Myles Younger is a Senior Director on the Marketing team. To help digital advertisers understand the critical choices and game-changing solutions that lie before them, Myles blends his past experience as a builder of ad tech (specifically, a DCO platform) and B2B marketer. Outside of work, he’ll sing in your Judas Priest cover band if you have one of those.
As first-party data takes on “strategic asset” status, brands are engaged in high-stakes jockeying to decide who will take the First-Party Data High Ground. Will the outcome hinge on company size? Speed and agility? Technology? Agencies and solutions partners?
The jury is still out, but new research on first-party data from MightyHive sheds some light on those marketers that feel they’re pulling ahead of the pack. MightyHive partnered with Advertiser Perceptions, surveying 200 brand marketers and conducting five in-depth marketer interviews to attitudes, goals, and expectations around first-party data (timelines, ROI, ease of access, etc).
Interesting Patterns in the Data
When we crunched the numbers from the survey data, we saw some interesting lines get drawn between marketers. Not only were some marketers more confident in their timelines and expected ROI, but these “data-confident” marketers exhibited clearly different characteristics than their less-confident counterparts. Our research found that data-confident marketers:
Use a more diverse range of technologies to manage and activate first-party data
Are more likely to use partners such as agencies and consultancies to help unlock first-party data
Put a significantly higher priority on certain first-party data types such as ad serving data and mobile app analytics
Admittedly, our survey asked marketers to make a self-assessment, so one can’t assume that just because some marketers said they expected higher ROI from their first-party data that those marketers will actually achieve higher ROI. But…when you consider that, for example, this higher-ROI cohort also indicated significantly greater adoption across every one of the four different partner types we asked about (Agencies, Management Consultants, Systems Integrators, and Ad Tech Vendors), it gets more difficult to chalk higher ROI expectations up to marketer hubris.
Even our survey responses seemed to reinforce the stakes in advertising’s current era of upheaval. When asked what the primary reasons were for using first-party data, these were marketers’ top four answers:
Improved performance / ROI
Accuracy / data quality
Lends to more precise targeting
Improved measurement and attribution
Perhaps not coincidentally, those four answers can be rearranged into a loop or cycle:
It is urgently important to understand that that cycle is a positive feedback loop. Meaning that once marketers “crack the code,” the feedback loop will kick in and they will amass an ever-larger and richer trove of first-party data that they will do a better job of activating in campaigns and customer funnels that throw off yet more first-party data. We’ve already seen this happen with DTC brands that have, in a few very short years, sprung from out of nowhere and are giving incumbent brands a run for their money.
Over the next several weeks, expect more analysis digging deeper into the report. We’ll also reveal additional data and insights that were left on the cutting-room floor. So when you register for the report, opt in for email updates, or follow MightyHive on LinkedIn and Twitter.
Toshihiko works as a Digital Analytics Specialist in the MightyHive Tokyo office. He brings extensive experience in digital analytics and aims to help Japanese brands and agencies improve their business with data-driven marketing. He spends his free time running marathons and cross-country skiing.
MightyHive recently announced the opening of our new office in Tokyo, which will serve to connect Japanese marketers with local MightyHive talent, supported by our network of digital experts across the globe.
Our team in Tokyo is looking forward to partnering with marketers in the Japanese market, helping to guide them as they take control of their digital futures through unified media and analytics. The team brings a wealth of programmatic knowledge to the APAC region, and we are very excited to welcome everyone in Tokyo to the MightyHive family.
We wanted to get to know both the team and Tokyo better, so we asked Toshihiko Honda, a Digital Analytics Specialist based in our Tokyo office, to answer a few questions about the best parts of living and working in Tokyo.
Check out the interactive map at the end of this post for more information on each of Toshihiko’s recommendations.
Q: What brought you to MightyHive?
The Tokyo office just launched in February 2019, and we are establishing MightyHive’s footprint in a new market. The Japanese digital market is huge and unique in many ways, making this an exciting opportunity to build a new branch from scratch.
“After meeting with different members of the MightyHive team, I was really impressed with MightyHive’s collaborative culture and that’s what really drew me to the company.”
Although we are based in Tokyo, we are collaborating with the entire MightyHive team globally, which allows us to tap into MightyHive’s collective expertise to support our Japanese clients.
We have a challenging task ahead of us, but working cross-functionally makes it a very exciting opportunity!
Q: What’s your favorite part of working in Tokyo?
Everyone here is very professional and projects usually proceed on time, as everyone is punctual.
The city itself is very clean and traffic is managed efficiently, so it’s easy to get around and you don’t need to worry about traffic delays.
Another bonus is the food. You can find really tasty food at a reasonable price. Good Japanese food and sake are everywhere in Tokyo!
Q: What are your favorite restaurants in Tokyo?
One of my favorite places to eat in Tokyo is in Shinbashi. It’s a business area, but it’s also famous for its many delicious Japanese style bars and restaurants (They are called Izakaya:居酒屋).
Uokin: If you come to Japan, you can’t miss the seafood. You can find fresh seafood at a reasonable price at Uokin. They have some branches in Shinbashi, but you’ll have great seafood, no matter which branch you choose.
Jidoriya: Jidoriya is one of the best traditional yakitori (grilled chicken) places in Shinbashi. As the restaurant is pretty small, I would recommend you to go with a small group.
Coco Ichibanya: Coco Ichibanya is a Japanse curry rice restaurant. Japanese curry is a little different than Indian curry, but it’s a Tokyo staple. Coco Ichibanya is a chain restaurant, so they have locations all over the city.
Q: Favorite cultural activities?
One of the best ways to experience the culture of Tokyo is to simply walk (or sometimes run) around the different neighborhoods. In fact, I usually spend the weekend walking around the city with my wife.
Since Tokyo is such a large, populated city, each neighborhood has its own unique personality. For instance, Otemachi is a very upscale business neighborhood around the Imperial Residence where you will find a nice view and a lot of runners. Shibuya is a very busy and crowded tourist area with shops, nightlife, and restaurants. Ginza is an upscale tourist area, and Harajuku is a popular cultural and shopping area for teens.
Just walking around the different areas in Tokyo will give you a rich cultural experience.
Q: What are some of your favorite off the beaten path activities?
Good question! Because there are so many people in Tokyo, it’s important to find a non-touristy location to spend your weekend.
My recommendation is Koganei park. Although it is located a little far from the central area, the park is not crowded and you can spend a slow afternoon enjoying the day. The park is also home to the Edo-Tokyo Open Air Architectural Museum where you can explore architecture from the Edo era.
SOURCE: Edo-Tokyo Museum Facebook
Q: Where are the best places for a great cup of coffee (or tea)?
You should go to the Okushibu neighborhood (located near Shibuya) for the best cafes.
SOURCE: Fuglen Tokyo Facebook
My favorite is Fuglen Tokyo. The interior of the cafe is from Norway, and the mood is very relaxing. There are great cafes all over Okushibu, so no matter which one you choose they all serve high quality drinks.
If seats are not available at the cafe, take your coffee to Yoyogi Park. It’s the biggest park in Tokyo and a good place to relax.
Q: Finally, what’s your favorite neighborhood in Tokyo?
While not technically in Tokyo, you should definitely visit a hot springs area while you are in Japan. It’s a big cultural activity here and is very relaxing, so one of my favorite places is the Izu Penninsula where you will find many of these hot springs.
One of my favorites is Izunagaoka Onsen. As it takes about 2 hours by train, I recommend making your visit a weekend trip.
We are excited to bring MightyHive’s services and solutions to Japan to offer full regional support, helping Japanese marketers take control of their programmatic futures and providing unified media and analytics solutions across regions. MightyHive is currently looking to fill a variety of roles in the Tokyo office. Visit our careers page to learn more and apply
The MightyHive team is thrilled to be attending the sold out ANA In-House Agency Conference. MightyHive CEO Pete Kim will be joined by Josh Palau, VP Media Strategy and Platforms at Bayer to deliver a pre-conference session on Paving the Path to In-Housing Success.
Other speakers from PwC, Nationwide, Verizon, Clorox, GlaxoSmithKline, Bank of America, and more will discuss topics like driving cost efficiencies, project prioritization, building a strong culture and fostering talent, and the future of in-house agencies.
Paving the Path to In-Housing Success
March 13, 2019 | 3:45 PM EST
The decision to go in-house should not be taken lightly, nor is a 100% in-house model right for every organization. Marketers who jump in without building a thorough business case for change are likely to struggle; a smooth transition requires planning, prioritization, and patience. In this session, Bayer and MightyHive will break down the organizational choices that, when examined preemptively and thoughtfully, will pave the path to in-housing success. We’ll consider how to decide what to bring in-house, cost calculations, managing expectations, partner selection, hybrid resourcing models, and more.
Interested in learning more about how you can take control of your digital future? Schedule a time to meet with the MightyHive team onsite by emailing firstname.lastname@example.org. We look forward to seeing you there!
See What Makes Confident Marketers Tick
If you’ll be onsite, be sure to visit the MightyHive table to get a cozy gift and a first look at our new report: The Data-Confident Marketer. We partnered with Advertiser Perceptions to survey 200 marketing decision-makers to find out what makes data-confident brands tick. Pick up our report to see how you measure up.
Cutting-edge advertising technology is often marketed as “powered by Artificial Intelligence.” Some marketers get scared away, believing that they aren’t prepared or educated enough to leverage the latest tech. However, many marketing and advertising applications of Artificial Intelligence (AI) lean heavily on a subset of AI called Machine Learning. Essentially, Machine Learning (ML) is about improving predictions from the data you already own. As a decision maker within your organization, you should understand what basic ML applications entail and how they can be properly employed.
This blog post will cover three basic ML applications:
We’ll explore some use cases and avoid grandiose concepts that are too complicated to execute when getting started. I believe that beginning any Machine Learning endeavor requires baby steps. Like all technology development, it’s an iterative process that requires due diligence, realistic expectations, and never-ending trips back to the drawing board. Most importantly, this information should provide marketers with a reasonable understanding of ML concepts so that you feel empowered to make educated decisions when investing in projects.
Making Predictions with Regression
Regression is the process of estimating the relationship between variables. For example, a basic Return on Ad Spend (ROAS) model: given $100 of ad spend (X), how much revenue can I expect to generate (Y)?
Another useful example is predicting morning traffic. How much does the length of my commute increase for every inch of rain, day of the week, or time of departure? Traffic and ROAS predictions for that matter depend on more than just one single input variable. These models can combine tens and possibly hundreds of variables. Predicting ROAS could be a function of budget, time of day, average frequency caps per user, site content ratings, ad visibility, ad size, button color, ad copy, etc. This is known as MULTIPLE REGRESSION.
A multiple regression model might tell you how inches of rain, day of the week, or time of departure affect commute time. (Photo credit: Wikipedia)
The beauty of multiple regression is that the model dictates the relative importance of each input in determining the output variable or prediction variable (e.g., ROAS or commute time). Simply stated, regression is best used when attempting to forecast a result.
You might hear analysts or data scientists talk about regression analysis to predict future performance. Regression uses historical data points to create a model that allows a program to plug values into the weighted equation and get back a predicted result.
Using the example above, let’s say we’ve determined that our ROAS metric is on-site revenue ($OSR). We’ve trained our regression model with some of the possible variables considered above. Let’s assume our model is:
$OSR = (1.03 * budget) – (0.05 * time of day) + (0.63 * avg. frequency caps per user) + (0.005 * site content rating)
In making a prediction for on-site revenue generated, you could plug any combination of values into this equation and get a predicted amount of on-site revenue ($OSR).
A simple example of linear regression. In practice, ML regression models are often significantly more complex. (Image credit: Wikipedia)
Any machine learning system should constantly re-train on new data, as results are always changing and the relative importance of each input variable can change significantly based on actual results. The model should adapt as your data changes. Your predictions are only as good as the data you feed the system.
Classification is a form of categorization via pattern recognition. At its simplest form, logistic regression is the model of choice for determining the likelihood that something is either Category A or Category B.
A common example might be an email spam classifier. A more relevant example for advertising would be a classifier that determines the probability of a user converting after seeing an ad. This type of classifier powers the automated bid optimizers found in many DSPs. In theory, such a classifier would take an array of inputs related to a user —often gleaned from detailed cookie data— and provide a probability or likelihood of conversion. The higher the likelihood of conversion, the more your DSP is willing to pay for the impression and thus a higher bid. However, not all classification has to happen within an auction.
If you’re curious to get a deeper dive into how supervised classification algorithms work, this video from Wolfram walks through several examples:
On-site experiences can be significantly improved by employing classification. Understanding who is visiting your site by age, gender, location, estimated income level, interests, and so forth can help you tailor on-site content such as text, images, calls to action, recommended products, and much more. No classification system is going to be 100% accurate, but even a slight improvement in UX that can be driven by a classification system can move the needle in terms of revenue.
Enterprise analytics platforms (such as Google Analytics 360 or Adobe Analytics) often offer demographic reporting that marketers find useful. These reports are often driven by a combination of classification and clustering algorithms employed together.
What’s the Difference Between “Supervised” and “Unsupervised” Machine Learning?
In the case of classification, “supervised” ML means that the algorithm is provided with preset classification labels or categories. The classification algorithm can then work through a data set and classify items according to the available labels and categories.
On the other hand, “unsupervised” ML is required when there is a data set, but the categories, patterns, or meaningful relationships that might exist in that data set are unknown before the algorithm (e.g., clustering algorithm) has run.
Classification and regression are typically SUPERVISED learning models, meaning the input data is labeled for a particular set of outputs. Clustering algorithms are UNSUPERVISED—the process of finding patterns and hidden structure within unlabeled data. Think of unsupervised models this way: “What patterns can I find in a set of data that I don’t already know much about?”
Audience or customer segmentation are prime examples of applied clustering. Your Customer Relationship Management (CRM) system is filled with a breadth of customer-related information. Wouldn’t it be valuable to figure out who you should reach out to for a customer loyalty promotion? Or, what kind of client is likely to churn and what can you do to retain their business?
Clustering algorithms can identify the similarities between your users/customers and group them accordingly. K-means clustering is a simple but powerful algorithm that can be implemented with minimal inertia. While k-means clustering is often a stepping stone to more sophisticated clustering algorithms, it can whet the appetite for what can be further uncovered.
This animation shows a hypothetical K-means algorithm working iteratively to identify clusters of data points. (Animation credit: Wikimedia Commons)
Distribution models employ a more statistical approach to clustering, where membership is probabilistic and members can belong to more than one cluster. Distributive clustering is actually quite useful in audience segmentation. Your users may exhibit behavior that is commonly found in high-revenue customers but may also exhibit behavior in customers that are likely to leave for a competitor.
Machine Learning Doesn’t Just Work Right Out of the Box
No machine learning system is complete without cross-validation, feature reduction (removing variables that don’t impact the model), and other techniques that improve the accuracy of predictions. Ensemble machine learning systems (that blend multiple ML approaches) are increasingly more complex and sophisticated and often require the help of experts to develop and maintain. It’s important to understand and start with the basics and decide if machine learning is worth the investment for your organization.
Taking the Next Step with Machine Learning
You should now have a better grasp of common Machine Learning algorithms and their everyday applications in marketing and advertising. Getting started may seem daunting, and with the volume and depth of data at your disposal, hiring an expert to get you off the ground can pay major dividends to your business now and in the future. Exciting breakthroughs by ML practitioners and the availability of open sourced, enterprise-quality SDKs (such as and StatsModels and scikit-learn), along with scalable cloud infrastructure at your disposal, has made it easier than ever to discover what insights your data may hold.
Laurent is the Global CRM lead at MightyHive, where he manages a CRM ecosystem that includes Salesforce Marketing Cloud. Laurent has 10+ years of experience in CRM systems across different industries and geographies. 6x Salesforce certified. Based in San Francisco, in his spare time he’s also an AFOL (Adult Fan Of Legos).
Salesforce and Google, the tech giants joining forces to solve the traditional gap between offline and online customer journeys could have an unexpected impact on the digital advertising industry.
Uniting the Online and Offline Customer Experience
First announced at the end of 2017, the Salesforce and Google global partnership has come to fruition with an enhanced integration between Google Marketing Platform and standard connectors for Google Analytics 360. One of its greatest benefits is making online behavior and insight data available across Salesforce Sales Cloud and Marketing Cloud, enriching traditional CRM customer data (offline) with live (online) metrics.
Simultaneously, Salesforce has been advancing Journey Builder, the customer engagement tool that is central to its Marketing Cloud package. The result is a powerful, robust tool capable of delivering responsive and highly personalized data-driven automated campaigns.
Composing a Symphony of Data
Think of Journey Builder as an orchestra conductor making sure the musical ensemble is playing perfectly and following the partition. Previously, the conductor was only hearing from one ear (offline data) and adding Google Analytics 360 to the mix let the conductor hear with both ears (online). While performing well before, suddenly the musical ensemble is able to play a much larger repertoire.
In this sense, the integration between Google Analytics 360 and Salesforce gives marketers a more complete view of their customer data, with Journey Builder enabling a path for improved engagement.
As Google and Salesforce describe it, it’s a match made in marketing heaven. This match has also created additional benefits for marketers looking to upgrade their technology but wanting to balance their needs against those of their organization. This results in alignment between the goals of the CMO and CIO. The CMO benefits from a powerful actionable tool mapped on the entire B2C customer journey and provides the CIO a cost-efficient alternative to custom integration and implementation.
Salesforce and Google Marketing Platform Today
Marketers today have the ability to view Google Analytics data within Marketing Cloud, associating those site usage goals and e-commerce data points to email metrics.
Capturing online customer behavior and activities allows marketers to trigger automated, personalized journeys through the Marketing Cloud engagement channels via Journey Builder.
While the latest release is packed with new features, it’s hard not to see the exciting future ahead of us as the product roadmap is expected to include deeper links with the Google Marketing Platform stack including Google Optimize 360, Surveys 360, and Data Studio.
Looking Forward: The Impact on Programmatic Advertising
Journey Builder always seems to have been destined to enter the digital advertising arena. With the goal of fulfilling a marketer’s dream of enabling 1:1 marketing, Journey Builder is set to become a valuable tool for helping advertisers streamline and personalize their campaigns in real-time for a better customer experience using online data collected from Google Marketing Platform.
Allowing Journey Builder to connect with the other platforms in the Google Marketing Platform stack such as Campaign Manager or Display & Video 360 would open the gates for even smarter and more efficient programmatic ad buying.
The integrated ecosystem will ultimately encourage and help facilitate brands who are thinking of bringing their digital advertising in-house. With the hyper-targeted audiences activated via Marketing Cloud, marketers will also be able to drive overall acquisition costs down, making the business case for in-housing their digital advertising more compelling.
MightyHive is a leading partner for brands looking to integrate Google Marketing Platform and Salesforce. With a focus on uniting media and analytics, we help our clients own their analytics and insights to inform a data-driven campaign strategy.
To learn more about how MightyHive can help you activate a unified data and media strategy with Google Marketing Platform and Salesforce, please contact us.
As Director of Marketing at MightyHive, Lizzie leads brand activation and event strategy, helping to facilitate the team’s ability to share their expertise as thought leaders within the industry. Previously, Lizzie had the very meta job of marketing marketing conferences to marketers. In her spare time, Lizzie enjoys playing with her dogs Bud and Hank, reading voraciously, and going to concerts. She is also a staunch supporter of the Oxford comma.
Attribution is an integral component of any successful marketing plan. But where should marketers start with attribution modeling, and what happens when digital metrics fall short? On January 17, practitioners from across the industry met at MightyHive NYC over pizza and drinks for a deep dive into the triumphs and challenges of digital analytics, and where advertisers often go wrong.
Rachel Adams, MightyHive’s Director of Accounts, moderated a panel entitled Attribution Solutions: How to be a Hero when Attribution Models Fall Short. On the dais for the evening were Ben Rudolph and Emma Tessier, both MightyHive Project Leads, as well as Cullen Urbano, an Enterprise Consulting Lead at MightyHive. Ben, Emma, and Cullen drew on their experience with clients and their in depth industry knowledge to provide a well-rounded perspective about the best strategies for attribution modeling.
Left to Right: Rachel Adams, Director of Accounts; Cullen Urbano, Enterprise Consulting Lead; Emma Tessier, Project Lead; Ben Rudolph, Project Lead
We Are Gathered Here Today…
Attribution is a hot topic. The MightyHive office was packed with people who voluntarily passed up a night of binge watching Netflix to talk about it. But what makes attribution so challenging for advertisers in the first place?
Tech Companies Aren’t Great at Sharing
Our panelists all cited tech limitations and walled gardens as major factors that make it tricky to figure out how marketers should optimize their media spend. As Ben pointed out, “In the ecosystem a lot of players want to demonstrate their specific value, so they put up walls and are unwilling to share data that will give marketers a more complete picture.” He continued, “There’s also no north star for advertisers to look to that isn’t last-touch, so it can feel risky or intimidating to look at different models.”
Also, Robots Aren’t Perfect
Another often-overlooked factor when dealing with high-tech, automated solutions is human oversight. Cullen stressed the importance of evaluating every model with a human gut-check. “If you do that, you can avoid instances where an MTA model might recommend a mass reach partner with sub-20% viewability and +30% frequency.”
Rachel echoed: “Even though all the decisions we make are highly data-driven, at the end of the day there’s an element of ‘does this feel right?’ Does something seem off? Are these impressions even viewable? Is there an instance of fraud? Are we utilizing all of the verification tools we have at our disposal, etcetera, that only a human can evaluate.”
But Humans Aren’t Perfect Either
However, before even talking tech, all agreed that marketers need clearly defined KPIs and a full view of business objectives before they can tackle attribution modeling. Emma noted, “So many clients freeze when you ask, but they need to understand their KPIs and the platforms they’re running across. Someone who understands multi-touch attribution realizes the need to address the full funnel, whereas someone who’s not as intimately exposed might think it makes sense to allocate budget to last-touch because that’s what they see as driving conversions. They don’t understand how different touchpoints work together.”
Cullen and Emma both pointed out a common problem: clients who optimize towards the wrong goal. For example, if an advertiser makes most of its sales offline, they may be better served by optimizing for top funnel brand awareness than web traffic.
Attribution Challenges IRL
When asked about interesting attribution problems they’ve helped clients crack, Ben referenced a client with the “pragmatic goal of trying to be less wrong.” This comment elicited a chuckle from the audience, but he continued, “It’s a great way to think about attribution because there’s never really an end-state. This client wanted to move away from last-touch, so we shifted to a fractional model which was very successful for them.” Although the fractional model helped the client optimize budget, it’s still an iterative process as the ecosystem and consumer behavior are constantly in flux.
Cullen and Emma both pointed out a common problem: clients who optimize towards the wrong goal. For example, if an advertiser makes most of its sales offline, they may be better served by optimizing for top funnel brand awareness than web traffic.
Similarly, Emma recently worked with a client who was focused on brand awareness, but stated KPIs as ROI and sales. Emma guided the client towards an MTA model that incorporated offline and third-party vendor data to create a less biased full view across channels.
The MightyHive team, well-versed in Salesforce and Amazon Advertising solutions and globally certified across the Google Marketing Platform and Google Cloud, is uniquely qualified to help marketers solve complex attribution challenges. In fact, when asked what’s most exciting about working at MightyHive, the panelists all indicated that they love tackling new and interesting problems.
“We’re in a new frontier where we get to come up with processes to help companies figure out problems no one else is helping them solve.”
– Emma Tessier, Project Lead, MightyHive
“One Size Fits All” Isn’t MightyHive’s Thing
One such problem, which Cullen and the MightyHive consulting team recently addressed, was working to onboard a client’s first-party data to make it available to different attribution systems. While the most straightforward answer was to use an onboarding partner, sometimes that can yield match rates below 30-40%, which makes it difficult for advertisers to glean useful insights.
“We did some interesting things where we combined several approaches,” Cullen described. “We started with an onboarding partner, and for unmatched records, we used third-party and DMP data to model out lookalike audiences. We also included second-party data partnerships, and rounded them out by amplifying first-party login info, married to offline data where possible.” Using this strategy, the team was able to produce much more indicative results, giving the advertiser a 70-80% match rate.
We’re Hiring… Big Time
If learning about new ways to approach complex digital analytics challenges excites you, MightyHive is hiring around the globe! Check out our careers page to learn more.
Attribution is an integral component of any successful marketing plan. It’s crucial to understand what’s working (and what’s not), and continuously optimize based on data-driven learnings. As we find more reliable ways to understand what impact a given channel had on producing a conversion, marketers are moving away from last click and fractional models that only tell part of the story. But what happens when even multi-touch attribution doesn’t cut it? And how can brands form a more complete attribution picture between digital and offline channels?
RSVP here to join MightyHive subject matter experts for a timely conversation about how to bridge gaps left by even robust attribution models. We’ll discuss tracking limitations, how to tackle walled gardens, and new tactics for quantifying success across disparate metrics. You’ll come away with actionable insights that will make you an attribution hero.
Plus, complimentary pizza, cocktails, and gifts will be provided.
Where: MightyHive New York, 43 W. 24th Street, 6th Floor (between 5th and 6th)
When: Thursday, January 17, 6:30-8:30pm
Dress Code: Casual. Wear your networking pants. Bring your business cards. Don’t be shy!
You: An inquisitive mind. An experienced, hands-on-keyboard ad tech enthusiast. Someone interested in connecting with others to geek out about new technology over pizza and drinks instead of binging on Netflix after work.
Us: MightyHive is a new breed of media consultancy that partners with global brands and agencies seeking transformative marketing results in a time of significant disruption. We provide consulting and services in advanced marketing and advertising technologies, media operations, training, data strategy, and analytics. And we’re hiring, big time.
Whether you’re looking for your next career move, you want to expand your skillset, or you just like to chat with like-minded industry folks, we hope you’ll stop by.
“The very best columns are engaging, on-point and fun to read. Our 10 most popular op-eds of 2018 below delivered on each count.
We have to give a gold star to Martin Kihn (formerly of Gartner and now at Salesforce), author of three of our most-read columns. Not far behind is MightyHive, the consultancy recently acquired by S4 that contributed two of our top columns.”
Sam is an Account Manager based in the MightyHive Sydney office. As well as managing a number of agency and brand clients across all of their online advertising, she is also a Search SME (Subject Matter Expert) for the APAC region.
Historically, search engine marketing has been considered a tactic for driving lower-funnel performance, but marketers often overlook search when it comes to brand marketing. Smart marketers know that a well-constructed search campaign can create and support brand building in addition to driving lower-funnel KPIs like clicks and conversions.
Here are some guidelines to help you create a powerful campaign that will work harder for your brand.
Brand vs. Search Generic Campaigns Explained
Search campaigns can typically be categorised as brand (containing brand-specific keywords), or generic (keywords not containing a brand name). While the purpose of a generic campaign is to raise awareness, a brand campaign nudges the user down the funnel towards a conversion.
TYPES OF SEARCH CAMPAIGNS
Contains brand-specific keywords (e.g., ‘Nike’)
Typically lower-funnel, since the search is usually specific to both a brand and a product (or product type)
Limited to generic, unbranded keywords (e.g., ‘basketball shoes’)
Typically upper-funnel, since the prospect is interested in a product or service but has not yet decided on a brand
Bidding on Your Brand Keywords
Users show a high level of organic intent when searching for specific brand names. Despite this, campaigns should not rely solely on organic results and exclude brand search terms. In fact, brand searches provide enhanced opportunities for advertisers, and the benefits can far outweigh the incremental Cost Per Click.
Let’s look at an example. Myles, our hypothetical shopper, is interested in getting his wife a new backpack, and he’s heard good things about Herschel. He goes to his search engine of choice, and types “Herschel backpack.”
A budget-savvy advertiser might argue that organic results from Myles’ search are adequate to encourage conversions, and consequently, no further budget should be allocated to bid on brand keywords. However, there are benefits to bidding on brand keywords:
Controlling search engine results page (SERP) real estate
Leveraging a high Quality Score for brand search terms
Brand consistency in multi-channel campaigns
Controlling Search Engine Results Page (SERP) Real Estate
Significant Search Engine Results Page (SERP) real estate is a benefit of combined organic and paid results. SERP placement is best achieved when advertisers bid on their own brand keywords. The investment ensures that prime real estate on the results page (the top positions) is dominated by the searched brand, with less opportunity for competitors to win impressions.
Leveraging a High Quality Score for Brand Search Terms
Due to the positive impact of site content relevancy and ad copy on the Quality Score, brand keywords will always win bids over competitors, and will tend to be less expensive due to the high Quality Score.
Despite these benefits, there are some instances in which brand search terms do not require paid investment. For example, if Myles searches “Herschel backpack reviews,” indicating he is in the research phase, a brand keyword bid would be crucial to ensure he doesn’t click on a competitor’s ad. However, if Myles searches “where to buy Herschel backpacks,” his granular search term indicates he is lower in the funnel and well on his way to conversion. Organic results should suffice in this case.
Hot tip: Optimising a brand campaign in this way can be done by negatively targeting lower funnel brand keywords, or by lowering the max CPCs on these high-converting search terms.
Maintaining Brand Equity Through Paid Search
Search is one of the most creatively restrictive mediums for brand messaging. Search forces advertisers to support their brand without the aid of visual cues such as colour palette, logos, or imagery. Without visual identity, the only levers left to pull are the brand name, and the verbal tone & messaging (which is further limited by a strict character count).
When crafting your search campaign, it is imperative to adapt brand messaging to work in a search engine environment. Creating a compelling search ad requires far more thought than cutting existing taglines to fit character limits. Maintaining consistency between keywords and ad copy is crucial. The tone of the ad copy should mirror the tone of the brand and resonate with the correct purchase funnel stage.
Brand Consistency in Multi-Channel Campaigns
Consistency across an integrated brand experience from above-the-line mediums to SEM is important for brand recognition in a competitive environment. If an advertiser is running a holiday promotion, for example, it’s a best practice to mirror that message in search results. This could require sending the user to a promotional landing page instead of the site homepage.
In our backpack example, let’s assume Herschel is running a 20% off holiday promotion, which Myles learns about offline. In this case, if Myles searches for “Herschel backpack promotion,” organic results could lead him to the Herschel homepage, not the holiday deal landing page.
Using paid search, Herschel can push Myles from the middle to lower funnel using consistent promotional messaging and granular sitelinks. Paid search ads that mirror landing page and offline promo messaging give the advertiser more control to show the promotion.
Now We’re Talking!
Investing in paid search for brand campaigns can be extremely powerful. Using a strategy that optimises towards your brand KPIs and funnel stage-specific messaging, branded keyword bids can raise brand awareness and push customers from research to conversion. Now it’s time to consider: what can search do for your brand?
Historic proxy metrics like CPC, CTR, and CPA are dying. MightyHive’s Chris Brook argues that proxy metrics are distracting marketers from measuring the true impact of their campaigns. Instead, he argues that marketers should be focused on measuring the success of their campaigns against business outcomes. By measuring against actual outcomes, marketers gain a clearer picture of their audience and the return on their programs.
As digital advertising continues to evolve, it’s evident that the industry needs to set a new bar for media and consumer measurement, and begin to sunset legacy metrics in favor of real business objectives. While breaking decades-old habits is never easy, it is critical that we change our way of thinking, or get left behind.