Measurement works fine without third party cookies. It’s been years now since third party cookies were a reliable source for indexing or matching consumers. In the best of times, third party cookies worked incrementally: Our experience was 4 to 8% improvement under the best of circumstances. And, it’s only gotten worse.

First party data rules the road. And, PII (personally identifiable information) should be managed very, very carefully, as outlined by GDPR, CCPA and a whole bunch of other rules: Don’t use PII to build identity graphs, or to measure advertising effectiveness. It crosses into the targeting lane, and isn’t just measurement anymore.Measurement and analytic providers must — and can — work between these guard rails. And, they should showcase prospective clients the service contracts, privacy policies, compliance management and information security protocols behind their sales material.C3 Metrics is an independent measurement and analytics provider, building first party data, working within all the privacy guard rails, and delivering results. We’ve invested a decade of time and millions in resources to develop the privacy and security procedures to use and protect first party data, and to avoid the pitfalls of PII and third party data.

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]]>Choose a Lane: Measure or TargetGregory CollinsWed, 16 Aug 2023 17:25:13 +0000https://www.c3metrics.com/datalab/cdpltvmtacta629e2bb546d330177c50055f:629e318a169ddd62b8dea94e:64dd0484c347d41db6bc3118Customer Data Platforms (CDP) promise to bring next level thinking to Life Time Value (LTV), just as Multi-Touch Attribution (MTA) advances the thinking about Cost to Acquire (CTA). CDP and MTA solutions complement each other, solving different needs and enabling different actionable insights.

Customer Data Platform (CDP) is the new acronym in the marketing industry, and it is simultaneously an overhyped concept and a foundation for true insights into customer value and segmentation. The hype — to be avoided - is much like that around other “Big Bang” notions — projects that will be wildly expensive and lengthy, with massive promises coming at the end of implementation and testing. Sales Automation, then Customer Relationship Management (CRM) presented similarly-exaggerated potential, while also providing a basis for dramatic, tangible, achievable improvements.

Multi-Touch Attribution (MTA), on the other hand, collects data about advertising and addresses the intricate landscape of customer interactions across various touchpoints before conversion. MTA goes beyond simple first-touch or last-touch attribution models, acknowledging the complexity of today's customer journey. MTA tracks and assigns relative credit to each touchpoint a customer encounters, providing marketers with a clearer understanding of how different marketing channels contribute to a conversion. These insights are crucial in optimizing marketing budgets and strategies, as they enable businesses to allocate resources more effectively based on the actual impact of each touchpoint.

In the context of these two concepts, the analogy CDP:LTV::MTA:CTA draws a parallel between Customer Data Platforms and Life Time Value, just as it relates Multi-Touch Attribution to Cost to Acquire.

Customer Data Platforms (CDP) and Life Time Value (LTV)

A Customer Data Platform collects, organizes, and consolidates customer data from various sources, creating a unified profile that marketers can use for better personalization and targeting. This rich dataset empowers businesses to understand their customers on a deeper level, predict their preferences, and tailor marketing efforts accordingly. This newfound customer insight contributes to optimizing the Life Time Value (LTV) of each customer by nurturing long-term relationships, encouraging repeat purchases, and maximizing the value generated from each customer over their entire journey with the brand.

Fundamentally, CDPs enable marketers to understand a customer’s value, by segment, action or product line(s), and define or categorize the worth of that segment, action or product line.

Multi-Touch Attribution (MTA) and Cost to Acquire (CTA)

Multi-Touch Attribution (MTA) systems tackle the challenge of determining which touchpoints in the customer journey should be attributed credit for a conversion. By analyzing the influence of each interaction, MTA helps marketers understand which channels are most effective at driving conversions. This knowledge is invaluable for making informed decisions about marketing spend. In a similar vein, Cost to Acquire (CTA) measures the resources expended to acquire a new customer. It considers the cumulative costs associated with various marketing efforts, including advertising, promotions, and sales efforts. When coupled with MTA insights, businesses can optimize their acquisition strategies, focusing resources on the most impactful touchpoints and minimizing unnecessary expenses.

Fundamentally, MTA enables marketers to understand their advertising spend, and what it costs to acquire a customer.

So, there’s overlap and both MTA and CDPs can be components of a broader, universal marketing data set — but they really are best utilized for different insights and actions. MTA data could play a key role in a CDP’s understanding the costs associated with acquisition - perhaps providing a segment- or product-driven cost to acquire. Similarly, LTVs developed by segment or product from a CDP could inform an MTA’s output on which advertising channels impact which products.

Because MTA develops and presents insights on existing spend, MTA programs have a time-to-results advantage over broader-based CDPs. Building and acting on a unified understanding of a company’s large advertising spend presents ‘low hanging fruit’ savings to drive longer-horizon investments in customer and channel data.

1. Path Visualizer: Clients focused on consumer journeys want a quick, easy way to see common cross-channel journeys. Our Path Visualizer, available within our standard and custom dashboards, provides an always-accessible approach to viewing the most common, most recent consumer journeys.

2. Scenario Assist: The most common request for analytics support is scenario planning, which is a great use of C3’s combination of data, modeling and analytics support. Our Scenario Assist functionality provides a starting point for answering complex scenario questions and predictive analysis. Clients may ask for the isolated, or longer-tail impacts of a particular channel (e.g., TV, social), and Scenario Assist presents an initial series of assumptions and likely outcomes that can be used by clients and internal analytics team members to build and test assumptions.

3. C3MMM: MMM (media mix modeling) is a complementary modeling exercise to MTA (multi-touch attribution), and can take advantage of the seed and historical data developed during C3’s measurement programs. C3MMM is our approach to MMM offerings, and can be added to our programs. MMM and the effort surrounding its delivery can provide a solid foundation for budgeting and campaign introductions, and it’s a natural step for many of our programs.

4. Brand Manager: Several clients are extending their measurement programs to additional brands and associated campaigns, under common management. This presents opportunities to compare and manage measurement programs across similarly-characteristic programs. Under one umbrella program, C3 Metrics’ Brand Manager offers a simple, compelling dashboard-based approach for combining and parsing information across an entire campaign portfolio.. Of course, Brand Manager retains C3 Metrics’ commitments to tenant isolation and privacy across programs and consumers.

5. C3API: Some clients want to visualize C3’s measurement data in their own dashboards or BI tools, and we have standardized the format, content and cadence options for data delivery in our C3API service offering. In addition to standard or custom dashboard availability, C3API provides our clients with flexible, secure and tested data delivery. Data can easily be delivered into a clients’ CDP, data lakes and warehouses and BI tools.

C3’s model and dashboards received general enhancements, as well. New conversion reporting and quality assurance testing are available, as is custom select and filtering in consolidated and custom reports. Improvements were also made to data sourcing and help / FAQ / change logging functionality. And, fraud filtering functionality is continuously updated and improved.

All of these improvements enhance C3 Metrics’ proven approach, which emphasizes:

• Outcomes / results focused

• Privacy centric

• Tag-based, first-party data

• Enterprise ready

• Filtered for fraud

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]]>C3 Metrics Announces 2023 YTD Product Improvements and ExtensionsGregory CollinsTue, 11 Jul 2023 15:55:44 +0000https://www.c3metrics.com/datalab/site-traffic-outcomes629e2bb546d330177c50055f:629e318a169ddd62b8dea94e:64ad7769ed9a5c665a5b36b4Website traffic is a crucial metric that businesses and marketers track religiously. After all, the number of visitors to a website seems like a direct measure of success, right? Well, not quite. While site traffic is undoubtedly important, it’s essential to recognize that it is merely a touch point along the path to achieving meaningful outcomes. In this blog post, we’ll explore why site traffic should be viewed as a touch point rather than an ultimate outcome in your digital marketing strategy, and emphasize the value of connecting advertising to real business outcomes.

1. Understanding the Purpose of Website Traffic:

Website traffic serves as an important indicator of how many people are visiting your site, and it can offer valuable insights into user behavior. However, it’s crucial to remember that the purpose of driving traffic to your website is not just to accumulate large numbers but to engage and convert those visitors into customers or achieve other desired outcomes. Website traffic is the starting point, the initial touch point where the real work begins.

2. The Quality vs. Quantity Dilemma:

Focusing solely on increasing website traffic numbers can be misleading. It’s the quality of the traffic that matters most. It’s better to have a smaller number of highly targeted and relevant visitors who are genuinely interested in what you offer, rather than a massive influx of random visitors who have no real intention of engaging further. It’s important to consider factors such as audience targeting, demographics, and user intent to attract the right kind of traffic that is more likely to convert into desired outcomes. Website traffic is easier to manipulate and easier to mis-measure. As a result, we consider website traffic a gross or rough estimate for activity; not suitable on its own to assess the effectiveness or efficiency of a campaign, or refined enough to be used alone as a signal to match with other (e.g., off-line) signals.

3. From Traffic to Outcomes:

To achieve meaningful outcomes, it’s essential to look beyond website traffic and consider the subsequent actions visitors take on your site. These outcomes - usually called conversions to differentiate them from traffic - could be diverse, ranging from making a purchase, filling out a form, subscribing to a newsletter, or engaging meaningfully with your content. By focusing on optimizing your website for conversions and tracking the desired actions, you shift the emphasis from traffic numbers to the outcomes that truly impact your business.

4. Engagement Metrics and User Experience:

While traffic numbers can provide a high-level view of visitor interest, engagement metrics provide deeper insights into the user experience on your website. Complexities such as bounce rate, time spent on site, pages per session, and ultimate conversion rates are necessary to filter gross traffic to net traffic. Improving these metrics requires attention to user experience, website design, content relevance, and navigation, which ultimately leads to better outcomes. 

Simpler still: connecting to outcomes cuts through those other analyses, by focusing on business goals.

5. Aligning Metrics with Business Goals:

To truly measure the success of your digital marketing efforts, it’s crucial to align your metrics with your business goals. Consider what outcomes matter most to your organization—whether it’s increased sales, brand awareness, lead generation, or customer retention—and establish key performance indicators (KPIs) that directly reflect those outcomes. By focusing on the metrics that drive real results, you can gauge the effectiveness of your marketing efforts more accurately.

C3 Metrics uses website traffic as gross or rough estimate for activity; not suitable on its own to assess the effectiveness or efficiency of a campaign, nor refined enough to be used alone as a signal to match with other (e.g., off-line) signals. Website traffic is too rough a metric to be considered an acceptable substitute or proxy for a business outcome.

Website traffic is undeniably important, but it should be viewed as a touch point rather than an ultimate outcome, and there are major hurdles and stumbling blocks if website traffic is used as a proxy for outcomes in measurement programs. By shifting the focus from traffic numbers to user engagement, conversions, and other desired outcomes, measurement programs can connect more directly with business and marketing strategies.

“So what?” An equally valuable sentiment, rooted in the same great thought.

Incrementality, the sort-of-new-kid-on-the-block in advertising measurement, is neither curious or explanatory. There are real-world limits on its utility.

Incrementality attempts to isolate the change in a result, or the impact of a channel or campaign or tactic, by asking ‘what if’ that spend were non-existent. It’s a good first-level thought, but almost always needs to be placed in context. The notion makes sense: If we can isolate both the spend and the result, and identify a correlation between the two, we can say “if a, then b.”

But, that logic reaches its limits quickly, when second-level questions remain unasked, or context is lacking.

So, here’s the next question to ask: Does the conclusion (explanation) stick out above the noise? To see if it does, try an X-Factor challenge:

If we isolate 1 percent of the results, and 1 percent of the spend, we might identify a strong correlation between the two. It may look overwhelming, when we compare the 1 percent of results with the 1 percent of the spend.

But, so what? What if there’s just 1 percent noise or uncertainty in the overall data set?

What if an X Factor of just 1 percent of the total is included in the results? Do the results still stick out? Do they extend above or below the noise?

What if we assume — as we should — that the data has some inherent variability and volatility?

What if we are concerned about the quality of the data, or its independence and bias?

A problem with incrementality is that it does not challenge itself. If the process finds a simple answer, it stops there, and then extends that logic, untested, beyond its knowledge.

Incrementality is looking for change on change: That might actually occur, but some sense of confidence and context is necessary before that insight is extended beyond the test. If the identified result can be explained just as easily with a 1 percent X-Factor, confidence should be pretty low.

So, incrementality is a useful tool in an advanced measurement set. But only with context, curiosity and pragmatic, genuine skepticism.

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]]>X-Factor: 1 — Incrementality: 0Gregory CollinsFri, 10 Mar 2023 18:01:00 +0000https://www.c3metrics.com/datalab/incrementality-is-marketing-measurement-101629e2bb546d330177c50055f:629e318a169ddd62b8dea94e:640b706a678ebf36303e2651Incrementality is a flawed measurement technique, best applied early in an analytic program’s development, and with full recognition of its base assumptions and flaws.

1. Incrementality relies on a false premise - that all else is equal. It’s the most simplistic or reductive of causal relationships. Incrementality’s base assumption is that change or impact can be explained through one variable. It’s too single-minded or simplistic to be of use in an advanced analytics model. It might be a helpful starting point, but that’s where its utility ends.

2. Incrementality is one-dimensional. Modern consumer journeys extend through multiple channels, and through time. Channels impact those journeys directly, and indirectly. Incrementality attempts to measure a channel’s impact in a single dimension, and isolated from pragmatic realities.

3. Incrementality is a dead-end. While programs that rely on incrementality might promise near-term conclusions or a faster cycle to hypotheses for marketers to use, its potential ends there. The approach’s value diminishes over time.  So, these programs burn out themselves.

Finally, and most limiting, incrementality analysis depends on a channel’s self-reported data. The fundamental weakness dooms the practice by reinforcing and validating a knowingly-biased data set. Those errors and voids are more than ample to overwhelm the subtle signals that incrementality portends to differentiate.

Incrementality can be a valuable introduction to marketing analytics. And, for those ‘students’ interested only in basic knowledge, or whose application extends no further than a single channel, or requiring a simplistic rationalization of spend, it could be adequate.

For those organizations working in multi-channel, real-world environments, multi-touch attribution (MTA) is massively more compelling. MTA is the advanced, graduate-level program analog, enabling marketers and data scientists to answer questions across channels, journeys, users and scenarios. 

MTA builds first-party data, and checks against the biases and mistakes that come from a single-channel view. MTA is the obvious choice for sophisticated advertisers.

Ready to graduate to the next level? Be in touch with C3 Metrics to talk about advanced measurement and analytics programs.

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]]>Incrementality is Marketing Measurement 101Gregory CollinsThu, 05 Jan 2023 14:48:20 +0000https://www.c3metrics.com/datalab/5-misconceptions-about-multi-touch-attribution629e2bb546d330177c50055f:629e318a169ddd62b8dea94e:63b6e3348f3a74516bc39573

We see in print and hear during conversations a number of perceptions and misconceptions about Multi-Touch Attribution. So, we’re going to address the top 5 today. These aren’t end-all-be-all responses, but they do provide some context and color in considering how MTA works, and how it fits within measurement programs.

#1 Misconception: MTA can be done in-house

Reality: A great systems and services partner significantly increases the odds of a successful program

Great outcomes from measurement and analytic programs require excellent support across data ingestion, modeling, analytics and program support. C3 Metrics brings those four forces together, leveraging our investments in product and support, and focusing on client-specific needs and objectives. We’re independent from all advertising channels, and our success can be tied directly to a program’s success; absolutely no other agenda items.

C3 Metrics adds to program resources, and complements people, processes and technology already in place.

#2 Misconception: MTA models are black boxes

Reality: C3 Metrics provides high visibility to modeling decisions and selections

Perfect is truly the enemy of great. And, we’re trying to provide great, usable data, in a format and timeline that will impact decisions and results. MTA programs are much better off when they build confidence around output, and present alternatives and opinions for discussion. So, we’ll always err on the side of highlighting the model decisions, and work to identify areas where we can improve.

#3 Misconception: Alternative measurement approaches, like incrementality, make MTA obsolete

Reality: MTA is a versatile tool, supporting multiple objectives and program goals

We’ll be the first to admit that MTA-based programs are not for everyone. Our most successful relationships tend to share characteristics, and we put together a Scorecard that helps identify situations more likely to work. MTA is a powerful tool, and C3 Metrics is a great working partner for the right clients.

MTA programs are typically faster than MMM, more sustainable than A/B or geo-testing, and more flexible than aggregated reporting and in-house analyses.

#4 Misconception: MTA is dependent on third party cookies and other privacy-prohibited data

Reality: MTA is a great source of, and repository for, first party data

C3 Metrics’ MTA programs are a great source of first party data, powering data-based decisions on their own, and in concert with CDPs and other efforts. As a rule, C3 Metrics does not collect data that would be subject to most privacy regulations. And our tag-based infrastructure is dedicated to measurement and analytics, not retargeting or other ‘creepy’ usage of consumer information. We can work with existing identity resolution and device-graph relationships, but we don’t share data outside a specific client engagement.

An investment in C3 Metrics is an investment in a future-proofed, cookie-less world.

#5 Misconception: MTA provides data in real-time.

Reality: Attribution results are available as consumer journeys complete and channels report.

C3 provides ‘real-time’ access to a dashboard, with model results. But, results come in over time, and are best viewed and analyzed after the source data has been qualified, and several buying cycles are completed. MTA looks back from results, and works to look back through the entire journey. For some D2C purchases with digitally-focused advertising, that may be a day or two. For a B2B, television-rich campaign, that could be six months or more.

We establish and reinforce timing expectations from the first conversation onward.

If MTA is on the company radar, awesome. Let us know what you are thinking about, and we can give you specific feedback on the goals, MTA’s fit, and C3’s ability to help.

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