Deep Analytics: Why Retailers Need It Now More Than Ever
In this episode of our Talk Retail to Me podcast, we focus on deep analytics. Parker Avery experts Clay Parnell and Sam Iosevich discuss how retailers and consumer brands can embrace analytics into their business models to enable cross-functional collaboration and deliver meaningful financial results.
In the podcast and transcript below, we explore questions like:
- While the recent disruptions have put an even stronger focus on integrating digital and physical channels and deeply understanding customer behavior, it certainly makes sense to involve analytics to some extent, but are these the most important areas right now?
- What are other top key areas or business challenges on which retail and consumer goods companies should focus their analytics investments and efforts?
- How can an analytics system integrate into a company’s existing operating model without major disruption?
- What prerequisites—besides data cleanliness and governance—are needed for a company to begin embarking on its analytics journey?
- What skills or resources are critical to ensuring successful outcomes?
- What benefits have been realized by retailers and consumer goods companies who’ve successfully adapted an analytics-driven mindset?
We hear the word analytics everywhere these days in different contexts and industries, and while the recent disruptions have certainly put an even stronger focus on things like integrating digital and physical channels and deeply understanding consumer behavior, are these the most important areas for analytics investments?
Sam: The integration of the digital and physical channels is first and foremost a business process challenge—where analytics is an enabler. Analytics can help us optimize your digital traditional and marketing spend, inventory across the physical assets, assortment, and pricing—but it all comes back to the business process.
Clay: I absolutely agree. First of all I think this is a great question to start with—I’ll share that in my many years of consulting with retailers, the consistent challenge we always hear for business teams, especially in merchandising and supply chain, is the fact that they’re always focused on execution and solving problems and dealing with daily fires. There’s always a strong desire to be able to spend more time and energy on analyzing results, working smarter, and just having time to truly manage their business, so it’s even more important today for obvious reasons.
You touched on the omni aspect, the digital aspect; in the past, consumers shopped almost only in stores and there were less options for the assortment, the channel…and pricing was relatively straightforward. Today in the COVID world, you can shop anywhere and get inventory in a myriad of different ways and from a myriad of different sources, whether it’s the store curbside, the traditional direct-to-consumer—so the channels are endless, the level of consumer specific data, and personalization is immense.
In today’s world of increasing omnichannel complexity, it’s not possible to understand and drive effective decision making without advanced analytics.
So I truly believe in today’s world of increasing omnichannel complexity, it’s not possible to understand and drive effective decision making without advanced analytics.
What are the other top key areas or business challenges on which retailers and consumer goods companies should focus their analytics investments in their efforts?
Sam: Analytics should be integral to every business function. Anything from planning, marketing, pricing, fulfillment, operations. I don’t think there’s a process where analytics is not key these days. Analytics not only serve to optimize these processes but align them as well. These functions are all looking to satisfy customer demand. A common understanding of the demand levers that each of these functions controls is necessary for collaboration. For example, look at something like planning, think about the products you can bring in, your marketing dollars can be focused there as well; your pricing and your fulfillment are aligned to the same demand signal. And, of course your operations—stores, transportation—are aligned to make all of that work together. So analytics is part of every business function—not only does it optimize it, but it brings it all together and aligns it. Very often you’ll hear about business challenges related to working in a siloed environment, and analytics can play a key role in bringing that all together.
Clay: As far as other business areas, just focus on what makes sense for you. Just because a competitor or peer you’re talking to is focused on forecasting or market basket or something else—figure out what makes sense for your business: What areas have real challenges? What opportunities do you think are worth additional analysis? I agree with the silos and collaboration; one of the unheralded benefits of analytics is the ability to drive collaborative behavior and cross-functional integration. The classic example is that you can use analytics to improve your overall demand signal, and it’s great to start off with using that to drive merchandise planning, but it can be that same demand signal to drive other functions, like replenishment, allocation, supply chain, etc. Just having that common signal itself can help break down those silos.
How can analytics system integrate into a company’s existing operating model without a major disruption?
Sam: The analytics systems are the grease that enables improved business process. You don’t ever do analytics for analytics sake. Necessary disruptions should be driven by business process and strategy improvements—not analytics. Analytics is there to facilitate not drive the change. It’s important that everyone understands this before embarking on the analytics journey—how analytics will be the enabler for process and strategy. From that standpoint, change management is a crucial component.
Necessary disruptions should be driven by business process and strategy improvements—not analytics.
Clay: The analytics itself shouldn’t be disruptive, the results can be—and leadership needs to embrace and think about what analytics can provide. Listen to the numbers and don’t prejudge what the answer should be based on bias or previous results or what you’re reading in the latest business journals. Test and learn—and keep moving forward. And in that regard, speed is important. It’s important to show results, even in a test and learn situation or a pilot, as quickly as possible. Especially in today’s environment, be nimble, be agile, and that will help win support across the business. And just continue to iterate through additional improvements.
We hear a lot about being “nimble and agile” and when it comes to integrating analytics into an existing environment, can you give a real-life example of what you mean by this?
Sam: We will hear a lot about people focused on a process and almost being a hamster on a wheel in whatever process they’re involved in—they’re constantly playing catch up. And that’s because they’re pulling the levers for everything they do. There is a lot of automation that analytics can bring to where we can focus associates on managing by exception. As an example, there’s no need for a demand planner—either retail or CPG—to be touching every plan, for every item, in every geography. They should be managing by exception, and analytics can enable that.
Besides data cleanliness and governance, what prerequisites are necessary for a company to begin embarking on their analytics journey?
Sam: Deep data science expertise are necessary, but not every company is going to have access to that expertise. If you look at the bigger organizations, like Walmart and Amazon—they likely have access to dozens of data science experts in-house—but some of that expertise can be brought in. It’s very important for the entire organization to understand what data science can do for them, and it starts with the business functional leaders. They don’t need to have a profound understanding of machine learning, neural nets, etc. are. What they need to understand is the benefits that analytics can bring to their function.
It’s very important for the entire organization to understand what data science can do for them, and it starts with the business functional leaders.
Clay: I will always harp on the need to have a solid strategy and plan. I always emphasize, “Don’t just buy the new shiny object for the sake of having a new shiny object.” Define what your team needs to focus on first, but don’t go any further than that because you’re likely going to learn new information and change your mind anyway. The team needs to have spent some time on the business processes. Many companies just look for a new solution that sounds like it will fit, but they haven’t thought through how and where it will fit in the workflow: what roles will drive the solution or be impacted by it and how it’s going to work with what’s already in place. I’m a firm believer that analytics without a business process focus is a recipe for futility. And as with any new initiative, communicate and keep over-communicating, tell people about the investment in analytics and how it’s going to be done, including expectations and success measures. But recognize that your people are your experts: ask for input and feedback along the way. And like any other major initiative, don’t underestimate the change management requirements that will likely come forth.
What other skills or resources are critical for analytics to be successful?
Clay: Few, besides some of the largest retailers have those data science skills in-house. I’ve worked with retailers where we’ve tried to find and recruit data scientists and the right analytics skills, and it’s neither easy nor cheap when they have so many other options. But there are options to “rent” or lease skills as well as software-as-a-service platforms. One easy option is to find some partners out of the gate as you build up your skills and bench strength, it’s an option to consider. One other key point is that a lot of retailers continue to pigeonhole people, with mindsets like, “my merchants are my buyers—they’re product people” and “my planners need to know the numbers—they’re my numbers people.” In today’s world, you can’t have that dichotomy. Everyone has to be a numbers person to some extent—not that they all have to be data scientists, but they must understand the results of the analytical tools and the implications of those results. It’s very important to not assume that the data scientists are down in a dungeon throwing out answers and results without interpretation.
As far as benefits, what have you seen in retailers and consumer goods companies who have successfully adapted an analytics-driven mindset?
Sam: That’s the right question because the focus should always be on financially measurable results. There’s no reason to embark on an analytics journey if you’re not focused on the bottom line. Personally, I’ve seen financially measurable results in the hundreds of millions of dollars. These benefits have been driven by better planning, pricing, marketing, fulfillment, and operations. Although many of these benefits have come from automating tasks such as ordering, most of these benefits were realized in enabling people. Therefore, adoption is key.
Clay: Some of the automation ties in with efficiencies and letting people attain results without having to churn through huge spreadsheets that are both hard to maneuver and typically can’t get the level of detail required anyway. But the efficiencies are very minor compared with the financial results. The ability for analytics to impact a medium-to-large enterprise is huge: impacts on demand signals to drive sales, impact margin or GMROI, to impact inventory turns and how companies are buying and setting replenishment targets, how they’re allocating to channels and stores. Analytics will fit well for retailers that focus on key metrics and set improvement goals for those key metrics. The ability of analytics to drive improvements in those metrics is significant.
Don’t be afraid of getting started, but also don’t expect to define a huge, rigorous project.
Any last bits of advice for retailers who are just starting on their analytics journey?
Sam: You probably have already started on that journey, whether or not you realize it, so embarking on it will not be as scary as perceived. These days, anyone coming out of university will have some level of exposure to data science, and very likely some of your existing tools have evolved to already use some of the analytic tools, so you’re likely well on your way. I wouldn’t let the phrase “advanced analytics” scare you in any way, the focus needs to be on business process and strategy—and understand that analytics is the enabler.
Clay: Just get started. The conversation needs to continue; people are going to keep asking questions, and you need to dig down into your data. Don’t be afraid of getting started, but also don’t expect to define a huge, rigorous project. Expect quick results—maybe not significant results right out of the gate—but expect improvements over time. And keep the conversation going cross-functionally; don’t say it’s only a forecasting or planning or supply chain project—make sure everyone is engaged and involved because it is going to have many tentacles and touch points across your organization.
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