The 3 R’s of Inventory Planning

by Bill19. March 2014 08:00

Today’s blog post comes from inventory planning expert and Perkins Consulting ally John Rusnak. With more than 30 years’ experience developing demand forecasting methodologies and inventory planning strategies in multi-channel retail industry,
John’s insights and expertise are invaluable resources in the evolving retail climate.


In this post, you’ll learn John’s unique framework for avoiding the common pitfalls of generic, sweeping inventory decisions, and for improving your business through data-driven inventory planning. We hope you enjoy the read!

- The Perkins Consulting Team


The 3 R’s of Inventory Planning

By Guest Contributor, John Rusnak

Going to school as a child, you were probably taught the importance of the three R’s: reading, writing, and arithmetic. Today, the rising tide of environmental awareness has made us all familiar with the three R’s of recycling – reduce, reuse, and recycle.

But chances are you haven’t heard of the three R’s of inventory planning: the right item, in the right place, at the right time.

Why such an emphasis on inventory?

In the retail world, inventory is often the largest asset on your balance sheet. But it’s not just the size that’s formidable – your inventory is constantly changing, and can be incredibly challenging to manage.

Which is precisely why inventory planning has a place at the heart of your business strategy.

At its core, the goal of your retail business strategy is to convert inventory into sales, profits and ultimately satisfied customers. Ensuring you have the right item, in the right place, at the right time will benefit your income statement by:

  • Optimizing your gross margin;
  • Reducing storage costs;
  • Increasing cash flow;
  • Reducing finance charges;
  • And increasing customer satisfaction.

Want some proof?

Let’s walk through a few examples of how the three R’s of inventory planning can benefit your bottom line.

1)   The Right Item

Consider a retailer that has multiple locations in the same city. Variables like demographic characteristics or local competitive environments can largely affect the sales potential of a given retail item, even though the stores may otherwise be identical. That means any single item can have a different sales potential at each location.

While it makes sense for retailers to stock similar inventory across locations, quantitative analysis offers greater level of depth into optimal inventory assortments, and how to ensure that each store stocks the right items.

For example, you may find that the optimal inventory assortment mix across two stores includes 85% - 90% common items, and 10% - 15% of unique items that reflect the different attributes of each location.

Figure 1: Example of an optimal inventory assortment mix across two stores.


2)   In the Right Place

How often have you visited a retail clothing store looking for a specific item, only to discover that your size is nowhere to be found? Turns out, another location has your size – but it’s halfway across town!

This is the perfect example of a retailer who has the right item, but it’s not in the right place. In this example, each clothing retail location will have a unique size profile based on the demand in their given market. As you saw in our first example, this demand is driven by the demographics, ethnicity, and certain competitive characteristics of that market.

Figure 2: Example of size profiles across two clothing retail locations.


As you can see in Figure 2, the size profile for Store A is skewed towards smaller sizes. That means proper inventory planning can ensure that customers are able find what they are looking for. It makes little sense for Store A to carry large and extra-large items that will be left unsold on the clearance rack, while their customers are frustrated that their smaller sizes sell out so quickly.

3)   At the Right Time

If you work in the retail world, you’re probably familiar with the term “sales lift.” What we’re talking about here a model that predicts the “lift” in sales that you can expect to see when you lower the price of a given item.

The fact is that when an item is advertised with a discounted sales price, the sales lift realized for that item will often vary by store and/or region due to customer demographics and their sensitivity to price.

Figure 3: Example of the sales pattern for a single item in two different stores.


As shown in the example in Figure 3, weekly sales of a single item in Store B are systematically lower than in Store A. However, when that item is advertised with a discount, the sales lift is much greater for the item in Store B than it is in Store A.

These findings should shape the way the retailer prepares for inventory of that item. It shows that less inventory is needed in Store B prior to (as well as after) the sale, but that the inventory planner must anticipate the greater sales that will occur during the week of the discount, and stock accordingly.

So, how do you get a passing grade in the three R’s?

The three R’s of inventory planning all have a common, under-riding theme – they’re based on attention to (and analysis of) inventory and sales data.

In order to leverage these concepts to benefit your inventory planning initiative, your retail business must develop a culture of using data-driven analytics to make decisions and develop strategies for growth. The good news is there’s a clear framework for those companies ready to take the next step towards improving their business through optimized inventory planning.

Figure 4: Framework for establishing an analytics-driven inventory culture.


  1. Develop the Database – This means proper documentation and storage of accurate, timely, integrated data at the enterprise level, and helps ensure that all teams are using the same numbers and definitions to measure success.
  2. Establish Descriptive Analytics – Leverage your database to answer the question “What has happened?” Descriptive analytics may include basic formatted reporting, some exception reporting and simple trend analysis.
  3. Conduct Statistical and Predictive Analytics – Leverage your database and descriptive analytics to answer the question “Why did it happen and how/when will it happen again?” Predictive analytics will drive insights into how the behavior of your products and customers will impact your business.


About the Author: John Rusnak specializes in working with retail teams to optimize performance improvement through demand forecasting and inventory planning. John’s analytical skills and 30 years of experience in the field inform his approach to helping organizations gain insight into business drivers, identifying risks and opportunities to drive business performance.



Want to learn how your retail organization can leverage data for optimized inventory planning and improved business performance? Give Perkins Consulting a call at 503.221.7582.


The Latest Data Revolution? Measuring People Performance.

by Bill12. February 2014 18:38

It’s no secret that data is revolutionizing the way we interact with our world and with one another. Businesses are at the forefront of that movement, leveraging analytics for a competitive advantage and to benefit the bottom line.

But the opportunities to gain valuable insights from data are in a constant state of growth and evolution. So much so, that it’s easy to fall behind the curve and miss opportunities for profitable change.

The measurement of people performance – on both the team and individual level – presents one such opportunity for improvement through analytics.  It matters to your bottom line: the true performance of your people is way beyond a function of HR - it’s a function of your business’ operations as a whole. The truth is, time and effort on the job is only a fraction of the performance story. How good are your people at doing what they’re needed to do?

It’s time to rethink your approach…

There are three key reasons why it’s time to re-examine your approach to measuring people performance:

1. The very nature of work is changing.

This data revolution begins at the very nature of industry itself. The way that we work in America is becoming more complicated. No longer are individual workers siloed to a single routine - we are asked to perform complex tasks and solve problems as members of a team. This makes it challenging to measure how well we perform on the job.

2. At the same time, data is opening new doors for insights into employee behavior.

You may have heard of “data exhaust.” The idea here, is that everything you do using technology, you leave behind a trail of data with traces of information. That trail (or “exhaust”) can be tapped for previously unavailable insights into your employees’ behavior.

3. Data Exhaust is a more accurate (and often more informative) representation of people performance.

Collecting and analyzing information that your team members leave behind from their activities is more accurate and, in most cases, requires less effort than asking them to keep track of what they do all day. There are numerous activities in today’s work environment that create a data trail: emails sent, phone calls made, documents checked in or out of a system, door badges swiped, etc. These insights help capture and accurately quantify peoples’ behavior, allow you to correlate behavior to actual business performance, and deliver distinct visibility into your teams’ and individual members’ contribution to the bottom line.


Examples in Action

The retail industry provides some great examples of how data is revolutionizing the measurement of people performance.

One of our clients is a home improvement store that utilizes a standard time in attendance system to track the hours worked by their team members. Unfortunately, work hours doesn’t correlate clearly with business performance in retail.

The Perkins Consulting team helped our client to add analytics to a system for connecting individuals and teams to sales performance. We wove together data from point of sale, time in attendance and other systems to paint a clear picture of each individual’s performance as a function of their contribution to top-line revenue.

Our client can now look at a number of employee performance metrics at both the team and the individual employee level. Not just how many transactions did they contribute to, but did they sell the big ticket items? Did they remind the customer to supplement or accessorize their purchase (i.e. drill bits on a drill purchase, deck screws on a lumber purchase, extra batteries on a flashlight purchase)? Did the customer return their purchase, indicating a poor sale?

This type of tracking and analysis is happening all around you. National retail giant Nordstrom is an easily-recognizable example, whose system for measuring people performance is very similar.

When you shop at Nordstrom, you’ll notice that the sales person who helped will often attach an additional barcode sticker to your receipt. The barcode is traced through Nordstrom’s point of sale system, which documents exactly who helped you and what you purchased – quantifiably linking an individual salesperson’s performance to business goals.

It doesn’t stop there.

Businesses everywhere are embracing the opportunity that data exhaust presents to their bottom line. As technology continues to evolve, and we – as a society – continue to accept and adopt innovative systems, data exhaust presents opportunities in business that extend far beyond measurement alone.

Join the revolution!

If you’re ready to get ahead of the curve, leverage untapped data, and improve the performance of your people to boost your bottom line, give Perkins Consulting a call at 503.221.7582.


On the Horizon: 5 Big Data Trends to look for in 2014

by Bill8. January 2014 08:00

2013 was a big year for big data, with businesses everywhere embracing analytics and data-driven decision-making over gut feelings and accepted norms. Moving forward, it’s time for businesses to sharpen their focus on big data and place a greater emphasis on analytics projects and the new generations of cognitive-intelligence apps. Companies will begin to carve out space for their new position of Chief Data Officer to foster a big data culture, while saying goodbye to data scientists.

In an effort to help you stay ahead of the curve and keep focus on where big data is headed, we’ve put together this list of 5 ways business will evolve with big data in 2014.

What to expect in 2014…

Mixed (or Connected) Data

Since the birth of the movement, big data has been about obtaining as much data as possible – the assumption being that to data-driven insights required massive datasets. But in the coming year, businesses will start to see that the most important aspect of big data lies not in the volume of a dataset, rather in the ability to derive insights from connecting several, smaller, datasets.

The company Premise provides a great example of this powerful application of data. Premise is an economic monitoring platform that derives global insights through the connection of isolated, local data sets.

This realization will, effectively, democratize the big data movement. Smaller businesses that do not have the exabytes or petabytes of data will begin to derive valuable insights by mixing and connecting several data sets.


The question of the hour for businesses is how to speak the language of the consumer. And in 2014, data is going to drive that effort more than ever.

Consumers are creating massive amounts of data every hour. Every click, like, tweet, cell-phone call, purchase and self-tracking application adds to the growing portfolio of data that’s assigned to a consumer profile. Data that is ripe with insight into how consumers think, and what they might respond to.

Companies like Amazon have historically used these kinds of data to create personal online shopping experiences that offer recommendations, personal homepages, personal discounts or personally targeted mass-email campaigns to their consumers.

And the coming year is going to bring more heat to that fire. In 2014, expect to see more organizations delivering a personalized experience with their offerings, be it online or offline.

Australian shoe retailer Shoes of Prey is off to a great start. They’ve customized their online experience in a way that not only benefits the consumer, but also enables them to analyze individual customer-spend and profitability. The result? A top notch customer experience that empowers the retailer upsell based on the fashion tastes of its clients.

Data Privacy & Security

Just as businesses have more access to data files than at any point in our history, so to do governments around the world.

And, in the wake of the NSA scandals of last year, businesses everywhere are focusing more heavily on securing their data to protect the privacy of their customers, making concerted efforts toward building security, privacy, and governance policies into their big data processes.

In addition to asking for aggressive reform of the NSA’s surveillance activities, more and more businesses will look to big data techniques in an effort to secure their IT infrastructure and prevent from being hacked, being monitored, or having data stolen.

Usability & Data Quality

In a recently released survey from InformationWeek, 58% of surveyed business intelligence consumers claim that “accessing relevant, timely, reliable data” is the biggest barrier to their organization’s big data success. Ease-of-use and self-service data access and analysis are at the top of mind for these businesses heading into 2014.

Software costs and ease of deployment play a role in how quickly companies can embrace new products. The fastest-growing business intelligence vendors (and the biggest market share gainers in recent years) have been the companies focused on ease of use, self-service, and data visualization.

Data that Interacts with Its Environment

The past year saw the first companies leading the way with innovative applications of data – applications where data interacts with its environment in real time.

The ground breaking #lookup campaign from British Airways is a shining example. The airline installed digital billboards in prime areas throughout its home city of London. But these billboards are also interactive - capturing real time data from British Airways airplanes to create an engaging digital experience every time one of their planes flew overhead.

The result? An interactive campaign that captures attention and raises their offering above the noise of the competition.

Do you have any additions to our 2014 big data predictions? Feel free to include them in the comments section below!


Call Perkins Consulting at 503.221.7584 to learn how we’re leveraging big data to give our clients keep a competitive edge in 2014.



Tomatoes and Business Performance: What’s the Connection?

by Bill3. December 2013 20:43

Tomatoes… You probably never realized how much they have to say about the global economy.

But David Soloff, co-founder of the tech startup Premise, has different ideas about this common vegetable.

Premise has developed a mobile app that is being used by people in 25 countries to snap photos of tomatoes (as well as other foods and goods in public markets) to send back to Soloff and his team.

You are probably asking, “Why the vegetable photos?”

The New York Times explains: “By analyzing the photos of prices and the placement of everyday items like piles of tomatoes and bottles of shampoo and matching that to other data, Premise is building a real-time inflation index to sell to companies and Wall Street traders, who are hungry for insightful data.”

Did you catch it? The single phrase in that sentence that explains how something like photos of produce can create an accurate image of the global economy?

Maybe not what you expected. But, it’s true across all analytical disciplines.

The fact is, data that is connected to other data is orders of magnitude more useful than unconnected data.

What we’re talking about here is the concept of “data connectivity”. This is a concept that businesses can easily overlook, even when they’re riding the big data wave.

The Current Environment

Of course, “Data” is all the rage these days.

It should be. It’s a decade of awakening in the business world. In order to keep up with the market, business executives have to be thinking innovatively about their data – how to effectively capture, store, and use it.

When we use data to shape the way we think about a business problem (or solution), then it can improve outcomes and drive performance. But, there’s more to it.

Connectivity > Sum of its Parts

Let’s go back to the case of the tomatoes and the global economy.

An isolated picture or two of a pile of tomatoes won’t lead to any substantive conclusions about the economy. But, when we connect pictures of food piles around the world to other sets of data like weather forecasts and rainfall totals, then you have revealing information that people like stockbrokers or buyers for grocery chains can leverage.

This story is a great example of a common rule in analytics: Isolated data can give you information for a discrete value happening in a discrete environment – but not much else. And, taking this approach in your business means you are probably missing out on valuable information and insight.

But Businesses Fall Short

You may be surprised to learn that businesses of every size, across a variety of industries and markets struggle to harness the power of connected data.

And the Perkins Consulting team is no stranger to this dilemma.

One of our clients, a major developer of investment products in the US, didn’t have a robust picture of their sales model until they connected their three disparate systems that tracked their direct sales transactions, sales to distributors, and housed their investment product definitions respectively. Armed with this new information, our client was able to sharpen their management focus by delivering the most profitable products to the right channels.

We helped another client, an international jewelry distributor, integrate their product data with their financial data in order to analyze financial results through complex categorization of their products. Now that their data is connected, our client is able to answer product performance questions such as:

  • Are smaller diamond rings more profitable than rings with larger diamonds?
  • Are bracelets more profitable than rings?
  • Does selling bracelets necessitate a larger inventory?

This information transformed our client’s approach to sales and operations planning, allowing them to implement a data-driven strategy based on facts rather than speculation or hunches.

It’s More Complex Than It Sounds

Housing information in separate silos tends to be a default for businesses because it is often a logical way to store data. It often requires an experienced eye to detect where this methodology cripples business potential, and then to take the steps necessary for identifying and integrating meaningful data.

But like many analytical methodologies, the concept of data connectivity has been around for a very long time. With the right strategy and expertise,

you can leverage this simple methodology for improved insight and powerful gains in your business performance.

And, you don’t even need tomatoes.


Call Perkins Consulting at 503.221.7584 to learn how your business can overcome the limiting effects of data siloes, and boost performance with connectivity.


How Accurate are Your Forecasts?

by Bill6. November 2013 08:00

Sales and demand forecasting are recognized across industries and roles as essential business functions.

But what if I told you that the commonly-held understanding of forecasting is not enough? That if you’re not looking beyond history as a predictor or the basis of forecasting, you might be doing it wrong?

It’s a Pain

The fact is, even the best data-driven sales and demand forecasts – supported by a wealth of collated data and the best sales analytics – will be inexact. And it’s a pain for business leaders everywhere.

81% of CFOs surveyed in 2011 about their key business issues said that forecasting performance (accuracy, cycle time, efficiency) is important or very important. Similarly, demand management and forecasting ranked #2 when surveyed their readers on top 10 supply chain concerns.

In order to alleviate this pain, to dampen the drudgery and bring some light to the end of the tunnel, business leaders need to make a strategic shift in the way they think about forecasting. They need to depart from traditional forecasting practice to embrace reality.

Which is what, exactly?

The reality is that your historical sales data is not the most accurate indicator of what’s to come.

In today’s markets, demand for products and services are under constant influence from a broad spec­trum of variables; variables including (but not limited to) the weather, the economy, seasonal changes, holidays, display and promotional activity.

Don’t get me wrong, we can learn a lot from the past. And historical performance should certainly inform our approach to sales and demand forecasting. But the reality is that we can’t learn as much from the past as we think.

Think Outside the Box

In order to get more accurate with your forecasts, you need to understand which variables are correlated to your sales.

The first (and most obvious) step is to compare how well your actual sales match up with historic sales data. But the subsequent investigation is where strategic thinking comes into play. What other variables might be affecting demand for your product

or service?

At Perkins Consulting, we’ve helped many clients achieve a more accurate understanding of what drives their sales. And the variables that we’ve found to accurately predict sales run the gamut, from overarching economic metrics to weather patterns.

For one of our retail clients, we discovered that the daily high temperature had a higher correlation coefficient to sales at the week level than the sales history from prior years for that same week.

We’re also able to apply product and customer segmentation to our forecasting activity, enabling us to identify those products or services that correlate to different external indicators. For a home supply client, we found that the sales of garden products, unlike the sales of bath fixtures and accessories, are strongly correlated to historical data.

Not to be Taken Lightly

This is no trivial matter. When your sales forecast varies widely from your actual results, multiple facets of your business operations (and ultimately, your bottom line) are compromised.

Increasing the accuracy of your sales and demand forecasts improves cash flow, sales volume and, ultimately, company profitability through:

  • Targeted demand planning
  • Effective scheduling
  • Optimized inventory planning
  • Efficient utilization of working capital
  • Improved workforce planning and hiring

So, when it comes to sales and demand forecasting, don’t settle for an educated guess. When your forecasts are accurate, they support profitable strategic decisions and resource allocation, and, ultimately, drive business performance.

Call 503.221.7584 to learn how Perkins Consulting can boost the accuracy of your business forecasting.



Bayes’ Theorem for Business

by Bill9. October 2013 08:30

‘Big Data’ is creating a whole lot of buzz these days – and it’s easy to see why. The widespread adoption of business analytics has made industries more efficient, more profitable, and more competitive. The companies that choose to ignore the big data boom run the risk of being left behind – and fast – by their competition.

But as much press as big data is stirring up right now – the analytical methodologies and frameworks that businesses are implementing today to gain a competitive edge have been around for a very long time.

That’s not to say that these methodologies are stale. The fact is that, up until very recently, data has had an image problem. What has been historically considered a predominantly academic (read: nerdy) endeavor is today being called the sexiest job of the 21st century. While we don’t use the generic term “Data Scientist,” we’re referring here to the field of analytics, and the tried and true methodologies that have brought data to the forefront of the business world.

Bayes’ theorem is the perfect example of one of these methodologies that has stood the test of time. It continues to play a big role in the business world, as well as for many other scientists and researchers who are looking to answer big questions with limited data.

Bayes’ Theorem

Conceived in the 18th-century by Presbyterian minister Thomas Bayes, Bayes’ theorem applies probabilistic mathematical principles to address an essential question: “How should we modify our beliefs in the light of additional information?

Stated scientifically, Bayes’ theorem provides a method to recalculate the probability of a hypothesis given the introduction of new evidence. Intuitively, this makes perfect sense. Without getting into the quagmire of the math, there are huge benefits that come from applying incremental insights and building a framework of understanding that evolves (closer to accuracy) as new information is introduced.

Nate Silver, the popular statistician who correctly predicted the 2012 election, describes how he uses Bayes' Theorem:

"A nice attribute of Bayes' method is… that over time you converge toward the correct results," Silver said. "People can begin with different beliefs, and if they abide by Bayes' Theorem, in the end they converge toward a consensus as more and more data is accumulated."

Applications for Business

Segmentation is a great example of how Bayes’ theorem informs analytical methodologies and business strategy today.

At its core, segmentation is the act of inferring some truth about your business based on the information at hand. You can segment any group of individuals – customers, distributors, vendors – based on pre-existing information. By assigning an artificial grouping to this set of individuals, you can uncover additional information that helps advance your understanding of how each group contributes to your business goals. Then, you can adjust your strategy based on that updated understanding.

Segmentation: Bayes’ Theory In Practice

Let’s say a financial services executive decides to segment the distributors of their financial products by volume of sales, thus creating three subgroups: high-volume distributors, average distributors, and low-volume distributors.

With this artificial grouping, analysts can then examine the revenue and costs associated with each group, and check against business models to learn if a high sales volume contributes to profitability.

Segmenting a data set has, in turn, created an additional layer in the information hierarchy – an additional layer that can be built upon for a heightened understanding of profitability.

For example, if analysts confirm that higher sales volume leads to higher profit, that information can (and should) inform the executive's decision making and business strategy. How can they best support high-volume distributors? How can they incentivize other distributors to sell more?

But if the analysis shows that volume is not a key indicator of profitability (in many cases margin, rather than volume, drives profit), then executives must update their models based on this new information, and shift business strategy based on an updated understanding of profitability.

Not Exotic, Just Sexy

You may look at this example and scratch your head. The progression is a logical, and by no means exotic, way of thinking about segmentation and business strategy.

The point here is that segmentation of information is a tried and true technique that’s been around for a long time. Sure, analytics is way sexier in the age of Big Data, but every business is capable of leveraging these historical methodologies to make better business decisions.

Want to learn more about how Perkins Consulting can help your company leverage data for insights? Give us a call at 503.221.7584


Decision-Making: How Humans Make or Break Business Intelligence

by Bill4. September 2013 02:41

Time and time again, research has proven that humans are inherently prone to pervasive cognitive biases. These biases weave their way into our subconscious, clouding our judgment and compromising our ability to utilize and interpret information for optimal decision-making.

And human error, as a result of suboptimal decision-making, can create massive costs in the business world. In today’s economy - where a knowledge worker’s primary deliverable is a good decision - human error is becoming even more costly.

And the fact is, with as much time and energy that’s been spent understanding how humans make decisions, we have little understanding of how to help individuals overcome their many personal biases and behave optimally.

Enter Business Intelligence

“Business Intelligence” (BI) is commonly used as an umbrella term to describe a conglomerate of processes and systems that transform raw data into insights for improved business performance.

Many business leaders see “Business Intelligence” (BI) as an opportunity to leverage technology to mitigate the risk introduced by cognitive biases and human error. But at Perkins Consulting, we see this approach fall short time and time again.

Technology Alone Won’t Cut It

Even the most robust BI technology and framework can’t ensure that humans will make insightful, data-driven decisions.

Think about it. No matter how many dashboards you generate, graphs you chart, or reports you see, business performance won’t improve if you’re not making smart decisions based on that data.

You may think this is secondary to the technological applications of BI. It’s not. We’ve seen companies implement robust BI technology to little effect, and have traced the shortcomings to the culture and structure around how employees make decisions from their information.

With technology, the right information can be accessed, analyzed, and presented at the right time, to the right people, in the right format. But our inherent cognitive biases mean that we have a really hard time utilizing that information to drive informed thinking and improved decisions.


Reframe Decision-Making

If technology is not enough, and humans aren’t rational – how can businesses generate a sufficient competitive advantage from their BI investment?

An Analytic Services Survey published by the Harvard Business Review identified some of the emerging leaders in business intelligence and data-driven decision-making. Beyond sharing a high level of analytical maturity, these companies make a concerted effort to address inherent human shortcomings and foster a framework for evidence-based decision-making with:

  • A data-based decision-making culture
  • Decision-making transparency
  • Corporate-wide decision-making processes
  • Greater use of analytics in real-time decision-making
  • Emphasis on the use of managerial insights as a supplement to data
  • Continual refinement and testing of new ideas.

These findings are especially compelling given that these leading companies hail from different industries, regions, and sizes. Their shared, comprehensive approach to data-driven decision making has yielded substantial benefits and greater impact from BI across the board.

This isn’t to say that entrepreneurial spirit and business judgment aren’t still important – these elements are still imperative in any competitive decision-making framework. But the risks of personal bias, repeating past mistakes, acting on guesses or following hunches unnecessarily, can be limited when you foster a culture of evidence-based decision-making throughout your business.

The bottom line is that any company, anywhere in the world, of any size, in any industry can create a competitive advantage by developing a decision-making framework that fosters a data-driven culture. And when you take a comprehensive BI approach that mitigates human error and sets your team up for decision-making success, your business will see improved financial performance, increased productivity, reduced risks and costs, and faster decision-making.

Want to learn more about how Perkins Consulting can help your company develop a decision-making framework for business intelligence success? Give us a call at 503.221.7584


The Myth of the Data Scientist

by Bill7. August 2013 09:00

Surprisingly, one of the systemic issues faced by our clients has to do with people, not data.

At Perkins Consulting, all of our clients are trying to improve their ability to make decisions from quantifiable information, but many of them face internal challenges with implementing systematic change.

Some find that their teams are hesitant to accept their new framework for data-driven decision making, and choose instead to rely on their instinct or on the common knowledge they’ve been operating under for years. Others are simply unaware of best ways to utilize and leverage the new information that’s been made available to them.

These issues with acceptance and implementation point to a failure in human (as opposed to data) systems. The fact is, the technology and tools for collecting and storing information has raced ahead of our skills to make full use of it. A recent study from IDC Digital Universe demonstrates the gap between opportunity and capability, confirming that less than 1% of the world’s data is analyzed while the amount of useful available data continues to expand.


The Myth

Many believe that one of the keys to filling this gap is the emergence of important new talent known as the “Data Scientist.”

Thomas H. Davenport, a senior adviser to Deloitte Analytics and prolific data-focused author, defines the Data Scientist as “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.” Unfortunately, this description is incredibly vague, and leaves much to be desired in terms of actionable insight. Olaf Swantee, Ceo of EE, takes a stab at firming up the concept, describing Data Scientists as “PhDs in physics and maths and more esoteric fields like astrophysics.”

You’re probably asking how a math or science jock is the “solution” to the overarching problems that your company faces with human acceptance and implementation systems. The reality is that the Data Scientist is just one of a number of roles that need to be filled in order to make the most use out of your data and analytics strategy. Only with a fleshed out and capable “Big Data Team” will your business see a shift in human acceptance and implementation of strategic information systems.


Five Roles You Need On Your Big Data Team



In a recent HBR Blog Post, Matt Ariker, Tim McGuire, and Jesko Perry of McKinsey identify five roles that are essential for success in any big data team. We’ve identified them below, explaining why each is essential, and why it will take more than PhDs to bridge the big data gap.

  1. Data Hygienists make sure that data coming into the system is clean and accurate, and stays that way over the entire data lifecycle. Data Hygienists are like the gatekeepers of databases. They monitor and scrub your data to ensure congruence and consistency across the board. What good are analytics is they’re based on “dirty (inaccurate) data”?
  2. Data Explorers sift through mountains of data to discover the data that you actually need. This is a huge task because so much data was never intended for analytic use and, therefore, is not stored or organized in a way that’s easy to access or utilize. Think of Data Explorers like a filter, they delve into and streamline the expanse of your database so that it’s lean and relevant.
  3. Business Solution Architects put the discovered data together and organize it so that it’s ready to analyze. The Business Solution Architects utilize comprehensive understanding of large-scale, cross-functional processes to paint the big picture –current state of operations, gaps in the system, resources available to address those gaps, and possible process solutions. They are strategic in addressing user needs, arranging the data in a way that can be usefully queried to develop relevant reports and insights in appropriate timeframes.
  4. Data Scientists (there they are!) take this organized data and create sophisticated analytics models that answer a certain question or shed light on a given problem. Data Scientists are generally math or science jocks that understand the manipulation of big data, and are responsible for delivering insights to the user in an efficient and sustainable way.
  5. Data Implementers turn the models into results. They have a thorough knowledge of the practical and technical systems needed to deliver results, and are focused on putting the information to use in business initiatives.

If you’re feeling challenged in getting your team to accept and utilize new information, you’re not alone. Many of our clients are stuck trying to accomplish the work of a successful analytics team with only one or two positions. As you can see, the “Data Scientist” is not going to be enough to accomplish your organization’s goals.

An approach to data and analytics that fails to account for all five of these roles will fall short. Time and time again, we see businesses that are crippled by insufficiently broad people capacity struggling to improve their decision making processes. Start by identifying the gaps in your data team (two of the roles we often see missing are the Data Explorer and the Data Implementer), then develop a map for how data will move through your team. Once you have these steps completed, you can begin to write accurate job descriptions for the positions you need to fill to construct your ideal big data team.

Want to learn more about how Perkins Consulting can help your business create a profitable role map? Give us a call at 503.221.7584.


Team Performance – Insights from Billy Beane

by Bill3. July 2013 08:45

Chances are, you’ve heard of Billy Beane – whether you realize it or not. He was the inspiration behind Michael Lewis’ book, Moneyball, which Hollywood released as a biographical sports drama in 2011 to critical acclaim.

Through analytics, Beane built a template for profitability, turning the league's lowest-salary budget baseball team into a winning organization.

Game-Changer (Pun Intended)

For those of you still in the dark, Billy Beane is the general manager of the Oakland Athletics, the East Bay baseball team endearingly referred to as the “A’s.” He took the reins of the empire in 1997, following severe budget cuts that slashed his payroll and left him high and dry in the game for all-star players.

The future looked dim for the A’s. But in 2002, Billy Beane changed the game – not just for his franchise, but for the entire world of baseball. His disruptive approach to evaluating individual players transformed the way that Major League Baseball thinks about team performance.

How Did He Change the Game?

Billy threw conventional baseball wisdom the wind, and raised many eyebrows in the process. Let’s take a look at the key elements of Billy’s strategy and how your company can incorporate these same principles:

  • He understood team performance as a function of individual attributes. In an industry driven by Babe Ruth-ian dreams and flairs for the dramatic, “exciting” players tend to be immensely over-priced. Billy knew that even one all-star, at the expense of a comprehensive, strategically adept dug-out, would cripple rather than bolster his chances of success. So, players like lead-off sluggers, base-stealing runners, or closing pitchers with high price tags were of little value to him.Instead, Billy took a strategic approach to building the A’s bench by pursuing affordable additions to the roster who brought specific skills to the team. In short, Billy valued players as the sum of their segmented contributions, and pursued a team with a well-balanced portfolio of capabilities.
  • He relied on data-driven insights. Billy made a conscious decision to build his team from statistical data rather than the “instinct” that was championed by the scouts of the old guard. With the help of Paul DePodesta, an out-of-place Harvard economics grad turned assistant manager, Billy mined the landscape of baseball data, unearthing the most meaningful statistics that indicated a player’s ability to contribute to the A’s success.

This approach was in direct conflict with conventional baseball wisdom and scouting practices. Billy and Paul only sought players who had accrued statistics from college or league play, steering clear of the high school prodigals that were favored by the rest of the recruiting world. They ignored body size, body type, personality, and baseball form – all traits that factored into a traditional scout’s views on a candidate. Instead, they honed in on the players with the right stats to allow the A’s to win.

Want to learn more about Billy Beane’s sports legacy? Check out this documentary short from ESPN.

Bottom Line

Healthy teams are successful ones. Business is like baseball in that there are finite, measurable traits that can be analyzed and tied to team and ultimately business performance.

The challenges that Billy faced in Major League Baseball are present in competitive markets everywhere. And, advances in technology make data-driven insights an efficient and affordable option for businesses to improve team performance and gain a competitive advantage.

The time has come for businesses to shift their approach to team performance analytics. The existing paradigm attempts to value all employees through the same lens, rather than approaching team performance as a function of diverse individual attributes. Understanding individual contributions to your team’s performance and isolating meaningful data that affects your bottom line is the first step to creating a “winning team” in the footsteps of Billy Beane.

Give Perkins Consulting a call at 503.221.7584 to learn about our People360 solution for measuring and optimizing team performance.


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The Psychology of Discounting – Pricing, Packaging, and Profitability

by Bill5. June 2013 12:00

Price means everything in the retail world.

For a supplier, it’s the difference between operating in the black or in the red. For the consumer, it’s the single-most informational valuation of a product’s worth.

But prices are far from concrete, and executing a well-informed discounting strategy can significantly improve your bottom line. Your consumer group is unique, and analyzing your sales data will help you understand their purchasing habits and inform your approach to discounting.

Discounting Methodology

Discounts are a direct vehicle for realizing more retail profits.

By driving down the price per unit on under-performing merchandise, retailers attract customers and speed up the sales of those products, making room for higher-margin inventory. Though delayed, the retailer realizes increased profits overall – accepting a smaller immediate loss on those slow-moving products will ultimately result in increased future profits when new merchandise sells faster and at full price.

But what happens to consumer perception and behavior when prices are thrown into flux? And how does that affect a retailer’s bottom line? More importantly, how can retailers be strategic about discounting decisions to realize greater profits? And, what can retailers do to leverage their existing data to make better decisions about how their own customers are likely to behave?

Consumer Behavior

Basic market theory postulates that consumers are logical creatures, making rational purchase decisions that will maximize their utility. But this isn’t the whole picture. We’ve learned that consumers are far from rational!

The psychology of sales is fraught with nuances – different people assign value in different ways. And different groups of consumers have different preferred purchasing habits.

The way that products are delivered and marketed has a huge impact on their salability. Things like packaging, brand presence, shelf placement, and store positioning all play into a consumer’s perception of value along with price point - this is where strategic discounting comes into play.

The Findings

A recent study out of the University of Minnesota found that consumers are significantly more likely to purchase a bulk discount offering than they are to purchase an identical product marked-down to the same price per unit. In this side-by-side trial, researchers sold 73% more hand-lotion when it was offered in a bonus pack (50% increase in quantity with fixed price) than when it carried an equivalent discount (33% off original price).

But what drives this trend? Why do consumers make this choice?

Some researchers postulate that it’s a result of humans’ inability to understand basic fractions – and this is probably a large factor. But consumers may also place higher value on the idea of “acquiring more,” as opposed to “paying less.”

Another example from the auto industry: when advertising a new car’s efficiency, sales professionals are more convincing when they present the number of miles per gallon, rather than the equivalent percentage fall in fuel consumption. Be aware that this purchasing trend may not be universal, and nuances in consumer behavior can vary by product, by market, or by industry.

So What?

An understanding of how consumers interpret and value components of a product or offering is the first step in developing a discounting strategy to realize more profits. Because nuances in consumer behavior are so variable and unpredictable, hard data is the most accurate vehicle to achieve this understanding.

The next step is to leverage your own data to understand your group of consumers’ preferences and purchasing habits.

The good news is that today’s retail industry is chock-full of sales data. The challenge lies in the analysis. What data do you have? Where can you acquire it? Which data is important in answering these questions? And what do the numbers mean?

Interested in learning more about how your retail company can execute a well-informed discounting strategy? Call Perkins Consulting at 503.221.7584