Cashing in on Analytics in Retail 2
One thing is clear: the retail industry is not what it used to be. The combination of new channels, growing
digital competition, and faster product launch cycles has created a constantly changing business sector with
tremendous opportunities—but also with significant challenges.
The web, mobile, and social media channels mean more ways for you to touch your customers, but greater
competition as well. The customer has choices and you’re not just competing with another store down
the street or across town; you’re up against virtual entities hundreds of miles away or on the other side
of the globe.
What’s more, you no longer hold the power in the retailer-customer relationship. Increasingly tech-savvy
and highly informed, customers visit comparison-shopping web sites to quickly search for the lowest-cost
products and use their smartphones to scan barcodes and compare prices between local stores. Moreover,
they can influence others to buy from you—or not—with just a few keystrokes on Twitter, a blog, or an online
Empowered customers expect more from you than ever before. They want personalized offers for highly
relevant products and services, when, where, and how they want it. Blindly push products or offer promotions
at the wrong time and you can irreparably damage your brand and business image.
In order to win—or even simply compete in—the customer attention war, you need deeper customer insight,
including knowledge about customer preferences, profitability, life stage, and more, to create the personalized
products and services today’s consumers demand.
But the massive amounts of industry data you collect each day—on market trends, competitive moves,
product developments, and, most importantly, customer preferences and desires—can paralyze even the
“Big data: The next frontier for innovation, competition, and productivity,” McKinsey Global Institute, May 2011.
According to a McKinsey
report, retailers that effectively
leverage Big Data and analytics
can experience as much
as a 60 percent improvement
in operating margin.1
3Cashing in on Analytics in Retail
Wall Street darling Amazon’s
revenues jumped from $48 billion
in 2011 to $61 billion in 2012.
What’s behind this phenomenal
growth? Analytics. As early as
2009, the company attributed
about 20 percent of its total
revenue to its successful product
from market basket analysis.
The key to winning the war is not just to collect the data but rather to quickly access and blend all the
disparate types of data, analyze it to distill new, micro-level insights, and share those insights with relevant
decision-makers within the organization to facilitate timely, effective decisions.
Whether you are in marketing, merchandising, supply chain, store operations, or real estate and finance,
analytics can help you gain an advantage over your competition. Here are some of the ways you can get started,
even if you don’t have the most sophisticated analytical capabilities.
Growing competition, declining customer loyalty, and an uncertain economy combine to intensify the pressure
on retail marketers. On one hand, you need to create a differentiated experience for customers, marketing to
segments of one. On the other hand, your marketing budgets are the same or even shrinking. Using analytics,
you can gain critical insight into your customers and their purchasing behaviors in several ways:
• Customer Insight: Simple segmentation, based on just one or two variables, can help you answer important
questions, such as: Who are your most—and least—profitable customers? Which products are they most
likely to buy? And through which channels will they buy them? Armed with this insight, you can now target
them with more personalized messages and promotions to improve campaign response rates.
• Market Basket Analysis: Using internal transactional data and third-party panel data, you can easily
determine which products sell best together and which products are complementary or substitutable.
Then, you can use that information to make cross-sell recommendations, pinpoint up-sell opportunities,
and develop cross-recommendation programs.
• Multi-Channel Analytics: Blending and analyzing your cross-channel transactional and click-through
data can provide you with a single, unified view of your customers across channels, so you can determine
their preferred channels and paths-to-purchase. With a better understanding of product-channel affinities,
you can more effectively determine which products to promote through which channels and how to best
allocate your advertising spend.
Cashing in on Analytics in Retail 4
• Marketing Effectiveness Analysis/Marketing Spend Optimization: You need as much help as you can get
to stretch your marketing dollars. Techniques such as what-if analysis and scenario modeling can help you
determine the impact of a promotion or marketing event on your demand, revenue, and margin. You can
A/B test ad performances and track actuals against target to optimize media mix, adjust plans mid-course,
and determine which competing campaigns or promotions to fund.
• Social Media Analytics: Retail is no longer only about influencing the buyers. Social media has made
influencing the “influencers” even more important for retailers. Analytics can help you understand customer
sentiment toward your brand, product, or service, score the influence level of a customer, and keep up with
competitive activities and market trends in general. Armed with this information, you can improve and
prioritize service, introduce new products, and better align your messaging with customer needs.
Savvy retailers know that
analytics can help optimize
marketing decisions, and the
growing IT buying power
of CMOs reinforces that fact.
In 2013 alone, CMOs and other
business unit heads helped
increase IT spend by more
than $11.6 billion.2
“Black Ops IT Spend: When IT Spend Starts Being Paid Outside of the CIO,” IHL Group, August 2, 2013.
A southern retail chain of over 300 mid-range and upscale department stores found itself struggling to leverage the vast amount of customer data
collected across multiple channels. The challenge? Pull together data from 13 disparate databases and use the insight to improve its marketing reach
and the customer experience.
Using Alteryx, the company quickly blended together the different data types and enriched them with third-party demographic, geospatial, and
census data to get a single, unified view of the customer. By analyzing the attributes of customers shopping via multiple channels, the retailer
targeted customers with like attributes and doubled the number of multi-channel customers. What’s more, by monitoring the customers’ path to
purchase and identifying their channel preferences, the company adjusted its media mix to optimize marketing spend. Thanks to Alteryx, the retailer
increased net new customers by 20 percent and grew diverse spend by 10 percent, resulting in higher overall margins.
5Cashing in on Analytics in Retail
Retail merchandising is part art, part science. Tightening margins and fickle consumer trends have led to greater
analytic adoption within merchandising. Savvy merchandisers now leverage historical purchase data with
consumer trends, trade area demographics, population changes, and other factors to improve the effectiveness
of merchandising efforts and drive greater value for their organizations. Some of the most common ways you
can use analytics to drive merchandising include:
• Demand Forecasting: Past purchase history and intuition alone cannot help you predict what
customers will purchase and when. Accurate demand forecasting requires you to not only look at internal
transactional data, but also at customer demographics, attitudinal data, competitive activity, economic
markers, seasonality, promotions, and more. Using data blending and advanced analytics, you can now
accurately predict consumer demand, by item, category, and department, from the individual store to
the corporate level.
A highly diversified, branded lifestyle apparel, footwear, and related products company, VF Corporation, serves consumers worldwide
through 35 brands and multiple distribution channels. With brands such as The North Face, Nautica, JanSport, Lee, Wrangler, Splendid,
and Vans, which garnered sales of $10.9 billion in 2012, the company wanted to improve corporate profitability, support significant retail
expansion, and maximize the performance of its more than 100,000 SKUs at over 10,000 retail locations.
Using Alteryx, VF Corporation was able to better match products to consumers and specific stores, thereby moving inventory into the right
locations at the right times. Based on simultaneous analysis of POS data, demographic information, and more than 200 lifestyle variables, the company
improved sales and reduced merchandise markdown and return rates. What’s more, Alteryx enabled VF Corporation to better track sell-through
rates of its fast- moving inventory and improve the efficiency of its forecasting function, leading to more accurate replenishment plans and better
forecasting for the company’s top 100 accounts.
Cashing in on Analytics in Retail 6
• Hyper-local Assortment Planning: The “one-size-fits-all” approach to assortment planning no longer applies
in today’s retail environment. Customers expect you to understand local sales and consumer trends—
and tailor assortments accordingly. With analytics, you can intelligently cluster stores based on like
attributes, assess sales performance by products and channels, and combine trade area demographics,
census, and demand data along with past sales history to create locally optimal product assortments
for each store, trade area, or channel.
• Inter-department Mix Optimization and Space Planning: Floor space is expensive and limited. Using analytics,
you can determine which departments or product categories to place in which location and how much space
to allocate to each department—accounting for trade area demographics and local demand trends—thereby
maximizing financial performance of your floor space.
• Promotional Planning: With so many competing products and categories, it is challenging to allocate the
right amount of promotional dollars for each product. Predictive analytics let you analyze the impact of
a promotion on overall demand, including complementary and cannibalized sales, so you can decide which
products to promote and when. You can even analyze the impact of multiple promotions within a specific
time period on your sales and margin goals to optimize the overall promotions plan.
New sales channels, globally expanded operations, and need for higher customer-service levels have all added
to the complexity of retail supply chains. To avoid lost sales or high operational costs, you need better visibility
into your inventory and transportation costs and improved collaboration with your suppliers. Using analytics,
you can improve the efficiency of your supply chain in the following ways:
• Inventory Management: Combine sales, inventory, and shipment data across multiple channels and
systems, and standardize product-naming conventions to get a complete visibility of what is stocked where.
Forecast demand based on sales history and demand trends to determine which products to stock in what
quantity, where, and when, and measure inventory turns and fulfillment rates to establish stocking levels
and re-order thresholds.
Under- and over-stocking of
merchandise cost retailers
worldwide more than $800
billion each year. Even more
alarming? The problem
is growing by nearly $50
billion each year.3
“2nd Annual Inventory Distortion Study,” Tyco Retail Solutions and IHL Group, May 10, 2012.
7Cashing in on Analytics in Retail
• Supplier Performance Management/Spend Optimization: Managing your extensive supplier network
without visibility into the associated risks and spend levels is a recipe for disaster, yet most retailers lack
a holistic view of their suppliers. With analytics, you can combine all your supplier data to rank suppliers
by quality, price, on-time delivery, and other factors. You can also calculate total spend by item, category,
and supplier to consolidate contracts and rationalize your supplier base.
• Distribution Network Optimization: Newer channels, changing demographics, and a sluggish economy
combine to change your retail footprint. As you open new stores, close others, and transition certain product
categories to newer channels, you need to rethink your distribution network. With data-driven insights,
you can reliably forecast which products and quantities to stock at different distribution centers and model
the impact of alternate distribution and service center locations on delivery time, fuel costs, and inventory
carrying costs, helping you to optimize network design.
Southern States Cooperative (SSC), founded in 1923, is one of the largest farmer-owned cooperatives in the United States. Owned
by more than 300,000 farmer-members, it purchases, manufactures, and processes feed, seed, fertilizer, farm supplies, and fuel.
Thanks to strong customer loyalty and very high brand recognition among agricultural professionals, SSC serves more than
1,200 retail locations in 23 states and sells products to farmers and rural American customers.
Wanting to reduce its inventory carrying costs and free up working capital while still stocking the right inventory in the right stores at the right
times, SSC turned to analytics. Using Alteryx, the cooperative segmented its inventory by seasonality and turns to identify slow-moving inventory
and establish in-store start and stop dates to stock seasonal merchandise. Thanks to the new insights, SSC reduced inventory by 31 percent while
maintaining planned service agreements, thereby freeing approximately $20 million in working capital per year.
Cashing in on Analytics in Retail 8
The performance of retail operations depends on a multitude of factors, including where you locate your
stores, how well you manage labor and how closely you monitor and manage store and overall organizational
performance. Here are a few ways you can use analytics to enhance your corporate and store operations:
• Labor Scheduling and Optimization: Labor is a huge cost in retail, yet most retailers struggle to synchronize
labor with actual demand. With predictive analytics, you can forecast labor demand across departments and
stores; conduct what-if analysis to understand the impact of promotions, seasonality, and other marketing
events on demand and labor needs; and measure, track, and monitor the impact of labor changes on category,
department, and overall store performance.
• Store Performance Analysis: Information about store operations can help maximize profitability, but with
hundreds of stores to manage, retailers struggle with issues related to labor, metric inconsistencies,
and overall operational efficiency. Analytics can help you determine the impact of promotions,
With more than 3,000 salons throughout the United States and Canada, Minneapolis-based Great Clips is the world’s largest and fastest
growing salon brand. The company’s salons employ nearly 30,000 stylists who receive ongoing training to learn advanced skills and the
latest trends. Great Clips wanted to better support its growth strategy by accelerating the new site assessment and selection process
for their franchise salons, while reducing the cost of that process.
Great Clips now puts the power to find and qualify potential new franchise locations directly in the hands of its real estate managers with Alteryx.
With analytics, Great Clips has reduced the time required to assess a potential site by 95 percent, enabling the company to assess three times as many
sites at a much lower cost, eliminating backlogs. What’s more, the company uses Alteryx to proactively target top site locations with the greatest
revenue growth potential as well as more quickly open new franchises in locations that have the greatest potential for success
9Cashing in on Analytics in Retail
refurbishments, and competitive activities on performance, compare results with other sister and
competitive stores in your local area, analyze the variance between actuals and targets, and share results
through easily consumable graphics.
• Site Selection and Trade Area Optimization: Despite the growing influence of e-tailing and other alternative
channels, brick-and-mortar retail stores continue to drive 85 to 90 percent of total retail sales worldwide4
making site selection and trade area optimization critical to retail operations. With analytics, you can
optimize market expansion and contraction plans based on population trends, competitive locations,
and other factors. You can also use analytics to improve retail store or fulfillment center site selection,
taking into account sales forecasts, drive time, sister store cannibalization, and competitive activities.
Despite the promise of analytics—along with proven results—do you still continue to hesitate? Are you worried
about your organization’s limited analytical skills? Concerned about executive approval for funding an analytics
initiative? Or nervous about adding to the workload of your already overburdened IT staff?
You’re not alone. According to KPMG, 80 percent of all retailers agree that data analytics are important, but only
12 percent claim high analytical literacy. And a recent McKinsey report ranked the retail industry in the lowest
quartile of all industries in terms of analytical skills and data-driven mindset.
Hesitate no more. Alteryx can help.
Alteryx is the ideal solution for you to start your retail analytics journey. By providing an intuitive workflow
for blending internal, third-party, and cloud data, Alteryx enables you to build sophisticated analytics
quickly and easily, so you can gain deeper business insight in hours, rather than weeks required with
“Retail: On-line versus Bricks and Mortar Sales—A Landlord’s View,” Vornado Realty Trust.
Visit Alteryx Retail District at
to access pre-built retail apps.
Cashing in on Analytics in Retail 10
Single Workflow for Data Blending and Advanced Analytics
Rather than cobbling together multiple tools from various vendors to get the functionality you require, Alteryx
delivers everything you need in a single, integrated solution, from data blending and exploration capabilities
to advanced analytics and reporting. Go ahead—optimize your marketing, merchandising, and other retail
operations—without incurring expensive integration costs or forcing analysts to use multiple, complex tools.
Intuitive Solution that Delivers Results Fast
In retail, IT resources are scarce and data scientists are difficult to come by. With Alteryx, you no longer have
to worry about IT availability. Built specifically for line-of-business analysts and managers, Alteryx’s intuitive
workflow for data blending, analytics, and reporting makes analysts productive in hours rather than days.
With pre-built data connectors that help you access and integrate virtually any data source, spatial and
predictive analytics tools, and an easy-to-use, drag-and-drop visual workflow, Alteryx simplifies the complex
tasks of gathering and blending the relevant data and building advanced analytics to help you quickly
answer your complicated business questions.
Built-in Third-party Market Data
Optimized retailing depends on access to the right data, but your internal transactional system or click-through
and social media data alone are not enough to give you the context and insight into your customer and
The Alteryx intuitive drag-and-drop
interface puts powerful data blending
and advanced analytic capabilities
in the hands of analysts
11Cashing in on Analytics in Retail
market environment you need. That’s why Alteryx uniquely includes the industry’s leading third-party customer
and market data out of the box, giving you the demographic, attitudinal, and trade area geospatial insights
you need to localize your assortment decisions and optimize your retail network—no additional expense
or integration effort required.
Spatial and Predictive Analytics Together
In the retail industry where location is all-important, Alteryx enables you to simply and easily bring a spatial
element to your organizational intelligence. Using the powerful Alteryx spatial analytic tools, you can turn
basic names and addresses into location information and get valuable visual insights about your customers,
such as preferred customer proximity to store locations. However, spatial analytics are not enough to meet
the need of modern retailers, which is why Alteryx also includes built-in, advanced predictive tools. The result?
You can create and promote campaigns to the customers most likely to respond based on drive times and
optimize store and distribution center locations—all without leaving the intuitive Alteryx workflow.
With more than 300 customers, Alteryx is the leader in the data blending and advanced analytics market.
Leading retailers, grocers, and restaurant chains, such as Walmart, Levi’s, Kroger, Lowe’s, McDonald’s, and Yum
Brands, all rely on Alteryx across functions from multi-channel customer insight and localization of assortments
to inventory management, labor planning, store performance analysis, and trade area optimization.
To compete with leading retailers, such as Amazon, Target and Walmart, you need analytics. Don’t let a lack
of analytical skills or the scarcity of IT resources hold you back.
Give your data analysts—those who know your business best—the power of sophisticated yet easy-to-use
analytics at their fingertips with Alteryx. No longer a complex, confusing morass, the data you so painstakingly
collect everyday becomes a strategic weapon in the war for customer attention. With Alteryx, you get the
insights you need to drive your business forward—without breaking the bank.
Visit us at www.alteryx.com/retail to learn more. Or call at 1.888.836.4274 to talk to a retail expert.
Alteryx includes the following
third-party customer and
market data out of the box:
• Experian household,
• Dun & Bradstreet organiza
tional firmographic data
• Tom Tom geospatial data
for location intelligence
• 2010 US Census data