Telling Data’s Story With Graphics

Experiments with graphic presentation of data are making it easier for sales people to see how they’re performing right in the field, according to Joseph D. Bruhin, chief information officer of Constellation Brands.

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U.S. consumers might not recognize the name Constellation Brands, but if they drink beer, wine, or liquor, they have probably consumed at least one of the brands it markets and distributes: Corona Extra, Modelo Especial, Clos du Bois, Robert Mondavi, and SVEDKA Vodka are among the company’s more popular offerings. Constellation is a publicly traded S&P 500® Index and Fortune 1000® company, with 2015 net sales of approximately $6 billion and 7,600 employees. It is headquartered in Victor, New York, with operations in the United States, Canada, New Zealand, Italy, and Mexico.

One of Constellation’s marketing goals has been to reach customers where they live. The company’s 2015 annual report notes that, “Mobile marketing is helping to build awareness — and loyalty — for our wine and spirits brands through targeted emails, online sweepstakes and coupons, shopping and food-pairing apps, dedicated social media pages, contests, and more.”

But making sense of the data generated by those kinds of marketing and sales efforts is a particular challenge, and one that is top of mind for Joseph D. Bruhin, Constellation’s senior vice president and chief information officer. Bruhin’s role includes developing new tools for sales people to use in the field and new measurements for understanding how products are performing at retail. He has been particularly focused on making data easy to analyze and understand.

In a conversation with Sam Ransbotham, associate professor of information systems at the Carroll School of Management at Boston College and the MIT Sloan Management Review guest editor for the Data and Analytics Big Idea Initiative, Bruhin details the particular challenges faced by companies in the beverage alcohol business and the role that graphics are playing in helping employees more easily track sales against metrics.

Can you give us an overview of Constellation Brands’ approach to analytics?

As anyone in analytics can tell you, obtaining meaningful measurements is not always easy. Our company has a diverse portfolio of more than 100 beer, wine, and spirits brands. They cross many categories and price points. And within the U.S. beverage alcohol industry, a three-tier system of distribution has been established that brands must follow, making data tracking even more difficult.

Tier 1: Constellation makes a product and sells it to distributors. Tier 2: Distributors sell the product to retailers. Tier 3: Retailers sell the product to consumers. The challenge of the U.S. three-tier structure is that we have limited visibility to our product as it goes through that process, changing from distributor to retailer. There are lots of anomalies in the data, different rules across different states, and we can’t see the movement of our product into the consumers’ hands as easily as we’d like to.

Data for distributor sales to retailers are calculated by third parties: Beverage Data Network (BDN) for wine and spirits, and Vermont Information Processing (VIP) for beer. VIP and BDN synthesize the information and then sell it back to us. That’s how we see how much of our product is sold into retail by distributors. There is often a lag to that data, however, and it’s limited to the information that the distributors are prepared to share with BDN or VIP.

Are there other challenges besides data lag?

The challenge isn’t just the lag of the data, it’s also the kind of data. Of course, we would like to have the most detailed level of data possible, but we also have to respect the wants, needs, and confidentiality of the distributors. And there are rules and agreements regarding what Constellation can and can’t share as well. These complications make tracking even basic sales constructs, like promotional evaluation and effectiveness, more difficult.

Research has shown that up to 70% of consumers make wine purchasing decisions while they’re standing at the shelf. If you ask a shopper, “What kind of beer are you going to get?” they’ll likely mention a specific brand. If you ask about a spirits purchase, the consumer may not have a brand in mind but will mention a category, like vodka. If asked about a wine, however, a consumer may say, “I don’t know, white, red …” — they often won’t even go to varietal. Brand loyalty and brand adoption elements for wine can be very different.

So reaching consumers before they get into a store, and knowing the store environment, must be critical.

Our challenge is finding a way to capture the hearts and minds of consumers either before they shop, which means marketing in the home, via TV or internet, or at the retail shelf. So we need to know what products are there, and what sells.

Visibility of data is a critical piece, and the engine of analytics behind that is hard to put together. In a perfect world, we would know where we’re running that promotion and who the participating retailers are in real time, so that we can course-correct within a promotion. It also helps us understand if this was a successful promotion and if it should be replicated on a larger scale. And to do this most efficiently and effectively requires an investment in data and analytic technologies.

How are you using information technology to solve the problem of limited visibility?

We feed the distributor data into our database, as we always have. But now we are able to put a graphic presentation layer on top of our data, showing how retailers are performing with our products. We developed a comprehensive retailer map by leveraging TDLinx, a data source that contains all beverage alcohol retailers, used our VIP and BDN distributor data to match distributor sales to retail, and put teardrops with GPS coordinates onto a digital map. The pins are color-coded based on how they’re performing against our metrics for either that geography or that type of retail.

All of our sales people can see this, and now — this is the beauty and power of it — it’s also going out into the hands of our distributors. They’re now able to see their data better through our representation than they can see from their own. That’s the power of the analytical engineer. We’re transforming and changing the way that people use systems. Even though it’s not the industry norm, distributors are saying, “Can we please use your tool?” That’s transformational in this industry.

What other benefits do you derive from this change?

I think the primary driver for distributors’ interest in this data is the visibility of how products are moving. We incentivize distributors to move our products and distributors are required to meet certain standards. This deeper level of data lets distributors focus on locations where execution improvements are most needed, helping them — and Constellation — meet their goals. These insights are driving behaviors by allowing distributors to clearly see how effective they are and where they could improve.

But this has also facilitated deepening relationships with our distributors by clarifying what’s important to the company and fostering a deeper mutual trust. Both partners are now looking at the same data and making decisions together on how to best proceed with “the good, the bad, and the ugly.” When incentives are now set, everyone has a visual representation of the work to be done in closing the gaps and can collectively work toward the best outcomes.

Can you give us an example?

We talk about closing the point of distribution (POD) gap. We might say to a retailer, “Here’s a bottle of wine we think you should include in your product mix, because in similar stores we’re tracking that product and it’s doing very well, selling side-by-side with the same products you’re already carrying.” We can show them that there’s a correlation of product movements to make the case for why they should be selling another product of ours. In essence, we’re taking the art of the retailers’ intuition and complementing it with graphical data.

Owners of small retail businesses appreciate the affirmation of their intuition, while larger chain retailers like Kroger or Walmart have their own analytical horsepower that allows their buyers to quickly assess our data and make intelligent adjustments to their product mix. And as I mentioned, we’re giving it to them in a graphical presentation that’s appreciated, easily translated and understood.

How were you able to get people to trust and use the data?

We validated our analytical results with pilot algorithms that, once we defined them, were moved into their tool and begun testing in five locations, as well as with one of our distributors. I’m looking forward to lessening the gap between desired and actual product mixes at retail over time. I think we’re cutting-edge on this one, I really do. I’m excited about the potential this has to move the needle on Constellation’s bottom line growth and success.

So, have you measured any results from these changes?

We’ve been seeing an uplift over the last couple of months. But I’m waiting for a few more quarters’ worth of data to really see validation. All technology has a “hype cycle” where people get excited by the tool, put it into play, and cause an uplift just because people are having different conversations. What’s really important is that it’s sustainable over the long-term.

What are you going to tackle next?

For years, we’ve been flirting with radiofrequency ID (RFID) technology. The concept is that you walk out of a retailer with your shopping cart full of items containing RFID tags and sensors that read everything and automatically put it on your credit card. We’re not there yet, but it’s a great concept, and at some point, maybe the retail space will get there.

In the meantime, IT will bring other things to the forefront of the marketing and sales space. For example, imagine if we could more easily match consumers to products that they really care about. If we’re able to connect with consumers through their phones or other wearable technologies, and we know someone is interested in our product because they’ve opted in to receiving messages from us, we can notify them as they approach our products: “Hey, Sam, did you know that we have a special just around the corner? We’ve got your Robert Mondavi Private Reserve. Would you be interested in buying a bottle? Here’s a coupon.” Or maybe you’re coming up to a display of Corona Light and Kenny Chesney starts singing you a song on your phone. All the pieces of this technology play are already there, it’s just a matter of figuring how to put it together in a way that delights our consumers.

We’re already collaborating with other teams across the organization to explore that space in Constellation’s wineries. If you go to Robert Mondavi Winery and you have a phone with Bluetooth, you can walk around the winery and in certain areas a transmitter will recognize you and start telling you a story about the vineyard. You can learn about the history, understand how harvest is managed, and hear what our senior winemakers have to say. And when visitors leave the vineyard, the tool still knows who you are because you opted in. That technology is very early in its inception, but there’s real promise there. We’re taking deliberate steps to deepen our partnerships and work collaboratively on translating analytics into long-term growth and success.


Competing With Data & Analytics

How does data inform business processes, offerings, and engagement with customers? This research looks at trends in the use of analytics, the evolution of analytics strategy, optimal team composition, and new opportunities for data-driven innovation.
More in this series

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Comments (3)
MinhChau Tran
Article is about data with graph, but i don't see any graph, could you please upload some examples. Thanks
john Bangs
Got to agree with Eric. I was expecting to see some fascinating and insightful graphics and maybe some ways of representing data that I was unaware of ....
For an article about using graphics, one would think you would show some visual examples.  Also, when MIT uses the word 'experiment', I'm expecting a scientific method not a marketer's 'let's try something and roughly guess that it's working.'