Tag: MMM

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. 

Countdown to Black Friday: Retail Media Strategies for Holiday 2024

Posted on by Caroline Lewis

Retail media has proven to be particularly effective during seasonal periods. Retailers live and die by seasons, which create natural peaks and valleys in consumer demand. From New Year’s resolutions and Valentine’s Day to Easter, summer sales, and a myriad of retailer-specific holidays like Prime Day and Back-to-School, these events offer an excellent tempo for organizing retail media campaigns.  

With fewer days between Thanksgiving and Christmas this year, understanding and leveraging these opportunities can significantly enhance the effectiveness of your retail media strategy. 

Understanding Seasonal Baselines 

One of the key insights for maximizing retail media success is recognizing the importance of seasonal baselines. These periods often come up quickly and create sharp spikes in consumer activity. Therefore, having a clear understanding of historical data and trends is crucial. This data helps in setting realistic expectations and in measuring the incremental impact of your campaigns. 

During these peaks,  methods of test and control may not be feasible. Retailers and advertisers are often unwilling to engage in controlled experiments during their busiest times. Instead, you should focus on gathering as much data as possible from previous years, and use your MMM model, to establish a robust baseline against which you can measure your results. 

Leaning into the Spike 

Retail media strategies need to lean into these seasonal spikes at least two to three weeks ahead of the event. For online sales, this means starting your campaigns earlier, as online consumer behavior tends to ramp up gradually before peaking. In contrast, in-store sales often exhibit a much sharper increase during the actual event period. 

By planning your campaigns to align with these behaviors, you can ensure that your messages are reaching consumers at the right time. This early engagement can help build awareness and anticipation, leading to higher conversions when the peak period hits. 

Flexible Budgeting and Keyword Strategy 

As seasonal events approach, the competition within retail media networks becomes fierce. Events like Black Friday, Cyber Monday, and other major sales can lead to inflated media costs. To navigate this, a flexible approach to budgeting is essential. Shift your focus to higher-margin, longer-tail keywords that may be underpriced compared to the highly competitive terms. This strategy allows you to maintain visibility without overspending on the most contested keywords. 

Additionally, consider diversifying your spend across less saturated retail media networks. For example, delivery apps and hospitality brands may offer lower competition and better value during these peak periods. This approach can help protect your digital shelf and ensure your products remain visible even when competition is at its highest. 

Leveraging Multi-Brand Platforms and Influencers 

During peak seasons, the saturation of ads can lead to banner blindness, where consumers become desensitized to the overwhelming number of ads. To combat this, consider leveraging multi-brand platforms or ads. These collaborative efforts, reminiscent of old retail tactics like tri-brewer ads in the beer business, allow multiple brands to share the spotlight in a single advertisement. This not only reduces costs but also increases the ad’s appeal by offering consumers more choices in one place. 

Influencer partnerships are another effective strategy during these periods. Influencers can cut through the noise and deliver your message in a more personal and engaging manner. Their followers trust their recommendations, which can lead to higher engagement and conversions during these critical times. 

Conclusion 

Seasonal strategies for retail media require a combination of historical data analysis, early engagement, flexible budgeting, and innovative advertising approaches. By understanding the nuances of seasonal consumer behavior and leveraging the right mix of tactics, you can maximize the impact of your retail media campaigns. Remember, the goal is to anticipate the spikes, prepare accordingly, and execute with precision to ensure your brand stands out during these high-stakes periods. 

 

This article was based on a recent webinar, “Navigate Retail Media for Maximum ROI: How Marketers Can Ensure Profitability and Incrementality with RMNs,” hosted by i4i’s Mark Garratt with featured guest, Nikhil Lai of Forrester. 

 For more insights on navigating RMNs, check out our guide here.   

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.

Attribution Analysis Letting You Down? Why Not Return to MMM?

Posted on by Kevin Youts

Marketing attribution analysis was great in theory. What are the things consumers see and hear that influence and prompt them to act, specifically to buy a product or service?

It would be nice if it were simple and binary: Consumers see this, they buy that.

But let’s start with the essential data and the connections brands need to make, to support decisions that impact media planning effectiveness.

Visibility into what a consumer bought: Possible in digital (if brands own their own data or can get it easily from e-commerce distribution partners.) Harder in physical stores. In both cases brands need first party data from the retailer to know what was actually purchased.

Visibility into what the consumer did to get to the point of purchase: Did they go to a retailer’s website and search for the product? Did they see a digital ad, click on it, and it took them to an e-commerce page? Did they see the product on their neighbor’s kitchen counter and make a mental note to purchase it next time they were in the grocery store?

Visibility into what advertising campaign was seen or heard before the purchase: Was it the digital ad on their favorite app? Was it the TV ad, or the OOH ad on a bus shelter? Was it an audio ad on a podcast or on the radio during their commute from the office? If it is digital media and a digital purchase with accurate UTMs embedded and an effective tracker to see what messaging converted, an advertiser may have half a shot of making a connection of the channel and the campaign to the actual transaction. But it is not a precise media planning tool, particularly if the campaign intent was brand awareness, versus activation.

Connecting all these data points to create optimal media plans

Here’s the dilemma. Attribution modeling has been described as limited, in part because it may account for one or two online channels compared to each other, but in terms of all other media and marketing exposure it can’t definitively say which is THE ONE that can be attributed to the purchase. What is more likely is a combination of factors in aggregate, in context of other behavioral conditions, drove the consumer to put the product in their virtual or physical carts.

Folks at the digital marketing agency Atomicdust back in 2021 wrote an interesting piece provocatively entitled “Marketing Attribution is Killing Common Sense”.

Still quite relevant, it contains some of the better definitions we have seen on the types of attribution (“last interaction”, “last non-direct interaction”, “linear attribution”, “multi-touch”, etc.) commonly tracked in attribution modeling. More importantly, the author basically describes how each of these models as flawed in their own way.

Why are they flawed? Two reasons, actually.

  • Attribution models are primarily limited to digital marketing, which is highly dependent on the infamous cookies that are going away due to privacy concerns.
  • These models don’t reliably accommodate the attribution of sales that are conducted in physical retail settings, in which case an advertiser may have no idea what was the last (or first) ad the shopper saw.

Frankly consumer behaviors are sufficiently complicated as to defy predictions based purely on the last or first impression of the product. Everything from seasonality and weather to time of day (particularly in the case of impulse buys at quick service restaurants or convenience stores) will be influenced by the confluence of when and how an advertising message is delivered in any given media channel.

So, if not attribution modeling, what can brands employ that is more evidenced based than “common sense”, to make better media buying decisions?

The Return to a Trusted Tool: Marketing Mix Modeling

FMCG companies have relied on Marketing Mix Modeling since the 1960s. Of course, retail options and even the media channels available to advertisers were far less complicated then. Decisions on how, where and when to plan media spend could be done at a comparatively more leisurely pace than the frenetic programmatically driven speed required in today’s digital environment.

But the beauty of MMM is its inherent data analytical sophistication, capable of incredibly complicated models with far more degrees of granularity, is that it can tackle the combination of many new kinds of data feeds at different levels of specificity (target audience, time granularity, geographic unit, type of metric, etc.) And this level of complexity requires data analytics robust enough to parse the results at a much more sophisticated level to much better media planning support.

As retail has also evolved to include far more distribution outlets (including e-commerce) and media followed suit with the introduction of digital advertising, MMM provides the perfect solution to making highly complex decisions about media buys. But you need accomplished data scientists running the models, equipped with the expertise to interrogate the algorithms with a brand’s specific objectives in mind.

  • MMM when done right, can provide far more detailed road maps for achieving ROAS (return on advertising spend) encompassing virtually endless variables that can impact spending.
  • MMM done right can provide guidelines for spending just enough in any one media channel so as not to overspend in terms of overall return on investment.
  • And we could (and will) dedicate and entire post to the unique benefit of MMM to facilitate targeting not only the many more channels in the new world of media used by people, but the situation and the journeys that got them there. Attribution never really fulfilled its promise to get a whole lot closer to this level of targeting visibility.

Brands are moving away from strictly asking themselves: “Should we spend less on TV and more in digital?”, to the much more relevant question: “What’s the role of the big-screen in my campaign and which new channels are going to replace the ubiquity of TV?” To guide the resulting media planning choices, the best MMM provider/partner will actually surface the more discrete evidence as decision support. The ideal MMM partner will reveal which of all the digital and other channels available will yield the best contributions to ROAS compared to each other, so media buying decisions can be more fine-tuned and effective.

Attribution may be letting you down. But have no fear. Our data analyst teams at in4mation insights are prepared to help you tackle media planning, delivering optimized scenarios to achieve return on your investments and ongoing growth for your brands.