How Starbucks Employed AI for a Better Customer Experience

In 2017, Starbucks launched what would become its most advanced AI-driven initiative yet. “Deep Brew,” the brand’s custom-made recommendation platform was built to reach customers across multiple channels, including the Starbucks ordering app. 

Today, Deep Brew is driving growth, providing deeper customer understanding, and allowing Starbucks to seamlessly adapt to changing customer preferences with little effort. With this foundation in place, the company is already planning several other AI-driven projects to enhance the customer experience.

Unprecedented Growth


Part of the reason Starbucks executives have pushed for more AI technology is the brand’s recent growth spurt. The industry-leading Starbucks Rewards Program has continued to flourish since its introduction in 2007.According to their website, “Membership has grown more than 25% over the past two years alone, climbing to 16 million active members as of December 2018, a 14% increase over the prior year. Starbucks Rewards transactions accounted for 40% of tender in U.S. company-operated stores in the same time frame.”

Central to the rewards program is a focus on nurturing customer loyalty. A new tiered structure was created, along with new rewards options. Customers can earn points to received customized drinks, food options, or Starbucks merchandise – sometimes as soon as 2-3 visits after becoming a rewards member.

And while stocks have slipped in recent months, there’s an overall uptick in value since the beginning of the year.

“Deep Brew will increasingly power our personalization engine, optimize store labor allocations, and drive inventory management in our stores,” CEO Kevin Johnson told reporters. “We plan to leverage Deep Brew in ways that free up our partners, so that they can spend more time connecting with customers.”

How It Works

So what exactly is Deep Brew and why is it so valuable? True to its name, Deep Brew utilizes deep learning technology to gather information from unstructured data. To understand the innerworkings of Starbucks’ new AI deployment, we need a lesson in reinforced learning capabilities.

Reinforced learning is a type of machine learning that allows organizations to determine the best possible course of action in a specific situation. Simply put, it’s a type of AI that answers the question: What option should be chosen based on what is currently known? The algorithm learns through trial and error, using each piece of feedback as evidence supporting its next decision. Reinforced learning works sequentially and continuously collects customer data to create a unique profile around their tastes.


Using Microsoft’s Azure cloud infrastructure, factors like location, weather, inventory, and price are taken into account for each person’s order. If a customer frequently buys non-dairy drinks, it will automatically be remembered. This technology is how Starbucks is able to recognize and adapt to the desires of its millions of customers each week. Starbucks Analytics and Market Research VP, Jon Francis sees these tailored recommendations as an extension of the in-person care a customer would receive from a barista. It further strengthens that connection and sense of trust with each buyer.

Along with personalized recommendations, Starbucks has another AI trick up its sleeve. Their Mastrena machines, originally debuted in 2008 to release shots of coffee faster, contribute to the customer experience in a more covert way:

“Those machines have Internet of Things sensors built into them. And so we get telemetry data that comes into our support center. We can see every shot of espresso that's being pulled and we can see centrally if there is a machine that's out there that needs tuning or maintenance. And that allows us to improve the quality of the shots that we're pulling…with the Deep Brew and our predictive analytics, we're going to be able to determine if a machine needs preventative maintenance on it before it breaks.” – CEO, Johnson

All store equipment is synced up to Azure Sphere, and over 5mb of data can be collected in a single 8-hour shift. Rather than shops being slowed down by sudden equipment issues, they’ll be prepared and aware of any potential problems.

Starbucks VP and CTO, Gerri Martin-Flickinger says their next project will be leveraging data for a better drive-thru experience. Because personalization is more difficult in a drive-thru line than in a mobile app, store sales history and other criteria will be used. Drivers will be greeted by a customized drive-thru screen that displays what is most likely to interest them.

The Future of Coffee

In an industry where convenience, speed, and customization are so pivotal to the customer experience, there’s plenty of room for optimization.

Starbucks self-serve kiosks are popping up around the world, providing more evidence that the brand intends to fully embrace automation and AI as a key customer service feature.

Aside from Starbucks, a company called Cafe X is gaining fame for its ‘robot barista,’ which can make 120 cups of coffee per hour. Such inventions have many people asking, “Can a robot make a better coffee than a human barista?” And if so, will it eventually mean a radical shift in how coffee shops operate? Café X’s machine is said to eliminate margin of error and is expected to become more popular in upscale commercial buildings in the next decade.

It’s safe to say the coffee industry is in the early stages of an innovation boom as AI becomes more accessible and affordable. We’re just beginning to see how these developments will reshape the coffee-drinking experience in years to come.

Auditing Your Customer Experience for Better Personalization

Most organizations are now familiar with data in some capacity, but extracting customer insights can still be a challenge. Even those who have mastered data analysis can struggle to meaningfully act on the insights they uncover.

From Mass Marketing to Niche Marketing

The modern organization’s struggle with personalization is understandable - The customer experience wasn’t always a hot topic in business and marketing. Before data, everything was marketed in the same way to all people. Often, this simple strategy of tossing out persuasive ads and hoping they were relevant to someone, somewhere, actually worked. The customer experience was more uniform and predictable, and for the most part, marketers didn’t need to care.

In those days of mass marketing, your niche could be broad. A vacuum salesman’s niche was vacuums, plain and simple. Today, it’s a different story. Consumers have access to millions of businesses on the internet – not just the businesses they live near. A quick Google search, YouTube video, or email campaign can bring dozens of new businesses to the forefront of a customer’s mind. How do they choose?

The first key component of personalizing your customer experience is to carve out a more distinct niche so you can reach the right audience. Everyone has preferences, but you’re only concerned with those of your unique customers.

Customer Expectations are Shifting

One of the biggest reasons organizations must personalize the customer experience is consumer habits and expectations. Advancements in technology have drastically changed the way most people experience ads, research products, shop, and make purchase decisions. Social media, mobile devices, email marketing, ecommerce, and other sweeping trends have contributed to a whole new way of experiencing the world – and that absolutely includes the customer experience.

From start to finish, the typical journey of the consumer has been revolutionized. Most importantly, they have more options and more opportunities to get distracted or confused when interacting with any of your touchpoints. For this reason, personalization is a tall order, but one that you can’t afford to ignore.

Understanding Before Personalizing

As touchpoints multiply, it becomes increasingly difficult to understand each facet of your customer experience. That means we must rely on data to reveal the problems our customers are having, what frustrates them, what makes them happy, etc. These insights are nuanced, but with the right data, general trends begin to emerge. These trends will then illuminate next steps for organizations that are hoping to make themselves more efficient.

Whether it’s the checkout process, email newsletters, or landing pages – personalization must be front and center of the design process. When decisions are made from customer behavior insights, the results of your marketing efforts become more predictable.

Conducting a Personalization Audit

Auditing your customer experience requires attention to detail and finding out exactly what your customers want. To do this continuously, you need relevant data.

“By analyzing data like their ‘add to cart’ rate, page views, product views, or the referral channel bringing them to the website, brands get a much richer profile of their customers’ behavior before creating an experience for them.” source

  1. Track the Customer Experience and Identify Touchpoints

No organization can get far without in-depth knowledge of the customer journey, both online and in person. Look at the path customers travel as they get closer to you. This may start with social media or an ad they click. It may end with them becoming a monthly subscriber to your service, or purchasing a product and then unsubscribing. The customer journey that doesn’t end how you’d like is even more important to analyze. 

"Your most unhappy customers are your greatest source of learning." - Bill Gates

  1. Determine the Significance of Each Touchpoint

Data analytics now allows organizations to weigh the impact of each touchpoint. This helps you know what areas of your website, for example, to place the most focus and effort. It also allows you to see weak touchpoints that customers aren’t meaningfully interacting with so you can improve or remove them.

  1. Identify Silos and Work Toward Integration

“If you have signs that, for instance, your ecommerce programs aren’t syncing with your storefront programs through viable integrations, you know you have a problem. Customers, naturally, expect seamless integrations between digital and physical worlds, and it doesn’t always happen.” - Timothy Burke

  1. Look at Existing Feedback and Resources

Sometimes making improvements doesn’t require a ton of analysis. Consider what questions you often receive from customers. Ask experienced team members what problems or complaints they hear from customers the most. Oftentimes a simple conversation can reveal the low hanging fruit – problems you can resolve with a few quick changes.

Personalization Equals Retention

“Customers always have an experience - good, bad, or indifferent.” - Sachin Datta

When customers feel understood, they have a better experience and they’re much more likely to stick around. This is when breakthroughs happen and retention becomes less of a struggle.

Personalizing the customer experience can be simple – learn about your customer preferences and meet those expectations, over and over. When you move from guesses and assumptions to factual observations, things like poor design choices and customer confusion are less likely to occur.

2020 Predictions Based on Current Quantifiable Trends

“Data and analytics leaders should actively monitor, experiment with, or deploy emerging technologies. Don’t just react to trends as they mature. Engage with other leaders about business priorities and where data and analytics can build competitive advantage.”
- Rita Sallam, Vice President Analyst, Gartner

It’s no secret that technologies like machine learning, AI, and predictive analytics have revolutionized how organizations are developing. But within these broad categories, several more specific data trends are particularly relevant. Organizations that pay attention to these now will thank themselves later when they’re able to keep pace with the next set of emerging trends in years to come. Here are the most disruptive data analytics trends of 2020 that will continue to mature over the next five years.

Augmented Analytics


Augmented analytics (AA) allows for optimized decision-making beyond what many professionals are accustomed to. AA uses machine learning, AI algorithms, and a process called natural-language generation, which transforms structured data into our normal, natural language. Rather than pulling up the most relevant insights manually, organizations can automate this process and save loads of time. These insights can be made available to all important players, and the process saves them from having to be (or rely on) data scientists and analysts. AA has a lot of potential to be disruptive because it addresses the collective Achilles heel – that there are not enough data scientists and expertise to manage all the data that organizations are accumulating.

Data Fabric

Data fabric is a platform that allows for the convergence and management of data from different sources. Through this framework, data can be transported, combined, designed, managed, and protected across channels. For organizations that don’t want to convert or migrate data, data fabric offers a solution that doesn’t waste as much time. Closely tied to managing augmented data, data fabric allows organizations to support data at scale from diverse silos like cloud, SQL, and more. In the past, organizations aimed to have all their data stored in one warehouse. Today, we’re moving beyond this to a more comprehensive goal.

Explainable AI

Explainable AI will be essential for organizations to successfully manage the rise of AI in business. With such complicated models being employed more every day, it’s critical for organizations to learn how to understand and explain their results for internal monitoring. Detecting for bias and privacy issues, and ensuring regulations are observed, is of growing importance and difficulty.


Explainable AI is critical because it addresses the growing “black box” problem in which data experts do not know exactly how and why an AI tool came up with the answer it did. Many AI algorithms can’t be examined after they generate insights, leaving organizations to wonder about the accuracy of the information. With explainable AI, the models and steps involved can be more carefully scrutinized. This way, organizations can repeat these processes and have them be transparent rather than hidden.

Continuous Intelligence

Continuous Intelligence is as useful as it sounds, providing the ongoing capacity to make real-time business decisions based on analytics. Organizations with situation-specific data can make informed decisions or receive predictions on what to do next. Central to this process is a focus on outcomes and automation. This may sound like what many organizations are already doing, but it goes a step beyond that. You can analyze data as it is created, visualize aspects of your organization, make predictions faster, learn from unstructured data, and automate actions immediately. In today’s world of developing technology and ongoing change, continuous intelligence is a no brainer for the future of business.

Mobile Intelligence

Perhaps the least surprising trend, the mobile intelligence (MI) framework will be built upon to achieve better results. Mobile app development will be geared toward a better work experience, stronger internal communications, and stronger B2C communications. With consumers using mobile devices for more and more tasks each year, it’s clear that mobile is being embraced on all levels. This means organizations need to have MI at the front and center of their strategy. Much like continuous intelligence, MI allows for real-time insights that help organizations act more quickly.

Data Diversity


In 2020, we’ll see an even stronger focus on bridging the gap between diverse data sets as organizations grapple with what tools to use and how to combine data in the most efficient way. When data sets become too large, sometimes the simplest solution is to break it up into smaller, more meaningful sets. The key to truly diverse data is to look beyond what’s obvious, not just focusing on the data that would be easiest to collect.

“Non-representative data sets are less likely to yield workable insights than those which cover all facets of the issue under investigation. It’s also true in terms of the variety of data available. With the sheer divergence of datasets available, it’s more critical than ever, as insight can often be found in unexpected places. Thanks to breakthroughs in technology such as image analysis and natural language processing, meaning can be extracted, in an automated way, from video, handwriting, recorded speech and the text of emails and social media posts.” - source

Organizations that dig in and engage with these trends now will be much better prepared for data advancements in the years to come. Meanwhile, those that don’t may have a lot of extra headaches later on.

Being data-driven is no longer a luxury reserved for the biggest and wealthiest organizations. It’s an accessible reality for all. Moving forward, focus will shift toward refining data management processes and accessing deeper layers of expertise for sharper results – better customer service, higher marketing ROI, and more confident decision-making. That’s how data-driven organizations will move onward and upward in 2020.

Data Orchestration is the New Data Automation

The advent of the big data age had companies scrambling to not only collect more data but automate processes to help manage the workload. Businesses were learning in real-time – through trial and error – how to collect, store, clean, and analyze heaps of information.

More than a decade later, tech advancements haven’t slowed down. There are more tools to use, more skills to learn, and, according to scientists, about 295 billion gigabytes of data in the world. How do businesses extract precise insights that can guide their marketing strategy and boost ROI? In other words, how do we put all this data to good use – and prove it?

Needless to say, this process was challenging enough, and many marketers, organizations, and data scientists are still wondering whether their efforts to utilize data are making a real impact.


But as the dust settles, many businesses have landed on their feet, wiser and better equipped to thrive. Now that we’ve gotten a handle on data automation 101, we’re beginning to see an even greater level of advancement on the horizon – and that’s data orchestration.

What Is Data Orchestration?

In a nutshell, orchestrating data works just like orchestrating a symphony – all instruments must be in tune, in rhythm, and working in unison with all other parts. When this falls into place, being a data-driven business is much easier. But when data processes are not in harmony, it can lead to all sorts of kinks throughout your data pipeline.

From preparing data to analyzing, drawing conclusions, and taking action, your data may travel through various applications and departments. So what happens when the right synthesis doesn’t take place?

Disorganized Data (The Problem)

One of the biggest issues that can arise from a lack of data orchestration is unusable data. Whether it’s poor quality, inaccurate, or not in the correct format to use, this is the dreaded ‘dirty data’ problem that thwarts many companies. The impacts of disorganized data are surprisingly weighty. According to an Experian report, companies from around the world feel that an average of 26% of their data is dirty.


Another problem arises when data history can’t be tracked.

The provenance of data products generated by complex transformations, such as data orchestration workflows, can be extremely valuable to digital businesses. From it, one can determine the quality of the data based on its source, provide attribution of data sources, and track back sources of errors and iterations. Data provenance is also essential to organizations that need to drill down to the source of data in a data warehouse, track the creation of intellectual property and provide an audit trail for regulatory purposes.” - Chris Scalgione

Part of eliminating data silos is eliminating disparate tools that are difficult to use in tandem. Ideally, teams will have access to the same data and know how to use the same platforms to manage it holistically.

Cohesive Data Management (The Solution)

So what can a business do if it’s wading through the swamps of a data disaster? Data orchestration means carefully mastering each interaction with data from start to finish.

Let’s start at the beginning with data collection. Customers are interacting with your brand at many touchpoints – advertisements, websites, social channels, and, perhaps, in person. Each of these data sources can provide a wealth of insight – if you’re equipped to collect it in real-time.


Next, data integration. Coming from various sources and in various formats, your data needs to be merged in one place. Once data is profiled, you can decide how it needs to change to create one uniform data set. This step is integral for accurate interpretation later on.

From here, data-enrichment processes can increase the quality of data, readying it for analysis and, eventually, decision-making. Enrichment allows for a better understanding of customers and what they respond to over time.

The tools and platforms you choose play a pivotal role in ensuring each of these processes is carried out efficiently. Currently, many data platforms have one or two strengths they specialize in, but can’t orchestrate the whole process. For this reason, many organizations must manually examine their data pipeline to find weak spots and refine the procedures.

The Future Is Orchestrated

In the end, automation is only as valuable as the tools you use to carry it out. High data quality and careful coordination must come first. The places we mine data and the uses we find for it are multiplying, along with data privacy regulations. Thus, it’s more important than ever to have a sophisticated data management system that leaves nothing to chance.

In the next ten years, more businesses will begin their trek toward a data-driven future. It’s through this transformation that companies leading the pack will be able to scale comfortably and make truly data-informed decisions.

It's All About Location: The Untapped Potential of Location Data

It’s clear that location data has immense value – especially for brick and mortar businesses that want to build a reputation in their area. But all too often, better results are left on the table when teams are unaware of all the ways location data can be used.

Location-based marketing is just what it sounds like – targeting your audience based on where they live, where they’ve recently visited, or where they’re currently at. Thanks to the widespread adoption of smartphones, it’s now possible to understand how most consumers move and interact in the real world – not just online.


Aside from brick and mortar stores, brands that are participating in limited-time events or hosting pop-up shops can benefit from targeting local consumers who will be in the area or attending an event. Location data can encourage people to visit a specific location and track those visits as they occur.

Reach the Right People with Geofencing & Beacons

Geofencing technology uses cell towers to allow businesses to draw boundaries around the areas that interest them. These targeted area can be as small as a one-block radius or as large as an entire city. If a business owner needs more information about customers in a given location, geofencing is a direct way to obtain nuanced data insights. You can set geofences to determine which days or times are most popular for visiting a certain neighborhood. Geofences work best for larger areas, while beacons are more appropriate for reaching customers in the immediate vicinity.

Beacons allow you to target consumers who are just steps away from your location with special offers and deals. Beacons use Bluetooth technology to automatically recognize devices – typically smartphones – in the area. This allows for the right people to see your offer at the most convenient time – when they’re close by. Unlike geofences, beacons are more flexible in that they can be moved as needed. Businesses with multiple locations can make use of this data, as well as businesses that are considering opening additional locations.

Crack the Offline Attribution Code

Location data helps solve one of the most challenging problems marketers face: tracking offline customer behaviors. It’s a gray area many marketers struggle with – how to identify how a customer moves from Point A (learning about an offer), to Point B (making an in-person purchase). But interestingly, the majority of businesses have not yet implemented location data to fix this blindspot.


Businesses need to understand the full customer journey, and technology makes it easier to do so when everything is online. For example, a customer can click an email marketing link and makes an online purchase – no mystery there. But what about a customer who is targeted with a mobile ad and then visits a store?

Whether you’re running a social media campaign, print ad, mobile app offer, or anything else, you need to know its true ROI. Location data helps illuminate which offers customers are taking action on. This empowers businesses to see which marketing channels are most effective and which ones need work.

Track Visit Frequency to Create Customized Offers

Because most consumers don’t make a purchase the first time they interact with your brand, having several touchpoints is key to building brand loyalty.

What can businesses do with visit frequency data (how often customers visit stores)?

First, they can intervene if their own customers are switching to a competitor’s products or services. In the same vein, they can reach out to a competitor’s audience who is dissatisfied and shopping around for a new brand. Brands can also target more frequent shoppers for special holiday offers and sales.

Get Creative with Consumer Insights

Location data isn’t just about drawing customers to your location – It’s about getting creative and finding ways to meet your customers where they are. Dominos launched their outdoor hotspots service in 2018, showing how location intelligence can be used to give customers a convenient new way to interact with brands. Dominos customers began ordering pizza from beaches, parks, and other hotspots.

Aside from reaching customers where they are, brands can learn about their needs and interests based on where they go and why. Discover what types of stores certain demographics prefer, or how far they’ll travel to a competitor’s store. These detailed insights can help fill in the gaps and complete the story of your target audience.

With the vast majority now owning smartphones, location data is quickly becoming ubiquitous. Whether it’s GPS, weather reports, local news, or app-based delivery services, most mobile users are already benefitting from location data.

Because data is now being shared so rapidly and at such scale, it’s important for companies to make sure insights are being accessed ethically. This means taking proper security precautions and ensuring user data remains anonymous. This will undoubtedly be an ongoing conversation as businesses and consumers navigate new territory in information sharing. If companies harness data responsibly and with consumer privacy in mind, it will be a win-win for all.

The Evolution of Black Friday: From In-Store to Online

Black Friday falls on November 29th this year. With the renowned shopping holiday just four days away, we’re looking back on Black Fridays past — drawing predictions and analyzing the bigger story behind this annual gold mine for retailers.

Black Friday in Review

The term “Black Friday” first appeared in 1869, the day of an epic stock market crash in the US. Clearly, the term has done a 180, now signifying the opposite of economic collapse as consumers gear up for some of their biggest purchases of the year.

The day following Thanksgiving has been associated with shopping since the 1930s, when advertisements in the Macy’s Thanksgiving Day Parade enticed growing crowds of onlookers on the streets of New York City. Fast forward to today, and Black Friday is now an internationally observed day of spending.

Black Friday 2018: From In-Store to Online

Black Friday has gotten a makeover in the information age as retailers notice shifting consumer trends and try to keep up. Unsurprisingly, the most notable of these trends is the popularity of online shopping and how fast it’s growing.

Research shows that in-store Black Friday traffic has been declining since 2016. But thanks to ecommerce, that doesn’t mean dwindling profits for retailers.

Black Friday 2018 raked in 6.2 billion in online sales, a growth of 23.6 percent year over year.” And the kicker: Cyber Monday yielded $7 billion worth of merchandise soldmaking it the largest online shopping day of all time in the U.S.”

One source showed that smartphone sales reached an all-time high of $2 billion, and more shoppers chose to buy online and pick up in-store than in previous years. source

Of course, many shoppers want to avoid the chaos, crowds, and long lines to shop deals from the comfort of their home – but how exactly are they shopping online?

On Cyber Monday 2018, direct website traffic ranked highest for driving revenue at 25.3 percent share of sales, followed by paid search at 25.1 percent, natural search at 18.8 percent, and email at 24.2 percent. Similar to past years, social media continued to have minimal impact on online sales at a 1.1 percent share.

Large retailers, on average, had more success with smartphone sales, while small retailers offering more specific items did better with desktop sales.

All of this data brings us to the fundamental question: Is Cyber Monday slowly phasing out Black Friday? Aside from Cyber Monday, we’re seeing other trends that draw focus away from shopping on Black Friday itself.

Black Friday 2019: What’s in Store

In the past, not waking up early to brave the crowds meant missing out on deals. But ecommerce has changed the game, providing easier ways to buy.

Some suspect that 2019 may be the first year Cyber Monday deals overshadow Black Friday deals.

The likelihood of trend continuing is high, considering how web shoppers aren’t really missing out on anything. Historically, it’s been shown that most supposed in-store only deals are actually online too. source

Along with Cyber Monday, Small Business Saturday has also gained significant traction in recent years to encourage consumers to not forget about small businesses. In addition, big brands like Best Buy and Walmart have already started announcing deals the week before Black Friday. The Kohl's online Black Friday sale is already underway as of today (November 25th).

With all of this hype leading up to Black Friday and after it, the holiday is quickly extending into a full week.

“As stores moved their Black Friday Sales on Thanksgiving Day, they faced a backlash. To avoid the backlash, more and more stores are moving their sale online on Thanksgiving Day by still keeping their physical stores closed. As a result, Thanksgiving Day is emerging as one of the main days for online shopping. The Wednesday or more specifically Thanksgiving Eve has also emerged as another time when several stores start their Black Friday Sale.” source

This brings us to another fundamental question: will Black Friday eventually expand into a month-long series of deals and discounts?

Both consumers and businesses are reverberating this pattern as more shoppers report starting their holiday shopping in October. It’s also not uncommon for retailers to start advertising their Black Friday week deals in October.

Data Drives Retail Decisions

Data allows businesses of any size to take maximum advantage and forecast the best Black Friday results. There are several ways retailers are already doing this.

For big brands, using the prior year’s Black Friday data to prepare and make predictions for this year is standard practice. With more in-depth analysis, businesses can also identify the most in-demand products so they know what deals to offer. Tracking revenue also helps businesses aim higher each year and know what decisions helped them achieve that lift.

Machine learning helps brands predict how much a customer will spend using deep neural networks. These networks take unstructured data sets and comb through layers of information. Just as Netflix offers recommendations based on a customer’s unique views, businesses can offer specific products to customers who are most likely to want them. By experimenting with different models, data scientists can extract precise information that gives brands the best chance of success – and on a holiday like Black Friday, that can have a major impact on revenue.

Black Friday Prep for Small Businesses

If you’re offering Black Friday deals, there’s lots to do in preparation for the big day. Checking inventory, making sure your site can handle the extra traffic, selecting items to discount, beginning email marketing campaigns, optimizing for mobile buyers, and, of course, tracking your performance.

Studies show that retargeted ads are hugely impactful for Black Friday sales. “Apps running retargeting enjoy a significant revenue uplift on Black Friday compared to those that don’t. The gap was most pronounced in the US, with a staggering 14 times difference.” source

Whether you’re a Black Friday fan or not, it’s useful to observe the digital trends that have transformed the holiday over the years. It’s these same trends that are influencing small business sales both on and offline.

To Infinity On Demand! Data Is Driving The Way We Consume Content

Back in August of 2017, Disney announced that it would be pulling all of its content from online streaming services like Netflix to focus on its own streaming platform. The wait is over; the on demand entertainment platform Disney+ launched just last week. It’s an exclusive streaming service with hundreds of films and thousands of episodes worth of Disney content – all ad-free for $6.99 per month. Subscribers can watch classics, new releases, Disney+ originals, and content from Pixar, Marvel, Star Wars, and National Geographic.

Founded in 1923, Disney is a rare example of brand sustainability, still blazing a trail nearly ten decades since it was established. From the conception of Mickey Mouse in 1928, to the creation of Disneyland in 1955 and Disney World in 1965, the company has shown it can keep pace and entertain its audience year after year.


Platforms like Netflix and Hulu were some of the first big disruptors of the streaming market. What does the future of entertainment look like with a data-driven, on demand video streaming market?

Content Consumption is Changing

In the past, far fewer options existed for how we consumed media. People watched sporting events, shows, and movies on cable from their home television when they were on, and if they missed out, that was that. They couldn’t watch later at their convenience on their phones or tablets.

As we transition into the age of on demand, multi-device streaming services, the way we consume content is also changing. Where, when, and how consumers view content is dictated by their own in-the-moment preferences rather than whatever happens to be on.

What does this mean for modern brands that want to evolve and align with these changes?

Becoming Compatible with Change

Not every business is in the on demand entertainment industry, yet content consumption is at the forefront of every brand’s marketing strategy. We all know there’s a graveyard of brands that have failed to adapt to these advances in technology over the past few decades.

Disney+ is another great example of how Disney is staying compatible with change. It’s compatible with most popular streaming devices like Amazon Fire TV, Android TV boxes, Apple TV, iPhones, and PlayStation 4.

By not keeping pace with broad trends and changes, brands become outliers – using outdated and obsolete channels that consumers have largely abandoned. Ultimately, it means that consumers will skip over your content as they become more accustomed to newer, faster, and simpler ways of consuming their favorite media.

Where You Communicate


From old Mickey cartoons on film reels to VHS classics, DVDs, and now on demand streaming, Disney is no stranger to the art of adaptation. As content mediums come and go, the company has remained relevant and present on each important medium as it rose to popularity.

It’s no secret that the mediums brands use to publish content are largely dictated by technological advancements – and those happen fast. What was big 15 years ago might now be considered vintage. 

Aside from TV and movie streaming services, we see the consumption of music and podcasts radically changing too. Platforms like Spotify, Pandora, and Apple Music provide millions of songs and episodes at your fingertips.

This also means new challenges for advertisers as ad-free internet streaming platforms like YouTube Premium are established.

How You Communicate

It’s easy for smaller businesses to assume that data analytics is only useful for mega-brands like Disney. But analyzing content consumption trends doesn’t have to be done on a massive scale, or with a million-dollar budget.

“While big data provides top-level trends, small data helps companies to connect with consumers on a more intimate level, including marketing to them on a more localized and personal level.” - Sergey Bludov

Businesses creating all types of content now use data to analyze key performance indicators to decide not only where to reach people, but what people want to hear about. These insights allow brands to map out how they present their brand story and what the content production process entails for them.

Better, Faster, Stronger

With the promise of 5G cellular networks in the near future, it will only become easier to stream content anywhere, anytime. Consumers will always gravitate to what’s simpler, faster, more affordable, and more convenient.

The where, when, and how of content consumption has radically shifted, but the why remains the same. Your customers are still looking for that personal touch - entertainment, education, or whatever it is you uniquely offer.

The question is: How will you communicate your brand’s message now?

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.

AI Has Arrived: What Can It Do For You?

“Artificial Intelligence (AI) is no longer the next big thing. It is now a big thing in digital marketing.” - Richa Pathak

From young startups to large corporations, companies are reaching for AI-powered marketing tools that make life easier. From better marketing campaigns to smarter advertising, predictive analytics and AI-based automation are already transforming how marketers work.

Many companies have barely had time to recover from the rise of data analytics that turned day-to-day operations on its head. But there’s no time to waste as AI emerges to the forefront with an even more impressive set of capabilities for small business. 

A Quiet Arrival

One of the most interesting things about AI is how it can be so seamlessly integrated into our lives – often without us even knowing it. According to a 2016 survey by HubSpot, many people didn’t know they were using AI when they actually were. Another 2019 study showed similar findings – that the majority of those surveyed were already using AI-powered tools or devices and didn’t realize it. 

Illuminating Customer Needs

The focal point of every successful marketing strategy is meeting a need. To do this, digital marketers must understand customer motivation – what matters to them? Knowing your customer’s needs is about as easy as mind-reading. Often, the educated guesses and assumptions businesses make turn out to be surprisingly false. 

Social listening is one strategy that has become more popular as businesses recognize its importance. AI tools are helping businesses keep their finger on the pulse of what customers are saying about their brand on social media. In addition, some AI tools are now able to use past industry trends to predict what customers might want in the near future. What’s better than a brand that knows what its customers want before they even express interest?

Confidence in Strategy

Perhaps the most coveted thing AI offers business owners is peace of mind - knowing that their marketing strategy wasn’t chosen haphazardly. Instead, strategic decisions are based purely on statistical findings about customer behavior - what worked and what didn’t. And as these findings inevitably shift, businesses will be aware and able to shift their strategy along with them. 

The value of analytics is that businesses can start to recognize surface-level patterns that lead to even deeper knowledge about how, when, and where to interact with customers. One of the limitations of things like customer surveys is that often, people aren’t sure why the prefer one thing or another. AI takes human error out of the equation and shows what customers reliably do and how companies can meet them where they are. Whether it's making changes to the website, choosing new content marketing topics, or redesigning a social media campaign, AI provides key insights for improvement.

A Restructured Workspace

Marketing and analytics departments are currently taxed with the burden of managing data – reviewing reports, crunching numbers, and figuring out what it all means. Imagine if technology handled more of these complex tasks, freeing up entire marketing teams to get creative and dream up new ideas. This is just what Chief Data Officer Dale Lovell is proposing:

There’s just too much data in many ways for the human mind to process. So you could either hire a thousand people in your team — which is not scalable — or you could use an AI tool to help create insights that inform your marketing and effectively lets marketers do what they do best which is be creative.

He also eases workers’ fears about being replaced by AI technology, saying that jobs will instead change and become more specific. Instead of a marketing professional having a broad range of overwhelming tasks, they will have a specialty area to focus in. 

Ecommerce Chatbots

Another facet of the AI revolution is its implications for online stores. Customers are apparently far more open to chatbots than many initially expected. 47% of HubSpot survey respondents said they would be comfortable getting assistance from a chatbot that gave personal product recommendations. A Ubisend report also found that 40% of respondents wanted to receive special offers and sale notifications from chatbots.

While many businesses have already employed basic customer service chatbots on their websites, AI enhancements would take it a step further to provide each shopper with a more customized experience. Brands like Sephora, eBay, and H&M have already popularized this technology, using simple questions to guide customers to the precise products they’re seeking. Companies can rest assured that customers may ask questions and have problems solved by AI 24 hours a day rather than waiting to reach a real person.

Already a billion-dollar industry, the predicted growth rates for AI in business are striking. As far as how many companies are using AI now, estimates vary widely from source to source. But one thing is for sure: Adoption rates are skyrocketing. A Gartner survey of more than 3,000 CIOs from around the world revealed a 270% jump in AI adoption from 2015 to 2019.

And of course, it goes without saying that businesses must learn how to use AI responsibly, thoroughly testing any tools they use from a UX lens. Brands can shape this new technology in ways that feel helpful rather than intrusive for consumers who are on the fence about AI.

For business owners looking to get started, Entrepreneur recommends looking at industry-specific use cases to see how different platforms work. Talk to companies who’ve already integrated AI, try out demos, and explore the options. There is no one right way to implement such a multi-faceted tool, so it’s crucial for business leaders to get clear on their goals. 

NBA Season Kicks Off With New Data Insights

The 2019-2020 NBA season kicked off on October 22, and all eyes are on big performers like Anthony Davis, LeBron James, Kawhi Leonard, and Paul George. With a new distribution of superstar players on teams like the Lakers and the Clippers, it’s likely to be one of the most competitive seasons we’ve seen in years. Not only are there changes in player-to-player dynamics, but in fan-to-team dynamics.

In the early days of the NBA, data analytics was still in its infancy. Today, there’s not a single NBA team that doesn’t use this technology in some way, with most teams having an analytics department. Whether it’s galvanizing fans to attend a pivotal game, buy merchandise, or check out the latest game recap, data analytics informs NBA teams in how to best engage their fanbase. How did the NBA transition from rarely using data, to it being an integral piece in the marketing process? 

Personalized Email Marketing

Remarkably, statistics show that less than 1% of NBA fans have attended a game in person. This problem isn’t totally unique to the NBA, as estimates have shown dwindling attendance at MLB games in recent years. Meanwhile, the NFL has been slowly working its way back up to its 2007 attendance rates. Outside the U.S, a similar downward trend in attendance was seen for other sports like rugby. 
Has a sports engagement problem prevented fans from filling seats in their local arenas? The answer seems to be a clear yes. But as NBA teams share the results of implementing data marketing, it seems this problem can easily be rectified with the right insights.

Jared Geurts, Senior Director of Marketing Analytics for the Utah Jazz spoke openly about how the franchise made some simple changes that led to a 61% increase in digital revenue and a 42% decrease in digital spending.

Ticket sales exploded from $25,000 to nearly $1 million – strictly revenue earned from email campaigns. In the years prior, the team would send out general emails to all fans in order to sell tickets and bolster engagement. The only change they made? A bit of data-driven personalization. They segmented fans into different interest groups, such as those who prefer weekend games, and tailored email campaigns accordingly. This created a more authentic connection with fans as their specific interests were heard.

“We’ve actually, by quite a large margin, been the best team at converting new leads. That wasn’t the case before we really started focusing on personalizing those ads,” Geurts explained. “Just from some simple personalization and caring more about our customers than what we want to sell.”

Personalized Experiences at the Game

Another exciting development in NBA trends is how analytics is generating insights about fans while they’re at a game. By knowing more about who is showing up for games, teams are beginning to create interactive experiences that appeal to the demographics in the building. As Deloitte notes, something like requesting a song or earning rewards after a certain number of trips to a concession stand helps fans feel like they’re not only at a game, but part of the collective experience. Much like email campaigns, stadium screens are becoming more personalized as well. Fans sitting in certain sections may see different promotions based on who they are/what group they are in attendance with. 

Drawing Outsiders Back In

Data insights from sports apps may even help teams reach those elusive fans who’ve yet to attend a game or haven’t attended in many months. With the right targeting, it can be just a reminder these fans need to remember they’ve been wanting to attend a live game. By forging partnerships with apps and offering special deals to fans on those apps, teams build a promising bridge for connecting with fans on the fringe.

Giving Fans More Details

NBA Engagement Channels

Relying on new technology has changed things for both fans and players. It’s not a new practice to rest high-performing players as certain points in the season. Yet, fans have been much more vocal about their disappointment on social media in not seeing their favorite players at the games they attend.

“Analytics have become front and center with precisely when players are rested, how many minutes they get, who they’re matched up against,” said NBA commissioner Adam Silver.

Teams are also using analytics through biometrics and wearables to determine when players are becoming fatigued and may need rest. Silver mentions the delicate balance of upholding their obligation to fans, while simultaneously keeping star players in the best health possible. 

In addition to fan engagement, Silver told interviewer Daniel Pink that analytics are now used for scouting new players, generating more detailed game stats, and even aiding players in improving their game on the court. Data also allows players to prepare for games with detailed stats about the opposing players they’ll be covering. With so much room for perfecting and maximizing success both on the court and in the stands, the sky is the limit for NBA teams implementing data analytics this season.