Make the IoT Work for You

In the time it takes you to make your morning coffee, thousands of Internet of Things (IoT) devices have already collected significant amounts of data all around the world for unique purposes.

IoT is a term that references our growing connectivity through internet-connected devices and objects. These objects automatically collect and send data online, contributing to a mass network of information accessible from potentially anywhere. And whether that excites you or freaks you out a bit, ignoring the IoT revolution will soon be impossible.

The IoT Takeover

With on-demand streaming services like Netflix and Disney+, delivery apps like Grubhub and GoPuff, and customized product recommendations from online retailers, it’s no secret our consumer landscape is driven by convenience, affordability, and technology. IoT is the next logical step on that path, providing even greater convenience and instant access to anything you might need – whether at home or at work.

Devices like Alexa introduced us to the reality of objects as assistants and connected information sources with vast potential. But IoT isn’t just a consumer fad. According to most research, the greatest potential lies in the business sector.



IoT at the Office

It’s impossible to pigeonhole IoT as having one particular use or a strong point for organizations that employ it. Instead, we need to take a 360-degree view to understand the variety of uses for IoT and the roles it can play in organizational efficiency.

No one likes to feel like they’re doing menial labor, but the reality is that someone – if not everyone – on your team gets stuck with it during their workday. Smart printers are self-monitoring, letting you know when they’re low on ink or paper or need service (and yes, they can even make the refill order themselves). Similarly, smart vacuums handle the cleaning and let you know when they need to be emptied. IoT sensors can even provide a heatmap of your building, revealing overly congested areas, rooms where meetings are taking place, etc. These are just some examples of IoT seamlessly sharing the workload and helping you manage daily operations.

Of course, it isn’t just a helping hand at the office that IoT provides. The notoriously labor-intensive process of collecting, storing, and retrieving data can be automated using IoT sensors. In this way, IoT provides teams with a revolutionary capacity to use data for decision-making and become truly data-driven (without having to hire a huge team of data scientists to manage it all).

IoT for Capturing Data

From the minute details to the big picture, IoT has a place in any data-driven organization’s structure. Use cases from several industries are revealing some of the ways this technology is being put to good use.

For example, any in-person gathering - like an event a retailer holds for its customers, a company-wide training, or a networking event – can become a ‘connected event.’ With this use of IoT sensors, event-holders no longer have to rely on guesswork and qualitative data to determine if the event was a success. Sensor-captured data provides real insight into who was engaged in what activities, when, and where. IoT video analytics is also an emerging security trend, allowing you to monitor building activity and detect abnormal behavior in a given space.

Just as event-holders need real-time insights and control over their space, product-makers need an understanding of their customers’ experience. Tracking product deliveries is one of the simplest ways to monitor experience, along with inventory management. Some companies are finding they can reduce costs on overstocked products while making sure they don’t run out of best-selling products. It’s a delicate balance that is best left to smart devices rather than human guesswork.

In addition, IoT sensors now allow companies to monitor which product features customers are using the most. This opens to floor for tailored email communications, a more effective content marketing strategy, and design edits that enhance future product releases.

“Leveraging IoT has evolved from a connectivity strategy to a business transformation strategy, and has proven results, including increased profitability.” Currently, 45% report IoT has helped boost profits by 1% to 5%, and another 41% say the impact has boosted them by 5% to 15% annually.” source

Getting Started with IoT

Getting started with IoT can be intimidating, as it typically requires investing in new software or hardware and learning the ins and outs of new technology. Furthermore, getting team members on board may require training to allow for a more data-driven culture. You’ll need to consider which devices to employ based on your specific goals, which means you’ll need to consider things like battery life, scalability, and data processing capabilities. In addition, how you connect is a big consideration – cellular, Bluetooth, Ethernet, RFID, NFC, satellite, and WiFi are some of the many ways to connect IoT devices.

Thankfully, with the average cost of a sensor falling every year, implementing this technology will only become more affordable in the years to come. For both B2C and B2B users, possibilities abound. The IoT revolution is creating a web of ever-growing connectivity – and that means real-time information that yields insights you can act on.

In what ways are you most interested in employing IoT to work for you?

Descriptive, Predictive, and Prescriptive Analytics: What's the Difference?

Descriptive, predictive, and prescriptive analytics use data to answer fundamental questions that help guide organizational strategy. If you have any interaction with data at all, you’re using one of these analytics styles to glean insights. But because data analysis is still new in many professional environments, there’s plenty of confusion around these three terms.

How do you know which type you’re using? Which type you need? And how do you know if it’s the right time to implement a new analytics model? Ultimately, it’s not about choosing the right one, but learning how all three work together.

Descriptive | What happened?

If you’ve had limited experience with data, descriptive is likely the kind you’re familiar with. Using descriptive analytics, organizations can generate crystal clear hindsight from mined data. Rather than launching projects and being uncertain of the full outcome, descriptive data tells a detailed story of what happened (e.g. How many units were sold and when? How many people signed up for the mailing list last month, and where were they from?)

Descriptive analytics is useful in many circumstances, including:

  • Comparing figures from different time periods
  • Assessing the success of a marketing campaign
  • Generating reports that show progress over time
  • Revealing problem areas where goals are not being met

With simple data visualizations, descriptive analytics allows organizations to get a full picture of what happened: whether last week, last month, or last year. With real-time analytics, many organizations even have access to what happened 5 minutes ago. While descriptive may be seen as ‘old news’ in comparison to the next 2 analytics models, it’s still highly relevant and valuable today.

At its core, having descriptive data is like having a mathematical genius on hand who can spit out numbers, facts, and figures about your organization in a moment’s notice.

Predictive | What will happen?

Predictive analytics goes a step further, taking past data and using it to forecast what will happen in the future. This is done using a machine learning model that assesses trends and patterns.

For example, predictive analytics is useful for:

  • Forecasting how much inventory will be needed
  • Predicting what product features customers will respond to
  • Automating follow-up emails with customer segments

This doesn’t mean that predictive analytics can tell you what will happen with absolute certainty. It simply uses all the information available to generate a statistical best guess, and often this is much more accurate than human assumption. With predictive analytics in place, organizations are better prepared to make quick decisions and avoid being surprised by a mishap.

A perfect example is Starbucks use of IoT devices that predict when a piece of machinery is likely to need service. With several data points, Starbucks can prevent last-minute repair costs and potentially disgruntled customers (for more on this, check out our blog: How Starbucks Employed AI for a Better Customer Experience).

“Research firm Aberdeen found that companies homing in on customer needs and wants through predictive analytics increased their organic revenue by 21% year-on-year, compared to an industry average of 12%.” source

At its core, predictive tools are akin to having a genie in your office who seems to sense things and provide ample warning before they happen.

Prescriptive | What should you do about it?

Running a business has long been considered a risky endeavor, and startup failure rates remind us of this all the time. Not knowing what actions to take – especially when things aren’t going well – is a struggle even the largest, most successful companies know well.

Prescriptive analytics can help you:

  • Determine what factors are impacting costs
  • Identify what changes may improve an employee training program
  • Assess which of two decisions may yield a financially safer outcome

Predictive analytics goes a step beyond both descriptive and predictive analytics and provides actionable advice: Considering what this data shows, what is the next step to take? Prescriptive analysis shows some possible decisions you can make and what their implications are likely to be, giving you the best possible chance of making an informed decision.

Google’s self-driving cars are one of the most famous examples of prescriptive analytics and AI being used in groundbreaking ways.

At its core, having prescriptive technology in your corner is akin to having an extremely wise business strategy consultant on hand 24-7.

In Tandem

By now, you can start to see how these analytical components are not competing – nor do you need to frantically ditch your old strategies the minute a new analytics tool is released.

A strategy that encompasses multiple data analysis methods can help you build a beautifully flowing system that runs your organization like clockwork.

Advanced analytics operates under rule-based algorithms, but it is anything but fixed. It can help you initiate radical changes and pivot your strategy as often as you need to. This iterative process allows you to refine your data usage continuously over time.

In the near future, organizations will graduate from putting out fires and scrambling to keep up with customer desires. Instead, the data revolution that’s currently unfolding will allow for an unprecedented level of confidence and calm. Rather than clinging to rigid operational models, organizations can continuously adapt, learn, grow, and take new action. A data-driven strategy is one of constant optimization, and thus a constantly evolving ROI.

Looking for an easier way to put your data to work for you? Get in touch with us to start building your systems of engagement, intelligence, and optimization.

Incorporating Data Visualizations for Better Understanding

“Psychologist Albert Mehrabian has shown that 93% of communication between human beings is in the nonverbal area. This means that an image can be immediate, while language requires time to analyze. Data visualization can cut to ­the chase, saving critical time and allowing the next step in developing solutions.”Visual Matters

Data visualization is an image that displays data about a particular topic. Whether it’s a traditional chart, a pie graph, or a more unique illustration, the purpose is the same: to make information easier to understand and act upon. When insights can be drawn faster and with greater clarity, organizations can truly call themselves data-driven.

Data’s Hidden Story

When we break down what data visualization actually does on a granular level, we can see why it’s so effective. Since the beginning of human existence, storytelling has been one of the most effective means of communication. The problem with numbers and data is that it lacks structure and organization – and there is a lot of it. No conceivable pattern exists yet, and so most professionals struggle to make sense of the data they gather. By translating information into visual elements, we frame it in a way the brain can quickly understand and remember.

Hubspot gives several examples of how successful brands have used visualization to get a point across. DensityDesign used a simple dotted map to show where each of the world’s 2,678 languages were spoken. FiveThirtyEight showed in a series of line graphs the full history of each NFL team. And in an effort to quell misinformation, Bloomberg Business created a sequence of simple charts to show which global factors have contributed the most to climate change since 1810.

In each case, public understanding of these complex trends was greatly enhanced by simply looking at an image. Furthermore, visualizations are interactive, allowing users to input parameters and derive more granular insights from smaller data segments. These visuals also empowered viewers to share the stories of the data with other people.

Of course, visualizations don’t always need to be presented to an outside audience. They may be just as useful – if not more useful – for internal purposes. 

Why Employ Data Visualizations?

“On average, those using data visualization tools report it would take an average of nine hours longer to see patterns, trends and correlations in their company’s data without data visualization.” – SAP

The ability to recognize patterns with less time spent analyzing is an obvious benefit of visuals. But the implications of this go even deeper. When organizations are aware of a pattern, they can immediately take advantage of it or act to prevent it, depending on the situation. Thus visualizations put leaders square in the driver seat to make the most informed decisions possible. In addition, they reveal outliers and segments of data that should have less impact on decision-making. Because data analysis is so complex, time-consuming, and multifaceted, visuals reveal insights that might have otherwise gone unnoticed.

Data visualizations also make it easier for your team members to get on the same page. Too often it is only those directly handling data that have a real understanding of it. But when data is driving organizational policies (especially changes), it’s critical that all team members have an opportunity to see the data and come to their own conclusion about it. When data is crystal clear, team members get on the same page. When they know why a certain action is being implemented, collaboration and productivity become easier.

Ultimately, visuals create awareness around particular trends or patterns. This might mean your team gaining a better understanding of customer needs. Or it might mean management figuring out where budgets are being wasted. This allows organizations to increase ROI by investing in what works. The key lies in choosing the right visualization for the situation.

When to Use Each Type of Visualization

Source: Tableau

The more diverse types of data you have, the more decisions need to be made. Whether you’re managing data on website performance, revenue, sales, customer behaviors, marketing campaigns, or anything else, there are infinite ways to interpret it. But just like a good story is carefully sequenced, data must be carefully presented. Choosing the wrong type of visual can create even more confusion around an already ambiguous subject.

So how can an organization determine the best way to illustrate its data? While dozens of visual styles exist, a few considerations can help you choose a simple one:

  • Are two or more things being compared? If so, a bar, pie, or line graph can be used.
  • Do you need to show different parts of a whole? A waterfall, stacked bar, or pie chart is best.
  • Are you trying to understand data distribution? Go with a scatterplot.
  • Do you need to uncover a trend? A dual-axis line or column chart will do.

Other attributes that make for better understanding include color consistency, clear labeling, and logical ordering. Visualizations need to be simple enough to understand, but also detailed enough to allow for the analysis of more complex types of data.

Whether you need to cut costs, engage more customers, or boost productivity, the ability to visualize your data will continue to be a key tool for business intelligence moving forward. As avenues for collecting data continue to multiply, it will be even more critical that teams have the tools to simplify and interpret their information.

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. 

Data Mining the Right Numbers

Advanced data is an increasingly overwhelming necessity in marketing. All you need to do is look at the burgeoning growth of competing software, platforms, dashboards, and measurement tools to grasp just how large of a market it's becoming


It’s a growing industry that's evolving because of its value to marketers and their clients. Every answer to their questions lies within the numbers. But it’s a matter of extracting the data that matters—the ones below the surface—that requires the help of data mining software.


Data mining, defined as “the practice of examining large databases in order to generate new information”, discovers “patterns in large data sets involving methods at the intersection of machine learning and statistics.”


Basically, it finds patterns and correlations in large datasets you would never be able to fully dissect because the datasets are that massive. Those insights it finds are what end up being used by companies, especially large ones that deal with upwards of millions of customers, in finding patterns.


For example, did you know Amazon is a long-time user of data mining? How else would they know about “Things you might be interested in” when you add a particular item to your cart?


It’s obviously worked. “Amazon has reported a 29% sales increase that has arguably to do with their recommendation systems.” Only their direct mailing efforts can boast a higher percentage increase.


The same rules apply to any store you’ve been to that constantly asks you to sign up for their loyalty program. Do you really think it’s all about signing you up so you can get better deals? Of course not. They want you in their system so they can more easily track your buying habits:


“Grocery stores are well-known users of data mining techniques. Many supermarkets offer free loyalty cards to customers that give them access to reduced prices not available to non-members. The cards make it easy for stores to track who is buying what, when they are buying it and at what price.”


These companies, and digital marketing agencies alike, are intent on making the buying process as cost-effective and fluid as possible. Convenience is the ultimate objective in the buying process, and if a company has to lose a few dollars through loyalty programs to profit more by better understanding buying habits they’ll take that hit.


Data mining comes into play here because of those different factors and variables it can distinguish:


"It ploughs through millions of combinations looking for groups of consumers who share attributes with one another. If it finds enough people whom meet a particular set of attributes, and if that set of attributes is relevant to the question at hand, we deem it a statistically relevant pattern. By studying those patterns, especially those in which a conversion occurs, data scientists can pinpoint key drivers of conversion.”


“Computer algorithms can slice and dice everything from a customer’s age and gender to credit scores and buying history. By carefully mining this information, analytics software can help identify patterns in customer behaviors that can increase sales and reduce customer turnover.”


But it's not only for large businesses. Even small-to-medium-sized businesses can find value in software doing the dissecting for them.


Grasshopper, a virtual phone system business, observed these factors to stoke a significant drop in their customer attrition:


“Since deploying its analytics engine in February, Grasshopper has reduced customer attrition by more than 25%....


For years, more than 10% of its customers canceled their accounts within the first 30 days. Analyzing things like location, usage rates, type of credit card and email domain has helped keep customers on board.”


Sway, a women’s fashion retailer, saw similar drastic changes:


“Only about 20% of our customers were opening the emails…That all changed when Sway turned to Retention Science’s predictive analytics software in March. Since then, Sway’s email marketing campaign has helped increase online revenue by 300%. Not only that, but now 40% of recipients are opening the emails and the number of click-throughs have tripled.”


The trends they found were able to identify high-risk customers who hadn’t engaged with the brand for an extended period of time:


“We created a ‘We Miss You’ campaign. It lured laggard customers with a 10% or 15% discount or free shipping based on previous buying behavior.


The result: A threefold increase in revenue from past promotional offers.”


Sometimes, data mining doesn’t even have to be limited digital marketing to optimize the physical customer journey:


“The use of Indoor Positioning Systems in large department stores is currently used to gain more insight into the journey of a customer in the department store. For example, IKEA could use this to check whether customers find their showrooms interested enough by measuring the duration of stay in front of a certain showroom.”


In order to understand buying or subscribing habits, businesses first have to understand their buyer. Digital marketing, and data mining as a result of the industry, are ushering in a way to obtain the information necessary to learn more about their customer, even before they buy anything.


It’s gained via data mining software, which then upload the data into data warehouses that are stored in in-house servers or a cloud system. Marketing professionals then determine how they want to organize that data, picking out factors and variables they think would be most important to achieving their goals.


The data is then sorted based on the user’s results, which is then presented through graphs and charts made by the marketer.


Still, while data mining makes it easy to find the answers, it’s still ultimately on the marketer to create an effective marketing strategy that takes these factors into account.

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.

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.

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How Hotjar Provides Landing Page Insights Metrics Can’t

Understanding audience tendencies is imperative to facilitating success, no matter the cause.


In digital marketing terms, it is especially important when creating a landing page or website that serves your audience. It must create enough convenience that the user easily finds everything they were looking for.


As important as metrics are, they can only tell us so much. Sure they can tell us how many times a button was pushed, but can it show us the user’s behavior beforehand? Can it show us where they had to scroll to find it? How far down they are scrolling to find what they need before giving up?


Hotjar, which considers itself “a new and easy way to truly understand your web and mobile site visitors”, specializes and excels in measuring this type of audience behavior.


Our team of analytical experts heavily utilize two key features to improve landing pages and websites:


  • Heatmaps
    • “Understand what users want, care about and interact with on your site by visually representing their clicks, taps and scrolling behavior




  • Recordings
    • Identify usability issues by watching recordings of real visitors on your site as the click, tap, move their cursor, type and navigate across pages




These features provide us with the advantage of seeing exactly how our audience behaves on a specific website we are monitoring.


When finding out just how much it helps, I asked Emira Oliveros, One Twelfth Performance Ninja (her words, not mine), to provide a specific example.


I got two.


She regaled me in a pair of success stories that centered on a recently constructed website for a new client; one about the placement of a ‘Learn More’ button, the other about the lack thereof.


The subscription service being offered on the website wasn’t receiving as many clicks as it should have. A lot of visitors were also leaving way too early, especially on mobile.


When she looked at the mobile website on Hotjar, she noticed the ‘Learn More’ button was well below the fold. With the main CTA being so low on the mobile site, users gave up and left. Considering the importance of mobile optimization, the inefficient design was practically turning away conversions.


It wasn’t until we recommended placing the button higher that subscriptions via mobile began to pour in. Users also stayed on the site longer.


The other case is something we’ve all dealt with at one point. You go to a website, see the product information on the top image, and assume this is where you click to access the products.


Instead, users were met with a dead end. The image wasn’t clickable, no matter how many times the user clicked. We were able to see this thanks to the Recordings feature, which allowed us to see sporadic clicking from numerous users on the website’s main image.


Our recommendation was to create a ‘Learn More’ button overlying the main image that directs users where exactly to click. Suddenly, the random clicking around stopped and memberships started to roll in.


The main learning was to include as much relevant info and a direct CTA above the fold that would require little scrolling.


While you who made the page may understand where everything is, you have to put yourself in the shoes of your audience and assume they know nothing. Do everything you can to help them out by placing all the key info and CTA’s they need to convert right in front of them.


People want everything provided to them instantaneously. Make it as convenient as possible to ensure as little critical thought is necessary and that they don’t need to go on a scavenger hunt.


Where Hotjar really came through was the fact that this was a new business that was only just starting to gain traction. Sample sizes were going to be too small to appreciate a collective outlook, so we had to take an individualized approach.


Our client being a new business actually helped our cause because it allowed us to set the foundation for our audience’s behavior. Since we were able to understand where people clicked and what drove them away, it set a precedent for the future of this website and any other pages that may follow.


Measurement tools like Hotjar stand out because they offer marketers a different take on indicating marketing success. It should be in the best interest of every marketing professional to utilize every resource necessary. Hotjar obviously won't help you figure out the best strategies for your website, but it can help you create a fully optimized, easily navigable website.


Have you ever used Hotjar? Leave a comment on our Facebook and let us know what you thought of it!

Part 3: Chatbot & Artificial Intelligence

As history has proven to us time and time again, humans have an insatiable appetite for making life easier for themselves, often to the point where we harness the intelligence and brawn of others for our benefit.


But even the Universe has its limitations. Not allowing that to stop our progress, however, we created a new one: A digital one, where the possibilities have no limit. You’ve seen it in factories, with humans being replaced by far more efficient machines. Now we're experiencing it in the era of making customer service more efficient.


Welcome to the Future, narrated and coordinated by Artificial Intelligence.


Since Deep Blue, a supercomputer with surely no intention of world domination, defeated Garry Kasparov, the world Chess champion at the time, humanity has marveled at A.I.'s extraordinary potential. As a result, humans couldn't help but develop and advance it more, pushing its capabilities further and further.



This trend of A.I. puffing its motherboard out at the intelligence of lowly humans was featured for all to see on Jeopardy!. The streak of human victories was snapped when ‘Watson’ entered the fray and destroyed the likes of noted Alex Trebek foil, Ken Jennings.



Deep Blue and Watson are examples of artificial intelligence's capabilities. They act as humans, but only at specific subjects; Deep Blue’s sole purpose knowing how to win at Chess, while Watson’s was knowledge aggregation.


The small-scale A.I. we experience daily has a sole purpose of aggregating customer info and assisting them with orders. Thankfully, they're not sentient so we can avoid embarrassing scenarios like these:




The previous objective of artificial intelligence improvement was to get it to the point where it would pass the Turing Test. It should respond and act like a human would. The result of these goals has led to breakthroughs in A.I. practicality, where it needs to be just human enough to carry on a simple conversation.


These useful breakthroughs come in the form of a ‘Chatbot’, an artificial intelligence program mainly used for commercial purposes. The logic is that online customers will have an improved shopping experience if they have a salesperson accompanying them, rather than if they explore the store on their own.


Facebook has already taken advantage, with over 11,000 companies using Chatbots on its Messenger service. Companies such as 1-800 Flowers, as well as the Weatherman app ‘Poncho’, are among them.



1-800 Flowers offers customers the opportunity to place an order. The Chatbot will interact with you, asking for your name, card message, recipient destination, and billing info, all under the illusion of speaking with an actual representative.


Ask ‘Poncho’ how the weather is and you’ll be greeted with a timely response of whether there’s sun or showers in the present or near future. You’ll never have to momentarily look out a window again! Welcome to the world of modern day convenience.


As if Chatbot apps couldn’t be more helpful, there is one currently in development that’s designed to help make you richer. BOND acts as your personal financial advisor, instructing you on better money management and budgeting. Advice on refinancing your loan, stock tips and finding a credit card with less interest are dispersed at will, all without the meddling and fallibility of a human being.


But not everyone agrees that Chatbots are as useful and reliable as they may seem. They can be taken advantage of. IKEA had to abandon its website Chatbot after 10 years, simply because they made it too human. Customers were excited, but for all the wrong reasons.

do-you-enjoy-reading-the-bible-2As the Chatbot’s responses became more human, the more vulgar the questions became. Around 50% of the questions asked were sex related, because we're humans and we can't have anything nice.


Chatbots, for better or worse, are the way of the future, and we’ve already experienced an early offshoot of it for years on the phone. Simply yelling ‘REPRESENTATIVE’ at your computer may not work (Perhaps that’s in Beta stage), but benefits, such as accessibility and convenience, still outweigh the drawbacks.


Plus, as noted at the beginning, technology has a way of evolving to further convenience and lessen frustration. There is a beneficial purpose behind this innovation, and with roots firmly dug into the dirt, we should expect this massive industry to only grow.


Here at One Twelfth, we enjoy looking into the future and being on top of new technologies and mastering them. We do this because of the intrigue, but, more importantly, because we want to provide the best possible service to our clients. You can reach us at