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10 Ways to Use AI for Better Ads

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10 Ways to Use AI for Better Ads

In our recent post about OpenAI’s ChatGPT, we unpacked what the tool is and how it works, and why we don’t see its popularity as a threat to search engines like Google. In this post, we’ll be diving further into the OpenAI Playground, and how PPC marketers can use that tool along with ChatGPT to save time on research, ideation, execution, and more.

The Playground is a basic UI built on top of OpenAI’s API. OpenAI has recently added ChatGPT to their API. When accessing ChatGPT through this UI, users have the ability to customize the model being used for each query (or continuation of the “conversation”) as they progress through their work.
 

How to Write ChatGPT Prompts

 
When working with tools like ChatGPT, it’s important to be as clear as possible in what you ask, and how you ask it. As you write prompts for ChatGPT to work with in retrieving and displaying the information you need, remember that you are giving instructions in a more direct way than you might if conversing with a colleague.

While another person may have contextual insight into what you’re really looking for with your question, tools like ChatGPT take language more literally, tailoring their response to the information you explicitly provide in your request.

ChatGPT will consider every element of your ask, so don’t give generic prompts. The more information you provide the tool in your prompt, the better it will be able to generate what you’re looking for in its response.

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Example: Let’s assume you’re using ChatGPT for dinner inspiration…

  • Generic prompt (least likely to return what you’re looking for): Give me 10 recipe ideas for a home-cooked dinner
  •  

  • Slightly better prompt: Give me 10 recipe ideas for a home-cooked dinner with squash as the primary ingredient
  •  

  • Even better prompt: Give me 10 recipe ideas for a vegetarian home-cooked dinner that I can make in an air fryer in 20 minutes or less with squash as the primary ingredient

See here and the examples below for more information and inspiration on crafting strong prompts.
 

How to Start Using the OpenAI Playground for PPC Marketing

 

To get started with the OpenAI Playground, create an account using your personal email address at https://platform.openai.com/. Once you’re logged in, navigate to the Playground page to access the interface and begin making requests.

screenshot of open ai playground

The right-hand sidebar provides some options for different modes and GPT submodels, as well as Codex models, which are primarily used for generating code. The Complete mode is selected by default, along with the text-davinci-003 model. The other models within the “Complete” mode are typically faster and cheaper but are also less advanced, so they may be viable alternatives depending on the nature of your needs. ChatGPT can be accessed via the Chat mode and is what we used for the examples below.
 

OpenAI Playground Tokens and Settings

The billing model for using this service is constructed around the concept of tokens. Each new user gets $18 of free credit (900K tokens) that can be used during their first 3 months from sign up; after that, it’s $0.02 for every 1,000 tokens.

There is a token counter in the footer of the Playground display which can help you keep track of how many tokens you are using. 1 token is approximately 4 characters (or 0.75 words), with token usage measured against both your prompts and the responses.

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You can limit the number of tokens that can be used in a response by toggling the Maximum length slider on the right hand sidebar, which is set to a 256-token cap by default. If you make an inquiry that requires an elaborate response, you may see the response get cut off before completion; in this case, it may be helpful to increase the Maximum length.

There is a maximum of 4,000 tokens that can be used in a single “request” (single session), i.e. a series of questions within the same Playground. Once you’ve hit that limit, all you need to do is delete your earlier prompt questions and answers, or save them as a “preset” before moving on to a new prompt.

open ai playground screenshot with arrow highlighting button to save your preset

Note: The use of tokens is required in the OpenAI Playground, but not when using ChatGPT natively. As of the time of this writing, ChatGPT is still free to use. A paid version of ChatGPT with advanced features and benefits is also available—ChatGPT Plus.
 

OpenAI Playground and ChatGPT Temperature

open ai playground screenshot highlighting where you can adjust the temperature

The Temperature setting controls randomness; lowering the temperature results in less random completions. As the temperature approaches zero, the model will become deterministic and repetitive. For most PPC purposes, we recommend a temperature range of 0.6-0.8 as optimal.
 

10 Ways PPC Marketers Can Use GPT to Improve Workflow Efficiency

 

“In terms of use cases, there are many different ways in which people working in all industries, and all fields of expertise, can lean on tools like ChatGPT and the OpenAI API to improve their efficiency and automate certain redundant tasks. This technology can help with smaller, repetitive tasks, such as breaking down a long document into a bullet point summary. However, when it comes to critical thinking and understanding the implications of things, I would be very cautious about over-relying on AI.”

Portrait of Josh O'Donnell
Josh O’Donnell, Sr. Strategist, Paid Search at Tinuiti

A couple of important things to consider before diving into our examples below:

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  1. ChatGPT/GPT language models training data cuts off in 2021. They do not have any knowledge of current events, and cannot accurately respond to questions about such topics. ChatGPT is not aware of things like who won the big game last night; it is not even aware of what day it is.
  2.  

  3. ChatGPT/GPT language models do not have access to the internet or any other kind of external data retrieval; they can only answer questions based on the knowledge acquired from their training data. They cannot verify facts or provide references, only generate responses based on their own internal knowledge and logic.

 

1. Keyword Research

Whether you work on the Paid Search side of marketing, or the organic side, you know how important (and time-consuming) thorough keyword research can be. One of the most important rules of marketing is to know your audience—which includes knowing what they want, and how they search for it—and the OpenAI Playground can help you find those answers faster.

Sample Scenario:

You’re just getting started building a new PPC campaign for a client that sells running shoes. To kick off your initial keyword research, you want to get an idea of which related keywords are being searched most often. You want a Top 20 keyword list, and GPT can generate a list for you to help you get started.

The prompt: Provide me with a list of 20 running shoe keywords for google ads, list them in descending order based on expected search volume in the United States.

The result:

screenshot showing how open ai playground can help with keyword research

Note that since OpenAI enables you to continue the “conversation” beyond your first query, we also asked it where it got the returned information from (above photo); it’s always important to consider the source when relying on AI-generated responses. This is a good example of why it’s important to take the outputs with a grain of salt, using them as inspiration to get you started, but not the finished product.
 

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2. Competitor Research

Comprehensive competitor research and analysis is a crucial part of a marketer’s job, helping inform and guide their campaigns. However, just like keyword research, this is also an ongoing, time-consuming process.

When you work in a complex space—or your products or services are part of different spaces—it can sometimes feel overwhelming to assure you’re accounting for everything and everyone. The OpenAI Playground can help make short work of initial research in a variety of ways.

Below, we showcase the results provided by three different prompts aimed at unpacking competitor insights instantly…

Sample One: Ask for a list of top US competitors ranked largest to smallest with accompanying website URLs to get ideas for custom audiences, messaging, and product positioning.

screenshot showing the results when asking open ai tool for a list of top running shoe companies in US ranked largest to smallest with website URL

Sample Two: Ask objective questions about your competitor and their product.

screenshot showing results when asking open ai playground to describe advantages of a competitor product compared to another product, including which is more geared toward price-conscious consumers

Sample Three: Ask about pain points for competitor products, and use that info to inform your own product messaging & marketing strategies.

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example of using open ai to uncover competitor pain points
 

3. Generate Ad Copy

In the below examples, we used the URL of the ad’s landing page to help inform the suggestions from ChatGPT, providing character limits in our prompt to help direct the output. If your original result doesn’t meet your expectations, continue to sculpt with additional follow-up prompts. GPT cannot access these web pages in real-time, but it can use the context from the URL structure to inform the output.

example of using open ai playground to help with ad copy headline ideas

example of using open ai playground for help with writing google ads descriptions

“It’s more of a utilitarian thing, where you provide the tool with the data, and ask it to manipulate that data for a better output. One example is to provide it with a web page, and ask it to generate some ad copy based on the URL text; it can provide fifteen or twenty options within seconds. I would never recommend simply taking those headlines and pasting them into an ad, but you can now start off your project with a list that you or a teammate can garner inspiration from, and strategically refine or tweak to fully optimize. This gives the practitioner more time to spend on critical thinking, with ChatGPT taking away the more mundane elements of the task.”

Josh O’Donnell, Sr. Strategist, Paid Search at Tinuiti

The copy itself should be quality, but the important aspect of parity between what you’re saying on the ad and what’s on the page can be efficiently solved for.
 

4. Translations of Copy & Headlines

In the example below, we asked ChatGPT to translate the 5 English language ad copy options generated above into Spanish. Additional options currently available include French and Japanese translations.

example of using open ai playground for copy translation
 

5. Answer Questions on Demand

Similar to ChatGPT, the OpenAI Playground can also be used for Q&A purposes. Just remember that answers can only be generated based on the tool’s current knowledge.

screenshot of Q&A information from open ai website

Source: https://platform.openai.com/examples/default-qa

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This can be especially helpful during calls with clients when you need a fast and simple answer to keep the conversation moving forward.
 

6. Simplify Complex Concepts

When talking about digital marketing with other practitioners, we know our audience ‘speaks the same language’ and certain questions, concepts, or outcomes need no further explanation. However, those same complexities aren’t always as easy to communicate to newer team members or clients.

Even when our day-to-day contacts are digital savvy, they often have to convey information to those higher up the chain in their organization who might not be as familiar with the lingo, or even why certain things they’re highlighting matter.

For scenarios like these, OpenAI’s Summarize for a 2nd grader feature can prove especially helpful. Once you have the foundation laid out, you can add more color and context to paint the fuller picture without worrying the basics would be glazed over.
 

7. Generate Product Descriptions & Names

Working with accurate, well-optimized product names and descriptions is one of the most essential elements of effective marketing. Strong, descriptive names and product information help search engines and users alike in uncovering the items that will be most relevant to their needs.

screenshot from open ai website showcasing how their product name generator works

Source: https://platform.openai.com/examples/default-product-name-gen

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While names and descriptions will always require a human touch for proper refinement, tools like ChatGPT and the OpenAI Playground can provide a great starting point to build from.
 

8. Parse Unstructured Data

The OpenAI Playground makes it easy to organize long-form text into a table format. Simply specify a desired structure, provide a few examples to work from, and enjoy the time saved.

screenshot from open ai website showing a prompt for parsing unstructured data

Source: https://platform.openai.com/examples/default-parse-data

screenshot from open ai website showing a response from a prompt asking for structured data

Source: https://platform.openai.com/examples/default-parse-data

 

9. Call Summaries & Follow-Ups

Call summaries are an important aspect of keeping organized and ensuring everyone working on a project is clued into plans and discussions, even if they weren’t part of the original calls. Putting together these comprehensive, valuable recaps can sometimes take as much time as the call itself, but GPT can help.

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Below, we asked GPT to write a follow-up email based on a call summary.

screenshot of response when asking GPT to write follow-up email based on call summary
 

10. Convert text from first-person to third-person

We have found this feature especially helpful for turning our own notes into actionable steps someone can follow when shared. For example, if you want to share steps for completing a process with a team member or client, you can type naturally using “I” language to convey those directions. You can then quickly convert the text to third-person, adjusting as necessary for optimal clarity.

Screenshot from Open AI website showing how third-person converter works

Source: https://platform.openai.com/examples/default-third-person

 

Conclusion

 
The capabilities of advanced tools like OpenAI’s Playground and ChatGPT can make short work of mundane tasks, help quickly generate ideas and direction, and ultimately save us all time to focus on the elements of marketing and advertising where our expertise and strategic insights can truly shine. If you’re interested in more under-the-hood information about how ChatGPT works, check out Stephen Wolfram’s breakdown of ChatGPT. Also see here for additional application options, or reach out today to learn more about how our Paid Search team can bring your PPC advertising results to the next level!
 

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MARKETING

Unlocking Hidden Revenue: The Inbox Retargeting Methodology

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Unlocking Hidden Revenue: The Inbox Retargeting Methodology

Page conversion rates have ALWAYS been a problem. The simple fact is most people don’t convert even on the most optimized pages. 

What’s why traditional retargeting on ad networks has been so dang powerful. While retargeted leads come cheap, they still aren’t free. Worse, you’re back competing against your competition in the ol’ ad auction system.

For the last 6 years, I’ve been using a tactic called Inbox Retargeting to identify who lands on my key pages and directly reach out to them in their inbox.

No more ads. No more auctions. Just a targeted contact that showed they were interested, but didn’t quite take the leap yet.

Before I dive into the “What’s” and “How’s”, this tactic can only be used in the good ol’ US of A. If you aren’t in the states or don’t have clients in the states, you’re out of luck. Sorry!

How It Works

Inbox retargeting doesn’t take a lot of heavy lifting. I’ll share the strategy next but I wanted to start with some of the logistics.

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DISCLAIMER: I am not a lawyer or coder, so keep that in mind if technical or legal questions pop up.

If you have a website, you have tracking scripts, e.g.,  GA4, the Facebook Pixel, Heatmap software, etc…

To get started with Inbox retargeting, you just need to be able to copy and paste two scripts on your site:

  • A collection script: This fires and tries to identify the visitor

A suppression script: You’d fire this on your conversion confirmation pages, you don’t want people who converted to land in your Inbox Retargeting campaigns.

1710795438 253 Unlocking Hidden Revenue The Inbox Retargeting Methodology

The tech works off of a database of contacts in the United States that are eligible for emails, so it’s completely above board with your ESP. However, you’ll want to do a few things before you start treating them like a regular member of your email list.

We initially tested this on one of our paid media campaigns. We already had a really strong campaign that we wanted to squeeze more leads out of…and boy did we.

We were driving traffic from Meta (Facebook for the OGs) to this landing page:

1710795438 272 Unlocking Hidden Revenue The Inbox Retargeting Methodology

This page converts at 58%. Yeah, that’s a humble brag…deal with it.

Even with a 58% conversion rate, we’re still missing out on 42% of the traffic we’ve already paid for. That’s kind of a bummer.

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After we added the collection script to the page, they were able to capture a lot more leads. The conversion rate jumped from 58% to a very sweet 87% – that’s a 50% increase!

That was the impact on a single page, that’s when we knew it was time to go bigger.

The Strategy

Most of the tools out there, whether it’s Retention.com or Customers.ai, are going to charge based on the number of contacts. So it can get pretty easy to burn through contact credits if you run the script on every page you manage, your site and your clients’ sites included.

That’s why you’ll want to make sure to select pages that capture intent versus targeting all of your traffic.

ID Key Pages

Here are some of the pages you should consider adding the collection script:

  1. Campaign Landing Pages – If you’re paying to send someone to a page, the referring source piqued their interest. If they didn’t convert, you’d definitely want to follow up.
  2. Product Pages – If someone is viewing this page they’re evaluating a particular product they were interested in.
  3. High Intent/Value Content Pages – This could be your pillar content on your blog pages, podcast pages, or your top level service pages.
  4. Registration Pages – This is a subset of a landing page, but if someone got all the way to a registration or sigh up page, they’re a prime candidate for outreach.
  5. Cart Pages – People abandon carts all the time. If you weren’t able to catch their details during checkout, this is an ideal opportunity.

Effectively it’s any page where you’re pushing a specific action. While the above pages are the pages to choose from, a homepage is acceptable but will require a little more finesse when you follow up.

Map to Email Campaigns

Now that you’ve identified where you’re going to identify leads, you’ll need to map it to your automation tool.

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Unlocking Hidden Revenue The Inbox Retargeting MethodologyUnlocking Hidden Revenue The Inbox Retargeting Methodology

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Most tools have a direct integration with your email service provider, but worst case scenario you may have to pass the data through a no code integration tool like Zapier.

Once you’ve worked out the digital plumbing, you’ll want to follow up based on the page the contact was collected on. Here’s how you should approach follow up:

  1. For Campaign Landing Pages – Give them the specific asset. They were interested in it, you’ve got their contact information, just hand over the goods. This builds good will at the start of the relationship.
  2. Product Pages – Send over the details of the product or product category they were viewing. This could be as simple as a reminder or you could build goodwill with a special offer or coupon.
  3. High Intent/Value Content Pages – Send over some of your best content or freebies that move people to the next phase of the Customer Value Journey.
  4. Registration Pages – Treat these like an “abandoned cart” type of email and get them to take that next step.
  5. Cart Pages – Same as “Registration Pages” but it’s, you know, an actual abandoned cart reminder. Similar to the product pages you could entice them to come back with a deal or coupon.
  6. Homepages – If you do run these on the homepage, you’ll need to do more of a reintroduction then transition to showcasing your best stuff.

Email Structure

The initial message you send needs to have a very specific flow. There are four critical things that need to happen when they open up your Inbox Retargeting message.

First, remind them about who you are and how they know you. This can be as simple as a, “Hey, thanks for stopping by…” message. Have some fun with it.

Next, you need to provide highly specific value based on their browsing intent. If you get this wrong, they’re just going to file your message under SPAM.

After that, you’ve got to set expectations with what they’re getting and now you’ll be communicating with them moving forward.

And Finally, you need to give them an EASY OUT. These campaigns have our highest unsubscribe rate, but that’s because we outright ask people to unsubscribe if they don’t want any additional contact.

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Once you’e gone through this, you treat them like one of your regular subscribers with all your fancy ascension automations, content emails, and promotional emails.

Here are the email stats from one of our PPC Campaigns:

1710795439 568 Unlocking Hidden Revenue The Inbox Retargeting Methodology

With an average open rate of 53.87%, we know there’s a base line interest in the deliverable. The click rate is DANG good for messaging visitors who didn’t convert.

Sure the unsubscribe rate is a little high for this campaign, but that is intentional. We push them to opt-out in the first email so we don’t get dinged later with complaints.

The Payoff: An Additional 109k Last Year

I mean, who doesn’t want another cool 100 grand for adding a script to your website and writing a couple of emails? Here’s how the numbers work out:

Last year, we identified 3,714 leads using this method. IMPORTANT: When I was pulling these numbers, I realized we installed the code wrong on some pages and missed out on about another 2k leads…oops!

Our average lead cost was ~$7, so the leads themself were a $26,000 additional value. This alone would be a reason to use the tech.

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BUT JUSTIN, did they convert?!

Yes!

We closed $36,000 in IPPC business from this lead source. For what we spent on those leads we’re looking at a 750% ROAS. Not too shabby.

The rest of the money we made was by selling this service to our clients. Since we run paid ads for clients, this method is a complete no brainer. We ran a pilot program and only offered this to a handful of clients last year, we averaged about 4k/month in sales.

We sold clients the leads at ~$2/lead for some of the niches we work in, that’s a steal. 

If you decide to sell this you need to make sure the client knows these are lower intent leads and will require longer term nurtures. If you follow the email strategy I shared above, you’ll be good to go!

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Protip: Charge for building the follow up sequence! 

So that’s it! If you’re running your own business or are an agency owner, you’ve got to consider Inbox Retargeting. Though, I do have some bad news…

Not to be “Chicken Little” but this is starting to get way more attention, there are services popping out of the woodwork so this will become a table stakes method. So get ahead of this today.

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What’s Media Mix Modeling? [Marketer’s Guide with Examples]

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What’s Media Mix Modeling? [Marketer’s Guide with Examples]

Privacy


By Emily Sullivan

Have you ever felt in the dark when it comes to understanding the real impact your marketing dollars are having across multiple channels? 

Determining where and how conversions are occurring is crucial in optimizing your budget to drive the most impact with your marketing budget. Media mix modeling (MMM) is an analytical approach used to gauge the effectiveness of various marketing channels in driving sales and conversions. This method allows us to decipher the true influence of advertising spend across diverse platforms by accounting for a myriad of factors, both within their control (like media channel spend, promotional strategies) and outside their control (such as economic conditions, competitor actions, and seasonal influences).

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One of the key strengths of media mix modeling is its ability to incorporate long-term brand building effects alongside immediate sales impacts, offering a comprehensive view of marketing effectiveness. It helps in identifying which channels are most efficient, how different channels influence each other, and how external factors affect marketing performance.

Media mix modeling is a powerful tool for marketers seeking to optimize their marketing investments. By providing a holistic view of how various factors contribute to sales and conversions, MMM enables data-driven decisions that enhance marketing efficiency and business growth.

In this article, we explore how media mix modeling works, and how businesses can use analytics to drive smarter ad spend decisions.

What Is Media Mix Modeling?

Media mix modeling (MMM) is a type of analysis that measures the impact of media buys across multiple channels, showing the role various elements play in achieving a desired outcome—often a conversion or revenue KPI. With this information, marketing stakeholders are able to make specific adjustments to campaign spend to improve their progress toward reaching a given goal.

Media mix modeling can be used to address common brand marketing questions and pain points, including:

  • Which of our marketing efforts are having the biggest impact on reaching our goals—or, more simply—what’s working?
  • How big of an impact does seasonality have on our marketing performance?
  • How closely is our performance tied to promotional efforts? 
  • Are shifting consumer trends negatively or positively impacting outcomes?
  • Which specific mix of spend allocation drives the highest ROI?
  • How will these channels likely perform in the future based on their optimized spend allocation?

“Media mix modeling is a top-down , privacy resilient approach that evaluates how historical media activity, promotions, pricing, seasonality, and uncontrollable factors—such as economic activity—impact key business outcomes such as sales revenue. MMM is a scientific approach to attribution in the sense that it applies statistical methods to analyze and interpret marketing data, providing a systematic understanding of how different marketing channels contribute to overall business goals in the broader context of the market. The quality of insights derived from MMM heavily depends on the quality and granularity of the data used.”

— Annica Nesty, Group Director of Marketing Science at Tinuiti

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MMM leverages aggregate data, and can measure both online (digital) and offline (traditional) advertising channel performance, including (but not limited to): paid media channels such as social media channels, traditional print advertising, linear TV advertising, and other performance marketing efforts, organic media, operational factors like promotions, external factors like seasonality, economic conditions, outcome KPIs such as sales revenue, new customers, and conversions. 

How Does Media Mix Modeling Work?

The MMM framework is a type of statistical analysis that uses statistical methods and econometric models such as a regression analysis. This econometric model helps analysts determine the strength of relationships between a single dependent variable and an array of independent variables.

Media mix modeling analysis measures the impact of your media spend today, and is also helpful in predicting the future outcome of your marketing investments on a given variable.

Example:

Let’s assume a scenario where our target metric, or dependent variable, is revenue, a critical indicator of business success. We aim to dissect the influence of various marketing initiatives on this revenue. These initiatives, our independent variables, encompass a diverse array of digital advertising campaigns, including those run on TikTok, Instagram, Snapchat, as well as broader Display and Streaming platforms.

The number of independent variables under scrutiny does not dilute our core objective. The mission is to measure the relationship between the marketing endeavors and the revenue they generate. This involves not only identifying the direct contributions of each campaign to revenue but also understanding the nuanced interplay between them by observing how changing aspects of those independent variables impacts the chosen business outcome

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What can MMM Measure?

When using MMM to assess campaign success, marketers should leverage statistical methods and econometric models to get the most accurate picture possible. Data quality is essential in achieving an accurate media mix analysis, so take any needed time to clean your data before using it in your analysis. 

Key elements an MMM equation can measure include:

  • Base and incremental sales volume impact
  • Channel effectiveness and return on investment
  • Marketing spend saturation

Media Mix Modeling vs. Data-Driven Attribution Modeling

Like media mix modeling, attribution modeling also studies the efficiency of marketing strategies — but there are important differences.

Attribution modeling is a general term that refers to tracking engagement to better understand how specific tactics drive action at the user level. This modeling works well for analyzing specific customer touchpoints, focusing on elements like how a consumer converted, which creative on which channel led to that conversion, and what the expected ROI could be if more ad budget were shifted to that channel. 

Media mix modeling takes a higher-level, more comprehensive picture. This modeling isn’t designed to measure user-level engagement like impressions and clicks, rather its primary function is measuring the impact of an entire touchpoint on specific marketing objectives. 

Data-driven attribution modeling and MMM each have their own set of strengths. It’s not a matter of one being better than the other, rather one being better-suited to different types of marketing analysis. 

For example:

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  • The precision of the data-driven attribution: Let’s assume you want to invest more spend in a social ad campaign during the holiday season. While MMM is an option for determining where to allocate those dollars, data-driven attribution excels in dissecting the intricate customer journey, offering a microscopic view of user interactions. For instance, if you’re keen on understanding the exact value of a single click from your social media campaign, Data-Driven Attribution can illuminate the path. 
  • The holistic perspective of the media mix modeling:  Media mix modeling, can consider the impact of offline actions and initiatives. Unlike the more narrowly focused attribution models, which might overemphasize the first or last touchpoint, MMM assesses the collective impact of all channels over time. This makes it an indispensable tool for strategic planning and long-term investment decisions in your marketing portfolio.

“Attribution modeling is based on a bottom-up approach while media mix modeling takes a top-down approach. Media mix modeling provides a long-term view of the marketing ROI of media activity, while attribution modeling evaluates individual-level activity to provide a short term view of marketing ROI.” 

— Annica Nesty, Group Director of Marketing Science at Tinuiti

Why Does MMM Make Sense for a Post-cookie/Post-IDFA World?

In the post-cookie and post-IDFA landscape, where privacy concerns and regulatory changes limit access to individual user-level data, media mix modeling has become a pivotal analytical tool. MMM’s emphasis on overall marketing spend allocation and its proficiency in establishing cause-and-effect models, address the challenges posed by the diminishing availability of explicit conversion information, providing marketers with a privacy-respecting and insightful approach to navigate the evolving digital advertising ecosystem.

An Example of Media Mix Modeling

With the right media mix model, a business can measure their past marketing performance to improve future ROI by optimizing the allocation of the media budget by channel and/or tactic, including: traditional and digital media channels, promotions, pricing, competitor spend, economic conditions, weather, and more.

Example:

An international ecommerce brand wanted to forecast their second-half of the year and create an optimal media mix to make their marketing dollars work smarter. A combination of client data, marketing data, and machine learning were required to create a powerful, custom media mix model. 

To build the model, the business used 2+ years of digital marketing and revenue data, analyzing it by market, tactic, and day. The data was then used to create model to assess future spend showing how changes in investment across channels could impact revenue and sales.

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media mix modeling

The full digital media mix model gave the ecommerce brand a detailed analysis of where to optimize their spend across all digital marketing channels. 

One recommendation was to shift dollars away from social—which historically had been at or near 30%—to paid search. This recommendation came with another layer of insight: The brand realized they were overinvesting in awareness campaigns, and needed to invest more heavily in capturing current demand during the 2nd half of the year.

Results: Working with a robust media mix model, the brand was able to break down how much media spend was needed by each channel in order to achieve the 30% YoY revenue goal they targeted. 

The Benefits & Challenges of Media Mix Modeling

MMM helps you accurately connect all the dots, leveraging (ideally) a wealth of provided data, to understand how disparate aspects of marketing campaigns work together in helping you reach your business goals. 

Benefits of Media Mix Modeling

The benefits of MMM are multifaceted, offering marketers a strategic edge in navigating the intricacies of their advertising efforts. Let’s dive into each benefit in detail… 

Omnichannel Campaigns: MMM excels in providing insights for omnichannel campaigns, allowing marketers to understand and optimize the impact of their initiatives across various channels. This capability is crucial in today’s interconnected digital landscape, where consumers engage with brands through diverse platforms.

Improved Oversight Over Media Spend Impact: MMM provides a comprehensive view of the impact of media spend, enabling marketers to assess the effectiveness of their investments. This improved oversight ensures a clearer understanding of how each component of the media mix contributes to overall campaign success.

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Media Spend Optimization: With MMM, marketers can optimize their media spend by identifying the most impactful channels and touchpoints. This data-driven approach allows for strategic adjustments in budget allocation, ensuring that resources are directed towards the avenues that yield the highest return on ad spend.

Effective Targeting of Audiences: MMM’s analysis helps in refining audience targeting strategies. By understanding which elements of the marketing mix resonate most with specific demographics, marketers can tailor their campaigns to effectively reach and engage their target audience segments.

Forecasting with Certainty: One of MMM’s strengths lies in its ability to forecast results with a high degree of certainty. This forecasting capability empowers marketers to make informed decisions based on predictive analytics, aiding in long-term planning and goal setting.

Reduced Reliance on Personally Identifiable Information (PII): MMM minimizes the reliance on personally identifiable information for analysis. This is especially crucial in an era where privacy concerns are more important than ever. 

Media mix modeling is a comprehensive and powerful tool, offering a range of benefits that contribute to a more effective, data-driven, and privacy-conscious approach to marketing strategy and decision-making. While there are many benefits to MMM, there are challenges as well. Let’s look into common challenges of MMM in our next section.

Challenges of Media Mix Modeling

MMM grows increasingly complex as the media landscape becomes more fragmented, and the customer journey more personalized. Whereas in the past, advertisers may have wanted to measure something as simple as the impact of a print ad in a Cleveland newspaper, today’s consumers are exposed to brands in a wide variety of locations and formats, from a subway transit poster to a Sponsored post on Instagram.

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Working with high-quality data is important in any measurement initiative, but for MMM to work effectively, it also needs a lot of data to build a reliable model. For example, if you wanted your model to consider the performance impact of seasonality, it would ideally need at least three full seasons (three years) of data to consider in its analysis.

This makes media mix modeling a ‘long game’ initiative with infrequent reporting by its nature. Brands and advertisers who are more accustomed to daily or weekly updates may struggle with ‘waiting out’ the analysis.

Because it’s not designed to make considerations based on user-level data, instead providing aggregate insights, media mix modeling offers limited insights on brand impact, personalized targeting, and customer experience. However, advanced models are available that can provide highly granular insights, but traditional MMM provides aggregate insights.

Common Misconceptions About Media Mix Modeling

Media mix modeling, like many other analytics solutions, has also become a marketing buzzword that has generated its fair share of misconceptions.

Here are a few of the most common misconceptions around media mix modeling.

Media Mix Models Are Not Transparent

With large datasets and statistical analysis involved in media mix modeling, the methods behind the technique have been critiqued for their obscurity. If there is no perceived transparency in the process, how does a brand know if its media mix model is really accurate?

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Any organization specializing in media mix modeling should provide a transparent approach, with deliverables such as outlines, milestones, and performance reports. Additionally, you may want to consider partnering with an agency that truly understands how media mix modeling aligns with your needs and expectations. Every business is unique and each media mix model is based on multiple factors.

Media Mix Models Do Not Provide Real-time Data

Today, results are often measured by the timeliness of their delivery, with the current digital marketplace allowing for almost instantaneous real-time data. Media mix models do actually provide compelling real-time marketing insights, perfect for evaluating new campaigns, new competitors, and assessing pricing actions or changes in promotional strategies. 

A powerful partner in media mix modeling will provide sophisticated tools and real-time approaches to satisfy your business performance assessments. Your partner should also be able to provide forecasting, simulation, or AI- and machine-learning-integrated models to suggest future movements. 

Media Mix Modeling is Biased to Offline Channels

Though media mix strategies do integrate and consider offline channels in their approaches, media mix modeling also considers all digital channels — including display, email, paid search, social, and more. Remember—it’s considering your media mix. If that includes ten different channels and you provide enough high-quality data for each, they will all be considered in your marketing mix analysis. 

In fact, as customers have become more intertwined with digital channels, media marketing models have adapted to go even deeper into the analyses provided by those channels’ respective insights to support better budgeting choices and customer segmentation reports. 

Conclusion: MMM Closes the Loop on Marketing Performance

In an ever-evolving digital landscape, MMM’s adaptability to the post-cookie/post-IDFA world positions it as an essential tool for marketers. As businesses seek to connect the dots, leverage data, and make strategic decisions, MMM is a crucial ally in the dynamic realm of mixed media advertising.

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“At Tinuiti, we leverage measurement best practices such as MMM and incrementality to understand media effectiveness, predict future outcomes, create deeper insights, analyzing what-if scenarios to provide recommendations that optimize media performance. This helps brands understand what channels they should be investing in, how they should shift budgets (media mix), creating a high-level view of what channels are driving overall sales and ROI. Our goal here is to deliver growth for our clients by maximizing the return on investment through best in class measurement”

— Annica Nesty, Group Director of Marketing Science at Tinuiti

At Tinuiti, we know, embrace, and utilize MMM. Our Rapid Media Mix Modeling sets a new standard in the market with its exceptional speed, precision, and transparency. 

Our proprietary measurement technology, Bliss Point by Tinuiti, allows us to measure what marketers have previously struggled to measure – the optimal level of investment to maximize impact and efficiency.  But this measurement is not just to go back and validate that we’ve done the right things. This measurement is real-time informing what needs to happen next.

Curious about how we can tailor strategies to hit your unique marketing bliss point, including Rapid Media Mix Modeling? We’re eager to chat. Contact us today for details.

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Email Ready to Send? Make Sure to Tick These Things off First!

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Email Ready to Send? Make Sure to Tick These Things off First!

Designing and developing an email campaign is a complex mechanism; a few things will inevitably escape your attention during the process. So, before you hit that send button, you must draw up a foolproof checklist to ensure every single component in your campaign is in its rightful place. Wondering what an ideal pre-flight checklist looks like? We’ve carefully compiled everything necessary in this blog. Read on to find out!

Subject Line and Pre-header Text

A subject line can make or break your emails. It’s the first thing about your email that reaches the audience, and if it fails to hit the right notes, you’ll have a tough time convincing your subscribers to engage with your emails.

What makes a subject line tick, you ask? Let’s take a look!

  • Your subject line should prioritize an economy of words; this will help you on two accounts- firstly, a crisp and to-the-point subject line increases your probability of catching the reader’s attention. Secondly, longer subject lines run the risk of being clipped on mobile devices, thereby spoiling the subscriber’s user experience. By keeping your subject lines concise, you eliminate this possibility.
  • Ensure your subject line clearly explains what readers can expect upon opening the email. The more guesswork your subject line demands of readers, the less likely they are to open your email.
  • Steer clear of using words that might be considered spammy. With email filters becoming more and more sophisticated, usage of any sort of contentious term in your subject line will result in ISPs flagging your email as spam.
  • Personalize your subject line. In a climate of increasingly crowded email boxes, personalization is one technique you simply can’t afford to overlook.

Besides fine-tuning your subject line, you also need to pay attention to your pre-header text. Building upon the context provided by your subject line, pre-header texts give readers an additional nudge to open their emails. Two crucial things that you must keep in mind while curating your pre-header texts are:

  1. It must exist only as an extension of your subject line; it must not try to introduce any new ideas on its own.
  2. It must be mobile-optimized.

Broken Links

Given that the links embedded in your email eventually facilitate a conversion, it is imperative that you thoroughly evaluate their health prior to delivering your emails. Broken links aren’t just bad for business; they also spoil a subscriber’s user experience.

Here are a few things you must check after embedding a link in your email:

  • This might sound trivial, but do check if the link you have inserted is the one you intended to or not; the only thing perhaps worse than having a broken link is having an irrelevant one.
  • Check that the link is redirecting the user to the desirable destination.
  • If the download of a resource is supposed to be triggered by clicking the link, check if that’s functioning properly; you wouldn’t want subscribers clicking umpteen times on your link only for it to return nothing.

Accessibility

Apart from acing your content and design, you must also work towards making your email campaigns accessible; people making use of assistive technologies must be able to engage with and comprehend your emails in an absolutely hassle-free manner.

Given below are a few measures that will help you make your campaigns accessible to all:

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  • Organize your email content. Break down long paragraphs into small sections of 2-3 lines. Use bullets and subheadings wherever necessary. This will make it easy for assistive technologies such as screen readers to parse through your content.
  • Write descriptive alt texts for the images you’re including. Besides improving accessibility, alt texts also enable search engines to crawl your page more efficiently, thereby boosting your SEO.
  • Use semantic markup; this will help screen readers navigate your emails in a smooth fashion.
  • Try to stick to a single-column layout while designing your email template.

This email from AllTrails is an ideal example of an accessible template.

Inbox Preview

Different email clients render emails differently, even if only slightly. Hence, before sending out your emails, you must preview them across different environments and clients to check if they appear as desired. If you are designing your email for dark mode, too, it becomes that much more important to preview it before delivering.

Wrapping It Up

For your email campaigns to be able to drive maximum impact, they must be free of blemishes of all kinds. We hope the pre-flight checklist we shared above proves to be of help to you when you sit down to create your next campaign.

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