Tag: Marketing Mix Modeling

The Impact of Trade and Distribution on CPG Success

Posted on by Caroline Lewis

For the Consumer Packaged Goods (CPG) industry, a lot of focus in marketing analytics is placed on things like marketing mix modeling, consumer behavior, and brand strategy. But there’s another crucial piece that often doesn’t get as much attention: the role of trade and distribution in driving business outcomes. These aren’t just logistical details—they’re powerful factors that can significantly influence a brand’s performance in the market. 

As someone who works closely with CPG companies, I’ve seen how important trade and distribution strategies are to a brand’s success. Activities like in-store promotions, price discounts, and product placement deals directly impact what consumers buy. Similarly, the distribution model a company uses can either enhance or limit its ability to get products on shelves and into customers’ hands. These factors are often the unsung heroes behind a brand’s market share and overall profitability. 

The Importance of Distribution Models 

One distribution approach worth highlighting is Direct Store Delivery (DSD). For those who aren’t familiar, DSD involves delivering products directly to retail stores instead of going through distribution centers. This allows companies to have more control over how their products are displayed and stocked, which can lead to fresher products on the shelves and a quicker response to what the market wants. 

However, DSD isn’t without its challenges. Running a DSD operation is more complex and demanding than traditional distribution methods. It requires close attention to everything from delivery schedules to shelf space and product availability. But for companies that can manage these challenges, the benefits are clear. The extra control that DSD provides can lead to better sales and a stronger presence in stores. 

Modeling Trade and Distribution in Marketing Analytics 

Given how important trade and distribution are to the success of CPG brands, it’s critical that these factors are accurately reflected in marketing mix models (MMM). Unfortunately, many MMM practices focus more on media and advertising, often leaving trade and distribution underexplored. This is a missed opportunity, especially for companies using complex distribution methods like DSD. 

To truly understand the impact of trade and distribution on business outcomes, it’s important to use a modeling approach that matches the level of detail involved in these areas. This is where Hierarchical Bayesian models come in. These models are particularly good at handling the complexity and detail required to accurately represent trade and distribution activities in MMM. 

Hierarchical Bayesian models allow us to combine different levels of data—like store-level sales, regional distribution patterns, and national trade activities—into one cohesive framework. This level of detail ensures that the model captures the true drivers of business performance, providing more accurate and actionable insights. For CPG companies using a DSD model, this level of detail is not just helpful—it’s essential. 

The Takeaway for CPG Brands 

For CPG brands, especially those using complex distribution methods like DSD, it’s important that your marketing analytics efforts don’t overlook the impact of trade and distribution. While media and advertising are important, they’re just one part of the story. To really understand and improve your brand’s performance, your MMM should reflect all the factors that drive your business, including trade and distribution. 

By using advanced modeling techniques like Hierarchical Bayesian models, you can make sure that your analytics are as detailed and accurate as your business operations. This won’t just lead to better insights—it will help you make decisions that drive real growth. 

Trade and distribution deserve more attention in marketing analytics. These elements are crucial to the success of CPG brands, and their impact should be fully accounted for in any comprehensive marketing mix model. As the CPG landscape continues to change, those who can navigate the complexities of trade and distribution will be the ones who succeed. 

Understanding Media Attribution to Sales

Posted on by Caroline Lewis

A common question I often get about media attribution is, “How do you attribute media to sales?” This query always excites my inner data nerd! My typical enthusiastic response is, “We fit a hierarchical Bayesian model that relates adstock saturation transformed media to sales,” which usually leaves everyone more confused.

So, what does that mean in plain English?

Let’s break it down.

Hierarchical means we are stacking similar data together, such as by region, and sharing insights across these groups. This allows us to draw more robust conclusions by leveraging similarities and differences across the data.

Bayesian refers to the statistical approach we use, which not only helps us learn the effect of media on sales but also measures our confidence in these effects. It provides a probabilistic framework, giving us a richer understanding of the data and its uncertainties.

Adstock captures the time-delay effect of media. Most advertising has a delayed impact on sales, and adstock helps us account for this lag.

Saturation accounts for diminishing returns. After a certain point, additional media spend has less and less of an impact. The saturation function helps us adjust for this nonlinear effect.

So, in simpler terms, a better explanation might be “We adjust media for delayed impact and diminishing returns, then relate adjusted media to sales using a model that shares learning and estimates uncertainty.”

By adjusting for these factors, our models provide a more accurate and nuanced understanding of how advertising translates into sales. This approach not only helps in pinpointing the effectiveness of different media channels but also enhances our strategic planning by understanding the varying impacts across regions.

Understanding these concepts can significantly elevate your media attribution strategy, providing actionable insights and driving more informed decision-making in your marketing efforts.

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Breaking Down Silos to Unlock the Full Potential of Retail Media Networks

Posted on by Kevin Youts

Introduction: In today’s rapidly evolving marketing landscape, Retail Media Networks (RMNs) have emerged as a critical component of marketers’ media strategies. According to a recent article in Insider Intelligence, RMNs have grown exponentially across an exploding number of retailers, becoming a $30Billion channel in just 5 years.

With the deprecation of first-party data and the need for real-time insights into shopper behavior, RMNs offer a tantalizing antidote to both trade marketers and media buyers. However, to harness the full potential of these networks, brands must avoid the pitfalls of organizational silos, and provide the resulting teams with the optimal tools to make win/win decisions retail media decision that benefit the company overall.

Collaboration between brand and trade marketers and category managers, alongside media planners and buyers, can be crucial for maximizing return on investment (ROI) in RMNs and achieving both trade marketing and media effectiveness. Too often the two groups are functioning with separate budgetary parameters or not working off the same models to drive the most effective media buying decisions.

Let’s look at what each group brings to the challenge:

Brand and Trade Marketers:

Brand marketers, including shopper marketers, category managers and omnichannel leads play a crucial role managing sales, and coordinating merchandising and promotions with distribution partners at retailers. Their skill sets revolve around understanding consumer behavior, market trends, and effectively positioning products within both the physical and online retail environments. They typically possess the following key competencies:

• Retail Strategy: Trade marketers and brand category managers have a deep understanding of the retail landscapes in which their products are sold. They develop strategies to maximize product visibility, optimize shelf space allocation, and ensure effective pricing and promotion tactics are negotiated and well executed across an array of retail partners, increasingly in both physical and online environments.
• Merchandising Expertise: These professionals excel in creating compelling product displays and planograms that capture shoppers’ attention and drive sales. They have an eye for visual aesthetics and possess strong knowledge of consumer purchasing behavior.
• Channel Collaboration: Trade marketers and omnichannel leads and brand category managers establish strong partnerships with retailers, negotiating slotting fees, and ensuring optimal product placement. They possess exceptional communication and negotiation skills to drive mutual success.

Digital Media Planners and Buyers:

Media planners and buyers specializing in digital opportunities, play a pivotal role in targeting specific audiences and executing programmatic buying across relevant channels. Their expertise lies in leveraging digital platforms and various data analytics tactics to deliver targeted messages to the right audiences that can generate measurable outcomes. The following core competencies define their skill sets:

• Data-Driven Insights: Digital media planners and buyers are adept at analyzing consumer data and identifying targets and how best to reach them. They leverage sophisticated tools and technologies to extract actionable insights, enabling precise audience targeting and effective campaign optimization.
• Multi-Channel Mastery: These professionals have a deep understanding of various digital channels, as well as offline media, that in combination make up the optimal media mix. Their core competency lies in balancing expenditures and often dynamic investments across channels including social media, search engine advertising, display advertising, and video advertising. They are tasked with crafting integrated media strategies that reach consumers at multiple touchpoints.
• Programmatic Proficiency: Digital media planners and buyers excel in utilizing programmatic buying to automate media purchases and optimize ad placements in real-time. They possess a strong grasp of ad exchanges, demand-side platforms (DSPs), and supply-side platforms (SSPs). They lean on direction that comes from robust data analysis to retrospectively calculate ROAS, and proactively optimize their media plans to achieve optimal growth in the future.

The Pitfalls of Organizational Silos

Failure to bridge the gap between these two groups can lead to several detrimental outcomes. Budget disputes often arise when teams compete over limited resources, hindering the overall marketing effectiveness. Moreover, the absence of collaboration can result in one team allocating budgets without considering the expertise and domain knowledge of the other, leading to suboptimal decision-making and wasted opportunities.

Harnessing the Power of MMM for Optimal ROI

Retail Media Networks, especially if brands are investing in several, produce a maddeningly complex set of data to analyze (time, geo-spatial data based on physical location of stores, consumer behavior on and offline, and the staggering array of products many sales and media teams must account for).

There is renewed enthusiasm across the media analytics industry in the power of robust Market Mix Models (MMM) to make sense of increasingly complicated media planning required to optimize RMN spend as part of overall advertising strategies.

The best MMMs enable marketers to analyze behavioral, historical sales, and other causal data at the individual channel partner level across all media. With this granular understanding, organizations can identify the most effective tactics for driving sales within retail settings, analyzing overall media spend as well as the relative contribution of each media partner, including RMNs, in relation to each other.

Conclusion:

As Retail Media Networks assume greater significance in marketers’ media partner choices, organizations must dismantle the barriers of organizational silos to achieve optimal ROI.

By embracing collaboration between brand marketing and media management and providing them with effective tools like MMM, advertisers can leverage the strengths of both groups to make better-informed decisions that drive sales growth effectively.

By embracing these strategies, organizations can seize the opportunities presented by RMNs and unlock their true potential in today’s dynamic retail landscape.