As companies try to aggressively harness customer and marketing data, they often feel like they are in rough seas, overwhelmed with waves of data and no rudder.
How can they use data to drive more efficient decision making within their organizations?
Shardul Wartikar, VP & Global Analytic Lead at Kantar recently sat down with four experts in the field to discuss the issue of how to make data accessible and useful. Our experts (Cyrus Kelley, Vice President, Consumer Insights and Loyalty at Red Robin, Debra Mednick, Sr. Manager, Market Intelligence & Insights at BIC. Cecilia Dones, Head, Data Sciences at Moët Hennessy, and Doug Jensen, SVP Go-to-Market Analytics & Activation and Learning COE at The Estee Lauder Companies Companies) came from a variety of industries but all face similar challenges.
Wartikar recognizes several data challenges companies face: determining the most valuable data sets, layering in platform providers, coalescing different data sets, budgeting and socializing the results among key decision makers. As he says, “data demands ROI and if it can’t be proved it will not be funded.”
How should companies proceed? Our experts pointed to the following tactics:
- Putting the customer at the center of the data practice
- Connecting the data sets
- Implementing the analytics
- Budgeting for it all
- Riding the data roller coaster
- Getting the decision makers on board
Putting the customer at the center of the data practice
How do complex, often multi-national corporations determine the ROI on data investments and justify them to leadership?
There’s a lot of discussion about the value of data, analytics, and technology to help organizations become more effective and efficient in their marketing decisions.
Our experts agreed that a customer focus is at the center of any sound data practice. As Cyrus Kelley of casual dining chain Red Robin sees it, “brands have to put the guests at the center of every decision and make continuous investments to become more data driven.”
Red Robin focuses on leveraging first party and other data to create a deep understanding of their guests. They also have a robust and successful loyalty program that provides rich data. As Kelley says, “We are now able to leverage this data, tailor communications and messaging to the needs and preferences of the company's guests, going from broad-based messaging that resonates with all guests, to messaging that's customized to each guest need, based on their data” This helps drive engagement, and ultimately, customer lifetime value.
Cecilia Dones of Moët Hennessy agrees with the customer-first mentality. “Customer first - if we use that as a North Star - really helps to drive our decision making for data and analytics.”
“I'm part of a very successful legacy organization with a rich history and from that perspective, the organization has been making strategic decisions to drive their business forward for a very long time using data,” she says. “But the data was not in a database. Now we have databases and big data. We have new signals related to data and we incorporate that in the strategy.” Their data is focused around “the decision-making process, understanding how the business makes decisions and then aligning the data strategy and the analytical strategy.”
As she sees it, “That's how you start to get those quick wins, then build medium term wins and conversations around data investment in terms of longer-term trends.”
Connecting the datasets
The group delved into the problem of lack of access to customer-level datasets and the inability to connect disparate data sets to make decisions. This is combined with the issue of how to ensure insights tools are adopted and funneled into the decision-making process.
Doug Jensen of The Estee Lauder Companies spoke about harmonizing the various data sets the company possesses. “We’ve been on a multi-year journey to collect first party data, harmonize it and ensure we have a single view of the consumer.”
He makes sure that once the first party data is collected and harmonized, the company then reaches out to consumers to engage with them in customer life cycle programs.
“You acknowledge what they purchased from you, and then you put them on a pathway to loyalty which, in many cases, is a reminder of that purchase and offering a retention or replenishment journey,” says Jensen. “A large part of my job is to make sure that all stakeholders understand how to leverage campaign management tools and how to harmonize those tools worldwide to make it easier for users.”
The Estee Lauder Companies use marketing mix modeling or attribution modeling which requires a high level of data harmonization.: “I’m a big proponent and fan of these techniques to measure incrementality in return on investment for your advertising. For us, it's essential to analyze all of that data properly, and at the correct level,” says Jensen.
Implementing the analytics
Once data is pulled together in a central location and connected, the analytics can begin. Analytics for this group of experts can include media mix modeling, but also simply “finding meaning in what influences an outcome,” says Kantar’s Wartikar.
Debra Mednick of BIC manages business data across more than 30 countries and unlike The Estee Lauder Companies or Red Robin, has limited customer level data. “We’re working with secondary market data…we started off a little over two years ago with the objective of bringing all the data together to enable essentially a one-stop shop. While it seemed simple, securing high level buy-in and commitment was needed given the significant investment in funding and resources. While we’re pleased to have delivered on the harmonization and integration, the journey continues as we pivot to focus on refinement of the data visualization tools for extraction.”
The challenge as she says is that “You have hyper local data in countries but not necessarily accessible to everyone who needs access. Imagine, for example, that I'm in a central function like Investor Relations, and I want to be able to aggregate that data at the snap of my fingers. The journey was all about aligning the data – a process called harmonization – and storing it in a central system, a.k.a. a data lake, and then develop a platform to enable quick and easy customized reporting, enabling aggregation in any kind of way, shape, or form.”
Aside from centralized reporting, BIC’s goal was to “be able to see trends or signals early, have that crystal ball, which meant reporting data as quickly as possible to identify key trends and variations between different countries and regions,” says Mednick. “To easily extract these trends, we included plans to develop a BI system including advanced analytics to meet the additional objective of building stronger predictive and prescriptive capabilities. At BIC, this function is housed within the innovation team led by Market Insights and Data Science.”
Budgeting for it all
How do these companies decide which areas to prioritize and build a business case for investment that they can take to leadership for funding?
According to Dones of Moët Hennessy, “It's easy to say, but hard to do in practice. I look for the intersection of consumer need versus internal business stakeholder alignment. So how can you actually create value from data and realize that value within the organization? You need business stakeholders who are partners with you, trying to help push it through. Producing a new dashboard or a new analysis, getting a new number, that's fantastic; but unless somebody actually applies it to a decision and takes a different action than they would have before to impact a consumer experience, it doesn’t matter.”
Dones operates by putting the challenge into three buckets, starting with revenue generating. “If you have e-commerce capabilities, that one is so much easier,” she says. “The second one is around efficiency. There’s a lot of business processes that we do, for example, but if we can do them 25% faster, are we saving on full time employees? If we are externalizing, but we learned to do it internally with our own data, do we save agencies having to scope it out?”
“The third bucket is risk mitigation,” she continues. Once Dones knows these three factors, she proceeds in the consulting practice of 2 x 2 grids focusing on value against level of effort.
To determine whether an organization has the right data, she says that you must determine whether the data is already curated and readily available for use. “All those things go into level of effort. But then once you plot it on a 2 by 2, it becomes easier to have a conversation of, ‘this is low hanging fruit, this is medium.”
Riding the data roller coaster
Kantar’s Wartikar wanted to know whether in determining what projects to proceed with, the companies typically followed a linear progression based on revenue generation, efficiency, and risk mitigation.
Moët Hennessey’s Dones says, “The textbook answer is everything is linear. The reality is, as with most wonderful things that are complex and beautiful and high value, it is non-linear. The analogy I tend to use is that it’s a roller coaster - there are definitely ups and downs. But if you ride this right, it's way too much fun to not be doing this.”
Kelley of Red Robin, concurs. “The trick is that not all data is created equal in terms of cost and level of effort to analyze, and quite frankly, benefit to the business. And so, there are two approaches that I typically see on opposite ends of the spectrum, and then multiple flavors in between. On one end, you go big. You put together this large data project or team that focuses on bringing lots of disparate data sets together with a number of different benefits, and that helps to kickstart the company's investment. That has the benefit of creating company focus and getting more total resources to bring to bear to get the data.”
“Then, since there are a number of different uses for all of those different datasets, sometimes that can help ensure that there's a total ROI that pays for all of it,” Kelley continues. “Typically, there are some things that you can demonstrate ROI for easier than others, and so if there are a couple that really pay out, you can do the whole project. The challenge with this approach is that it can be unwieldy and lose focus. It’s really important at the core that there are a central set of objectives defined to ensure that, at minimum, you realize the benefits that you actually set out to achieve.”
On the other end of the spectrum is the approach of starting small and taking bite size chunks. As Kelley says, “You might start by manually pulling a smaller subset of data, in a more raw, and unstructured format, to work with ad hoc. That allows you to prove out your hypotheses, test the business value of a particular analysis, or use of the data, and then use those results to drive the business case for scaling up, or automating that analysis. This gives you more freedom to explore data early, under less pressure, and in some cases, demonstrate business value faster.”
Getting the decision-makers on board
The last challenge discussed was that of internal communications. As Mednick of BIC says, “Understanding the various stakeholder’s requirements upfront and getting their buy-in will save you time in the long run. We did this by identifying all potential stakeholders and then conducting a Road Show.”
Another learning was about the need for customization. “While the organizational focus was on harmonization and ‘one version of the truth’, we uncovered a strong preference for having the ability to customize so while we set out to build standardization, we ultimately delivered a platform that also provided flexibility without compromise.”
To ensure socialization and adoption Mednick started an internal newsletter. “We started publishing the newsletter to inform us of the progress, so people would get excited and start the adoption process. A major element included soliciting and curating testimonials by the early adopters who highlighted the benefits of the new global platform.”
Jensen of The Estee Lauder Companies also had a newsletter which celebrated the analytics being delivered. But more so than broad communications, he believes that having “the C-Suite support from your CEO and your CFO is essential. If they believe in data and analytics, as an enabler, for the brands or for the corporation, it’s critical.”
As Kelley of Red Robin says “If the data helps to keep everyone on the same page and build confidence and ultimately drive ROI, I generally find that people will use it. On the other hand, if they feel it slows the process down and reduces the ability to be nimble or didn't drive decisions they fundamentally agree with, they tend not to want to use it. Ultimately, we're all here to drive business outcomes, serve our guests, our team, and investor stakeholders. Our product is not data and analytics. Those are a means to an end.”
To listen to the audio recording of this webinar, or to register for any of the upcoming webinars in the series, please visit the Kantar Analytics Live website