4 Ways Data Can Guide Your Branding Strategy

It’s easy to think of branding as a one-and-done activity – something that is determined early on in a company’s development. Creating the website, logo, and style guide, employing a general content strategy, and hoping for the best is what many companies did in the past. But as our understanding of consumer-brand relationships grows, it’s clear that a brand isn’t just a static ‘set it and forget it’ entity. 

Nowadays, we have a fuller picture of what branding really entails. Thanks to major rebrands by corporations like Apple and McDonalds, it’s clear that no company is too big or well-established to revisit its branding strategy.

So where does data come in?

Ultimately, a business can’t revamp its brand without any insights to support the changes. Anything from the slightest adaptation to a total overhaul needs data to ensure that the changes reflect the company’s vision for the future are also compatible with what its audience can connect with. Without careful consideration and research, a company might join the unsavory ‘failed rebrand’ hall of fame –businesses that wasted thousands of dollars and had to undo the changes made soon after a rebrand.

Data-Driven Storytelling

Data-driven storytelling is a concept that has gained traction in the past several years as marketers learn more about social engagement. Countless social media studies have shown that content that evokes emotion and tells a story drives engagement. If a brand can tell a captivating story in bite-sized pieces, it earns salience in the minds of its audience –

“Beyond simply getting noticed, brand salience is crucial for a more subtle reason. It turns out people are not the rational, utility-maximizing creatures in the way traditional economists and marketers once thought. According to a study by Kantar Millward Brown, “consumers rely on mental shortcuts or heuristics when they make their brand decisions. One such heuristic is to assign greater importance to things that have ready mental availability, the effect of which is to choose the most salient brand.” source

Storytelling is an ancient art for a reason. It helps people get to know others as individuals. Thus it makes perfect sense for a business to employ storytelling to earn the trust of its audience and share its core values.

Data insights reveal what topics appeal to readers and what stories make the most impact. Brands that tell stories with their content show who they are, what they know, and most importantly, why it matters.

Data-Driven Experiences

Data can inform not only how you choose to market and advertise your brand, but also how your audience identifies and experiences your brand. Much like storytelling, data-driven experiences create positive associations with customers. This strengthens their understanding of what you offer and how you offer it.

To start creating and tracking data-driven experiences with customers, it’s critical to know about touchpoints. 

A touchpoint is any time a potential customer or customer comes in contact with your brand–before, during, or after they purchase something from you.source

Customers will interact with your brand via your website, social channels, software, in-person stores, ecommerce checkout, newsletter, and many other touchpoints. The question your data should answer is: How can we reduce friction and make each of these experiences more satisfying? 

Brand Research

There’s no room for assumptions when it comes to business strategy. If a brand seems to be falling short of what its creators envisioned, it’s time for some research. What brand activities or qualities are missing the mark? It’s important to explore all of the possibilities.

One brand might be creating content that doesn’t match the attention span of its audience. Another might be using antagonistic language without realizing it, or even unflattering colors that drive people away from its landing page.

In order to know what needs to change, brands must assess their current identity – Who are they now vs. who they want to be in the future. In addition, data analytics paired with market research has enabled many brands to keep up with important new trends. After finding that a growing percentage of coffee drinkers were seeking non-dairy options, Starbucks introduced dairy-free alternatives. With this single decision, Starbucks expanded its brand to be more inclusive of health-conscious coffee drinkers. 

Content Creation

When people think branding, the first thing that comes to mind is often ‘content.’ The content a business creates is closely tied to its identity– What you say and do needs to align consistently with who you claim to be. This is the precise formula that builds brand loyalty.

Data can show what types of content receive the most ROI and what types receive the most meaningful engagement. Data can also expose opportunities your brand is missing out on (e.g. Is there a topic your audience loves that you haven’t talked about? A problem they’re having that you can directly address?) As these insights are observed regularly, brands can build a routine around what works and budget only for that content.

Some marketers may worry that being too data-driven will ruin spontaneity or deflate creativity. But in reality, these qualities of a brand work best when coupled with a sound data analysis practice. With data at the foundation of your branding strategy, your business can adapt to market shifts and new customer interests with confidence.


ONE12th.io - Data, Marketing & Automation

Driven by Data or Lost in the Details?

5 Questions that Matter in Marketing

From the advent of the data age, businesses have struggled to harness new technology to make data-driven marketing decisions. From lead conversion to better ROI, and now customer engagement, marketers are eager to make use of the power data has endowed them with.

Every modern company aspires to be ‘data-driven.’ The problem is, achieving this coveted label isn’t so simple. With new power comes new complications – keeping data clean, separating facts from assumptions, tracking the right metrics, and choosing appropriate platforms and tools, just to name a few.

So how can businesses know if they’re truly data-driven or getting stuck in the various pitfalls of wielding unfamiliar technology? While the details vary for each business, a few simple questions can provide clarity.

1) Is a cross-channel strategy employed and is it getting results?

Most businesses know they need to reach out to their audience through multiple online channels. The easy part is creating accounts and having a presence on all of these relevant channels. The hard part is creating cohesion between them. A cross-channel strategy accomplishes two valuable objectives: It ushers customers smoothly from one step of the marketing funnel to the next, and it amplifies the impact and message of the overall marketing strategy.

A cross-channel strategy that is employed correctly will strengthen brand recognition and help customers remember specifically what it has to offer. A business without a cross- channel strategy ends up with choppier communications and a value proposition that lacks unity.

2) Is data being collected and analyzed in silos?

What happens when the findings of one dataset contradict another? How can marketers avoid comparing apples to oranges or drawing conclusions based on data that is too limited and specific? Ultimately, collecting and analyzing data on several separate platforms increases confusion and makes it harder to create a cross-channel strategy that makes sense. Platforms that allow for a centralized data hub give a broader perspective of the health of a business’ marketing efforts. These platforms also increase communication and collaboration between team members who should all be accessing the same data reports.

Fortunately, data silos are a problem that businesses are making great strides in solving –

According to a recent study by Teradata, “43 percent of respondents said they have achieved fully integrated data across teams, compared with just 18 percent in 2013.”

3) Is data being analyzed and action being taken?

It sounds all too obvious, but even highly developed marketing teams can get caught up in the chase – that is, collecting data endlessly as if collection is the goal itself. In addition, companies can easily lapse into using data strictly in hindsight, looking back at past results to understand what already happened. This neglects the bigger purpose of data, which is to forecast, improve future outcomes, and prevent problems. It’s crucial for businesses to stay focused on answering the right questions with data rather than endlessly collecting larger and larger pools of data. Bigger is not always better – especially if the ‘why’ behind the project grows hazier as the data size increases. If data is collected and analyzed successfully, but no clear information can be learned from it, it’s a red flag that marketers need to go back to the drawing board. Even the most skilled data analysts with precise conclusions won’t make an impact if decision-makers don’t head their advice.

4) Is testing central to how the organization operates?

At the heart of every data-driven organization is testing. Whether its split testing two versions of an ad campaign, swapping email subject lines, or anything else, a data-driven company tests whatever has an unknown outcome. A large corporation with several product lines might run dozens of marketing tests per day to pinpoint optimal ways to reach customers.
This testing culture is born from the realization that there is always another way to tweak, streamline, and optimize processes for best results. Thus data-driven organizations embrace testing as a means of continually growing their reach, understanding customers more intimately, and increasing revenue. As predictions are made about inventory, sales, or marketing, companies can later look back to assess the accuracy of these predictions and make changes to improve.

5) Where is most of the time spent?

A good rule of thumb is that marketers should be spending less time on data collection and reporting, and more time using those insights to create change. According to Hubspot, wasted time can be reduced by making small changes, such as automatically updating reports daily. Simplifying the data collection and reporting process makes space for data analysts to spend more time on what matters - generating thoughtful insights and using the information to solve problems. Companies must have sufficient time to reflect on the bigger picture underneath their data - What is the deeper meaning and why are these patterns playing out?

Ultimately, a business is only data-driven if it takes consistent action based on definitive findings. For that to happen, a business needs to have sound data collection and analytical capabilities. In addition, being able to segment datasets without going too far as to create silos is critical.

This delicate dance of using data intelligently isn’t easy, but for the persistent marketer, it pays off in spades. A data-driven company is a company that knows the health of its customer-base, where its revenue is coming from, and what marketing decisions to make next. Businesses no longer need to leave these immensely important choices to chance. With data, there can be certainty, and with certainty, peace of mind.


SOI: The Engines Of The 4th Industrial Revolution

We are in the midst of the 4th Industrial Revolution. From AI to IoT to machine learning to automation, companies must evolve to survive. Lennar International, the division in charge of bringing the dream of owning a US home to the world for Lennar, a Fortune 200 company, partnered with innovative upstart ONE12th to re-imagine itself as a data-driven, digital-first organization. The Systems Of Intelligence (SOI) connect data to business outcomes in order to maximize efficiency. ONE12th and Lennar International’s Lecia Rothman led the transformation through a System of Optimization, by placing the SOI as the engine at the center of every process. This enabled Lennar International to leverage, and stay one step ahead of, the data-fueled technological advances of the 4th Industrial Revolution… And so can you.

The 4th Industrial Revolution is bringing with it an accelerated pace of innovation, particularly when it comes to the vast troves of data being generated and the technology that is transforming the way we harness that data. At ONE12th, we believe that our historical focus on digital marketing has led us to the forefront of the disruption and opportunity being generated in its wake.

Systems of Intelligence (SOI) is a collection of systems that aggregates all of the relevant first and third-party data in a cloud-based database which is connected to both PII, through CRM, as well as real business outcomes. In the case of Lennar International, we had implemented Salesforce/Pardot combined with other systems in the mar-tech stack. This set of systems allow the International Division to capture inquiries, to then nurture those inquiries based on their geographic and language criteria. Their relevant digital behavior, as well as interactions with a call center, are tracked in order to deliver a personalized, high touch, international home buying journey. The systems qualify inquiries to become MQLs automatically as the inquiries meet certain criteria. At this point, they are put in the care of a human.

The 4th Industrial Revolution focuses on how technologies are interacting with humans’ physical lives. This point in Lennar International’s systems is where the rubber meets the road, and the automated marketing engine drives into the hands of a human, to keep moving forward. These specialized individuals communicate personally with each MQL in order to tailor the home buying journey to their unique needs. Once MQLs are qualified as SQLs, they move seamlessly through the sales process. The data from the final stages of this process continuously sync throughout the international systems so that it can be aggregated and leveraged to optimize demand generation, the automated marketing nurture, the actions of the humans and ultimately, the home sale process itself. And that, our dear friends, is data being put to work maximizing real business outcomes while improving the customer experience.

In order to build a truly Data-Driven / Digital First Organization, having the technology and data flows in place is not enough. Lennar International quickly realized this as it watched the technology advance away from their pre-established operations. It needed to redefine itself around it. 

And who better to take on this challenge than us, the win-win partnership team that dared to build their SOI in the first place. Re-enter Lennar International’s Director of Digital Strategy (“Change Agent”), Lecia Rothman and the ONE12th team.

The System Of Optimization Project (SOO) leveraged a data-first management consulting approach through which ONE12th transformed Lennar International into a digital-first organization. At the center of this organization would be the previously developed Systems Of Intelligence (SOI). 

The first key milestone was the implementation of a scientific brief to foster thoughtful, data-driven ideas both for digital activity and offline initiatives. Due to the “data science” brief, Regional Business Directors were now proactively developing and testing new digital-first ideas. Along with scientific briefs came new customized communication flows and interconnected collaboration tools that fostered these types of ideas. With all of this enhanced focus from the whole organization on data and digital; new nurturing processes, data structures, and team roles were developed, debated and implemented to maximize data use and automation. 

There were a number of key optimizations to the technological architecture. There were custom web services developed in Microsoft Azure to monitor the database for marketing/sales trends from the likes of Facebook, Linkedin, Talkdesk, Google Ads, Salesforce, Pardot, and other sources. KPIs were visualized through Power BI, set certain automated flows in motion and triggered predictive notifications for specific team members in Slack. Additionally, an Idea Management System leveraged Microsoft Onedrive and Slack for the budget approval process and automated campaign/project performance tracking through Salesforce. Last but not least, Slack channels brought in lead information as they progressed through the funnel to enable contextual discussions beyond the sales teams, maximizing collaboration around every international opportunity.

With the conclusion of this SOO transformation project for Lennar International, we are excited to be able to share a lot more about this work, the valuable insights we learned along the way and how they could be leveraged by any organization to position itself to lead in the midsts of the 4th Industrial Revolution. 


An Integrated Marketing System

Many organizations focus on individual media and technology platforms rather than approaching marketing as a whole. They concentrate on silos, instead. The truth is, marketing is a system -- an interconnecting network of small components, where individual marketing elements work together in unison. Here are some of the benefits of approaching marketing as a system.

Working in silos could result in duplicated data and errors. This is because data is spread across several locations, making it difficult to pull and analyze.

"Silo marketing can reduce the efficiency of a company's communications," says The Houston Chronicle. "Sheer volume leaves each channel competing for consumers' attention. Thus, the impact of each channel's message drowns in the collective roar.

A marketing system, on the other hand, gets more powerful over time. This system collects valuable real-time data that you can use to improve engagement and execute your workflows.

Why Marketing is More Interconnected Than You Think

Different marketing methods serve different purposes, but they are all part of something much bigger. Individual marketing elements should work in harmony to achieve a common goal. A marketing system combines all of these individual elements and provides organizations with actionable insights into their customers, and campaigns. It utilizes technology, data, and automation to execute these insights flawlessly.

A marketing system will introduce controls, workflows, procedures, and key performance indicators to your organization. It will enable the organization to scale and make sure all of the stakeholders within the organization are on the same page. Using the latest marketing technology, this system will also improve collaboration across different departments. There will be one centralized system, not several individual silos.

"Centralized data is easier for teams to work with," says Data Science Central. "All of the authorized people have access, and the data is readily available and can be updated and changed as needed. It makes the whole collaboration process so much smoother."

Linear workflows are no longer thought as the rule but rather as the exception, is simply not the way consumers behave. As technology evolves, marketing is becoming increasingly interconnected. Approaching marketing as a system will enhance sales and revenue, increase consumer trust and help track customers at all the different touchpoints.

System Levers

Levers are crucial components of a big marketing system. Unlike silos, levers provide a marketing system with agility.

Organizations use levers to fuel their campaigns and get insights into their customers in real time. These metrics provide organizations with valuable intelligence for solving problems and forecasting future trends. You can discover what your customers really think about you at various points in the sales pipeline, for example, and collect data from a range of sources for more detailed insights.

A Single Source of Data

A marketing system provides a single source where data is reliable and consistent, which improves transparency, accuracy, and business intelligence. This system will continue to evolve -- it constantly captures and analyzes data, which makes insights more accurate over time. Machine learning and artificial intelligence (AI) facilitate this process even further.

But what can you do with all this data? A data-driven marketing system, among other things, will help do the following:

  • Harness your existing business data: Utilize the existing data in your organization for better personalization and engagement, which will fuel your future campaigns.
  • Deploy marketing solutions throughout the customer journey: With a centralized marketing system, you can deploy marketing solutions at every stage of the customer journey. This can maximize your ROI.

Here are some of the benefits of a marketing system:

  • Proper attribution: A marketing system provides you with greater insights, so you can attribute value to your campaigns. Find out which campaign elements make the most impact, for example.
  • More visibility and control: A marketing system and its levers let you navigate your market in real time. You have greater control and more visibility into your marketing campaigns.
  • Turn your insights into money: A marketing system provides you with insights that you can use to increase revenue, boost sales and save money in your organization.

Here at One12th, we build Systems of Engagement, Systems of Intelligence and Systems of Record. We'll be happy to discuss how a systems approach can benefit your organization. Schedule a call to talk to one of our Marketing Systems Specialists today. 


What Are The Top, Most Advanced, Customer Journey Analytics Tools Out There Right Now?

“An organization’s ability to learn, and translate that learning into action rapidly, is the ultimate competitive advantage.” Jack Welch

As we rapidly approach 2019, it is critical that you make customer experience a top priority. In order to better understand these experiences, you must dive deeper into your customers' journey.

When you truly begin to understand your customers' behaviors, you can then develop a more strategic marketing plan. In turn, this will help you not only elevate the customer's experience but also increase your ROI. To assist your efforts, you need to hone in on productive, insightful analytics tools.

6 Customer Journey Advanced Analytics Tools To Consider for Your MarTech Stack

If you have recently made it your mission to map out the "customer journey" associated with your company, then you need to focus your attention on advanced analysis. Only then can you truly uncover the sophisticated behaviors and interests of your customer base.

Here are some of the top customer journey analytics tools available, all which offer their own unique features.

1. Google Analytics 360

Google has been a leader in relation to analytics, showing us that the more you know about your customers, the better equipped you'll be to make beneficial decisions. Google Analytics 360 was developed in order to help you "connect the dots." More specifically, this tool will allow you to link your offline and online information, helping you better understand user behavior.

Regardless of the industry, this analytics tool will provide you with a deeper understanding of your customer journey so that you can provide the best possible customer experience and more importantly, drive results. In fact, Google offers unique machine learning capabilities that will help you discover new insights (i.e. which customers are likely to convert).

The core benefits of Google Analytics 360 include:

  • The ability to see the complete picture, seeing how various channels impact your sites and/or apps. You can also easily connect other systems in order to measure point of sale, CRM, etc.
  • The capability to connect with Google's advertising and publisher products, such as Display & Video 360, AdSense, AdMob, Google Ads, and Ad Manager. You will also be able to share large amounts of data in an instant while using Google's configuration APIs. Google Analytics 360 also supports Javascript libraries, mobile app SDKS, and other collection APIs. Read more about these features here.
  • What we love about Google 360: Endlessly customizable, although it can be overwhelming at first. Natural progression from GA.

2. Heap Analytics

Known as the "new standard in tracking customer data," Heap Analytics automatically captures all interactions. Whether they be mobile, web, or cloud interactions, including events, clicks, emails, and more, you can now effectively and easily analyze your data without having to write any code.

In turn, you will be able to effectively measure the impact of each and every interaction. This is particularly true in regards to customer behavior, as you will be able to analyze and use data to increase conversion rates, better understand your users, and increase your overall revenue.

Overall, the core benefits associated with this tool include:

  • The ability to save time while increasing operational efficiency. Without needing any additional code, you can capture every interaction from your website or mobile app.
  • Access to retroactive data, allowing you to make better decisions. You can also capture on-page data via snapshots, enriching your datasets with ease.
  • The option to enrich data using third-party apps, including but not limited to A/B testing tools, payment processors, marketing automation tools, and CRMs. Cloud apps can also be integrated with just one click, including Stripe, Salesforce, and Shopify.
  • What we love about Heap: It allows for retroactive set-up of events. Mapping of events is extremely easy.

3. Woopra

Trusted by more than 5,000 of the world's most innovative companies, including WordStream and Hewlett Packard Enterprise, Woopra is known for its analytics, supporting marketing, sales, product, and support teams.

The benefits associated with this tool include:

  • Real-time analysis on an individual basis. You will be able to see how is interacting with your site, who is opening emails, who is making payments, and more. Helping you tie all of your data points together, will help you understand the customer journey and customize funnels accordingly.
  • In addition to retention reports, trend reports, journey reports, you will be able to create dynamic segments of users based on the criteria you choose. Woopra's visual interface showcases highly robust segmentation capabilities so that you can better understand various customer groups.
  • What we love about Woopra: CDP + Automations with a big library of integrations out of the box.

4. Yandex Metrica

The goal of Yandex Metrica is to help you better analysis both the intent and behavior of your users. Offering some unique and innovative features, you can take advantage of powerful segmentation and tagging capabilities. These features surrounding customer behavior include:

  • Accurate session replay, which helps you understand why a conversion was lost.
  • Click heat maps, which allow you to see exactly what your customers are clicking on so that you can better examine behavior patterns.
  • Form analytics helps you detect the pain points of your customers. In turn, you can see which form isn't performing as it should within a form-funnel.
  • What we love about Yandex Metrica: Great complement for GA. It allows to zero in on individual user's activity.

5. Indicative

Developed with product managers, marketers, and analysts in mind, Indicative will allow you to understand your customers are people, not as data. Connecting your data sources, this tool delivers a complete view of your customer. Whether you would like to optimize marketing campaigns or customer acquisition, Indicative provides you with the insights you need. Best of all, the standard version is free.

Benefit from:

  • The ability to segment customers in order to learn about different groups (i.e. how they interact with your product(s)). Then, identify behaviors that result in higher conversion rates before analyzing the impact of your A/B tests.
  • The opportunity to identify and eliminate points of friction across various segments. This will allow you to maximize your campaigns, as you're able to "slice" your funnel to compare various paths. Learn more about the unique features offered here.
  • What we love about Indicative: A robust platform for advanced segment analysis.

6. Pendo

As they saw over at Pendo, "we make software lovable." Like the above tools, you will be able to access robust product analytics without coding. There are many features associated with this tool, including funnels and journeys. Surveys and polls, user segmentation, product analytics and more.

This tool can help you:

  • Segment users for targeted messaging, allowing for more personalized engagement.
  • Benefit from a wide range of integrations, including everything from Wordpress to BigQuery.
  • What we love about Pendo: Incredibly dynamic tool for the analysis and execution of insights.

Better understand who users engage with your product(s). Whether you want to know which features drive engagement or view historical trends, all of this becomes possible -- and much more


Get to Know Your Next Customer with Predictive Analytics

You ready yourself for an oncoming storm because predictive analytics advised you it was approaching.

 

You stand at bat against a pitcher and swing at a certain area of the strike zone because predictive analytics advised you that’s where they’re most likely to throw.

 

You defend a basketball player and force them to drive left because predictive analytics advised you that’s where they’re weaker.

 

Predictive analytics are integral to providing a company, an athlete, or a storm-prepper with crucial info to get a forewarning. You use them "to identify the likelihood of future outcomes based on historical data." Without them, you're preparing with the storm on the horizon or guessing your way through at-bats and defensive possessions.

 

They're a necessity in a digital marketing, where success is contingent on analyzing data, before, during, and after a campaign.

 

All data analysis begins with predictive analytics; targeting groups based on variables, predicting customer behavior, and recommending certain products and services they’d be most prone to buying.

 

This is the most important segment of the analytical stage. It’s how and where you find your audience. You can have the Ernest Hemingway of copy and the Basquiat of graphic design on content. It won’t produce nearly the same results without segmenting, predicting, and filtering beforehand.

 

Utilizing predictive analytics is where retail giants like Amazon and eBay excel. They target groups based on numerous variables, including behavioral clustering, product-based clustering and brand-based clustering.

 

From there, they evolve from the segmenting phase to the prediction phase, utilizing propensity models. This is where customer behavior is predicted; variables such as engagement likelihood, and their propensity to unsubscribe, convert, or buy.

 

Then begins the filtering phase. This is where eBay and Amazon earns their notoriety. They’re always seem to know just what you want to buy and when you need it. They know this because of your past buying behavior. It allows those retail giants to predict what you’re likely to buy next will be in the same vein.

 

And it works.

 

It comes down to understanding people:

 

“Knowing the customer type or behavior you want to replicate, the predictive modeling starts with a sample of the consumers you want more of, otherwise known as seed. The predictive model is then able to create an audience that is tailor-made to your business and objectives.”

 

To reach the point of understanding your customer’s behavior, your predictive model must first identify “consumers based on who they are rather than exclusively focusing on a recent behavioral signal, thus exponentially expanding your pool of potential prospects”, based on the predictive model.

 

Furthermore, “predictive modeling evaluates all available data to classify the relative importance of each point in identifying your target audience. The resulting formula pinpoints which consumers to target, allowing you to capitalize on both scale and precision.”

 

The underlying current of predictive analytics is tracking the online behavior that takes them from point A to B. It’s focused on pinpointing who’s most likely to buy, when they are at their most willing to buy, what product or service they’re most likely to buy, and what’s going to prompt them to buy.

 

Even more important, however, is differentiating between high-value customers and those you might suspect of just browsing. Again, predictive analytics can aid in qualifying and prioritizing leads based on their likelihood to take action.

 

This is possible by “identifying and acquiring prospects with attributes similar to existing customers”. If your online patterns and behaviors are similar to the majority of customers on that website, you’ll be treated as a high-priority lead.

 

Here’s an example from Marketing Land on how this works:

 

“Applying predictive and analytics on a range of digital and offline data sets, we were able to identify just how valuable different online behaviors were to an offline, in-store transaction and activation later in the purchase cycle.

The data told a story with many elements we might have expected: Add-to-cart actions and beginning a checkout process were indeed predictive of an impending offline purchase, and locating a nearby store also showed up as an action predictive of purchasing intent. But browsing device galleries and using the chat feature were among the more valuable actions, and the single most important factor in purchase intent was interacting with the current special offers.”

 

Sounds like a science experiment, right? You lay out a couple of variables that act as triggers for your candidates and then wait for the results to play out. From this particular experiment, it was clear that offers were the trigger that turned the most potential customers into actual customers.

 

Notice how many variables were weighed as well. It goes so much further beyond whether or not a potential customer clicks through your ad. It comes down to what type of ad they’re clicking on, what they’re leaving behind in their cart, how far they went out in the checkout process, what they were browsing, and if they were using the chat feature.

 

The analysts went as far as tracking if their candidates were searching for stores nearby.

 

Ushering your potential customer is a delicate process that requires extremely precise timing, a task which links back to customer segmentation and leads to personalized messaging.

 

Predictive analytics also greatly assists in the fact that "73% of consumers prefer to do business with brands that use personal information to make their shopping experiences more relevant." So not only are you helping yourself in the long run, you're also assisting in directly getting sales through re-targeting efforts.

 

In fact, personalization overall greatly assists in drumming up more sales:

 

  • "86% of consumers say personalization plays a role in their purchasing decisions"
  • "45% of online shoppers are more likely to shop on a site that offers personalized recommendations"
  • "40% of consumers buy more from retailers who personalize the shopping experience across channels"
  • "80% of consumers like when retailers emails contain recommended products based on previous purchases"

 

This shouldn't be surprising. At every juncture of an Amazon transaction, the website is listing 'Top Picks for You' or 'Recommendations for You' or 'Customers who bought this also bought...'". All of these tactics are naturally going to elicit more orders. Your interest is already piqued in your purchase and you're likely excited about it, too.

 

It's kind of like a checkout line at a grocery store. You think you got everything, but don't you need some gum once you finish eating? And how about one of those magazines with the big headlines to relax with after?

 

Those weren't put there by accident. Stores analyze their customers' buying habits to see what they were buying at the end of a checkout line. Just like Amazon and eBay places certain recommendations before, during, and after your transactions, it's all based on using predictive analytics to forecast what you're most likely to buy along with that item.

 

In the same vein as any marketing agency's work, predictive analytics is utilized to get that extra sale that would have never been found without discovering who your customer is and how they behave beforehand.


Marketing Automation and Big Data: A Perfect Match

In an age where digital data is not only valuable but ubiquitous, organization and automation becomes a marketing agency's pillars of time management and financial advantage.

 

More needs to be done to understand the motivations of a consumer. Content creation and targeting are only the tip of this iceberg and the start of a deep dive to converting a customer into a lead or sale. It's data that educates a marketer on what makes an individual tick. Through data, they'll be able to establish what exactly triggers them and the most efficient way to do so.

 

To do so, you need to build a customer profile:

 

"Through marketing automation systems, we should be able to build better-rounded customer profiles through variable data field capture during different communication touch points."

 

Using big data can gain a marketing agency advantages when it comes to developing relevant content and messages, collecting and analyzing data on how customers interact, and delivering a more consistent, positive customer experience across devices.

 

Digital advertising isn't just posting an ad online and hoping for the best. Leveraging automation enables agencies to determine what type of content is best at attracting leads, how they find you, and why they chose to connect with you. It can help figure out how, when and where customers tend to interact with you, as well as what platforms and devices they're reaching you on.

 

Even though we're online, you still have to imagine a face and personality behind that screen.

 

Online marketing may have muddied the border between buyer and seller, but it hasn't completely eroded it. The intimacy of conversation may get down to bare bones quicker, but getting to know one another, in order to build up a level of trust from the seller's side and understanding from the buyer's side, has not been completely lost.

 

Now instead of asking questions, you're simply provided with profiles through those variable data fields we just mentioned. You get to know their behaviors, tendencies, and interests, while marketing automation and big data work "together to create an effective way to collect, sort and gain insight from thousands of data points about customers, campaigns and products or services."

 

This can partly be done by the miracle of predictive analytics, which can predict the future by mining the past. Consider Amazon; they gather past purchase data, wish lists, similar purchases and customer ratings to predict future shopping patterns. They simply acquire all the data they need to build up an accurate enough profile that will efficiently usher you from point A to point B:

 

"With the increased accuracy of self-learning algorithms, marketers will be able to better deconstruct big data to create incredibly targeted and optimally timed user experiences."

 

Getting a customer from each of those points requires a meld of data and automation; the data working as the blueprint, and automation working as the tools, delivering quickness, accuracy, and an improved user experience, one that puts the user in the driver's seat:

 

"They can access the exact information they want, how and when they want it. But every potential customer isn't necessarily going to want exactly the same information. With automation, you can also create multiple paths, so each person can have a different experience, based on their own needs and interests."

 

When "80% of your sales come from only 20% of your customers", automation is a necessity to pinpoint just what type of customers will react and how. For example, say you're running an email marketing campaign and you're trying to deliver the best possible user experience, you might monitor:

 

  • When your customer open emails
  • When they engage with content
  • What content they engage with
  • The frequency with which they choose to engage
  • Conversions that take place

 

Platforms like AutoPilot can deliver a tailored experience that accommodates each and every one of your leads as a unique individual, rather than just another part of the catch-all. Sure they might share similarities by way of being interested in what you're selling, but they all have different triggers and ways of going about things.

 

On the other end, the Zapier platform can help gather that data and turn it into data you can use to create a more efficient workflow and finish routine tasks quicker.

 

These platforms and tools will not only help you get better organized, but they'll help you draw in more leads. You can't treat your audience as a monolith. They might all like your product or service, but they all arrived there differently, are using different devices, react to different content, and come from different areas where the product or service might serve a different purpose.

 

You may not see them, and that disconnect and widening gulf isn't helping, but there's still a person behind the screen and the only way to turn them into a sale or lead is treating them like one.


7 Mobile Application KPIs You Need to Pay Attention to for Better Results

First and foremost, no mobile application metric or KPI is going to be more important than the star rating. Before we can delve into the crowded world of digital marketing statistics, let's just get it out of the way: the star rating's superficiality is what will enable many users to decide whether an app is downloadable or not.

 

A star-rating is so important that it could even deter users from downloading the app of a brand they like. When you see low stars, what immediately comes to mind? Probably crashes, long load times, misleading features; an app that doesn't deliver what it promises.

 

So to reach the point where an app's star rating is high, developers and whoever else is in charge of production, execution, strategy, etc. needs to focus on the numbers that will facilitate optimized performance and relevant, helpful, and engaging content.

 

Only through optimization and delivering a polished, quality product can you expect people to download the app and, most importantly, to continue using it.

 

It's natural to assume downloads would be the key metric. After all, that's how developers make some of their money back if they did in fact create an app that charges for use. However....

 

"'The number [downloads] means nothing without context. Downloads only enable an app to succeed, they do not indicate actual success,'says Brant DeBow, EVP of technology at BiTE Interactive. Too many brands are still concerned with eyeballs, treating apps as if they were a TV ad.

 

The best ads have stickiness and offer something inherently valuable to users.'"

 

That term stickiness is going to show up frequently here. What Brant says makes perfect sense. You can't simply create an app just for it to be downloaded. It needs to offer a "clear solution to a problem their users face with success affirmed by users visiting the app repeatedly."

 

It needs to have Lifetime Value (LTV), which is "the value of a mobile user as compared to a non-mobile user - if your mobile user is more loyal, spends more, and/or evangelizes more than your regular consumer, your mobile strategy is working."

 

Is someone going to recommend an app or give it a five-star rating simply because they downloaded it? Or are they going to give it that premier rating because of the features within the app? Getting an app downloaded is just good marketing. Retention within the app is the key KPI to measuring success:

 

"Retention is one of the biggest challenges of mobile apps today, as 65% people stop using them three months after install,' says Cezary Pietrzak, director of marketing at Appboy...Anyone can download an app, but it takes a special kind of app to compel people to use it with regularity. Your monthly active users (MAU) or daily active users (DAU) are your key users."

 

Now the question is how do you retain users? To start off, you need to consider the app you're marketing and which KPIs are more applicable and significant to that type of app:

 

"For games where ARPU (Average Revenue Per User) is naturally very low per individual user yet there may be many active users, a good KPI may be focused on keeping users engaging as long as possible. For a SaaS (Software as a Service) app where most users are freemium users, the best KPI is most likely focused on how well you can convince free users to become paid users."

Here's a few KPIs that every app should consider:

 

  • LTV
    • "How you quantify value depends on your vertical...The point is that knowing the value of various consumers means you can compare users and identify key segments of successful users as well as cohorts that need improvement, says Cipolla."

 

  • Session Time
      • "Just like page views versus time spent on the web, session length on an can help mobile strategists quantify the depth of a person's relationship with an app, says Pietrzak. You want a sticky, compelling app; stickiness lends an app toward longer sessions."

     

    • "Measured as the time period between app open and close. It indicates how much time your users are spending in your app per individual season. The more engaged they are, the longer their session length."

 

  • In-App Purchases

 

Basically what this means, and let's use a game as an example, is delivering the most basic tenets of the app, but holding out the best stuff for those that either play the game long enough (Retention!) or cave in and buy those extra incentives.

 

Take for instance a free Poker app I've become accustomed with. Now I can play hand after hand, day after day to reach a certain chip count so I can play with the high rollers, or I can shell out $10 or so and reach that point in a single transaction.

 

Or, as another example, the extremely popular The Simpsons Tapout game where you get to build your own version of Springfield. I can spend day after day giving characters tasks to complete so that I can have the money and XP to buy certain items. Or I could just spend $20 and get those items with a click.

 

These are the hallmarks of an effective gaming app. The games are addictive, entertaining, and free, at least to start off. It's not until you play for so long, however, that you're almost required to pay if you want to keep playing. You're left with the choice of either trudging your way through task after task or game after game, just paying to move up a level, or quitting that highly-addictive game.

 

  • Number of screens/pages visited

 

This KPI speaks not only to how engaging the content is on the app, but how high-quality the app's performance is as well. A user should be able to seamlessly launch the app, load new pages, make purchases, play the game, or whatever it is the app promises, without thinking, "What's taking so long?"

 

That momentary delay in seamless transitions can disrupt an entire experience. It's like reading a good article and stumbling across a grammatical error. It just throws you off. Even worse, it makes you want to experience something that isn't buggy and filled with problems that should have been worked out before.

 

  • Grant Permission
    • "A surprising yet important engagement-based KPI is when users grant permission for the app to access personal information. This KPI is important because it signifies a bond of trust between the user and the app which isn't inherently given to every app."

 

  • Performance
    • App crashes, app load per period, network errors, etc.

 

The number one reason an app gets deleted is because of technical issues.

 

This is where the editing and fine-tuning process play a critical role. It doesn't matter how much you strategize, how quality the content is, how addictive the game is, or how engaging the material is, your app will be deleted if it does not work, is laden with errors, or crashes upon opening.

 

  • Retention Rate
    • Highlights your most engaged -- and valuable -- users, creating better targeting opportunities and personalization of the app experience.

 

This, no matter the type of app you choose, is the most valuable KPI to build off of. It's how you know users are satisfied with the app because they'll keep coming back. Your app's accessibility, navigability, performance, content, and longevity have all passed the test if that's the case.


YouTube Demonetization and Why It Should Worry You

Sometimes what's good on paper doesn't mean it's good in practice. Sometimes it veils something far more nefarious in its intentions.

 

Take YouTube and their recent controversy. In order to combat their definition of 'extremist content', the worldwide video-sharing platform responded to threat of a mass advertising boycott by "implementing 'broader demonetization policies' around 'content that is harassing or attacking people based on their race, religion, gender or similar categories'".

 

Honestly, it's tough to blame them for this approach when "analysts are predicting that Google will lose roughly $750 million as a result of an international ad boycott that kicked off last month, when marketers discovered that their campaigns were running against extremist videos on YouTube."

 

"The latest companies to pull their ads from the video platform include Pepsi, Walmart, Starbucks, FX, General Motors, Dish, JP Morgan, Johnson & Johnson, and Lyft, Variety reports. They join AT&T, Verizon, GSK, and Enterprise Holdings, which pulled their ads earlier this week, citing the same concerns."

 

Sounds great, right? While YouTube is headquartered in America, the hub of equal and free speech, it still exists as a private company, meaning it can ultimately decide which content it wants on its platform. So if they find a video that promotes harassment and just blind hatred, they have the right to 'demonetize' those videos or flat-out remove them.

 

Demonetization is the process of decreasing the money a channel can make off a video once it reaches a certain view count threshold:

 

"While creators can get revenue from ads, individual views don't account for much money until they reach the hundreds of thousands. Making sure your videos can reliably have ads matched with them is essential for creators being able to have long-term revenue."

 

Here's a list of things that may result in demonetization, according to YouTube's new policy:

 

  1. Sexually suggestive content, including partial nudity and sexual humor
  2. Violence, including display of serious injury and events related to violent extremism
  3. Inappropriate language, including harassment, swearing and vulgar language
  4. Promotion of drugs and regulated substances, including selling, use, and abuse of such items
  5. Controversial or sensitive subjects and events, including subjects related to war, political conflicts, natural disasters and tragedies, even if graphic imagery is not shown.

 

How idealistic. Unfortunately, I, as you should as well, have two major issues with this. For one, most of it is completely subjective, and two, it's vague. The fifth point, in fact, is absurd in how broad it's defined:

 

"Guidelines that contain something as broad as 'subjects related to political conflicts' do not provide creators with useful information. It makes it sound as if YouTube is no longer going to monetize channels that cover current events, which of course is not the case."

 

And in the case of subjectivity, who is ultimately deciding what constitutes as hate speech, especially in this day and age where something as simple as challenging a different opinion can be defined as such. If I'm a conservative with millions of subscribers and I have thoughts on illegal immigration, what's to stop enough people with different beliefs and a large following to report me enough times to have my video demonetized.

 

Take for instance the YouTube Heroes program rolled out last September; perhaps one of the greatest attacks on free speech based on subjectivity you'll ever witness on a social media platform:

 

"YouTube heroes gives users the option to flag a video for being inappropriate, and as a result you can get your video demonetized by it becoming age restricted or removed completely, which will add a strike to your channel and possibly lead to it being deleted."

 

 

Oh, but it gets better. And by better, I mean much, much worse. Here's the five-step process:

 

  1. Become a hero
  2. Learn more in seminars
  3. Unlock super tools that allow you to mass flag videos
  4. Get behind the scenes access, contact YouTube staff directly, and try new products first
  5. Top hero perks, basically become a full-time unpaid Google employee.

 

Imagine my shock when I saw comments were disabled on the official video, which currently sits with a Like/Dislike ratio of 30,722:956,895.

 

This is where a huge problem lies. A video can get demonetized simply because it offended the wrong person or people. What offends some may not offend others. This isn't as simple as a hardcore racist saying "I believe Race X is better than Race Y and Race Z is worse than all of them!".  A vast majority of the time it comes down to innocuous beliefs that other people simply don't agree with.

 

But again it isn't as simple as that, either. What it appears to be is an outright attack on YouTube content creators with good intentions. Because this demonetization process isn't just attacking the likes of virulent racists like David Duke. It's going after creators like H3H3 Productions, Philip DeFranco and even Jenna Marbles, who "have all had hundreds of videos no longer qualify for advertising revenue, and other YouTubers are claiming they didn't have a chance to appeal to their demonetization."

 

 

 

"It isn't just large channels that are being affected by these changes -- YouTuber Tim TV, who has been a fulltime YouTuber for about six months, told Kotaku that he saw that his revenue was, 'tanking faster than ever before,' and that he found the changes 'terrifying'".

 

Here's a little background from H3H3's Ethan Klein on just how out of line and lacking in transparency YouTube can be when it rolls out these vague stipulations:

 

 

You heard that right. Even tagging things like 'Suicide', 'Rape', and 'Drugs' can get your video demonetized, not taking any of the context whatsoever into mind. That means someone who tagged 'Suicide' because they wanted to give advice on suicide prevention, or a rape survivor who wanted to tell their story and tagged 'Rape', or a doctor who wanted to give medical advice and tags 'Drugs' would have had their videos demonetized.

 

And the worst part of it all? YouTube didn't even warn the creators. Just read how lacking in foresight this approach was:

 

"In 2012, YouTube began demonetizing videos based on new advertising-friendly guidelines. This was not done by people, but by an algorithm that looked at metadata of videos and other factors to decide whether it was likely to be something as an advertiser wouldn't want to be associated with."

 

But don't worry, because everything is better now, right? Well..

 

"Google currently uses a mixture of automated screening and human moderation to police its video sharing platform and to ensure that ads are only placed against appropriate content."

 

Look, we get it. YouTube is a massive platform with billions of videos from all over the world. Sometimes automation is the only way to keep some things in check that a human can't reach. However, this is a significant issue when YouTubers like Matan Uziel is no longer getting ad revenue on their videos dealing with "women about hardship, including sex trafficking, abuse and racism."

 

Why did it get pulled? Isn't it obvious? One of those automated screeners saw "sexually suggestive content", maybe some "violence", and "controversial or sensitive subjects and events", and was programmed to demonetize the video of a creator with obvious good intentions.

 

But they're not alone:

 

"Dr. Aaron Carroll runs a channel dedicated to healthcare policy and research and discovered this week that 27 of his videos were demonetized and had been for months. It seems likely that the algorithm regularly flagged a program discussing prescription drug costs, the opioid epidemic, and treatments for diabetes because it thought those videos were celebrating illegal drug use."

 

How is that for a precedent set by YouTube? If you dare used your large following to discuss the evils of addictive drugs or tell the stories of abused victims, no ad revenue for you. Oh, and like Ethan explained in the video, they wouldn't tell you about it, either. You wouldn't get notified and your video wouldn't even become age restricted. Your video would just be demonetized.

 

Fortunately, this policy changed last fall. YouTube now:

 

  1. Lets you know when a video has been demonetized
  2. Shows a notice next to demonetized videos
  3. Allows you to request a manual review of demonetized videos
  4. Re-monetizes videos that the review finds to be not in violation of YouTube's ad-friendly policy.

 

It's a great gesture sure. But why did it take four years to correct, and why were channels not even notified in the first place?

 

It was a shoot first, ask questions second policy. By thinking they're doing the right thing and acquiescing to the demands of their advertisers (Not surprising considering YouTube operates at a loss), they negatively affect innocent YouTube content creators who treat the platform as a full-time job and livelihood.

 

As YouTuber Arin 'Egoraptor' Hanson' said, "he wanted YouTube to 'be more clear about what advertisers are opposed to having their ads displayed on. What can creators do specifically to make their content more advertiser friendly?'"

 

But to really get into the meat of YouTube and its advertisers' intentions with subjective censorship and constant threats of demonetization for ThoughtCrime, I don't think we can go anywhere until we explore what I have dubbed The PewDiePie Situation.

 

For those who don't know, PewDiePie is basically the face of YouTube. He has over 54 million subscribers, and his videos are basically him talking into a webcam talking about one thing or another. His audience is mostly made up of the younger generation, mainly middle and high school kids.

 

But about a month ago, PewDiePie was attacked, seemingly at random, by the Wall Street Journal who took some out-of-context jokes and videos and decided to go on a character assassination spree.

 

"According to the Journal's analysis, over the last six months the YouTuber posted nine videos that included either anti-semitic jokes or Nazi imagery, including one, posted on January 11th, that featured two men holding a banner that stated: 'Death to All Jews'. Another video, posted January 22nd, featured a man dressed as Jesus saying, "Hitler did absolutely nothing wrong."

 

The entire premise was based on Fiverr, a company that asks buyers to pay just $5 to do absurd things, like having two people dressed in traditional native garb to hold up a sign that says 'Death to All Jews', or having a man dress as Jesus and saying "Hitler did absolutely nothing wrong." PewDiePie was convinced they wouldn't do it because of how insane the statements were, but they actually did it.

 

Out-of-touch, narrative-driven journalists who worked for traditional outlets discovered the videos and went on a crusade to take down the evil PewDiePie empire. They went through his videos, chopped up more out of context clips in his videos, and said, "See! See! Look how evil he is! How can parents let their children watch this?"

 

PewDiePie was not contacted by the WSJ to defend himself for their first hit piece.

 

As a result of this attack, PewDiePie actually lost out on a partnership with Disney's Maker Studios. Also as a result of this attack, PewDiePie's 50 million+ subscribers realized traditional media outlets are using out of context video clips to defame the character of a YouTuber who had exhibited zero anti-semitic or racist tendencies in the past.

 

The Wall Street Journal, worth noting, has 2.1 million subscribers. It was also voted as one of the least cool brands by 18-24 year olds.

 

And isn't it just ironic that the author of the original hit piece of PewDiePie was written by Ben Fritz, who composed a tweet in 2009 stating: "Just attended my first chanukah party. Had no idea jews were so adept at frying." Here's another in 2015 talking about having a "hard on purely for the Nazis" and one more stating "well obviously I'm not counting jokes about black people. Those are just funny."

 

So what's the meaning of this? Why is the Wall Street Journal of all publications going after YouTube's most popular YouTuber? Well, I did some research into the WSJ and have a theory, but let me preface it with this response from PewDiePie on the whole ordeal:

 

"Old-school media does not like internet personalities because they are scared of us. We have so much influence and such a large voice, and I don't think they understand that. The story was an attack towards me by the media to try and discredit me, decrease my influence."

 

While I would like to personally cite and specifically quote the Wall Street Journal's findings and rebuttals, I can't because I need to pay for a subscription. It's exemplary of how a bitter, dying, and desperate publication from the old guard is lashing out and attacking the new; latching onto a statement or joke that could be misconstrued as racist or anti-semitic, which is basically a death sentence to someone working in the public eye, and selling that to uninformed users.

 

In perfect media collaboration, the Washington Post, Vox (who had the slimy audacity to, once again, use an out of context clip of PewDiePie raising his arm and equating it to a Nazi salute as their cover image for the article), Wired, and Salon were all quick to jump on the "Is PewDiePie a Nazi/Alt-Righter/Racist?" bandwagon.

 

YouTube content creators, people like PewDiePie, H3H3, and Philip DeFranco, are independent and don't answer to anyone other than what appeals to their subscribers. They don't answer to advertisers, high-profile donors, boards of directors, executives, or producers. These are people armed simply with a webcam, a microphone, and a platform reaching tens of millions.

https://www.youtube.com/watch?v=1BBmu_kFHrs

 

To the traditional media, this isn't just terrifying, it's a threat to their information monopoly.

 

Independent media, courtesy of the unbridled internet and social media platforms like Twitter and YouTube, have been on the rise and have shaken traditional media to its core. Distrust in these institutions is sewn as more and more people realize they're not getting the full story, while independent media, free of influence, is providing a perspective that's never discussed.

 

How do you attack these independents when they don't have a higher power that they answer to? It's simple. You hit them where it really hurts: Their ad revenue, their character through out-of-context clips, enlisting critics with opposing beliefs, employing other mainstream outlets to join your crusade, broad and extremely vague definitions of 'extremism', and using the platform they post on to crackdown on them.

 

But this isn't just an attack on popular YouTubers. It's an attack on counter-narratives and content creators not shackled by the constraining chains of producers, boards of directors, and advertisers.

 

So it's only natural that these dying publications in their death throes, like a cornered animal, are lashing out at its threats. Like YouTubers with over 50 million subscribers, or simply any YouTuber who is developing a following strong enough to take eyes off traditional outlets that are pushing a narrative delivered from on high.

 

Remember: "Whoever controls the media, controls the mind." There is nothing more integral to controlling the whims of the masses than the control of information. There should be nothing surprising that in the age of "fake news" a popular YouTuber is getting randomly attacked, advertisers are threatening boycotts, and traditional media outlets are doing their best to defame independent sources of info.

 

The only question that remains now is, just how long do the traditional media outlets think they have left?


Data-Driven Marketing is the Best Way to Improve Digital Performance

We live in a world driven by statistics and data. This new age we’re living in has made up-to-date metrics essential in companies deciding what's their next step. No longer do they need to rely on gut-instinct or intuition.

 

They have metrics do the job for them.

 

Modern technology has granted access to ubiquitous metrics that ultimately eliminate guessing over seemingly every aspect, in seemingly every industry. A retail giant can find which products sell and which don’t. A local government can judge the success of its funding efforts.

 

A digital marketing agency can base its entire philosophy on data. And for good reason. An agency’s job, after all, is to research, strategize, execute, and finally to report.

 

Notice what that proven plan is bookended by: Data-driven influencers. A marketer can’t begin to strategize and execute without first doing their research, nor can they report on their findings without heavily relying on data.

 

An agency without first doing its research would be the blind leading the blind. An agency then not reporting on their findings without utilizing data is misleading. It should be no surprise then that determining the successes and failings of a brand are contingent on what the metrics say.

 

Since statistics don’t lie, and never will, deciphering metrics for use in future campaign efforts is something every marketing agency should practice.

 

For example: Finding the right audience. According to Forbes…

 

“Whereas collecting and integrating large and disparate data sets to glean useful insights has been costly and time- and resource-prohibitive, technology has progressed such that the insights are ‘in the box’, can be tailored to the brand and business goal, inexpensive, and at your fingertips.”

 

These same technologies can be used to identify the best audience for a given campaign. Perform initial research into the brand by locating their audiences and then targeting them. You dilute your message less by sending it out to the broad masses. Instead, narrow the targeting to an audience that would be more receptive and inquisitive of the message for a more accurate perception.

 

Locating your audience is one of the most challenge parts of your campaign efforts because there seems to be a lot of guesswork involved. Technology, however, is catching up, as indicated by the same Forbes’ article:

 

“Front-end technology is catching up with the back-end such that ‘programmatic’ applies not just to the media buy, but also to the identification and creation of an audience.”

 

Targeting people who make $75,000 in the Northwest is good. But targeting people who make $75,000 per year, interested in mountain climbing, drive a Tesla, and likes Netflix and National Geographic is better. Your targeting yield might drop from 5 million to 1 million, but again you don’t want to dilute your message and waste it on those who it doesn’t speak to.

 

This way you can design campaigns around a 100% audience you know will listen.

 

This is all possible to identify through targeting. Facebook, in particular, allows marketers to target their campaigns through variables such as as income, location, interests, and behaviors.

 

Consider these before you run a campaign. That way you have a greater understanding of your target’s “actions, habits and propensities; their associations, networks and influencers; and the descriptive characteristics that influence and distinguish the group.”

 

That’s just one flap of the book, though. We can’t neglect the other side where we report on the campaign’s progress.

 

This is where metrics really start to shine, and where it showcases just how evolved this industry is. On the outside, metrics look to only be on the surface; likes, comments, replies, shares, retweets, etc. But indicating successes and failures goes far deeper, especially depending on the campaign’s purpose.

 

This isn’t to say those types of surface stats can be suitable indicators. They absolutely can predict which types of posts work well and which don’t. If one type of post is getting 100 likes on average, while another is getting only 25 on average, then it’s clear that one post obviously resonates and engages more with users.

 

But it’s the below-the-surface stats you really need to pay attention to; those available through deep insights and the tools needed to access them.

 

Surface stats won’t explicitly inform you of how many link clicks a post received. We actually saw this in practice with one of our premier clients. Although we were receiving tons of likes, comments, and shares, we noticed that we were basically garnering little-to-no link clicks on these same posts.

 

It wasn’t until we began to A/B test where we found the issue, and altered the posts. Only then were we able to boost our link clicks, albeit at the sacrifice of our engagement totals. Nevertheless, it was interesting to learn for future reference, such as running an awareness campaign vs. an engagement one.

 

But we can plunge even further into the sloping depths of digital metrics.

 

Metrics like bounce rates can indicate where users go after landing on your website. When you uncover and unleash the power of metrics, you can find out everything you need about the tendencies of people to improve your marketing approach.

 

As digital marketing grows, measurement platforms follow. With so many brands going digital, it only makes sense for ambitious entrepreneurs to take advantage by creating platforms that can measure and track metrics on their performance.

 

And since we live in a flourishing capitalist society, competition occurs that motivates these innovators to measure more metrics than the other. So when one platform can track how many seconds you spent on a specific website page, another platform sees that and creates a tool that does the same AND which page they’re going to after.

 

The insights just go deeper until marketers get the best available POV from their target audience. Remember that the greatest motivator to all of this is to nail down an audience’s behaviors and tendencies. That way a marketer can predict exactly what they do and how they make the transition from curious shopper to conversion.

 

This is the basis of what marketing was built on: Appealing to consumers within their sensibilities.

 

It was a lot more difficult to achieve that in the ancient time before measuring platforms came along. Marketers actually had to talk to people, hold focus groups, and stage surveys. Now they can pay a fee to have a website track what goes through the mind of their collective audience.

 

We wouldn’t want it any other way. Neither would you.

Read more