating reports generated throughout retail’s peak sales cycle, but did he?

On February 1, 2012, four months into his arrival, he launched plans to update the store designs to a town square model and simplify pricing that would put an end to sales coupons.   The ideas were bold, but not as daring as many armchair critics suggest.

Success required implementation excellence, akin to the level of APPLE retail but at the scale of Target and the execution precision of McDonald’s.  Was that the department store JC Penney?

FYI, Apple had spent a year developing ideas before hiring Johnson in 2000, and built a prototype store near Apple headquarters where they tested their concept.  In May 2001, they opened their first two stores in May 2001, in Virginia’s high-end Tysons’ Corner shopping mall and in Glendale Galleria in Glendale, Calif.  A little over two years later,Apple had opened over 70 stores in locations such as Chicago, Honolulu and Tokyo. (See the full WSJ reported story).  By contrast, Johnson when he arrived at JC Penney threw together a strategy and placed huge bets based on a short-lived prototype experience.

Where was the evidence that the chain’s mix of  products and  brands when pigeonholed into  the three-tier pricing strategy change would match customer assessments of their value?  Note, he  replaced the pattern of ongoing price adjustments and coupon offers with:  every day pricing  40% off list, with the suggested retail price removed;  distinct monthly special offers; and best  prices-clearance items.  All prices would end in $.00, not $.99 .

Casual observations

The 50% failure rate of new product launches Gartner and other studies explain as ” poor knowledge of what price the market will bear for a new product. ”  Greg Petro writing for Forbes 1/22/2013 shared these and other findings, which someone on Johnson’s team must have read and studied.  Price signals to consumers the relative market value of a product or service, but the market dynamics are challenging to manage.  Interestingly,  in 2011 several department stores began to play with intraday  pricing by connecting their awareness of external competitors prices and match or best them at the point of sale. The unique price advantage Apple holds also made Johnson recognize the operating efficiencies gained from static and more constant price communication to customers.  In seeing the efficiency he may have overlooked the market dynamics.

In 2007, Macy’s had its head turned around by customers after it attempted to cut by half the frequency of its coupons and sales.  Of course this move engineered by Federated, the new parent,  followed their large purchase streak  and coordinated efforts to re-brand under the Macy’s name a series of regional based retailers (i.e. the May company, Marshall Field’s etc). The idea was to help regionally loyal customers recognize the opportunities for price the bigger national Macy’s offered.  Consumers were unwilling to adjust and found local alternatives preferable.   There’s something different about what a retailer can do and a department store, Greg Petro learned and reported in a Pricing series for Forbes.

“Compared to Department Stores and Brands, Specialty (and Vertically Integrated) Retailers have the most control when it comes to pricing. Vertically Integrated Retailers control the entire process.  The best ones design product from the beginning to target specific price and margin points. They also control the in-store experience, which can’t be ignored when understanding the value of the brand and how it affects pricing.”

Refreshing retail experiences that  appeal to the millienials as they begin to raise families and need the value that Jc Penney was historically famous for delivering  has all the marks of a sound strategy on paper.  Johnson clearly has the bench strength in delivering both; but,  does he have the stamina and correspondingly the capital for the task.   (

The bigger the change, the more steps to implementation and the greater the chance to fail.  Shaking up JCP takes capital and cash, especially since sales growth depends on the successful integration of online and in store sales.  Until recently, no links existed between online and in store experiences. The onset of omni-channel  increases the ease with which customers can experience more integrated,  consistent connections.  The closer my online shopping experience finding merchandise, and having it in my hands as well as sharing the social experience of shopping with friends matches the experiences in store creates both great challenges and enormous opportunity for retailers to manage.  To avoid customer confusion, in store sales staff need to have access and awareness of online sales promotions, merchandise and pricing.  Historically, a gifted sales person who knew their customer and purchase history offered assurance of their choice and saving  the customer time. The results  increased their overall satisfaction with the purchase  which by association carried over to the store. Now, online tools offer what the sales associate did  and offering more control to customers who research at home and may venture into a store to get a complete feel but won’t necessarily complete the purchase on the spot.  It may not be reasonable, but the expectations of consistency of offer, price and service between in store and online keeps growing.

JC Penney’s margins, like many of its competitors were shrinking. Getting the experience for customers right  without changing pricing must have seemed ludicrous to Johnson, but from an implementation point of view,  it may have had more immediate impact on the top  line.  As customers adapt to omni-channel opportunities and sales people adjust their relationships with savvier customers, new segments and behaviors may emerge.

Perhaps, Johnson felt that there was no point in putting his team through a drawn out change process and felt it better to catch up all at once.  Being first, may have its advantages but it also comes at great cost which Johnson has begun to experience.

ARTICLES We Reviewed

1. Business model innovations looks at JC Penney

2. New York Times Nov. 12, 2012:  A dose of Realism for JC Penney

 3. MIT Sloan Business Review, summer 2012–Is it time to rethink your pricing strategy

AND as a  Bonus option,   the fitrade blog   5/26/2012

Why clothing retailers suck at posting amazing profits-year-over-year

BIG DATA: Big Deal or Just Big Business?

Technology evolves and for those of us who spend their lives adapting and endeavoring to keep up with the advancements it’s hard  not to notice a curious underlying dynamic.   Data and our ability to calculate or manipulate it for greater meaning is a little like resolving the chicken and egg paradox.  More of one begs more of the other, and yet we continue to ask which came first as if that question were important.  For many of us, our interest in  closing the uncertainty gap wishes for more data. We expect it will  help minimize the error or noise because the present picture of relationships remains a little too ambiguous. The constraint in this case is often our own experience and knowledge.

Professionally,  my own work warns against this unconscious bias.  I simply ask people to imagine three dots and ask that they line them up.  I then remind them that all three dots are coincident data points in time, and ask whether this new piece of information has changed their vision of the dots?  I then ask them to place the dots on an axis of time, and tell them that the dots now represent demand, growth or performance like ROI.  Does the way you’ve visualized the dots changed again?  I explain that the context I’ve added snapped into their own experience to create an image that creates a new puzzle as what they see fights with their expectation and they need more data to explain it.

The Economist in revisiting the Growth Matrix in 2009, put it another way. Bruce Henderson, credited with originating this framework reportedly believed  “while most people understand first-order effects, few deal well with second-and third-order effects. Unfortunately, virtually everything interesting in business lies in fourth-order effects and beyond”.

Big Data and the volume variety and velocity of its availability now has several partners,  real time processing power and plummeting data storage costs and lest we forget, simple access and manipulation tools  placing the data in an ever increasing number of users’ hands.  It is the number of people who now want to use the power of analytics that lends Big Data its influence, or at least that’s what several Chicago Booth alums who shared their thoughts last week recognized.

On May 18, 2012 Chicago was busy preparing for the arrival of NATO delegates and support.  The result was many businesses strongly encouraged their employees to work from home, leaving the monthly strategy discussion homeless.  We took advantage of the opportunity to launch our first virtual discussion combining a real-time interaction platform  (Group Systems Thinktank) and conference call (  Interest in the topic proved overwhelming prompting us to open up a second lunch interaction following  our usual early morning time.  The comments that follow represent a condensed version of the conversation.  Note, links to the discussion prep video and articles we encouraged participants to review in advance can be found at the bottom of this post. Also, a full transcripts are available to those who interested in seeing the automated output from ThinkTank, just drop me a note.

What’s the deal

Its a toss up whether mobility or big data has captured the imagination of business media more. The duel isn’t the point. Other driving forces and a growing need for critical thinking skills that were already in short supply.  Data reduction may be an emerging competency.  As the earlier references to Henderson point out, the questions you are trying to answer don’t get any easier just because you suddenly have access to more data. What to do with this new wealth of rich information are the bigger questions and challenges not merely for business but for consumers as well.

In the process of  generating the following list of examples, the interaction on Thinktank let participants also provide some links, raise new questions and add additional comments.

Twitter ,Telephone call records, Smart Grid, Real time Electricity meter  data , Nike + ,Scanner data,  Comments from Call Centers,  Providers’ case and disease management notes, EMR records, Geospatial (GoogleEarth, Navistar, etc), Mobile and GPS,  Gov’t DBs (big-data in an unstructured/non-uniform sense) , The quantified self, Output or processed Data from SAS,, other enterprise databases, Loyalty program, Amazon purchase history,, Moneyball (Big data in baseball), the new NSA data warehouse in Utah, QR, The internet of things,, The London Datastore, created by the Greater London Authority (GLA–Chicago has similar initiative) offers citizens open access , Netflix movie recommendations, SAP’s HANA usage (profiled in the report on their Sapphire Conference )

Twitter for example has evolved in ways that surprised their founders and also launched a number of new businesses with very unusual purposes. As one article pointed out routing the data can be equally important as tallying it, as illustrated by Procter and Gamble’s practice of funneling social media conversation/data to the appropriate person’s screen for monitoring and response.  In other words to be meaningful, sometimes just knowing something happened is enough, it doesn’t necessarily have to be mathematically manipulated to derive value.

Persistent challenges remain in dealing with the enormous variety of formats in which data are presented — some  sources are difficult to analyze — their pages long data dictionaries  often include details about its collection.Add to that the realization that Data is not just numbers anymore. The automatic semantic annotation required to make sense of this has also entered a new era.

Facebook, LinkedIn, Google+, Pinterest and similar social media sites would fit in here, as well.  All offer a richness of information, much of it real-time, that can be monitored, mined and used to drive decisions and actions. The customer center conversations , or customer audio recordings , transcribed to text, and then subjected to text analytics is helping improve performance management practice, allows for campaign conversion performance tracking etc

The impact or convergence of the evolving technologies with all of this new data, is as overwhelming as the scanner data that was available and stored historically but few had resource capability or interest in mining it. That era has passed and with it, new questions and promises arise.

Can we better understand and use consumers sentiment as in  do retail customers use more electricity/phone service/etc in a method that correlates to changes in the economy? weather? Or SAPs HANA data, now makes cancer DNA genome type analysis possible in minutes.

FB and Twitter,  may be drawing more attention, but those who  bring together the different streams  and mining more deeply old sources such as scanner data  are also causing quite a stir. Target found out that teenage girl was pregnant before her dad did using methods such as these.

Add the RFID  and imagine the benefit to stores knowing the quantity of each SKU that they currently have?  Can they coordinate sharing of product between their brick-and-mortar stores?

Perhaps the focus on twitter, etc., rather than scanner data comes from hoping that trending sentiment will precede and can be used to predict purchase, rather than log it as with scanner data. At, a participant shared  that vehicle search data on our website is predictive of sales.

It looks like QR codes might achieve the same benefits of RFID as smart phones are becoming ubiquitous.

The Geo-spatial data, according to McKinsey’s recent report on Big Data as the next big innovation, quantified billions in time savings from just helping consumers avert traffic.

Several other behavioral nudging based on real time feedback is now possible. Nike is exploring and furthering this automatic feedback. Check this article for more brands using the quantified self, and more information


SAP’s HANA, Google’s BigQuery , Splunk, Hadoop and NoSQL databases , Tableau, Tibco Spotfire, Omniture ,Pentaho (open source BI), Amazon’s Web Service suite (more of a platform), Cloud computing platforms, Data visualization, most business users, make the data preparation easier and allows them to focus more on analysis and develop insights in combination with Machine learning tools (neural networks, support vector machines, natural language processing, etc.)

Decision making , can and does BIG data make more accuracy possible?

It offers higher granularity — like Target’s ability to create coupon books customized to individual households, Or integrate GPS level stuff — e.g. texting coupons to customers right while they’re standing next to a certain store.  Or 2nd/3rd order analysis – correlating Target’s sales with weather data; creating ‘real-time’ personalized coupons; identifying ‘trend-setters’ among the customer base to influence ‘trend-followers’ coupons

The downside? Detecting or separating out spammers from these data sets or paid to  express a certain sentiment. Totally! Like those girls paid to say great things about clothes on Facebook — not maybe necessarily analyzing big data, but using the platform.

Greater real-time evidence  can reduce risk and  insure assumptions in product/service development and marketing are on or off target.

Hard to appreciate the analytic without the qualitative context or understanding;  but maybe some strange new ideas can come out of the data, like the “diapers next to beer” epiphany. Data needs some drivers to make it meaningful, as in cause and effect. In part because qualitative data is harder to analyze.

Is quant vs. qual or the social science methods to data collection really that different from the scientific data analysis approach?  Both approaches seek to explain cause and effect, or the relationship between a stimulus to produce a predicted response. The problem is  too many people will extend a model beyond it’s capacity. Claiming “the data said so”  lets people off the hook and avoids responsibility for  decision-making.

Suggested tips include  avoid extending a model beyond its capacity, or  understand and differentiate descriptive and predictive Stats. Likewise, be wary of  finding trends that don’t exist (e.g. “data mining” or “straws that look like needles”) and confusing correlation with causality.

Perhaps cross-validation from trained analysts can help avoid  these . Danger of expecting tools to automatically extract value from large datasets.  Need to ensure good analysis, disciplined hypothesis generation, etc.

The data, even when analyzed, does not represent the decision.  This is true with small and big data.



  • Big data is here to stay – need to figure out how to use it effectively.
  • I liked the point that lots of data is around and that people just don’t know what to do with it.  The best BIG DATA process or engine in the world still won’t create the insights that are needed.
  • Corporate culture is a huge factor–the problem is not availability of data, but commitment and focus of corporate leaders to shape a culture that moves the organization in that direction.
  • Big data is here. It’s a tool and like any other, it’s the latest and greatest on the block, with a bit too much hype. But it has a definite value in providing and stronger qualitative base to identifying trends and activities.
  • My realization  is that, once again, the technology is interesting, but it is the corporate culture and will that will matter. The culture and vision lead; the strategy and models follow.
  • sharing the questions with a wider audience confirms concerns and clearly lots of assumptions that need to be played out. There is a large dark side that we still don’t understand; but the positives and opportunities for real time decision.
  • Big Data! The piles get higher and  higher and wider and wider…to what purpose? That implies the need to “mine” the data, reduce it and subject it to analysis before it can be made useful.
  • Big data will revolutionize business but it is not strategy, potential for a lot of false positives .
  • The wise use of big data offers a huge opportunity for developing differentiating strategies and for finding new product/service needs.
  • “Big data” is the current term for things that have existed for a long time.  All types and sizes of organizations can benefit from big data if they recognize the importance of the human component (not just the data and software) and have specific objectives in mind before starting.
  • Much of the expertise about analytics developed over many decades still applies, and there are new dimensions to integrate and understand because of the availability of the technologies and data.
  • Everyone on the line has experience with Big data, so I don’t think it’s so scary.  Most people have business perspectives, wanting to teach the Analysts that their conclusions need to be driven by business needs.   My comment is that as leaders, and those trained with some behavioral awareness through business school, it is _OUR_ responsibility to try and massage the analysts towards an enthusiasm for our world view…;-)
  • The human side of utilizing the technology and expertise is just as challenging as ever. (Cognitive biases, communication skills, influencing skills) Garbage in – garbage out is a big risk without proper attention and skill in applying the technology and in communicating.  The data, even when analyzed, does not represent the decision.  This is true with small and big data.

In closing, let me return to my observations about the limitations of developing strategy rooted in an expectations of the experience curve relationship.  The frame with which you approach the problem often has far more bearing than the data, your analysis or the tools.  Or at least, in June we  plan to look at some of the assumptions around growth as the ultimate strategy.

Please throw your responses, or continue to post links for others as Big Data continues to be quite newsworthy as its impact and influence continues to unfold.

Articles and links:

We suggest an  Optional  short 5 min. video tutorial , EMC produced to understand what Big Data IS?
The following are required advance reading.

1. IBM’s Institute for Business Value, in collaboration with MIT Sloan Management Review  2010 research findings
Analytics: The new path to value: How the smartest organizations are embedding analytics to transform insights into action

2. Strata keynote  short 7 min. video by Google’s Digital Marketing Evangelist Avinash Kaushik- a bit irreverent and a little over the top – a bit irreverent, bordering on over the top – but not boring  effort to help us understand the problems and the approach undertaken by Google.

Big Data Imperative 
March 2012

3. Tom Davenport’s Culture of Analytics

April 5, 2011 

4. Inside P&G’s digital revolution
McKinsey Quarterly November 2011
overseeing the large-scale application of digital technology and advanced analytics across every aspect of P&G’s operations and activities—from the way the consumer goods giant creates molecules in its R&D labs to how it maintains relationships with retailers, manufactures products, builds brands, and interacts with customers. The prize: better innovation, higher productivity, lower costs, and the promise of faster growth…

One more optional overview

The age of big data
NYTimes, Feb 2, 2012