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Posts Tagged ‘analytics’

PepsiCo, Walmart and McKinsey Discuss Innovation in Retail and Consumer Shopping Habits, and Big Data Analytics

Friday, December 12th, 2014

While innovation is crucial for any company that wants long-term growth, it’s become increasingly difficult to anticipate and meet shopper needs through new product introductions. A massive 80 to 90 percent of new product introductions fail to meet their goals. This means that marketers must constantly update their understanding of what customers want and need in order to be part of the sliver of new products that do well.

Steven Williams, PepsiCo’s Senior Vice President and General Manager, Global Walmart Team, invited me to moderate a panel of senior thought leaders from PepsiCo, Walmart and McKinsey on this very subject during the recent 2014 Enactus Partner Summit. The panel discussed some recent innovations in retail and consumer shopping habits as well as Big Data analytics. Here are some of my top takeaways for achieving product success, whether you are a CPG manufacturer or a retailer:

Consider micro-marketing – Chris Turner, Partner at McKinsey & Company, noted that successful marketers are increasingly planning at the very micro level, such as focusing on their top 100 cities instead of the mass market. Micro-marketing can take this even further with campaigns that have a highly personalized view of individuals based on their previous direct interactions with a brand, such as through social media, email and other digital engagement. Focusing on this type of granular growth can help companies make the right choices about where to compete.

Big Data offers bigger challenges…and bigger opportunities – Walmart’s Senior Vice President of Global Customer Insights Matt Kistler discussed his company’s Big Data challenges – specifically the depth of data they have been able to acquire. Walmart has access to almost 30 petabytes of customer data (one petabyte has 15 zeros!) so just imagine the extensive data mining required to find relevant data and then map it to meaningful consumer insights. But Walmart is doing it – they’ve moved beyond just capturing traditional point-of-sale data into social media, analytical applications and weather patterns, and they use that data to optimize local store assortment, tailor promotions and more. With Walmart’s latest efforts to apply Big Data to marketing as well as its mobile strategies, the company is continuing its trajectory of using Big Data to drive retail innovation.

Effective data analytics can uncover gaps in the market – Simon Lowden, Senior Vice President and Chief Marketing Officer for PepsiCo Beverages, outlined how his team created a plan that leveraged behavioral science and demand spaces (aka market gaps) to better understand consumers’ decision making process and set growth strategies for the future. This included a landmark study that explored the true drivers of consumption for the company’s brands so that PepsiCo could map shoppers’ path to purchase. One key insight was to encourage consumers to pair PepsiCo products when eating and drinking during the day, hence the company’s recent Better Together marketing approach.

Innovation starts at the store level – understanding the real drivers of purchase behavior – so marketers must know how to meet the needs of retail’s specific customers. (Millennials, for example, like to be communicated with differently, and they want more transparency and choices.) Also, with the vast amounts of customer data now available, there are huge opportunities for companies to focus on personalization and one-to-one marketing, even at the store level.

Marketers still spend a lot of time and effort on traditional insights instead of looking at and leveraging data analytics for key decisions and foresight. Finding and understanding these hidden insights can set up brands for a successful future, more than just focusing on what’s happening today.

Pricing Strategy Moves Front and Center to Drive Sustainable Growth

Tuesday, December 9th, 2014

Harnessing the potential of big data in every aspect of a company’s operations is a highly attractive goal, but it’s one that can be difficult to achieve. IRI and Columbia Business School’s Center for Pricing and Revenue Management recently held their second annual education program on “Pricing Analytics” to discuss how leveraging big data to develop improved pricing strategies can create huge opportunities for organizations in any industry.

Those in attendance represented a diverse group of industries – CPG, financial services, ecommerce, healthcare, insurance, transportation.  They also included representatives from many disciplines within these companies. Day one included a pricing simulation that looked at factors such as shoppers’ willingness to pay, quantifying demand and price elasticity, pricing in a world of active competition, and more importantly, strategies for applying this information with attendees’ companies.

Day two focused on the techniques and technologies of pricing. The CPG industry is blessed with better information about demand elasticity than many, but struggles with how to apply this information to drive ongoing pricing strategies. The session dove into how to get decision makers from across a CPG company to align on pricing since, within many companies, this does not occur in a systematic way today.

IRI and Columbia faculty discussed several critical pricing-related issues and involved attendees in hands-on discussions about applying solutions within their organizations.   These included discussions about supporting better pricing and promotion decision with analytics, gaining buy-in for pricing analytics within an organization, translating pricing analytics insights into action and measurement, and the benefits improved pricing analytics provides to the CPG organization.

Attendees brought up issues such as a lack of alignment within their organizations about pricing strategy, concerns about communicating pricing strategy effectively with customers, how best to measure the impact of pricing strategies and the role of pricing in communicating the brand proposition.

An especially interesting session was the keynote presentation by Jeff Ansell, chairman, president and CEO of Sun Products Corporation. Jeff illustrated the critical role an effective pricing strategy can play for the success of a company.

He illustrated the perils of failing to address these points by using examples such as JC Penney and Netflix, where dramatic changes in pricing strategies not aligned with shoppers’ preferences led to unfortunate results.

Applying advanced analytics to big data can uncover new, important growth opportunities. Our program with Columbia is helping innovative industry leaders realize that effective pricing is an important cornerstone of long-term, sustainable growth.

We look forward to collaborating with Columbia again next year and hope more CPG leaders will attend!

IRI and Columbia Business School to Present Second Annual Program on Pricing Analytics

Thursday, October 9th, 2014

On Oct. 28 and 29, IRI and the Center for Pricing and Revenue Management at Columbia Business School will present our second annual executive education program on the Columbia University campus. Titled, “Pricing Analytics: The Art and Science of Profitable Growth,” this two-day course will integrate academic research and real-world examples, giving attendees key insights into the latest analytics innovations and strategies for sustained, long-term growth.

In order to thrive in today’s incredibly competitive landscape, manufacturers and retailers must leverage advanced analytics and predictive technologies. Throughout the two-day course, faculty will show executives how to develop better pricing and promotion strategies and translate those insights into customer segmentation and pricing decisions. Attendees will gain a stronger understanding of customer attitudes and behaviors, which they can use to develop pricing strategies to attract and retain shoppers, as well as actionable tactics for maximizing revenue and enhancing market share.

The course sessions will cover four critical areas:

  • Supporting better pricing and promotion decisions with analytics
  • Implementing pricing analytics within an organization
  • Transforming analytic insights into action
  • Quantifying the benefits of enhanced pricing analytics

After the success of last year’s course, I’m excited to serve as a faculty member alongside Robert Phillips, Ph.D., professor of Professional Practice, Decision, Risk and Operations, and director of Columbia University’s Center for Pricing and Revenue Management; Garrett van Ryzin, Ph.D., the Paul M. Montrone professor of Private Enterprise and chair of Decision, Risk and Operations; and my colleague John Porter, executive, portfolio and practice lead, IRI.

To cap off the program, Jeff Ansell, chairman, president and chief executive officer of the Sun Products Corporation, a leading North American fabric and household-care products company, will give a dinner presentation.

Click here to learn more about the program and register to attend.


Managing Analytics Quality

Wednesday, February 26th, 2014

Achieving consistently high levels of quality is a primary goal of all managers, especially those working with advanced analytics.  Quality management techniques developed over time across different industries can provide advanced analytics leaders with a blueprint to achieve this goal. For example, my first job was as a Tank Platoon Leader in the U.S. Army, where I learned to apply several quality management techniques.  More than twenty years later, I apply these same techniques to better manage the quality of IRI’s Advanced Analytics Insights project deliverables.

  • Process Management – This quality technique organizes work into standard, repeatable processes to ensure consistency and scalability of results. For my tank platoon, we created and documented standard operating procedures (SOPs) for key processes such as firing weapons systems, executing tank battle maneuvers, and performing maintenance activities. Applying this technique to advanced analytics today means defining work steps for our key deliverables as standard processes.  These core process steps are then documented into SOPs that include quality checklists and effort estimates that are useful references for project pricing, planning and management.
  • Quality Metrics – This quality technique includes measuring and evaluating key performance metrics to proactively manage quality results. As a platoon leader, I captured metrics on target accuracy rate, cycle time to shoot targets, and accuracy rate of crew fire commands to certify each tank crew in my platoon during gunnery training. For advanced analytics, my current team captures and reviews quality metrics on each project using a survey across the project team focused on a best practice checklist.  These quality metrics are a great management tool that serves as a key input to quality and performance management.
  • Process Improvements – This quality technique involves managing the quality processes and metrics to achieve continuous improvements. For the tank unit, each crew received metrics and evaluations of their training results through After Action Reviews (AAR) resulting in targeted process improvements. In the world of advanced analytics, my current team reviews the quality metrics and processes to identify and then execute improvement projects to improve quality, reduce cost, and lower project cycle times.

As you can see, quality management techniques used successfully in areas such as training a tank platoon for combat can also help advanced analytics teams consistently deliver high levels of quality that translate into increased value for clients.

Are you using these time-tested quality management techniques with your analytics solutions today?

Pricing and Merchandising of Multiple Categories

Wednesday, February 19th, 2014

As the National Football League prepared to play the Super Bowl in my hometown of New York City (northern New Jersey, anyway), I was disappointed my favorite Giants were not playing in their own stadium.   Aside from that regret, I turned my thoughts to the CPG categories that are often purchased together on a major party weekend   Let’s consider three of these categories:  salty snacks, beer and ale, and carbonated beverages.    After the big event is over, how do each of these categories respond to pricing and promotion changes throughout the year?

As a source of insights, we turn to our extensive IRI multi-category price elasticity database, one of the largest collections of price and promotion response insights in the CPG industry.  This syndicated results database contains price response measures for 120 product categories, spanning many manufacturers, brands, outlets, and retailers.   In particular, some gleanings related to these aforementioned football-friendly snack categories, across different outlets:

  • In grocery stores and drug stores, beer is the most elastic category of the three.   On average either a base price drop or a promotional discount will have over double the rate of rise in unit sales.   Carbonated beverages are only slightly less elastic in these two outlets.  Salty snacks are just moderately elastic of the three categories in grocery stores and drug stores.
  • In mass merchandizers, the relationship changes somewhat:  carbonated beverages are the most elastic for base price and discount changes, and salty snacks and beer are both a distant second.  In fact, some particular snack brands and products have an elasticity that could favor a modest price increase, as the units lost would not overcome the gain in dollar revenue.

What might this mean for a retailer who is trying to move products across all three categories?  What kind of merchandizing strategies can be applied for other upcoming major sporting events?   Our analysis suggests that salty snacks are the least elastic amongst these categories.  Armed with this knowledge, retailers can be creative in how they design their promotions around a peak demand weekend.  For example, for beer and carbonated beverages, they can continue to be aggressive in pricing, looking for the proverbial “bump” in sales. Alternatively, they could look at leveraging the strong consumer pull of salty snacks during sporting events by combining them with beer and/or carbonated beverages. Most consumers would buy these products anyway – by combining the promotions, the retailer may be able to benefit from the synergies between the two products.   Finally, timing of promotions is worth considering – the week before the major sporting event, shoppers are looking for a discount, whereas on game day itself, shoppers are less price sensitive as they seek those snacks and beverages.

Want to find out more, about your particular products, brands, or retailers? We at IRI would be happy to discuss these syndicated price elasticity results in more depth. In the meantime, I will be looking forward to future championship games where my favorite teams stand a better chance.

Analytics and Investment Advice

Friday, February 7th, 2014

A few weeks ago, I was having a casual dinner with a couple of my friends, Mark and Scott. We usually use these sessions to discuss the important things in life, like the latest in sports, our dream cars and latest escapades of our children, dogs or both. At one point in our conversation though, Mark got really excited to share a great experience he had with a new investment firm that he had engaged for financial planning. He explained that it was a firm started a few years back by a group who had cut their teeth with some of Wall Street’s finest firms, but they were really different and more innovative than the traditional brokerage houses.

Mark went into great detail about the new firm, telling us that they had built a highly automated solution for developing the right financial plan for clients. As he explained it, they had created a financial planning solution that took inputs from him regarding his current investments, financial goals, compensation, family dynamics and various other factors. They then submitted the data into a system that runs thousands and thousands of financial models in a few hours and provides an investment strategy designed specifically for him. He elaborated that they spent some time with him to explain the solutions and address his questions. Mark felt so emboldened that the new firm inspired him to move away from his typical conservative strategy to one that shifted investments into new emerging opportunities with lots of potential. Further, Mark was especially pleased that this new process was much faster and actually less expensive than he had experienced with his previous advisor.

“Want to know the best part though? My investments grew by 15 percent in 2013 alone,” Mark enthusiastically explained.

“Did you say 15 or 50?” Scott asked somewhat rhetorically.

“I wish it were 50 percent, but 15 percent is pretty amazing right?” Mark answered.

Now, our buddy Scott is an investment advisor and at times can be a bit blunt with his thoughts on financial matters. This turned out to be one of those times. “Well, you do realize that the S&P alone increased by over 25 percent last year and that is one of the most conservative strategies we typically advise our clients,” Scott said matter-of-factly.

“Are you telling me that I got taken?” Mark asked with a mixture of defiance and anguish.

“No, I wouldn’t say that. I’m sure their system is based on some pretty sound models and their approach sounds interesting,” Scott replied. He then went on the elaborate a little further: “It’s just that there is more to financial planning than just running lots of quantitative models based on your financial history and goals. I wish it were that easy, but the truth is most of the analysis comes with the expertise to understand the direction, pace and nuances of the market, and that requires some care and patience. I would never leave my clients’ financial fate to some fast tracked systematized routine; instead I work with them continually to ensure that we are meeting their objectives and if not, I immediately revisit the assumptions and trends in the market.”

“So maybe I didn’t get taken, but I left significant money on the table, didn’t I?” Mark realized.

Softening up a bit, Scott replied, “It seems that way, but this is nothing that I can’t help you out with… for a fee of course,” saying the last part with a smile.

As you might imagine, Mk felt like he was letting his family and himself down by not getting the most out of their investments. Recognizing our role in making him feel that way, Scott and I bought Mark a drink and made plans to help him get his investment strategy back on a stronger trajectory.

Now, the good news is that Mark’s choices didn’t really cost him or his family. As it turns out, though, everything regarding the nature of this conversation is true, with two notable exceptions: ‘Mark’ and ‘Scott’ are aliases for ‘client’ and ‘myself’. The names have been changed and the discussion was really between a client, a colleague and me regarding an initiative the client had been responsible for in 2013. At one point in the conversation, the client noted with some exasperation that they likely would not have engaged with the firm they did if they had to “pay out of their own pocket.” This conversation reminded me of one mentor’s wisdom to “spend the firm’s money as if it were my own,” to which I add the age old wisdom that “all is not as it appears at first glance” as we speak of a faster and cheaper world of analytics.

My client reached the same conclusion as Mark: had he been paying out of his own pocket, he would not have engaged with a firm that habitually churns data and uses standardized algorithms. Remember, as we speak of fast and cheap analytics: you get what you pay for.

A Polite Rebuke of the War for Analytics Talent

Wednesday, February 5th, 2014

Believe it or not, being an evangelist of advanced analytics was not my original career aspiration. Rather, it was one that evolved some time after completing my graduate studies in political science. In graduate school, my area of specialty was Foreign Policy with a specific focus on the study of international disputes and conflicts. Today, the common belief is that research in foreign policy is very contextual and not necessarily analytically rigorous. However, since as early as the 17th century, most international diplomatic relations have been rigorously defined using quantitative and qualitative measures to distinguish amongst events ranging from basic diplomatic disagreements to much more bellicose outcomes.

As one might expect, the most elevated event in the study of disputes is war: a significant outcome between two or more parties that is typically quite catastrophic in human, economic and cultural terms (at a minimum). One fortunate fact of history is that while wars steal the headlines and places on best seller lists, they are actually extremely rare events, which is a good thing given their tragic results. As such, I have a pretty cynical lens when I see references to war that describe events in pop culture or business. At their worst, such references trivialize the truly tragic nature of wars, and at their best, such descriptions are simplistic and typically devoid of relevant causal insight that even imply war is an appropriate descriptor.

I am able to dismount from my high horse long enough, though, to appreciate that hyperbole is a big business. There is no doubt that the so-called “war for analytics talent” has been a successful message to drive fear, uncertainty and doubt across the industry, thus delivering significant opportunities for consultants and pundits to provide their “solutions” to your problem. The problem I have with the messaging really isn’t that it offends my sensibilities regarding the term war; rather, it’s because it is simplistic and based on juvenile reasoning.

Those who espouse this “war for analytics talent” message are posing it as a resource issue that firms must respond to with immediate commitments to “talent centers” across the globe in order to avoid the impending carnage of the talent shortage. This relies on flawed logic though, that is primarily geared towards the basal instincts of the buyers. It’s analogous to saying that the best way to avoid a food shortage is to stockpile as much food as you can right now, regardless of capacity, spoilage or the impact on others.

To me, the reality is that productive analytics talent is a relatively scarce resource, but it’s not a numbers challenge, it’s a development one. The real miss by firms, consultants, pundits and the rest of industry lies more in failure to develop and/or reskill their existing talent to develop the advanced analytics skills that are most highly relevant to the business of the firm. Extending the food shortage analogy, winners don’t stockpile because that will eventually kill off consumers due to hunger or disease. Instead, winning firms seize the opportunity to develop incremental and systematic changes to dietary choices that resolve the shortage issue rather than exacerbate it.

As someone who has been significantly involved in recruiting advanced analytics talent over the past 10 years, I can say with great confidence that the dearth of development is the most pressing issue that the industry faces. Over the years, “lack of development programs” is the most cited reason for interest in leaving one’s current firm among advanced analytics candidates. To be clear, that reason is cited more than twice as much as “compensation” or “promotion opportunities” combined. The reason is pretty simple: many serious and talented analytics professionals tend to be driven by advancing their intellectual development as much or more than financial gain.

Firms that train and challenge their analytics leaders to engage with the business, be creative with solutions and consistently develop new skills are often rewarded with highly loyal and productive professionals. Unfortunately, many firms have been drawn to the siren call of the “war for talent,” and through their actions they have trivialized the value of the dedicated professionals already in their organization. The industry needs to retreat from the notion that developing analytics capabilities is a numbers game, as the consultants and pundits will have you believe, and recognize it is a development game.

I’m here to tell you that there is no real “war for analytics talent” that requires the vast mobilization of troops across the globe. Rather, more focus in developing and fostering current analytics talent will go a long way and ultimately drive profitable growth in both the short and long terms. Winning firms across the industry are recruiting on potential and leaning in to foster constant and consistent growth opportunities, while the rest are mired in slow or non-existent growth.

Managing Analytics Knowledge – It’s Not Just About Big Data Storage

Wednesday, November 20th, 2013

Data and other knowledge can offer analytics managers a wealth of information, but managing overwhelming amounts can get in the way of making high-quality decisions. According to International Data Corp. (IDC), Fortune 500 companies lose at least $31.5 billion a year by failing to properly manage knowledge. Meanwhile, leading analytics organizations are able to harness and manage their knowledge in order to drive improvements internally and provide clear value to clients. As part of a series on how to build better analytics organizations, IRI Advanced Analytics has successfully built a robust knowledge management program that we think may be helpful for you to know. What follows are some of the key components to help organizations drive value:

  • Analytics Knowledge Repository. We recently migrated to a robust knowledge repository platform. This new repository utilizes consistent structure and guidelines enabling robust document management across the Advanced Analytics team and other IRI business units. Unlike past project repositories, this new design captures detailed metadata attributes for each project deliverable, thereby enabling dynamic search capabilities across the entire site. Third-party, thought leadership papers enhance this site by bringing in outside approaches the teams can reference and use to grow industry knowledge as well as new modeling techniques. 
  • Analytics Modeling Norms Database. We produce large volumes of modeling coefficients that are valuable for benchmarking, reporting, and analysis needs. To better utilize this knowledge across our global team, we implemented a modeling norms database that houses robust pricing elasticities for items in the highest sales volume categories that are very useful for reporting and analysis purposes.
  • Analytics Best Practice Sharing. We created an internal forum to capture and share best practices through a peer review system. The program links to the corporate case study library and best practice presentations, which can be easily accessed by team members for reference.
  • Analytics Training. With the goal of broadening and deepening skill sets, we implemented a training program that goes beyond traditional offerings. The program is focused on the critical analytic service offerings supported by the practice areas of revenue growth management, marketing productivity, and retail productivity, starting with Marketing Mix Analyses. The training yielded materials that include process and best practice references for key product offerings, and the training sessions were recorded and housed in the shared knowledge repository for future reference.

Actively managing knowledge can help analytically focused companies increase their chances of success by facilitating decision making, building learning environments by making learning routine, and stimulating cultural change and innovation.

Are you managing your analytics knowledge to unleash business value today?


Advanced Analytics Success Requires More Than Big Data and Software

Thursday, October 24th, 2013

It’s not a secret that analytics has become a critical discipline for generating and maintaining growth.  Recent research conducted by the Bloomberg CFO Forum, for example, reveals that 91 percent of surveyed CFOs believe that insights from business analytics are more important today than they were a decade ago.  However, a study by the MIT Sloan Management Review found that just 4 percent of organizations utilize all the data they collect, and nearly 30 percent use “not much” of the data collected.

The benefits of successfully harnessing advanced analytics are irrefutable.  IRI research completed in 2012 revealed companies that implement advanced analytics can achieve dramatic increases in corporate performance, such as an average of a 14 percent increase in operating income and a 12 percent improvement in stock performance.

Generating high-quality insights with analytics that lead to sustained growth is difficult for many CPG and retail organizations to achieve with scale and cost efficiencies, and requires much more than petabytes of data and analytics software.  IRI has worked on this challenge since 2004 and has successfully created a robust model for generating high-quality insights that are scalable with a global outsourcing model. 

We believe there are lessons learned during this process that are valuable to CPG and retail organizations.  Among the critical success factors that have enabled our success to date:

  • Taking a world view.  Whether it’s data, talent or software, when identifying potential resources to create or augment the analytics function, look worldwide.  In today’s global economy, “best of breed” often occurs offshore as well as on-shore.
  • Forming key partnerships to ensure all critical competencies are in place.  The IRI U.S. Advanced Analytics, IRI Europe Advanced Analytics, and Genpact Advanced Analytics teams formed a long-term strategic outsourcing partnership starting in 2004 through which virtually all analytics data, modeling and insights work are executed across our global teams based across the United States, India and Greece.  This partnership integrates the IRI U.S. team’s deep industry and analytics expertise with Genpact’s outsourcing and process experience as well as the IRI European team’s knowledge in global analytics best practices to generate tremendous value.
  • Winning the war for analytics talent.  To attract and retain the best talent across the global teams, we have invested in talent management best practices.  Specifically, we have recruited talent from a diverse set of quality sources and hired the strongest candidates that matched our specific needs.  We have also developed and retained that talent with formal training curricula focused on role-specific hard and soft skills, aligned performance management processes, and targeted career development programs.
  • Creating an aligned global team engagement model.  We have modified the organizational structures across the teams to ensure global alignment across key roles and practice areas.  We also implemented a collaborative project engagement model with specialized roles filled across the teams that bring key skills to life and allow for scaling analytics projects.
  • Developing innovation partnerships to address discrete client needs.  We have consistently refined our global model through the use of innovation partnerships on high-value projects.  Examples include custom analytics engagements with clients where advanced analytic skills are applied to solve client business challenges across our practice areas of revenue growth, marketing productivity, and retail productivity.

Building an advanced analytics capability is a corporate imperative today. Boards and shareholders demand that managements invest in achieving increased and sustained growth.  Shoppers expect products and channel environments customized to their individual household needs. 

What has been the ROI of your organization’s investment in advanced analytics?

Empowering the Visual Analytics Organization

Thursday, July 11th, 2013

In my previous two blogs, I described the benefits of data visualization for analytics and called out particular visualizations for cross-platform, high dimensionality data. Now, let’s discuss some best practices on how to empower your organization to optimally leverage visual analytics. Here are some guidelines to consider, based on my experience across five companies and 15 years.

Supercharge Evangelists

Visualization is transformative, and significant changes don’t happen by themselves. You’ll need leaders who can passionately deliver the message, and show examples aplenty, of how dynamic monitoring with high dimensional visuals can spur top-line growth. The evangelists in turn entice and train other functional leaders to join the effort and spread the word.

Create Templates for Core Business Issues

Sure, highly visual charts, drilldowns and videos can appear impressive. However, the real impact comes when data visualization helps you solve your core business issues, whether they are identifying revenue growth areas, monitoring price gaps, or uncovering consumer shopping habits.  After some experimentation yields the “aha” visuals that turn your flat files into insights, define those as workbook templates for continual re-use. Then, regularly update those business-specific templates with fresh data for ongoing monitoring, and re-use them for similar projects on different product lines or geographic regions.

For example, for a targeting application to determine where to run a short-term promotion, a scatter chart using bubbles is a good cornerstone for a template. Choose opportunities that are high volume, high price sensitivity, and a large price gap. This general view can be refreshed weekly and applied across product categories.


Implement Analytical Data Marts

To get to the heart of your business issues, don’t let massive data warehouses and network bandwidth get in your way. Instead, design and build analytical data marts. A data mart is a small subset of a data warehouse that is oriented to specific business functions and offers information relevant to your business issues.

Also, include enough categorical variables like time stamps, category names, brand names, and geographies for discriminating charts with distinct shapes, colors, sizes, and trellis panels. This is  illustrated below for category-level, price-sensitivity checks.


Record Actionable Insights

Finally, continually remind your teams that the purpose of data visualization is to get to the insights that lead to business decisions. Record those insights as bookmarks within your visualization software for retrieval during presentations and for continual knowledge management.

By following these tips, you’ll get off to a fast start in deploying data visualization. If it’s conducted on an ongoing basis, you’ll soon experience the business value of accessing and understanding huge amounts of data in ways that otherwise would not be possible.