What’s Up with Retail: Invention easier than Transformation

 

170422153016-store-closing-signage-780x439The business headlines have been full of news about store closings.  Multiple chains are closing 100s of stores.  This once flourishing sector employed thousands, and now flounders. What strategic lessons can be learned from US retailers mistakes? The question and the learning opportunities for other sectors became the basis of the April 21, strategy roundtable’s diverse group of attendees.  A few articles  offered some summary context of the state of the sector, a little history about Macy’s and a series of growth related insights from disruptive innovation strategy firm , published in MIT Sloan Review offered by Fahrenheit212.

Surprisingly, after posting the articles in late March, a flood of store closings and their back stories continue to appear, leaving many to wonder what’s up for several of the biggest brand names in retail.  In the short horizon that  Macy’s lost $10billion in value, Amazon rose $300bil to reach its current six year low of $6bn, considerably off its peak of $24bn in 2006, Amazon’s current valuation just north of $370bn continues to accumulate market share.

The US Big Box Retailer’s current state isn’t just bad luck and it turns out to be less than easy to pinpoint. As Macy’s and other retailers struggle to maintain a fresh look, their poor performance ripples across the sector as one domino topples over another. No excuse for Management to become such terrible shop keepers, no matter how much online competitors divert their attention.  A poorly appealing store worsens its merchandise and thin staffing ratios erode service combine to turn off customers too.

Sears posted year on year decline over 9 percent, and 1.5x losses for its mobile application as it’ digital transformation efforts prove insufficient.

Cost cutting may help a business survive, but won’t deliver growth that makes it thrive. The creative fuel behind the early growth of Sears, Macy’s and the general merchandisers also expanded their market and showed their competitive capabilities. Amazon isn’t a new disruption and the blanket of doom seems to spread uniformly across the sector.

The Data vs. the Narrative

Did you know from 1990 to 2014, US retail employment grew by 2.3 million, of which the vast majority was among non-store retailers?  The total sector grew by 17 percent, while non-store employment grew 27 percent. In this same period, a combination of productivity gains and drops in labor compensation reduced the sector’s unit costs.

These reversals in historic trends pointed out by  Chicago Booth faculty Chad Syverson and Ali Hortaçsu in their recent review of US retail, should help large retailer’s right? More part-time workers, more automation and lower wages do improve operating margins, but doesn’t mean growth will follow.

The bigger strategic problem is timing and the ability to compete. The efficiency gains across the sector appear just as retail’s share of total US economic activity continues to shrink, and correspondingly its share of total US employment diminishes too.

Syverson and Hortacsu found ample research that relates technology, management, variety and productivity with shaping the success and survival of some retailers.  Surprise, greater productivity may or may not result from any one of these factors, and growth from greater productivity seems less causal too. Physical operations, however do prove important to both e-commerce and store based retailers. In other words, the formula for growth has grown more complex. Hold these thoughts.

Many explanations of changes in the sector, they say, “ …build on two, powerful and not fully consistent narratives, a prediction that retail sales will migrate online and physical retail will be virtually extinguished, and a prediction that future shoppers will almost all be heading to giant physical stores like warehouse clubs and supercenters.”

Their extensive data review also makes clear that the data does not support these narratives.  Online retail supremacy has not yet arrived, and likewise the scale and influence of the supercenter/Warehouse merchandisers continue to grow.

“Between 2000 and 2014, the fraction of all retail sales accounted for by e-commerce has risen steadily from 0.9 to 6.4 percent…The increasing share reflects an 11-fold increase in nominal annual e-commerce sales, in contrast to a 55 percent increase in nominal retail sales as a whole.”

The latest (4Q2016) US Census reports total retail sales of goods and services at $1,235.5Billion and estimates ecommerce at $102.7Billion (8.3 percent of total). Year over Year growth rates of 4.1 overall were considerably lower than ecommerce, which grew by 14.3 percent. Both grew only 1.9 percent over 3Q2016.

The analysis by category over time and projection of the trend lines summarized below makes the story more interesting.

Syverson and Hortacu’s                                                 Projected year that product’s 

 Table I: Product-specific E-commerce                        share expected (purple achieved):  as a Share of Product Total Sales

US census retail sales ecommerce table

The summary resembles an 80/20 analysis.  The categories with the biggest ecommerce sales represent the smallest category of total sales.

A Theory We tested

A recent edition of MIT Sloan Review published three “new” growth-related truths, and framed our discussion. The truths as you see, resonate with many of the common precepts around innovation focused strategy not just those of its authors from Fahrenheit212, part of Deloitte.

  1. Analysis won’t reveal your way to the future, you must invent it.
  2. Competition is not linear, it’s exponential and disruptive.
  3. Success depends on internal capabilities to catalyze an organization into action, and make something new happen.

First, analysis has broadened in many ways, but its purpose and practice still largely  determined by Leadership and its management priorities.

Limiting the scope of Analysis to tactical issues, confirms what works, or drives larger strategic projections as in what’s possible. Today, analysis and analysts review data in every function across the entire enterprise. Organization benefit from analysis when they also  commit to standards of consistency and integration, that also assure their results don’t confuse but reveal factors important to business growth.

Successful analysis also relies on the availability of data and analysts capable of its interpretation. Today’s connected world offers ever increasing opportunities to collect, store and process more data cheaply, and Enterprise Resource Planning Software systems greatly simplify and automate the reporting of standard views of activities in every function.

Planning and Process Improvements both suffer from a shortage of analysts capable of integration and interpretation of big data within a business context. The standards for business reporting reinforce old habits, rely on established metrics and existing interpretations, and thus miss cross-functional opportunities to share findings and develop new insights.

Is perspective and Interpretation hard to come by, or just hard to hear?

For example, to reach $150 million in annual sales, took Walmart 12 years and 78 stores. From its inception in 1994, Amazon took only three years. Further, Bezos reported 1998 net income remained close to zero as he his continuous focus on growth tirelessly plowed cash back into business development.  Not only did Amazon’s sales success patterns defy conventions of growth metrics, their unconventional use of data, analysis supports their creative capabilities and discoveries to understand what was contributing to their growth and working for their customers too.

In 1999, Forrester Research reported annual web retail sales as a whole jumped from $700 million to $20 billion, though it remained less than 1% of total retail sales. Growth was anything but linear—but the base too small to catch the eye of established, experienced retailers.

In 2001, Border’s CEO Greg Josefowicz was a very experienced and sophisticated retailer and no stranger to Ecommerce having come from Peapod. The fractional contribution of the chain’s online sales however led him to outsource the channel to Amazon. This was the same year, that Apple released the iPod. Unless you were closer to online data, and keenly understood its opportunity to track customer journeys and gain behavioral insights, chances are you too would have overlooked the value of further investment.

Image result for online vs in store customer journey

source: https://www.altocloud.com/blog/online-buyer-behavior.-what-we-can-learn-from-traditional-buyer-behavior

A 2016 interview with Michael Edwards, interim CEO Borders from January 2010 through its bankruptcy and liquidation in July 2011 revealed something else. Edwards buys into Fahrenheit212 philosophy that little can prepare you for wholesale disruption.  2010 was a period of widespread economic growth and US retailers sales were growing, but not uniformly; and not if you were in the book and electronics industry (aka ESMOH)—again hold that thought.

“The pivotal moment for me is when Apple launched the iPad,” Edwards said. “That foundationally changed the (book) industry forever.”Essentially, the iPad was a Borders in your hand. It had books, music and video. And people had access to millions of books.”

These hindsight claims made me wonder why Border’s didn’t feel any sales fallout from the iPod or Apple earlier, or when and why they misread Borders’ customers  change in shopping patterns?

Is your analysis reporting monitoring activity or action oriented?

What analysis and shared insights did Borders leadership encourage? Were traditional metrics misdirecting their strategic priorities and explain how their widespread physical presence was suddenly without value?

Remember the dominant narrative that Syerson and Horascu found?

Put that thought together with the analysts’ tunnel vision driven by elaborate ERP systems that accurately report established growth metrics. Monitoring Same Store Sales, Sales by channel or category breakdowns do reveal changes in shopping patterns, but are they actionable?  Even the Ecommerce reports from outsource vendor Amazon likely to include detail level data and helpful comparisons.

Different stories and trends emerge when analysis incorporates outside reference points. Benchmarking internal data to publicly available government statistics, for example, not just aggregate retail sales vs ecommerce but within their category might have raised alarm bells early.  Time pressures and priorities don’t have to stop anyone from creating a look similar to  Syerson and Horascu cumulative look.

What are you using to define your market and meet the needs of your customer?

Rethinking how to deal with consumers is more than a marketing plan it’s a strategic imperative.

At least that’s what Mike Edwards realized when he stepped up from the role of Chief Marketing Officer to help turnaround Border’s in 2010.

Conventional analysis techniques and formats don’t address deeper questions that test the validity of your strategy, or draw attention to important indicators affecting your results.

If you are a big retailer, and you moved online, you have big data. Macy’s and Sears both moved somewhat early to create an online presence before 2001. Maybe they saw online as an efficiency improvement to catalog sales, they still kept them independent of ongoing business activities.

Perhaps their experience relied too heavily on mass-market demographic information that large vendors like IRI made easy to digest.  Capabilities to analyze the flood of big data and the detail byte size moves of website visitors exceeded the capabilities of the most nimble and agile of digitally born players.

In 1998, the year Google was released, Wired reported the evolving capability of a website to gain intimate knowledge of their visitors. Excite, the leading search engine at the time, collected 40 Gigabytes of data daily in its log files based on 28 million daily Page Views. They only tracked directional patterns, though “for the first time, the continuously updated empirical evidence needed to assess relationships and deliver better experiences was available.”

A gap emerged between traditional marketing training and opportunities the web’s detail user journey tracking revealed. Do you appeal to demographic and assembled personas, OR are you responding directly to individual users’ needs?  This gap mirrored the unfolding of a larger competitive divide across all businesses and  further segregate online activities into separate operating units.

Bigger organizations’ centrally controlled decision making contrasted sharply with the emergent capabilities of online technologies and few recognized the important tasks required to rethink how to deal with evolving learning by their consumers and suppliers.  No problem for Excite, the leading search engine in 2001 and Amazon.   “Any active data we get, [Joe Kraus, VP of Excite explained] we put to instant use on the page…simulating personalization such as zip-code based weather forecasts.” Amazon without knowing any personal information, began to pass on simple recommendations based on the cookie data.

Cookies track specific behavioral data online, that was difficult to connect to purchase and profits, but still offered considerable strategic insights to anyone who took the time to look. Ironically, only a handful of advertisers possessed the technical and marketing experience with this growing data, which meant the playing field for ably using the information to optimize profits was wide open. Instead of investing and experimenting, many continued to apply the store sales success criteria to online sales.

Narrative Backstories: Perspective colors perception

I’m no retailer, but I did learn a few things from my father who created a handful of custom drapery stores that flourished in the 50s and 60s only to succumb to changing demographics in the 70s.

  • Purchasing frequency and customer loyalty aren’t accidental. Relationships build on more than serendipity.
  • Knowing your customer earns trust, most evident when your recommendations produce sales. Note, this approach doesn’t depend on markdowns or price drops to attract interest or make a sale.
  • Convenience is a perception not the reason passers-by cross the threshold (or click through).
  • Locations with heavy traffic create greater opportunity, sure, creative storefront displays (content) arouse interest or curiosity, and sales follow when entering visitors rewarded positively.
  • Invention matters but delivers greater value when balanced with conventional, basic goods and service options.

Drawing customers in, attractive presentation of merchandise has always helped successful merchants move what had to be moved. It’s been true for sellers regardless of their circumstance and environment.

Three longer term trends

Every trend has an origination point, successful analysts recognize the significance early because they often understand change as relative.  It’s easy to see the internet as a significant force today, but in the mid-1990s, analysis shown earlier documents  the case’s weaknesses and risks.

In 1995, Grace Mirabella, former editor of Vogue  broadens the context in her memoir In and Out of Vogue.  She describes dramatic shifts in the minds of consumers about department stores’ relevance compared to their hey day in the post-war period. 22 years later, her words don’t sound the least bit out of date.

“[B]efore malls and discount outlets and chain stores…[department stores] were the great halls of merchandise, and they provided an enormous variety of goods at much more varied prices than the present.  …each store aimed for a certain style, a certain specialty market, and a certain clientele, and you knew the minute that you walked into any one store, and smelled the perfume and saw the flowers and doormen or bargain tables, precisely where you were. “

Each family owned store’s attitudes and sensibilities she explains, accompanied the details that clued in customers, established unique contracts with manufacturers and made evident by the difference in merchandise they carried.  In the 1970s, Mirabella remarked on two major shifts:

  • Designers became all-powerful, cutting deals that promoted their name, and reducing retailers into commodity distributors who all carried the same things.
  • Consolidation by conglomerates followed.

“[The named department stores] started to take on the feel of the real estate ventures that they had become.  They lost their sense of purpose, of conviction.”[p. 45]

In 1994, Jeff Bezos, left his job as hedge fund manager for DE Shaw. Interviews reveal he spotted opportunity in the expanding internet, which led him to start the company he later names Amazon. His analysis skills suggest he was deeply familiar with another trend that began in the 1970s, one, that Mirabella in her backward look from her publishing perch misses–the evolution of Electronic Digital Interfaces (EDI) streamlining procurement.

In his first 1997 letter to shareholders, Bezos lays out his vision and writes:

“Today, online commerce saves customers money and precious time.  Tomorrow, through personalization, online commerce will accelerate the very process of discovery.  Amazon.com uses the Internet to create real value for its customers and, by doing so, hopes to create an enduring franchise, even in established and large markets.”

Personalization historically differentiated the high end of the market. Sales persons kept coveted black books that contained intimate notations about their customers ranging from size, color and style preferences to  special occasion dates and family details. Amazon wasn’t the first to collect user data, and was by no means able to mine it and yet they produced “personal recommendations” beginning in 1998 without investing in developing complex analysis capabilities. That came considerably later.

They took a shortcut that other websites  noticed satisfied customers. Chris Bayer writing about personalization for Wired in June 1998 explains it this way:

“The trick is to use technology to achieve the same economies that you have in a mass-marketing model, while delivering some personalized messages to the consumer,” says Rex Briggs of Millward Brown Interactive. A less visionary goal than one-to-one, surely, but far more realistic. It’s called mass customization, and if you can get past the oxymoronic bounce, you can see that its possibilities are not lost on the consumer-products retailers who have carved out a market for themselves on the World Wide Web.”

In 1999, Academics Joe Pine and James Gilmore publish The Experience Economy continues to shape many retailers strategic perceptions.  Their thesis builds on the retailer narrative and emotions Mirabella evokes, and connects to my own Dad’s experiences as a lifelong retailer.  Experience, they explain is now the metaphor of choice.  What else summarizes the combination of factors that attract and convert a visitor into a loyal, frequent customer and/or influencer?  Keeping  experiences relevant and meaningful amidst the backdrop of rapidly changing forces that impact every aspect of your business model demands rethinking of the employee not just the customer experience.

Direct learning

Unlike the leading CEO retailers failing, Bezos shares more in common with the great merchandisers of the past.  His digitally born and situated store front owes its business growth to continuous, bold experimentation as well as deep analysis. I don’t know what metrics are commonplace at Amazon, but their investments in data analysis capabilities and machine learning are self-evident by the efficiency and sideline cloud business they produced.

The speed in which consumers change their behaviors prove challenging for every retailer, non-store or store.  The online e-tailers’ unique environment, fully equipped to capture detailed user journey references and history can use the same mechanics to deliver immediate responses ranging from mass personalization to levels of deeper customization.

Amazon’s strategy embraces the principles of continuous learning at its core to control every aspect of the buyer’s experience. Similarly Apple, another company with astronomical market valuations entered the retail market in order to control and enhance the buyer’s experience.  Today, both have physical presence that emphasizes service and consumer education.

Retailers who miss the ability to construct a holistic strategy, increasingly are dying in the evolution of  responses or deeper customized, delivering valuable feedback enabling the business to continually improve its offerings and willingly take risks associated with invention. Amazon learned quickly how to draw customers online, present the merchandise attractively and yes move what had to be moved.  He didn’t have to balance the demands of managing existing outlets, nor accept established practices associated with large scale distribution networks, instead he invented his future.

In 20 years of online commerce, only a few companies strategy match Chris Bayer’s  observation that “”serious” companies are rethinking the ways they deal with consumers, and the idea of  mass customization ….using the trick of technology to deliver a personalized message that isn’t really personal at all.”

ARTICLES

Reframing Growth Strategy, Sloan MIT Review

http://sloanreview.mit.edu/article/reframing-growth-strategy-in-a-digital-economy/


Contrast in transition: Sears and Macy’s

https://centricdigital.com/blog/digital-strategy/how-sears-and-macys-are-transitioning-into-an-improved-digital-strategy/
Macy’s relationship trouble with Luxury brands

https://www.bloomberg.com/gadfly/articles/2016-08-11/macy-s-earnings-relationship-trouble-with-luxury-brands


Five Trends driving traditional retail towards extinction

https://www.forbes.com/sites/jjcolao/2012/12/13/five-trends-driving-traditional-retail-towards-extinction/#11487bd51efd

 

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How Old Metrics may strand you strategically

Ever stIMG_0267op to consider how the ever present changes going on around you make your own transformation easier?

John Hagel relatively recent blog post describes the opposite.

In a world of accelerating change, one of our greatest imperatives is to “unlearn” – to challenge and ultimately abandon some of our most basic beliefs about how the world works and what is required for success.

Accenture a few years ago noticed that many different companies had shifted their approach to strategy. Perhaps, the availability of cheap, powerful computing capacity and Big Data are responsible for driving changes in strategy development as more organizations using technology find it easier to build consideration of the future into their present planning.   Hagel, a long time fan of scenario planning would applaud these efforts too.

With the rise of automated business processes, analytics too get incorporated automatically to enhance decision making and may be simultaneously compromising management capabilities to internalize all of these changes or understand the underlying dynamics traditional measures mask. Several articles provided case studies in different industries provided the basis of discussion around transformation (see the bottom of the post for specific article links).

how to lie with staticsSuccessful organizations rely on their strategy to put forward action plans, realize new ideas while averting risk. Statesmen and management alike find themselves in precarious places when they assume a trend will continue without change. Many statistical methods and decision-makers use of data remain unchanged from 1954 when Daniel Huff first published How to Lie with Statistics. His timeless book describes very simply the perils of improper use of methods that were designed to capture and explain if not contextualize the significance of singular observations, or data.   The current transformations enabled by technology have done more to alter behavior than organizations seem to recognize. That’s the path our discussion took.

The capability for insight

Prospective vs retrospective cohort analysis  and data mining techniques are far from new. Though the volume and speed of available data to digest and process with ever The increasingly sophisticated tools and the ease with which volume and speed of available can be processed may help as well as hinder their digestion. Sure the time to test alternative scenarios may be faster, but how do you choose the model?

Do you begin with the intended outcome? The scientific method and numerous models from multiple disciplines make it possible to isolate factors, determine their significance, and estimate alternative scenarios and assess how these variations produce changes in impact.

Similarly, the cross pollination of data modeling from one discipline into multiple industries and use cases continue to shift management beliefs regarding the importance of specific factors and interactions in their processes. The perennial blind spot denies many organizations and their leadership the insight necessary to transform both their internal strategic thinking process and business operating models. Last month’s discussion of McDonald’s and Coca-Cola illustrated how easily leadership misinterpreted fluctuating performance as temporal issues versus recognizing structural factors. It’s one thing to balance efficiency and effectiveness, quality and satisfaction and another to manage awareness of change and insights necessary to your continued survival.

What else thinking

“…both the digital world and the physical one are indispensable parts of life and of business. The real transformation taking place today isn’t the replacement of the one by the other, it’s the marriage of the two into combinations that create wholly new sources of value.  “

The sudden availability of online data tracking provided many organizations with the proper capability to understand user behavior differently. A whole new industry arose to focus on interpretation while creating of new measures while also introducing new thinking about effectiveness in sales, customer service, training etc.  Metrics, once created to prove out a strategy or an idea, now leave many organizations vulnerable until they build up the capacity to understand this new thinking let alone make corresponding operational changes necessary to sustain their business.

This is not the story of companies who fail to adapt such as Kodak who invented digital cameras only to retain their focus on film; but maybe it is.

http://www.cognoise.com/index.php?topic=17598.0Computerized reporting dashboards summarize specific indicators or activity associated with managing process or business relevant factors. The time and reporting cost savings that result from the automatic generation and ready access to information by managers and executives reinforce existing thinking and leave little room for understanding wider changes that may be impacting their business. It wasn’t long ago that analysts, and teams of them, spent their entire day pulling data and then calculating critical statistics detailing the effectiveness and efficiency of organizational activities to create reports for senior management. These efforts also made them accountable by insuring the data was clean, verifying whether outliers were real or indicative of a model failing to fully capture the wider dynamics. I was once one of those managers.  Today, automated reporting has eliminated many of the people capable of deeper data exploration and who chose what data, which statistics and the context necessary to understand the situation. The second problem is that data shared graphically or in tables never tell the whole story, though infographics do try.

A good analyst is taught to review the data and results, double-check whether the model or calculated results makes sense. Sure managers and executives may be quicker to detect aberrations and then raise questions but , how many of them have the time, patience or skills to test their ideas or intuitions? I imagine very few if any. Where are these available resources and how widely known are they to questioning executives?   How might the dashboard provide additional information to help frame the results executives see as they too seek to understand or make sense of the results?

Outside in thinking

Established data flow processes and automated reporting do deliver great advantages but they may also explain why outsiders find it easier than insiders to create new business models.   Where’s the out of the box thinking? And how can different data help?

Sure, it’s easy to blame regulatory requirements or compensation structures incentivized to focus on effectiveness and efficiency that leave little latitude to notice opportunity. For example, in the airline industry route fares were once set by regulations. The minimum fares were intended to cover airlines operating expenses that both insured passenger safety and access to air travel in more locations where market forces may lead airlines to cut corners. Deregulation may have given airlines additional freedom but many manage their business using the same metrics that they report to the Department of Transportation. Likewise in Healthcare, the imposition of new regulatory requirements came with new metrics that forced hospitals to focus on patient outcomes not just their costs.

When executives bottom line focus limits their thinking as an exercise in how making corrections in operation may maximize that number they overlook other contexts. Data quality issues should surface quickly in most organizations, but what if another factor created the data issue? A misplaced data point, or inconsistent treatment of the content of a data field rarely explain all aberrations in the results.   Weather, for example exemplifies a ubiquitous, exogenous variable. Observable data fluctuations may be directly or indirectly responsible by affecting other more directly connected factors, such as a snowstorms that change people’s activity plans. I’m not familiar with any automated reporting system that will automatically create a footnote to the data point associated with the arrival of a snowstorm. The reviewer is forced to remember or manually if possible add the footnote for others.

Bigger transformations to come

Bain believes there are significant implications for every organization that result from this digital and physical combination of innovations , they call Digical. It’s not easy to keep up with the corresponding behavioral shifts that result from these rapidly changing technological capabilities.

Focusing exclusively on efficiency and cost data helped management measure impact in the old era, though still necessary today they may no longer suffice. Do you know how social behaviors of your customers impact your bottom line? The technologies to support your business, such as your website or your cash register misses out on the social behaviors evident on sites like Facebook, Twitter, Yelp or even their bank. Mapping the ecosystem and then aligning the digital tracking data can now be supplemented with sensor data that may be anonymous to specific customers but can inform movement and actions relevant to your engagement.http://intronetworks.com/making-amazing-connections-siggraph-asia/

Naturally, as mentioned earlier bias plays a role in our inability to notice the significance of new data. The more we automate and configure systems to measure what we always knew mattered, the less likely we are to be able to recognize new data and its significance. What should you the analyst and you the executive do to counteract these factors?

Takeaways

Monitor the activity of smaller companies as they experiment to learn what’s most relevant.

Don’t make assumptions, exercise strategic intentions to become more open receptive and curious about anomalies and be more creativity and persistent in identifying the drivers or possible factors.

Historically, metrics were an output designed to assess the validity of your strategy –did it work and/or deliver value. Not it’s time for strategic thinking to view metrics as an input. The use of statistics enabling analysis tools partnered with business knowledge and acumen must be part of communicating to higher levels in the business.

Often we measure the wrong things because the incentives are misaligned. Am I paid based on my proven ability to produce widgets at specific levels , or to produce effective, sustainable results for the business, not just my business unit?

Computers are useless they can only give you answers. For strategy, validating the questions may be important but so too is taking the time and effort needed to determine even better questions.

ARTICLES

Alternative case examples

Bain’s study and understanding of the state of “digical” transformation:
http://www.bain.com/publications/articles/leading-a-digical-transformation.aspx#sidebar
Fast Food
http://www.qsrmagazine.com/reports/drive-thru-performance-study-2014
Wireless
http://www.rcrwireless.com/20140812/opinion/reality-check-new-metrics-for-a-changing-industry-tag10
Television
http://fortune.com/2014/10/23/adobe-nielsen-tv-ratings-system/
Gaming
http://www.gamesindustry.biz/articles/2014-03-10-social-currency-has-real-value

Behavior Economics teams up with Big Data nudging Obama’s Re-election

Text message from Barack Obama campaign announ...
Text message from Barack Obama campaign announcing that Joe Biden would be Barack Obama’s choice for VP. (Photo credit: Wikipedia)

No surprise that this past election, campaigns took advantage of big data AND they also took advantage of the growing awareness of persuasion techniques and incentives that fall under the umbrella of Behavioral Economics…both topics we discussed in the past few months.

English: Nate Silver in Washington, D.C.
English: Nate Silver in Washington, D.C. (Photo credit: Wikipedia)

Nate Silver, and his 538 blog seems to have become an even greater rock star recently.  By applying his deeper understanding of probability and statistics , he successfully predicted the election outcome well in advance of poll openings.

Today’s New York Times carried an interesting story about the unofficial influence of Academics with expertise on Influence.  I urge you to read the behind the scenes story  Academic dream Team that Helped Obama’s effort.

Not to be undone, TIME magazine got to the real team working inside the Obama campaign to get the details.  After the election they were free to publish the following story:

Inside the world of the DataCrunchers who helped Obama win Re-election

If business needs further evidence that real time analysis of integrated data delivers value, the lessons learned during the election offers some great insights. The precedents set by  this new database and the quants analyzing it, suggests additional opportunities to make change that goes down more smoothly, like Mary Poppins advised using her spoonful of sugar.  Good or bad, the reality is that big data made a big difference in this election and on the persuasive front appears to have been effective in delivering the most relevant messages to individuals.  Hard to believe that general broad-based appeals such as bill boards and television advertisements will continue to call for  high level resource investments. Then again, that’s another story I’m sure.

I haven’t seen all the articles out there but more are bound to follow.  We will do our best to revisit these topics again  in 2013.

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 (freeconferencecall.com).  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, Salesforce.com, other enterprise databases, Loyalty program, Amazon purchase history, Mint.com, Moneyball (Big data in baseball), the new NSA data warehouse in Utah, QR, The internet of things, Data.gov, 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.https://i1.wp.com/www.connectedaction.net/wp-content/uploads/2012/04/20120421-NodeXL-Twitter-Global-Warming-Labeled-Groups-Network.png

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 cars.com, 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

TOOLS

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.

FINAL TAKEAWAYS

 

  • 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?    http://youtu.be/eEpxN0htRK
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

  http://public.dhe.ibm.com/common/ssi/ecm/en/gbe03371usen/GBE03371USEN.PDF

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
http://www.youtube.com/watch?v=CrSX97elHDA

3. Tom Davenport’s Culture of Analytics

April 5, 2011
http://smartdatacollective.com/clifffigallo/34719/tom-davenport-s-culture-analytics 

4. Inside P&G’s digital revolution
McKinsey Quarterly November 2011
http://www.mckinseyquarterly.com/Retail_Consumer_Goods/Strategy_Analysis/Inside_PGs_digital_revolution_2893
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
http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html