Navigating the complex waters of scaling and diversifying marketing channels can feel like charting the unexplored—be it the whirlwind world of TikTok, the grandiosity of TV, the ever-changing nature of influencers, or even the quirky realm of fortune cookie marketing. Expanding into new marketing channels is pivotal for businesses aiming to scale their reach and revenue. I've weathered these transitions, learning from triumphs and missteps, and I’m here to share these insights with you.
A Strategic Overview for Multi-channel Marketing
The Benefits of Multi-Channel Marketing
When to Consider Expansion To New Marketing Channels
Measuring Multi-Channel Marketing Success: Tools and Techniques
Understanding the Response Curve in Multi-Channel Marketing
Optimizing Marketing Spend Across Multiple Channels
Expanding into new marketing channels requires great ads, robust measurement, and strategic budget allocation to ensure success. Whether you're a seasoned marketer looking to refine your strategy or a burgeoning entrepreneur eager to make your mark, this article is designed to equip you with the knowledge and tools to effectively think about, measure, produce, and market across an ever-expanding array of channels.
The benefits of multi-channel marketing
In today’s diverse media landscape, sticking to a single marketing channel limits a brand's reach and impact. Eventually, a company must expand into the unknown. Below are the key benefits of adopting a multi-channel approach:
Increased Reach: Consumers have varied media habits, often limiting their consumption to a few preferred sources. Brands can connect with a broader audience by diversifying marketing efforts across multiple channels. This expansion is crucial because relying solely on a platform like Meta will not capture the full spectrum of potential customers.
Enhanced Trust: The prestige associated with certain channels, such as television, confers additional credibility to your brand. When consumers see a product or service advertised across multiple respected platforms, their trust in that brand typically increases. This perception can be particularly potent in channels like TV, which are perceived as more traditional or authoritative.
Improved Performance of Existing Channels: Advertising across multiple channels boosts the performance of the existing channels. For instance, a compelling TV ad increases engagement and conversion with the brand's digital ads. Broad-reach channels often introduce or remind a wider audience about a brand, subsequently making targeted digital efforts more effective. Essentially, these channels complement each other, with more expansive mediums like TV amplifying the impact of online ads.
Optimized Marketing Efficiency: Not all marketing dollars are equal in terms of the return they generate. The law of diminishing returns suggests that each additional dollar spent in a channel yields lesser returns beyond a certain point. Multi-channel marketing allows for the strategic reallocation of budgets across different channels, each with its own cost-effectiveness and reach potential. This method enables marketers to maximize overall returns by dynamically shifting resources to where they perform best based on real-time data and performance analytics.
Improved Retention: Multi-channel marketing strategies help acquire new customers and significantly contribute to retaining and reactivating them. Regular exposure across different platforms helps keep the brand top-of-mind, encourages repeat purchases, and strengthens customer loyalty.
Strategic Implementation for Maximum Impact
To leverage these benefits effectively, businesses need to:
Align Channel Messaging: Ensure all channels align with the overarching marketing goals and key aspects of the brand message. This coherence ensures that each channel reinforces the others, maximizing the cumulative impact.
Continuously Monitor and Adjust: Use analytics to monitor the performance across channels and adjust strategies as needed. This dynamic approach allows for real-time optimization based on actual performance data.
Experiment and Innovate: Regularly test new channels, marketing approaches, and modified messaging to find untapped opportunities. This helps find new avenues for growth and keeps the marketing strategy flexible and adaptive to changes in consumer behavior.
When to Consider Expansion To New Marketing Channels
Expanding your marketing into new channels should be a strategic choice supported by specific criteria. Here’s a three-step checklist to help you assess readiness for channel expansion:
Substantial Marketing Budget: Ensure that your marketing budget is robust enough to support expansion without detracting from the effectiveness of existing channels. A benchmark of at least $10 million annually in marketing spend indicates sufficient resources to explore additional channels effectively. Prior to a $10 million budget, Meta and Google are likely to outperform new channels.
Measurement Infrastructure: Have the necessary tools and systems in place to track and analyze the performance of new marketing efforts. Effective measurement is crucial to managing spend and optimizing strategies across multiple channels.
Refined Messaging: Before expanding, have a solid pitch that has proven to drive consumer purchases in your current channels. This pitch should be compelling, clear, and thoroughly tested to ensure it resonates well with your target audience.
Experience Creating Memorable Stories: Most channels require an effective pitch and an engaging story for success. Due to the expense of TV, podcasts, and other similar channels, it’s best to gain that experience through digital advertising on social media before expansion.
Understand Channel Requirements: Different channels have varying levels of forgiveness and cost implications. For example, television may require a higher level of polish and a more universally appealing message than digital ads.
Here’s an illustration from my perspective on the requirements for each channel:
Measuring Multi-Channel Marketing Success: Tools and Techniques
Measurement lies at the heart of every scalable marketing strategy—a diverse array of tools and techniques designed to decode the performance of channels and advertisements. It's crucial to understand that there's no "one-size-fits-all" metric; a singular measure of success is a myth. Instead, teams must adeptly navigate various metrics, each with unique insights and limitations, to paint a comprehensive picture of performance.
Diving deeper into the data can unveil insights that might initially seem counterintuitive but are incredibly revealing upon closer examination. Consider the scenario where the user clicks on a TikTok ad, yet during the post-purchase survey, they credit TV as the discovery channel. This discrepancy results not only in revenue being attributed to two different channels but suggests a nuanced consumer journey, where the immediacy of a TikTok ad click was likely spurred by heightened awareness from a TV campaign—a testament to the multi-touchpoint nature of modern consumer engagement.
Key Measurement Tools and Techniques
Pre- and Post-Purchase Surveys: Direct feedback from the consumer about how they discovered your brand is arguably the most valuable tool in a marketer's arsenal. This method is effective across every channel and provides insights that help correct misattributions and refine marketing strategies. If you don’t currently have one, add it now.
'How Did You Hear About Us?' Surveys: This survey presents users with a randomized list of marketing channels to identify which channel led them to your product. Including a 'dummy' option where you aren’t currently marketing to help filter out noise by identifying random selections. Providing an 'other' option with a textbox can capture data on channels you might not have considered or are less common, offering deeper insights into your audience's journey.
'When Did You Hear About Us?' Surveys: These surveys ask customers to recall when they first encountered your brand, which shows the time between ad exposure and consumer action. This data is crucial for understanding the delayed effects and forms the foundation for the response curve, how long a user takes to try your product based on the channel.
URL Parameters:
Essential for digital marketing, URL parameters track the specific campaign, ad set, or ad that brought a user to your website. Proper configuration is necessary to ensure accurate data collection. In the URL, www.amazon.com?AffiliateID=49&AdID=568437485, the first parameter is after the question mark, and each subsequent parameter is preceded by an ampersand.
SKAdNetworks and Device IDs:
On iOS, SKAdnetwork aggregates data to maintain user privacy, while Android uses UTM parameters combined with device IDs for more granular tracking. iOS also allows you to collect user-level information if the user accepts the App Tracking Transparency (ATT) prompt after opening the app for the first time.
Device Fingerprinting:
This method involves collecting data about a device at the time of an ad click and matching it with actions like app installs. However, its accuracy diminishes over a matter of hours, leading to false attributions.
Minute Level Lift:
For broadcast channels like TV or radio, analyzing traffic impact in the minutes immediately before and after an ad airs can illustrate the direct impact of the ad. When aggregated over multiple spots, this method helps quantify the immediate response to ads, which can be used to calculate the longer-term impact.
IP Sampling on Connected Devices:
Direct attribution is a method where the IP address from a smart TV ad view is matched with a device on the same network to track subsequent actions like website visits or app downloads. It bridges the gap between digital engagement and traditional media consumption. While IP matching only matches a small percentage of users, the team can use the limited data to verify the minute-level lift and model the behavior for the entire population.
QR Codes
QR codes link physical ads directly to digital actions, providing clear and direct attribution paths from out-of-home advertising efforts while improving conversion rates.
Promo Codes and Share Codes:
Unique promo codes can track conversions directly from specific ads or users, which makes them especially useful in influencer marketing, podcasts, and word of mouth. However, watch for the spread of codes on deal sites, which can distort performance metrics. To combat the issue, create unique discount codes yourself and publish them so individuals searching for a code find the general-purpose code instead.
Best Practices for Multi-Channel Measurement
Minimum Per Channel: Use a minimum of two methods to validate the performance data from each channel. Aim for three. This approach helps mitigate the risks of misattribution and enhances the reliability of the data.
Holistic vs User Level: Successfully optimizing across channels requires a holistic measure that identifies overall channel performance and a user-level sample to measure user quality compared to other channels.
Data Integration: To ease and automate data analysis, have the data loaded into your data warehouse.
By employing various measurement techniques, marketers can uncover nuanced insights into how different channels influence consumer behavior and contribute to the overall marketing objectives. This detailed understanding is crucial for optimizing strategies, allocating budgets efficiently, and driving sustained business growth.
Understanding the Response Curve in Multi-Channel Marketing
The response curve helps advertisers understand the timing of consumer interactions following exposure to an ad. It maps out the distribution of how long it takes for users to engage with a product after initially viewing an advertisement. Most measurements occur at a single point in time, leading to biases in the data.
How is the Response Curve Measured?
To construct a response curve, data is typically gathered through pre or post-purchase surveys where consumers are asked:
Where did you hear about us? – This question lists various marketing channels to identify where the customer learned about the product.
When did you hear about us? – Customers select from a range of time options (e.g., this week, this month, last 3 months, etc.), providing insights into the timeline from ad exposure to action.
Other measurements – Each measurement represents a specific point or set of points in time that can be used to plot points on the curve.
These data points allow marketers to plot the response curve, showing the time lag between ad exposure and consumer action across different channels.
Importance of the Response Curve
The response curve is crucial for several reasons:
Time Window of Effectiveness: Traditional attribution methods often have a limited effective time window, potentially undervaluing channels where consumers take longer to respond. For example, a customer might see an ad on a platform like TikTok and take weeks to act, whereas Meta drives quicker actions. Without a response curve, the delayed responses on TikTok might be overlooked or misattributed.
Accurate Attribution Across Channels: It helps correct the skew in perceived performance across channels by providing a more comprehensive view of each channel's effectiveness over time. This broader perspective prevents premature budget reallocations based on incomplete data.
Improved Financial Forecasting: Understanding the timing and rate of consumer responses aids in more accurate financial forecasting and budget allocation. The response curve informs how long it takes for marketing spend translates into consumer action, which is critical for predicting the revenue impacts of future marketing strategies.
Real-World Application and Considerations
Consider a scenario where two channels, Meta and TikTok, are used for advertising. Immediate device-level attribution might show Meta outperforming TikTok. However, early data would be misleading if TikTok users typically take longer to respond but eventually do so at a higher rate. The response curve would reveal this delayed and potentially greater effectiveness, arguing against a hasty budget shift from TikTok to Meta based solely on initial returns.
This example highlights the potential pitfalls of relying solely on immediate or short-term metrics. The response curve provides a more nuanced view that captures the delayed impacts of marketing efforts, allowing for more strategic decision-making.
Optimizing Marketing Spend Across Multiple Channels
Optimizing marketing spend across various channels is inherently complex and requires a nuanced approach to data analysis and budget allocation. While it might be tempting to simplify the issue by focusing on a single metric, such simplicity often fails to capture the full picture due to each measurement method's unique strengths and biases. Here’s a comprehensive strategy for analyzing marketing data and adjusting your budget to account for these biases effectively.
Establishing Accurate Attribution
Baseline Attribution with Surveys:
Use the "How did you hear about us?" survey to gather initial data directly from your customers. Many customers will respond, providing a reliable baseline that you can then extrapolate to the wider customer base.
Compare Survey Data with Direct Measurements:
Each marketing channel should employ at least one direct attribution method, where a customer is directly attributed to their origination source via click, URL parameters, promo codes, or IP matching. Compare these hard data points with survey responses to identify discrepancies, such as customers who report discovering your brand on TV but who are tracked via a social media ad click.
Account for Response Rate Variability:
Consider whether certain channels might produce lower survey response rates and adjust data interpretation accordingly. This step is crucial for avoiding skewed data that might misrepresent a particular channel's effectiveness.
Analyzing Revenue Impact by Channel
Aggregate Net Revenue:
Calculate the net revenue (gross revenue minus variable expenses associated with selling and delivering your product) generated by customers acquired from each channel. This figure represents each channel's effective direct return on investment relative to its marketing spend.
Assess Virality Effects:
Determine the viral coefficient for each channel using share codes or URLs. This helps in understanding how many additional customers are acquired through word-of-mouth.
Estimate the revenue impact of these organically acquired customers. If direct calculation is challenging, use the average value of an organic customer across all channels.
Calculate Reactivation Impact:
Ads do more than attract new customers; they also re-engage existing ones. Evaluate which channels are particularly effective at reactivating past customers and include this in your overall channel valuation. Every measurement method can aid in this process.
Estimating Customer Lifetime Value
Total Average Net Customer Lifetime Value (NCLTV):
Combine the net revenue from all sources—direct acquisition, virality, and reactivation—over a customer's lifetime by channel. This comprehensive metric helps determine the long-term value provided by each channel.
Determining Proper Allocation
Channel ROI Analysis:
Analyze the return on investment for each channel. Determine the discount rate where your Net Customer Lifetime Value equals the Customer Acquisition Cost (CAC).
Marginal Return Analysis:
Understanding the marginal return of each marketing channel is essential for optimizing your marketing budget effectively. This involves examining the returns on investment for each incremental customer acquired. Such analysis can reveal significant inefficiencies that average returns might mask.
What is Marginal Return? Refers to the return on investment from acquiring an incremental additional customer. It provides a granular view of the effectiveness of each marketing dollar. This approach is particularly useful for identifying the point at which the cost of acquiring new customers begins to outweigh the benefits.
The Importance of Analyzing Marginal Returns
Identify inefficiencies: Determine which segments of their spending are yielding diminishing returns.
Optimize budget allocation: Shift resources from less efficient to more efficient channels or within channels to maximize overall ROI.
Avoid over-saturation: Prevent overspending in channels that no longer contribute positively to the bottom line when scaled.
Example: The chart below illustrates the dangers of comparing average and marginal returns. On average, the channel generates a slight return. However, when analyzing the marginal return, it becomes apparent that half of the customers acquired have a negative return. The optimal course of action is to reduce spending and acquire only 40 customers.
This analysis is not limited to digital marketing but can be applied across various channels, including:
Social Media Platforms: Use look-a-likes to conduct a donut hole analysis, which you can read more about here.
TV, Radio, and Podcasts: Measure the performance of individual shows, spots, and channels, then cut the worst performing.
Influencers: Use promo codes to judge the marginal performance of individual content pieces.
Implementing Adjustments
Make Necessary Adjustments:
Based on the insights gathered, adjust the marketing spend across channels to maximize efficiency.
Optimizing marketing spend across multiple channels is about allocating funds and understanding the complex interplay between different marketing strategies and their impacts on consumer behavior. By employing a detailed and data-driven approach, marketers can more accurately attribute value to each channel, adjust strategies in real-time, and ultimately drive greater ROI from their marketing investments.
Pitfalls of Mixed Media Modeling and Multi-Touch Attribution
Mixed media modeling and multi-touch attribution are advanced analytical approaches used in digital marketing to allocate budgets and measure the effectiveness of different channels. While these tools can offer deep insights and help optimize marketing strategies, marketers must understand their significant limitations to avoid misguided decisions.
Understanding the Limitations
Correlation Does Not Imply Causation:
Both models work by fitting or correlating the model to the actual data. A classic example of correlation that does not imply causation is the high correlation between ice cream sales and shark attacks. This is not because one causes the other but because higher summer temperatures increase swimming, shark encounters, and ice cream consumption. In marketing, similar patterns arise where two variables move together but are influenced by a third factor that the analysis can’t control.
External Influences:
Models often struggle to account for external, non-predictable variables that can significantly affect the demand for a product. Factors such as a sports team's win record or weather conditions can all influence consumer behavior independently of marketing efforts. When these are not correctly accounted for, the model's predictions will lead to incorrect conclusions about the effectiveness of different marketing channels, as the variance can not be explained.
Quality of Data:
The principle of "garbage in, garbage out" holds particularly true in the context of mixed media modeling and multi-touch attribution. The accuracy of these models heavily relies on the quality of input data. Faulty data, such as incorrect sales attribution or engagements to the wrong channels, can skew results. It's essential for marketers to ensure their data sources are reliable.
Time Lag Issues:
The delay between marketing exposure and customer action can vary significantly across channels and campaigns. This lag complicates the attribution process and the math itself, as variations in the lag introduce a variable susceptible to overfitting.
Critical Considerations
Transparency and Understanding: Marketers must clearly understand how these models work, their assumptions, and what biases they might introduce into the decision-making process. Without this understanding, there is a risk of misinterpreting the data, leading to suboptimal marketing strategies.
Comprehensive Analysis: These tools must be used as part of a broader analytic approach, incorporating them alongside other metrics and observations. Relying solely on one model or data set can lead to overconfidence in flawed insights.
Continuous Validation: Regularly testing the predictions of mixed media models against actual outcomes can help identify and correct biases or errors in the model. This practice is vital for maintaining the accuracy and reliability of marketing analytics.
While mixed media modeling and multi-touch attribution can provide valuable insights into the complex dynamics of multi-channel marketing, they are not foolproof. Marketers must approach these tools critically, understanding their limitations and integrating them with a holistic view of marketing performance. By recognizing these pitfalls and working to mitigate them, marketers can more effectively use these advanced analytical tools to refine their strategies and improve their return on investment.
Conclusion: Navigating the Complexities
As the digital marketing landscape evolves, the tools and techniques we use to measure and optimize marketing spend must also adapt. These tools offer sophisticated insights that can significantly enhance budget allocation and channel effectiveness. However, these tools are not without their challenges. The opportunities for those willing to learn the art and science of marketing are enormous. Let’s embark on the journey together. Subscribe to learn more.