Big Data. Big Decisions
InformationWeek
Special Coverage Series


How Gallo Brings Analytics Into The Winemaking Craft

E. & J. Gallo, the No. 5 company in the InformationWeek 500, brings silos of data together for analysis and adds insights from social networks.

You might expect a 79-year-old, family-owned agricultural business to be a sleepy, low-tech affair, but not E. & J. Gallo Winery. It has all the product development, distribution, and customer research sophistication of a consumer products giant, backed by cutting-edge analytics.

With about 5,000 employees worldwide and $3.4 billion in revenue, Gallo is on track to ship 80 million cases this year. It has 60 brands and sells products in more than 90 countries, buying 85% of its grapes from independent growers and importing more than a dozen brands from Argentina, Australia, France, Germany, Italy, New Zealand, South Africa, and Spain.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Gallo took the top spot on our 2004 InformationWeek 500 based on its sophisticated supply chain. The winemaker has since ramped up its analytics capabilities and, in 2010, kicked off APEX, short for Architecture and tools, People and processes, Examination and analysis, and eXperimentation and research. APEX is about using IT along with viticulture and analytic science to make better data-driven decisions.

Gallo has more than 500 TB of data on everything from the vintages and varieties of the grapes it uses, to the brands it produces and ships, to detail on its distributors' buying habits. With point-of-sale information and retail data from third parties such as IRI and Nielsen, Gallo also knows what consumers are buying and has deep insight into local and regional trends.

Analytics Everywhere

Analytics at Gallo has been getting increasingly sophisticated. It started 15 years ago with comparative sales reports, distributor profitability reports, and demand planning information. Over the last decade, Gallo has gathered more data on consumer buying habits and the profitability of specific grapes and pricing methods.

With APEX, Gallo is bringing all of its data together and adding new sources, such as consumer feedback from social networks, in order to graduate into complex analytics, says VP and CIO Kent Kushar. "We had to bring it all together to clearly see the patterns on what people are drinking," Kushar says. "We're also using our research on grapes and varietals around the world to figure out what people will buy."

InformationWeek 500 Top 5: Vintage Gallo: Data Analytics
Kushar is bringing all of Gallo's and its partners' data together to look for wine drinking patterns

Gallo's analytics maturation mirrors the broader IT industry's move from rear-view mirror reporting to predictive and proactive analytics. Gallo uses the deep insight it gets from all of this analytics to develop new breakout brands.

To understand taste preferences, Gallo surveys consumers at tasting events and in tasting rooms at its California and Washington vineyards. If customers are wine novices, employees teach them about palate taste zones and the use of consistent descriptive terms, such as oaky, fruity, and syrupy, to describe the wine so that Gallo can accurately interpret the feedback.

These surveys have yielded five core wine style clusters: sweet and fruity, light body and fruity, medium body and rich flavor, medium body and light oak, and full body and robust flavor. Gallo maps its own and competitors' products to these clusters, and correlates them with internal sales data and third-party retail trend data to understand taste preferences and emerging trends in different markets.

In one brand development effort, Gallo spotted big potential demand for a blended red wine that would appeal to the first three of its style clusters. It used extensive knowledge of the flavor characteristics of more than 5,000 varieties of grapes and data on varietal business fundamentals--like the availability and cost patterns of different grapes from season to season--to come up with the Apothic brand last year. After just a year on the market, Apothic is expected to sell 1 million cases with the help of a new white blend.

After it came up with Apothic, Gallo used its deep sales data and marketing analytics to validate the potential of the brand, balancing price, volume, and margin trade-offs for Gallo as well as for its distributors and retailers. "With all the data that's available to us, we can model all of the choices we make in developing a particular brand down to the label and bottle, and we know the number of consumers out there that would like that particular style," says Jennifer Jo Wiseman, Gallo's VP of consumer and products insight.

Gallo then did test marketing with selected distributors and retailers, and in its own tasting rooms and at events it sponsored. Testing with partners yields valuable wholesale and retail sales data that helps Gallo validate pricing decisions, volume expectations, and a brand's appeal to the target customer. Gallo can analyze which wines sold at which stores, and with store demographics and aggregated customer loyalty card data, it knows whether a test product is reaching the intended customer. Gallo has confidentiality agreements with distributors and retailers, so their data is used strictly for internal marketing analysis.

 1 | 2  | Next Page »


Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.