Every marketer measures the impact of their activity but do they truly know the effectiveness of their activity and advertising spend? For the majority of marketers, the overwhelming answer is no. On average, marketers estimate they waste over one quarter of their budget (26%) on ineffective channels and strategies, according to a study by Rakuten.

Though marketing attribution may seem like a difficult task, in a world where ROI is king, accurate marketing attribution needs to be at the forefront of every marketer’s mind to maximize the efficiency of their activity in 2023.

This guide deep dives into how marketers are successfully moving the needle and implementing true multi-touch algorithmic attribution to measure and optimize their activity.

Don’t miss out on the opportunity to make sense of your marketing attribution and get to value quickly with our easy-to-follow guide. Visit Digital Marketing Depot to download the 101 Guide to Marketing Attribution from Snowplow.

The post Make sense of your marketing with the 101 Guide to Marketing Attribution appeared first on MarTech.

Below, we will introduce the basic concepts around attribution modeling and ways to get the most out of it. 

Table of contents

• What is attribution modeling? 
• Why is attribution modeling so important?
• The different types of attribution models
• How to choose the right marketing attribution model
• Skepticism about the viability of attribution modeling
• Additional resources

Estimated reading time: 5 minutes

What is attribution modeling?

Your prospects and potential customers can take a range of pathways to interact with your content and make their way through the buyer’s journey. These pathways are specific touchpoints throughout that journey, such as:

• Opening an email.
• Clicking on an ad.
• Commenting on a social media post.

Attribution modeling in marketing allows you to determine how beneficial each of those touchpoints is along the buyer’s journey. With it in place, you can identify which marketing channel best helps to convert a lead from a mere browser to a loyal buyer.

By assigning a particular value based on interactions with a marketing channel, marketers can decide where time, energy and money should be spent. Knowing what touchpoint actually converts a lead can make a great difference for both sales and marketing professionals.

Why is attribution modeling so important?

Attribution modeling is critically important for several reasons. We will discuss them below in the form of some questions you can ask.

What improvements can I make to the buyer’s journey? 

When attribution modeling is in play, you will start to see what works and what does not. Underperforming areas can likely be improved, and you will dig deep into the data to find out why and how to do so.

What is the real ROI from a channel? 

Understanding the parts within the buyer’s journey that push your prospects to convert can help you see the channel’s or sub-channel’s value. It will also allow you to determine whether you should spend more (or less) of your efforts and resources there.

Can I craft better content for my ideal client? 

The answer, with attribution modeling, is a resounding yes. Tailor more of your marketing campaigns to the working channel(s) and the ideal client most likely to buy from you.

The different types of attribution models

There are several different types of attribution modeling. Each model looks at the various channels you are using but may apply varying degrees of weight to them.  

Multi-touch attribution modeling takes into account each touchpoint and channel within the buyer’s journey from start to finish. It will determine which channels were the most beneficial and effective in influencing a customer’s decision to convert.

First-touch attribution modeling focuses on the first touchpoint or channel that the client interacts with within the buyer’s journey.  

Last-touch attribution modeling focuses on the very last touchpoint or channel that a prospect entered before making the decision to convert.

Time-decay attribution modeling gives equal consideration to each touchpoint and channel but gives the highest points to the touchpoint that was interacted with closest to the conversion.

Cross-channel attribution modeling is a form of multi-touch modeling that looks both at touchpoints within each channel and also at how channels work together.

Linear attribution modeling is a form of multi-touch modeling that gives equal range and weight to all channels and touchpoints throughout the full cycle of the buyer’s journey. 

These attribution models can be used in marketing campaigns based on your pre-determined goals and KPIs. The answer to what is working and what is not working may change based on the type of attribution model that you’re using. 

How to choose the right marketing attribution model 

Before settling on an attribution model, you may want to test them to see which one works best for your campaigns. There isn’t necessarily one that stands out above all others.

Consider your campaign goals and how quickly those goals were reached. This can help you determine what model might best be used.

In addition, as your marketing campaigns evolve, you may need to switch the attribution model you use. Stay agile and allow the tool you use to move flexibly with you and your team. Here are a few questions you can ask before picking an attribution model:

• How many touchpoints are within the journey for the prospect?
• What are the goals of the campaign? 
• What does the overall funnel look like? 
• What is the end result or expectation of the campaign?
• Will I be using a tool, or can Google Analytics give me what I need?

Skepticism about the viability of attribution modeling

While most marketers find attribution modeling an essential part of their toolkit, some skeptical voices argue that traditional attribution modeling cannot cope with today’s vast proliferation of channels and touchpoints and the complexity of today’s customer journey.

“At the end of the day, people have to give up the fantasy that [marketing] can ever be completely predictable,” says marketing strategist Kathleen Schaub.

The skeptics say it is not feasible to hit a certain ROI target by a specific date. Instead, the suggestion is that marketing analytics should be treated as a kind of GPS, where routes and even destinations can be optimized along the way.

Dig deeper

Want to learn more? Here’s some further reading that may be useful: 

• How can we understand and fully embrace attribution modeling? Let’s find out in, Modeled behavior: A future-proofed new measurement strategy.
• Opinions on addressing attribution in digital and physical marketing campaigns are sparking a debate among marketing OGs and professionals.
• Do you need a marketing attribution and predictive analytics tool? It could be that you do.  

The post Why we care about marketing attribution modeling appeared first on MarTech.

“Our belief is that generative AI will enhance human ingenuity, not replace it,” said Shantanu Narayen introducing Adobe Firefly at Adobe Summit today. Firefly is a new group of generative AI models focused on creating images, video and text effects. Firefly uses generative AI with graphics tools like brushes, color gradients and video tools to speed up production and make it easier for creators to make high quality content. The videos and images projected on-screen during the two-hour Summit keynote had been generated by Firefly.

Adobe’s generative AI. The generative AI announcement predictably stole the show although there were some other new product announcements. “Over time,” said Narayen, “AI will help us reimagine every aspect of marketing.” He could not resist adding that Adobe has incorporated AI in its creative products for well over a decade.

The first Firefly tools are available today in beta. Narayen also emphasized that Adobe is seeking to protect human creators, both by developing a model for compensating for use of their work and by moving towards a global standard “Do not train” metadata tag that creators could use in an attempt to ward off AI infringements on their content.

Summit also saw the launch of Sensei GenAI, natively embedded into the Adobe Experience Platform, although it was not immediately clear how this would enhance the platform’s existing Sensei AI capabilities. Again, the aim seems to be to allow users to work with generative AI capabilities within Adobe Experience Cloud rather than using independent tools and migrating results to Adobe.

Why we care. In many ways it would be shocking if Adobe had not taken this route to incorporating generative AI into its platform. Although Adobe was one of the first marketing clouds, its roots are in iconic creative tools like Photoshop and Illustrator. The promise, of course, is that Adobe users will be able to reap the benefits of generative AI within the Adobe ecosystem, rather than have recourse to one of the many independent tools rushing to market.

Dig deeper: Adobe CEO: Make the digital economy personal

Adobe Product Analytics. If overshadowed by Firefly, Product Analytics was a significant announcement. The Adobe Experience Platform already includes, among its many components, Customer Journey Analytics. Product Analytics offers a similarly drillable dashboard by presenting product-related information such as user growth and engagement with features of products and trends. The aim is to align the product team with other teams working on aspects of customer experience.

Adobe also announced Adobe Mix Modeler, a new dashboard giving a cross-channel view of campaign performance allowing real-time optimization of channel spend.

Additional reporting by Chris Wood.

The post Adobe announces Firefly for AI-driven creative appeared first on MarTech.

In this article, we’ll take a closer look at nuances in data measurement and various analytics software in action.

Looking at nuances in data measurement  

Digital analytics tools were never intended to work as accounting systems or sales registers. They were made to collect and quantify interactional user data into easily usable insights and reports. Over the years, these tools’ data collection methods have evolved. In turn, the way specific data points are measured also changed. 

Let’s say you changed your tape measure from imperial (measuring in inches) to metric (measuring in centimeters). The length of a desk might be reported as 39.4 in one and 100 in the other. The length of the desk didn’t change, but how you measured it has. 

Try switching between different analytic tools. Often, you’ll see that your numbers may be different, but trend lines remain similar. Each tool counts things slightly differently; the same issue frequently applies when upgrading software.

At one point, unique users were counted by combining the total number of unique IP addresses that accessed a website in a given period. Eventually, organizations started using firewalls/proxy servers, requiring all internal users to access the internet with a single IP address. How unique IP addresses were counted didn’t change, but the count of unique users dropped dramatically.

Counting of unique users evolved into using a combination of IP address, OS and browser (type and version), then the addition of a persistent cookie to better estimate unique users. Once again, no matter how you count unique users, if the user cleared their cookies and cache or switched computers (office vs. home vs. phone), no analytics tool will have provided an exact number. Nowadays, tools take other factors into account when counting unique users.

Dig deeper: Data analytics: Your stack’s past and limitations

How to think of your analytics data

Your analytics software is imperfect because of many factors beyond its control. Users might be blocking cookies or other tracking methods. Internet blips might prevent data from reaching the data collection server. The best way to think of your analytics data is by viewing it as a poll of user activity.

Everyone is familiar with polls at election times. A typical U.S. presidential election poll surveys approximately 10,000 people (or less) out of 150+ million eligible voters (0.006% of voters). This is why when news broadcasters report on the poll results, you hear something along the lines of “This data is accurate within 4 percentage points 4 out of 5 times.” This equates to it being off by more than 4 percentage points 20% of the time.

When it comes to your digital analytics tools, most analytics professionals estimate the loss of data to be no more than 10% and most likely around 5%. How does this translate into data accuracy?

If your site received 10,000 sessions in a reporting period but for various reasons, you could only capture data on 9,000 sessions, your data would be accurate within a margin of error of less than 1%, 99 times out 100. 

In other words, 99 times out of 100, your data is accurate and 1 out of 100 times, it is off by more than 1%. Simply put, your data is accurate, but it is not perfect (precise) and will not match your sales records.

Such data is more than accurate enough to determine which marketing efforts — SEO, paid ads, sponsored posts, social media marketing, email marketing, etc. — are working and even which ones drive traffic versus drive sales.

Dig deeper: Don’t apply wishful thinking to your data

Analytics in action

While analytics data may be accurate, even being off a small percentage in precision can call your analysis into question. This is especially true when the difference between two data sources changes. 

The key is to monitor the data and, where possible, compare it. If there is a sudden change in accuracy, you need to investigate. For example, was your website recently changed? Was this change properly tagged to capture the data?

A client once added a pop-up to their Shopify account after an order was placed but before the thank you page was generated. Their analytics tool records sales only when the user receives the thank you page. 

With the pop-up in place, the order still went through, but many users didn’t click through the messaging. As a result, a large percentage of sales were suddenly not being captured as no thank you page was generated. There wouldn’t have been an issue if the pop-up appeared after the thank you page.

Below is an example of monitoring sales and orders between Shopify and Google Analytics 4 (GA4). We can see how much data is being lost because of various factors. Using Shopify’s analytics as a record of true sales and comparing it to data collected via GA4, we see the following:

The daily variations in total revenue and orders varied from virtually 0% to nearly 13%. Overall, in these 24 days, GA4 reported 5.6% less revenue and 5.7% fewer orders. This data is accurate, especially when applied to marketing efforts to see what drove the user to the site to make the purchases. 

Should this company use GA4 to report sales? 100% no! That’s what accounting software is for.

If your organization demands even more accurate data, there are methods to push data directly to most analytics tools (server side). This avoids issues with user browsers and cookies. 

While sales data may be more accurate, other soft measurement aspects of user interaction may drop (e.g., scroll tracking). This is a complex and time-consuming method to implement for most organizations. 

You must ask yourself, “is this extra effort necessary just to capture another 2-5% of sales revenue in my analytics reports?”

Understanding your analytics data

Everyone needs to have faith in their analytics data. The key is ensuring your analytics software is installed and configured correctly. Understand that it can’t capture everything. 

Your analytics software simply takes a poll with a sample size of over 90%. This makes the results highly accurate (on target), if not 100% precise (actual numbers).

The post Accuracy in digital analytics: What marketers need to know appeared first on MarTech.

And although marketing budgets, as a share of revenue, rebounded last year to more than 9%, according to the 2022 Gartner CMO Spend and Strategy Survey, they are still lower than they were in 2020, forcing CMOs to achieve more with less. 

With increasing pressure to prove the value of marketing, smart CMOs are turning to marketing mix modeling, or MMM, to improve media performance and quantify their impact. 

What is marketing mix modeling?

Marketers still struggle to answer foundational questions about the impact of marketing on the business, such as “How effective is my digital marketing at driving in-store sales?” or “How will a 10% change to upper-funnel media impact bookings?”

Marketing mix modeling (MMM) can help answer them.

The goal of MMM is to measure the impact of advertising and promotions across channels while controlling for external factors outside of a brand’s control, such as inflation or consumer sentiment.  

The outputs from MMM are used in three ways:

1. As a scorekeeper, to show the overall incremental impact marketing investments are having on the overall business.
2. As a forecaster, to predict the outcome that raising or lowering marketing budgets will have on marketing’s contribution to the overall budget.
3. As a coach, to suggest shifts to current marketing investments that improve performance.

In its simplest form: MMM helps marketing leaders plan future marketing spend and measure the performance of past investments.

How impact is measured can vary; a focus on incremental revenue is common, but modeling multiple outcomes is a growing trend, such as store traffic or new account sign-ups. The details of the modeling approach differ, but all forms use aggregate (not user-level) data. This allows MMM to nicely sidestep user privacy and other digital tracking concerns, as well as consider a wide range of channels — both digital and traditional — and external influences.

Consider MMM in action: A regional bank uncovers large performance differences by channel and lowers overall marketing spend through televisions cuts, while still increasing top-line sales by investing in more effective magazine and radio placements.

MMM is a technique with a long history, and it continues to evolve. If you looked at marketing mix a decade ago and dismissed it due to “insights only at the channel level” or “results only updated quarterly,” your marketing organization may benefit from revisiting the technique.

MMM measures the financials of brand investments 

Inflation, coupled with shifting media consumption patterns among consumers, is requiring marketers to prioritize sustained brand investment now more than ever. 

MMM can help CMOs quantify one of their trickiest investments to measure: upper-funnel activities that build brand sentiment and consideration, but are not focused on driving immediate sales. With increased delivery cadences, MMM can provide monthly updates on brand metrics that are useful for filling in the gaps of less-frequent brand-tracking surveys.

Imagine a product or service with a six-month sales cycle. Sales are driven by a healthy marketing budget with investments across the marketing funnel to drive awareness, consideration and, finally, sales.

Since the average marketing mix model looks at three years of history, that means the MMM would capture, quantify or measure the vast majority of upper-funnel spend. MMM would capture an even greater portion of midfunnel impact.  It’s common to see digital video, TV and Instagram get a boost from cuts to paid search. This leads to a media mix that is more effective overall for the same budget.

MMM is a core measurement capability 

Of course, while MMM offers significant opportunity to increase returns on media investments, the models require consistent reevaluation to consistently deliver the expected benefits. Organizations who have trust in their MMM also report higher growth compared to their industry peers.

Additionally, because MMM typically provides the most holistic view of the ROI on marketing activities, it often generates findings that challenge conventional wisdom. 

Envision a compelling email which encourages a prospect to search a brand’s website the next day leading to an unfinished checkout that triggers re-targeting and an eventual sale.  Who gets credit for the sale?  Email, paid search or retargeting?  Measurement for each channel could triple count credit for the sale, while revealing no insights around relative channel contributions.   Marketing mix, since it looks holistically at the ecosystem, can partition credit for the sale across the channels.

Because these scenarios often lead to false reservations about the model itself, it’s crucial to get finance on board early on in the process and articulate how it’s essential to the company’s success among senior leaders.

So, the next time your team reviews your marketing mix model, consider the following questions:

1. Have you prioritized the insight objectives for your MMM? Is your marketing mix model actionable — meaning, do the outputs inform adjustments to marketing activities? This often involves adjusting spending levels, but could also include shifts to ad frequency or channel mix.
2. Are you sufficiently assessing your marketing mix model to ensure that the predictions are delivering true incremental business performance and can be trusted by executives across the organization?
3. Are you taking full advantage of the scenario planning, optimization and simulation opportunities that your MMM provides to improve future marketing efforts?

Across interviews with marketing leaders, we heard many talk about upcoming improvements to their MMM program, such as testing new data sources to better understand an external factor.

Everyone we spoke to could answer, “What is next for your marketing mix efforts?” So at least once a year — ideally more frequently — assemble key stakeholders involved in gathering the input data, building the models and using the results to adjust media plans. Then, discuss and commit to at least one improvement that focuses on prioritization, validation or optimization of existing efforts. 

It’s crucial to recognize that improving MMM is a journey — don’t stop at your first destination.

The post Marketing mix modeling: A marketer’s guide appeared first on MarTech.

Equipping marketing leaders with the skills, tools and data they need to prove ROI is like setting out to sea on a fishing expedition.

Rather than distributing equal bait to each rod of a marketing campaign despite not knowing which will produce the most bites, marketing attribution teaches marketers to assemble the best combination of bait before casting its line into a sea of prospects.

Register today for “Build a Winning Marketing Attribution Framework,” presented by Chanel99 and learn how to overcome the top three challenges in marketing attribution.

Click here to view more MarTech webinars.

The post Build a winning marketing attribution framework appeared first on MarTech.

Ad spend will continue to grow. The overall ad spend is projected to be up 5.9%, according to the IAB’s “2023 Outlook Survey.” Every digital channel is expected to see increased ad spend, with CTV leading the way in a 14.4% increase. Among category channels, B2B (20.8% increase), travel (20.6%), restaurants/beer/liquor/wine (17.1%), and financial services (11.1%) are projected to do the best. 

Top goals. Customer acquisition is by far the top goal for media investments in the coming year (61%). The next two goals are increasing brand equity (43%) and improving media efficiency (35%).

These goals all explain why the three things marketers plan to focus most on in 2023 are:

• Cross-platform measurement — 55%.
• Ad placement with publishers with first-party data — 53%.
• First party data acquisition/partnerships — 52%.

Good news for retail media networks. All of which means next year will be a really good one for retail media network (RMN) owners. Some 61% of buyers are investing or considering investing in RMN advertising next year, resulting in a projected ad spend increase of 28.4%. Onsite (owned & operated) ad investment is by far the top RMN ad tactic, being leveraged by 91% of buyers, according to the IAB.

Dig deeper: In this economy CMOs need to spend more on training, not tech

Why we care. Uncertain times call for data and spending certainty. Marketers aren’t waiting to find out when third-party cookies will finally go away. They are looking at first-party data acquisition now. Similarly, the chancy economic picture means re-evaluating media buying as frequently as possible. Also, all this means marketers are feeling far less adventurous: Only 26% said they will be significantly focusing on Web3 (including metaverse, NFTs, etc.) in 2023.

The post Economic uncertainty means marketers will re-evaluate ad buys more frequently in 2023 appeared first on MarTech.

By integrating spend data with measurement of outcomes, it also aims to provide visibility into the cost of generating those outcomes, allowing marketers to allocate budget more efficiently. The twin objectives are boosting pipeline while reducing acquisition cost. Channel99 is selling into B2B marketing organizations and the app is already being used by dozens of enterprise and mid-market customers.

Why we care. Two reasons. First, this could prove to be a major contribution to addressing the age-old attribution problem — a problem that increases in complexity as channels multiply: “Half the money I spend on advertising is wasted; the trouble is I don’t know which half,” as the old saying goes.

Second, Channel99 is the brainchild of Chris Golec who founded enterprise ABM giant Demandbase in 2006 and ran it for more than 13 years (he remains a member of the board). After a period as an investor and adviser, he’s excited to be back in the startup game — for the first time trying to do it remotely.

“Some of our engineers are here, some are in Poland,” he told us. “It’s just very hard to innovate and build a company remotely. I miss being in the office with people.”

After so many years with Demandbase, how does it feel to lead something completely new? “It’s fun,” he said.

Independent source of truth. The vision for Channel99, said Golec, is to be an unbiased source of truth for marketers by measuring the impact of spending on dozens of channels and the many vendors operating within them. It has raised $5 million dollars in seed funding led by Jackson Square Ventures.

“More than 90% of activity driven to a B2B website provides little to no value when it comes to driving new business,” said Golec in a release. “With investments across hundreds of vendors and channels, the industry desperately needs a single source of truth to consistently measure the efficacy of their efforts.”

The mobile experience. It’s unorthodox for a new B2B martech solution to lead with a mobile offering. “This notion of launching a free mobile app first is a way to engage with a customer a lot more efficiently than having them fill out a form and calling them 20 times over the following two weeks,” Golec told us.

The app provides insight into channel and vendor traffic, matching it to accounts and scoring it based on whether it fits a business’s addressable market. “It’s really an onboarding strategy because our next product will include financial information and really help marketers improve where they’re spending money and how they’re making decisions.” It will also include CRM data

“I wouldn’t call it pure attribution,” he said, “but it’s clearly going to show what’s working and what’s not.”

Dig deeper: Measuring the invisible – the truth about marketing attribution

Bad news for some channels and vendors? Of course, if Channel99 can indeed identify areas of wasteful spending, that might not be good news for the channels and vendors that are under-performing. “That may happen,” Golec conceded. “If anything it will expose opportunities for them to get better, but if you’re using a vendor or a channel that is not reaching your target audience, then you shouldn’t spend money there.”

Of course, just because a vendor under-performs for one business doesn’t mean it under-performs for others. “It all depends on who your target audience is.”

The post Channel99 will help marketers measure the performance of channels and vendors appeared first on MarTech.

Across the industry, there is no shortage of different initiatives and solutions attempting to tackle this — from the Privacy Sandbox to data clean rooms to the Unified ID 2.0.  Wading through the details of these solutions, it is understandable that any marketer will end up being overwhelmed by the various options. 

So, rather than worry about what you should do in reaction to something like Google Topics (short answer: not much until Google runs more concrete tests and provides evidence that it’s fully privacy compliant), there are two specific areas in which all brands should be focusing on in the immediate future.

The big tech platforms’ move to modeled data

User-level measurement has always been the North Star for brands. In a perfect world, it enables us to most accurately understand the impact of marketing campaigns to make effective optimization and budgeting decisions. 

However, in a privacy-centric era, platforms such as Google and Meta have implemented various enhancements to preserve user-level measurement as much as possible. This includes Enhanced Conversions and the Conversions API, each enabling conversions to be more accurately attributed to your marketing campaigns. 

Both features should be front of mind. That said, this will only cover a portion of your missing data and is where something like Google’s Consent Mode comes in. This leverages modeling techniques to account for users opting out of marketing/ analytics consent. 

There may be some skepticism about relying on modeled data within your reports. However, it is important to note that this isn’t anything new.

In fact, modeled conversions have been in place within tools like Google Ads and Facebook Ads Manager for many years. The requirement for modeling will only increase as known-user datasets continue to decrease. 

Although the big vendors predictably don’t make it very easy, with the right expert support, it is possible to compare what your unmodelled vs. modeled results look like. This will enable you to make more informed decisions about the numbers you report and their relative degree of accuracy. 

Rather than shying away from modeling, marketers should look to further understand and wholeheartedly embrace it. 

Dig deeper: Why marketing attribution is both a challenge and a necessity

Econometrics + attribution = modeled attribution

Attribution has been an eternal debate in marketing and was already challenging enough. All the more when we think about how to navigate the numerous walled gardens and privacy restrictions. 

Given the inevitable gaps in known data, a user-level attribution model is now very difficult — unless you are looking at a specific subset of channels that don’t cross walled gardens. Otherwise creating a robust cross-channel custom user attribution solution is now nigh on impossible. 

Yet, every business will still need marketers to accurately measure the performance of their media mix and make effective budgetary decisions. Intriguingly, the optimal next-gen solution is actually a combination of two historical approaches. 

Full-funnel view of marketing performance

Modeled attribution takes the best parts of MMM (media mix modeling) and MTA (multi-touch attribution) to give you a full-funnel view of marketing performance whilst being completely privacy-resilient. 

The foundation of modeled attribution is based on MMM, which uses aggregate-level datasets rather than user-level inputs (i.e., cookie data). This means it does not need to be concerned with MTA considerations, like user consent or how to navigate walled gardens. 

An additional advantage of modeled attribution is that by using a regression-based approach, it is far easier to incorporate all your marketing channels into your model without having to track everything within a single solution. 

You also have the ability to include external factors such as seasonality, stock levels or competitor activity to increase the accuracy of your model and isolate the specific impact of your media campaigns.

Dig deeper: Measuring the invisible: The truth about marketing attribution

A new granular approach

The historical drawback of MMM was that the outputs were at a very low level of granularity (e.g., TV vs. digital vs. print) and that results were only available every six months.

However, modeled attribution can leverage direct connections to each of your marketing platforms to pull in daily inputs at the most granular level. This makes it far more actionable for tactical planning and budget decisions.

While the initial setup requires precise planning and expertise, modeled attribution looks to provide all the detail you are used to with MTA while future-proofing yourself against further industry changes — which is all enabled through the power of modeling. 

So it turns out that the answer to our uncertain future was something that was in front of us all along. In many ways, we are going back to the future with our measurement strategies.

The post Modeled behavior: A future-proofed new measurement strategy appeared first on MarTech.

Marketing is complex and complicated. Many technologies, tools, and tactics promise to be an “easy button” to success. 

No matter what the latest trend is, experience proves that none of them can generate endless leads, consistently boost conversion rates, or predictably increase revenue.

But what if there was a “shortcut” — a straightforward, repeatable way to generate predictable results in marketing — that we have overlooked?

After advising and coaching dozens of marketing teams of billion-dollar brands, I believe such a shortcut exists.

If you want more revenue and results from your marketing, here’s the fastest way to success.

The two drivers of revenue and results

Marketing is a complex machine with many moving pieces and parts. This brings many challenges and a misunderstanding of what drives revenue and results.

Most people think of marketing simply as a creative endeavor. After all, the visual and written components that are the cornerstone of all marketing collateral are creative work.

However, marketing is more of a process than it is a creative effort. 

Does marketing require creativity? Absolutely.

But without the proper process, it becomes almost impossible to generate predictable results. 

Process creates predictability — and that’s especially true in marketing

To create predictability and maximize marketing revenue and results, you need two processes: experimentation and optimization.

• Experimentation helps you find out what works (and, most importantly, what doesn’t).
• Optimization lets you get the most out of your marketing (once you know what works).

These two processes go hand-in-hand and are fundamental to marketing success. Lacking one or both will cause your efforts to be stagnant and subpar. 

Unfortunately, most teams have no structure or systematic approach for either one.

Dig deeper: Driving marketing at scale: People, processes, platforms and programs

Experimentation

Experimentation is about testing things to find out what works. It’s a powerful tool every marketer should leverage.

In marketing, no one really knows what will work. Not you and me — nor your team, vendors, agencies, partners, and influencers.

Even if you have an idea, it might not last long because the one constant thing in our industry is change.

Dramatic shifts in technology, competitive landscape, customer behavior, and even the culture happen every day – all of which are constantly upsetting the status quo. This volatility makes our work so exciting, yet also highly uncertain. 

When you don’t have insights, you’re throwing darts with a blindfold on and hoping to hit anything. With insights, you’re standing inches from the dart board and can effortlessly hit the bullseye every time. 

The best way to get insights? Experimentation.

With experimentation or testing, you apply the scientific method to uncover answers to specific questions. It can lead to get better results such as increased conversion rates, reduced cost, higher ROI, and more revenue.

That said, it’s important to realize that improved results are a byproduct of testing, not the main objectiv.

What’s essential is that it allows you to generate insights to improve all facets of your marketing through optimization.

Optimization

Optimization is the process of making continual, incremental improvements to deliver a better result with the same or fewer resources. 

Want more revenue and results from your existing budget? Focus on optimization.

Just like with experimentation, you’d be crazy not to optimize your marketing. And yet, countless teams are too preoccupied with producing more content and launching more campaigns to be bothered with optimizing what they’re doing.

The benefits of optimization are apparent (who doesn’t want more from less), but it’s not as easy to do as it may sound. There are many moving pieces, and it requires building an optimization program to coordinate the efforts and the team to succeed.

First, optimization requires insights. You have to know there’s an opportunity to improve. Otherwise, you’re wasting your time. 

Benchmarks are the starting point of most teams but realize that almost everything your team is doing can be improved. 

Focus your optimization efforts on the areas that:

• Will have the highest impact (e.g., where you spend the most money).
• Are attainable (e.g., your team can actually execute).

Other times, knowing what to optimize isn’t quite as clear, like when a new landing page isn’t driving as many conversions as other, similar landing pages. What optimization should you make then?

This is why optimization and experimentation are inextricably linked. Use experimentation to develop and test your ideas to find what works and what doesn’t, then apply optimization to make the improvements across all of your marketing initiatives.

Once you know there’s an opportunity to improve, you have to make the changes.

Sometimes this is easy and simple, like turning off the underperforming ads. That’s fairly obvious, yet marketing teams routinely overlook such simple optimizations. 

Additionally, consider applying optimizations globally. If you developed new messaging for your email marketing that produced an increased response, don’t stop there. Apply that messaging to your social content, landing pages, and paid ads. 

Finding opportunities to leverage optimizations across your marketing is key to maximizing your results and the impact of your efforts.

Optimization is more involved than experimentation because it engages most of your marketing team. It requires coordination, collaboration, and communication to ensure that the necessary changes are made timely and their impact is measured correctly.

If you’re not optimizing your marketing, you’re wasting your budget and missing out on opportunities. Work smarter, not harder and invest in optimization.

Conclusion

How many experiments are you running every month? How much time and resources do you spend on optimizing your marketing versus creating and launching new efforts?

Both experimentation and optimization are essential. Each requires structure, diligence, and effort to execute consistently and effectively. Most importantly, they must be ongoing if you want to maximize the impact of your marketing efforts.

The most successful marketing teams invest in developing experimentation and optimization processes.

Because the more you experiment and optimize, the more revenue and results your marketing will produce.

The post The only two things that matter in marketing appeared first on MarTech.