Event driven marketing – is it missing the customer drivers?

Shopping cart moving quickly in a store.As we all know, marketing today is moving fast (although everyone was saying that in 2005, and will be in 2025!) Currently, one of the “accelerators” is that of digital marketing, with real time event driven campaigns being one of the latest mantras.

For example when a customer puts something in a basket, maybe you want to offer them some additional products, or if they pass through a geo fence, or near an iBeacon, maybe you want to offer something that is nearby, in stock, at discount, today only?

These event driven campaigns have proven to be very successful, as the contextual information – for example what the customer is doing, or where they are at that point in time - is typically very predictive.  A customer within one hundred metres of a store is far more likely to go there than a customer one hundred miles away.

Some would have you believe that identifying all the correct events is all you need to do to be successful in marketing. I see that as a very short term, blinkered view of the world. It’s almost as if trying to manage your entire life on your reflexes, forgetting everything you have done in your life up to that point in time, ignoring everything you want to do in future, and not using the rest of your brain!  There are two reasons I think that justify this:

  1. There are many other dimensions to why a customer makes a purchase decision.
  2. What happened in the past, and what will happen in future is relevant….potentially extremely relevant.

Let’s take a simple example of a customer passing near your store, bank or retail outlet.  You can send a message in real time letting them know about the latest offer in store, and sure, that will drive some additional footfall.  But because customers aren’t living their lives by reflex, they likely will be thinking about so many other things, such as: Read More »

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18 fun and handy facts about email marketing

Picture of a random teenager (my son) on his mobile phone.

My son not using email.

I have the hardest time getting my teenagers to use email. It's a generational thing that all parents probably contend with, and for marketers, it points to the growing importance of social media and mobile apps in the marketing mix. But unless teenagers comprise your largest target market, there's one enduring fact about email you should bear in mind:

Email still matters. A lot.

Email matters because it remains the most common form of business communications.

Picture of a random teenage girl (my daughter) taking a selfie.

My daughter definitely not using email.

It's very versatile, allowing you to attach files and embed links, and there are time-stamps and other important functions that give it enduring value. For my kids, they simply need to know that it's the preferred channel for their teachers, bosses and other adults (such as their parents). For communicating with most anyone over the age of 25, especially business decision-makers and influencers, email remains important.

With that thought in mind, I stopped by the booth of Emma - a company dedicated to email and survey communications - at a recent Content Marketing World. I was immediately taken by one of their hand-outs, enticingly titled: 18 Email Stats to Know, Love, and Quote at Parties. Their booklet includes the caveat, "Please don't quote these at parties," but nowhere did it warn me to refrain from blogging about these email stats.

So, after asking Content Marketing Strategist Jamie Bradley if I could blog about these statistics, I happily present them to you verbatim from their great handout:

  1. The average open rate for welcome emails is a whopping 50%, making them 86% more effective then email newsletters. So, an automated welcome is a no-brainer. (Source: MarketingSherpa)
  2. 51% of all email is opened on a mobile device. So, design for the smallest screen first, and use responsive design templates to ensure your emails look great on any screen size. (Source: Litmus)
    . Read More »
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Retailers facing most unpredictable Christmas ever

Once upon a time, the festive countdown began when towns and cities switched on their Christmas lights. We all sat at home, eagerly waiting for Christmas adverts to debut. Retailers could plan based on the number of weekends left until Christmas. But last year, all that changed – the Black Friday phenomenon arrived in the UK and totally changed the way we shop for Christmas.

This year, Black Friday week is expected to be more popular than the week before Christmas for festive shopping. One in five British shoppers plan to go bargain hunting, with 25- to 29-years-old being the most likely group to shop during that week according to our research. Younger age groups are notoriously difficult to predict as they are the most likely to compare prices online and are more open to purchases via different channels, incRetail Offersluding mobile. It’s putting retailers on the spot as so much of their revenue will be determined by a single day’s trading. Amazon and Argos have even started their Black Friday sales three weeks early to bring some order to proceedings!

But, retailers be warned. Brits may love a good price - three in four (75 per cent) of us are primarily motivated by price and half of us (51 per cent) are motivated by getting a bargain. But just under half of us (46 per cent) by the product being in stock. Even if retailers tick all the boxes to win over consumers, long queues are one sure way to lose sales. Consumer tolerance for bargain hunting is limited to one-minute for each one per cent discount when waiting for a store to open. This ‘patience ratio’ drops to about 40 seconds for each per cent discount at the checkout.

It all means retailers are facing the most unpredictable Christmas shopping period yet. Black Friday has changed market dynamics from a fulfilment and a predictability perspective, and for many retailers it’s set to be the busiest trading day this year. Price wars are extremely difficult to forecast and cater for when squeezed into a shorter timeframe. If retailers don’t attract enough customers they lose out, but if they can’t deliver on what they promise they also lose out.  It’s a difficult balancing act. And it doesn’t end there. The channel used must be evaluated – John Lewis announced earlier this year that it would now be charging for its ‘Click & Collect’ service as it was costing them too much to deliver it free of charge. Regional variations and weather patterns complicate the picture too.

Analytics can spare retailers a nerve-jangling finger-in-the-air experience this Christmas. By extracting insights from data in marketing, merchandising, supply chain, operations and more, it gives them the ability to make evidence-based decisions as to what is driving demand for which customers, when and via what channel based on their habits and preferences.  Only then can they make sensible decisions about pricing, stock levels and optimising their resources and supply chain.

It’s intriguing to see what happens next and who the winners and losers are.  Some US retailers, such as REI, have even pledged to remain closed on Black Friday this year – basically removing themselves from the game. One factor is that it’s a holiday period in the US, being the Friday after Thanksgiving, so there’s some feeling it should be a quiet time with friends and family rather than a time for frantic shopping.

To find out more about UK consumer spending habits this Christmas, check out the key findings from our 2015 SAS Christmas Shopper Survey.

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Translating predictive marketing analytics through visualization

Marketing analytics continues to explode with more data sources and fascinating predictive marketing approaches to solve important business problems, yet one challenge continues to bubble up. The ability to translate the technical math behind predictive analytics into easy-to-understand business language and visualization to help c-suite executives make data-driven decisions with confidence. Developing this business skill is highly valuable as leadership decisions will not be made with data-driven evidence without transparent understanding, and how one communicates to a senior executive within the C-Suite versus a departmental technical manager is very different.

This was the challenge I embarked to address at the 2015 &Then DMA conference in Boston, Massachusetts. Over the past few years, I have developed a personal frustration of attending various marketing conferences, and repeatedly observing high-level presentations about the potential of analytics. Even more challenging has been the recent trend of companies presenting magical (i.e. "easy-button") black-box marketing cloud solutions that address every imaginable analytical problem; in my opinion, high-quality advanced analytics has not reached a point of commoditization. There is a reason that the data scientist is the sexiest job of the 21st century, there are over 120 universities offering business analytic graduate degree programs, and U.S. President Obama appointed the first ever chief data scientist earlier this year . It is my personal belief that data driven marketing is on the rise, and will continue to provide competitive differentiation for organizations that invest in best practices and talent, as compared to others that select the short-cut approach.

When it comes to championing analytics within a marketing organization, part of the solution is to enable and perform effective marketing analysis that incorporates analytics across the spectrum - descriptive, diagnostic, predictive, and prescriptive. However, I strongly believe there are other important, and often, overlooked components that complement an analytic team's ability in becoming successful.

  • The ability to communicate and frame an analytics problem as it relates to a marketing challenge
  • The ability to explain the findings of the analytics process in sufficient detail (i.e. telling a story with data visualization) to ensure clear understanding
  • The ability to connect the dots between analysis, and empowering a downstream marketing process

As a principal solutions architect by day for SAS, and a professorial lecturer by night at The George Washington University, I take aim to raise awareness of these subjects to my clients and students. An individual's ability to communicate clearly, succinctly, and in the appropriate language vernacular when presenting analytical recommendations to the marketing organization is extremely important when focused on driving change with data-driven methods and visualization. My main intent is to prove that the days of leaving a business meeting where the CMO states “that was interesting, but maybe next year” are over.

Suneel Image 2

Did I succeed? You be the judge:


Let me know what you think in the comments section below. Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.


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Digital marketing, predictive analytics, and making personalization delicious

In anticipation of SAS Forum Portugal 2015, I wanted to kick off my first contribution to the SAS Customer Analytics Blogosphere sharing an interview I completed with Sofia Real on the topics of modern digital marketing, predictive analytics, optimization, and personalization. Does that sound like a nasty traffic jam you might want to avoid? Absolutely not, as the time has arrived for predictive marketing to have it's moment in the bright sun, and with Gartner recently naming SAS a Leader in digital marketing analytics, it's official - the 800 pound guerrilla in advanced analytics is locked in on solving complex issues facing the space of data driven marketing. Making digital personalization more relevant for target audiences is just like preparing a delicious meal; it all comes down to the ingredients and preparation process to rise to the occasion!

1. How can analytics help the everyday life of a marketer focused on website or mobile app content strategy and optimization? 

Optimization is a core competency for digital marketers. As customer interactions spread across fragmented touch points and consumers demand seamless and relevant experiences, content-oriented marketers have been forced to re-evaluate their strategies for engagement. But the complexity, pace and volume of modern digital marketing easily overwhelms traditional planning and design approaches that rely on historical conventions, myopic single-channel perspectives and sequential act-and-learn iteration.

Presently, marketers primarily use a variety of online testing approaches that include A/B testing and various methodologies within multivariate testing (MVT) for optimizing content. A/B testing is a method of website or mobile app optimization in which the conversion rates of two versions of a page (version A and version B) are compared using visitor traffic. Site or app visitors are presented either version A or B. By tracking the way visitors interact with the content they are shown – the videos they watch, the buttons they click, or whether they sign up for a newsletter – you can infer which version of the content is most effective. Multivariate testing uses the same core ingredients as A/B testing, but it can compare more than two variables. In addition, it reveals more information about how these variables interact with one another.

Lastly, for digital marketing practices with an advanced analytic strategy, the usage of data mining and predictive analytics to prioritize and inform the marketing teams on what to test, and to analytically define segment audiences prior to assigning test cells, is a best practice, in my opinion. Marketers are very creative, and can imagine hundreds of different testing ideas – which tests do we prioritize if we cannot run them all? This is where advanced analytics can help inform our strategies in support of content optimization. To bring this to life, check out a video example I created of predictive marketing analysis using SAS Visual Analytics and Decision Trees to provide digital-centric insights!


2. What are the advantages of using these various optimization approaches? Are they restricted only to the marketing department?

Online testing is appealing not only because it is efficient and measurable, but also because it cuts through noise and assumptions to help marketers present the most effective content, promotions, and experiences to customers and prospects. The evolving digital marketing landscape drives a greater mandate for online testing: to operate in more channels, handle more data and support more users. Online testing must move beyond traditional on-site experimentation to fully optimize a multifaceted digital customer experience.

The majority of today’s technologies for digital personalization have generally failed to effectively use predictive analytics to offer customers a contextualized digital experience. Many of today’s offerings are based on simple rules-based recommendations, segmentation and targeting that are usually limited to a single customer touch point. Despite some use of predictive techniques, digital experience delivery platforms are behind in incorporating predictive analytics to contextualize digital customer experiences.

There are three areas where current trends in digital personalization are falling short:

  • Single-channel digital interactions: Most online experience delivery platforms offer predictive analytic capabilities for a single section of a website in order to support marketing acquisition (rather than the entire digital journey), but do not provide (or limit) functionality for integrating predictive insights across multiple data sources (online and offline), primarily because cloud-based solutions were not designed to incorporate on-premises first-party offline data. In other cases, uploading that data would violate internal IT policies regarding the sensitivity of sharing customer data and associated risks.
  • Black-box vs. white-box scoring: Many digital experience delivery technologies offer predictive capabilities, but do not offer transparency. That is, they aim to provide insights for a specific scenario (such as next best offer recommendations) with algorithms that are more or less opaque. Marketers or their supporting analysts can’t see into the process of the prediction, limiting their ability to improve the predictive model while minimizing false-positives and false-negatives.
  • Extreme dependency on business rules: Other platforms rely heavily on predefined (or subjective) customer profiles and interaction campaign design. As firms who have adopted this approach begin to mature, these rules expand exponentially, forcing marketers and campaign planners to manage hundreds of rules. Business rules have a place in predictive analytics, but they are the bread, and predictive models must be the filling in between the bread.

There is a broad selection of standalone predictive analytics solutions that can support the delivery of exquisite digital experiences. These solutions enable any department (not just marketing), data scientists and developers to design, develop and deploy predictive models to websites and mobile applications. Standalone predictive solutions surpass embedded predictive capabilities that are found in many digital experience platforms because they have the ability to:

  • Incorporate large and varied data sets from numerous sources, producing unanticipated insights. Unlike the digital experience platforms, which aim to own the data, predictive analytic capabilities can support either cloud-based or on-premises platforms, enabling marketers to find customer patterns across a variety of internal and external data silos. Often, the goal-oriented nature of predictive analytics leads to unexpected customer insights that firms might not have found by using traditional segmentation methodologies. The key is to ensure that the data sources are available for real-time personalization applications, meaning that clickstream data (historical and in-session), demographics and other valuable inputs can be processed, analyzed, scored and treated within milliseconds.
  • Allow for monitoring of predictive models and adaptation to new developments. Over the long term, data-driven marketers must evaluate predictions for effectiveness. If a model’s predictive confidence level drops below a certain threshold, its business value decreases, and it might become no more useful than rules-based personas. When a model becomes unacceptably inaccurate, users should be able to modify the algorithms and variables that are used to make the predictions in order to return to higher accuracy levels.
  • Provide both the predictive insights and the logical rules. Despite their power, predictive models must also be constrained with information about the real world in order to deliver the most value.

I am a strong believer in supporting my thoughts and opinions with real evidence. Check out another video example I created using SAS Visual Statistics to perform approachable, analytical segmentation (rather than subjective rules-based approaches) using both clickstream behavioral data and third-party append data (sourced from a partnered MSP or digital DMP) to provide insight into informing personalization strategies and increasing relevance.


3. How does this all fit in a modern marketing omni-channel strategy?

Most organizations have several customer-facing web and mobile applications with varying levels of visitor traffic. Before undertaking a digital-personalization initiative, the organization has to first identify the most suitable digital application for personalization and its related content management systems. Some of the factors that go into this decision include:

• Average number of daily visitors
• Geographical and time-of-day distribution of visitors
• Purpose of the web application
• Existing hosting platform (cloud or on-premise)
• Ease of website modifications for personalization

After the most suitable web application and its related content management system have been identified, the following components (implemented by what I will refer to as engines) are recommended for a robust digital-personalization solution:

• Collection Engine: Collects digital behavioral data, for every session and every user accessing any of the digital properties of the organization
• Normalization Engine: Transforms raw digital behavioral data into a normalized data model, suitable for data-stitching with offline data, as well as for feeding business intelligence reporting, and predictive analytics
• Analytical Engine: Consists of all tools and processes used by organization to analyze the normalized data and build predictive marketing models
• Decision Engine: Uses the output of the predictive marketing analytical models and processes to perform decision orchestration in staged or real-time consumer interactions (both outbound and inbound processes)
• Personalization Engine: Presents optimized and contextually aware content across marketing channels (online or offline) using treatments received from Decision Engine

4. What are main steps a company must take to adopt this kind of procedures? Does it Imply changes in the traditional processes?

I would like to highlight three phased approaches, based on varying levels of digital marketing and analytic maturity of an organization:

Startup Phase

In this phase, the enterprise installs and configures the required tools and software to work in conjunction with its digital application to personalize content (by using a rules-based randomization model) and collect required data that will be used in upcoming phases.



Analytics Phase

During this phase, the organization assembles the data captured by the collection engine and merges it with internal customer data into a common analytical data mart for building models to support staged personalization.


Operational Execution Phase

During this phase, the enterprise monitors analytical performance and continues to improve its predictive models by periodically downloading data that was captured by the collection engine and deploying model scoring to the real-time decision engine.


For readers who made it this far, I thank you for your attention and commitment to this blog posting. If you enjoyed the content, and would like to dive deeper into my thoughts about making digital personalization delicious by leveraging predictive analytics, please consider downloading a technical white paper I authored earlier this year here, or viewing an on-demand webinar available here.

Lastly, if you would like to connect on social media, link with me on Twitter or LinkedIn.


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Data privacy can be a trick or treat

Marketers are walking a tightrope today with data privacy issues: Data can simultaneously bring customers and brands together and further drive them apart.

Recent data breaches, potential changes in data-privacy legislation and regulations loom large as customer expectations concerning marketing data continue to rise. As a result, today’s complex data issues are becoming a more like a horror flick. The outcome of these discussions, customer expectations, data-security lapses and rules-making efforts could change everything.

Consumers Refuse to Mask Their Data Privacy Concerns. Infographic courtesy of Direct Marketing News.

Consumers Refuse to Mask Their Data Privacy Concerns. Infographic courtesy of Direct Marketing News.

Amidst this backdrop, SAS conducted a global study on how consumers balance their need for privacy and personalization: Finding the Right Balance Between Personalization and Privacy.

Some of the findings are appropriately depicted in the chilling infographic to the right, created and originally published by DM News and included here with permission.

What is scary?

The complexity and fragmentation of the customer journey due to the explosion of digital channels, platforms, data and content has placed enormous strain on marketing to be contextually and personally relevant and responsive. Data privacy slips can easily obliterate brand choice.

Also, when different source systems capture the same customer information, it is seldom consistent. And inconsistency can lead to errors, which often results in angst due to privacy violations, plus unnecessary costs. To minimize the horror, technologies such as Master Data Management can take charge of key information assets – defining a “master hub” of core information that references the same key asset by all users and systems.

In this regard, the biggest complaint about data privacy that we hear from marketers is that the maze of privacy laws from various industry bodies and government agencies are often cryptic and sometimes in conflict with each other. The lack of harmonization within and across countries remains a source of dread, confusion and annoyance for most businesses.

For example, in the US, 47 states have attempted to legislate privacy in one way or another. Unlike the European Union, there is no overarching law at the EU level that sets a minimum standard for privacy. Every member country has its own laws. As a result, if you do marketing in the EU, you must deal with the privacy laws of 28 member states.

Scary, yes?

What’s not as scary?

Despite concerns over privacy, consumers are still willing to trade privacy for personalization. Our study found 69 percent of those surveyed said recent news—NSA leaks, social media privacy changes, data breaches, etc.—increased their privacy concerns. Yet, consumers are still willing to trade privacy for personalization. Our study showed that 57 percent still expect businesses to know their preferences and understand their needs.

In exchange for consumers' trust, brands must offer the following in every interaction: relevance, value and control. Consumers show trust by providing customer information in exchange. Brands can also assure customers that their data is managed well and give customers options to decide how their personal information is used.Consumers are okay with giving up some of their personal information for greater personalization--as long as they get to make that choice themselves.

For more helpful insights and take-always, I suggest reading Finding the Right Balance Between Personalization and Privacy in its entirety—preferably in a well-lit room.

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How localization helps retailers with millennials

Recently I read an article on National Retail Federation's "Halloween Headquarters" that 1 out of 6 millennials will dress up their animal for Halloween versus 13% of adults. With the rise in cat lovers and hipsters, I wasn’t surprised. I’m not going to lie, I once had a Pomeranian named Armani (yep, and a toy poodle named Gucci) and they both were known to be quite well-appointed furry friends.

Armani in a store-bought costume? Never!

Armani in a store-bought costume? Never!

Every year, I tried to dress my dog in a costume but he just wouldn’t have it. It was around the Paris Hilton era and all I wanted was for this little fur ball to sit in my purse and wear boots. *sigh*

How does this apply to retail? Well, does your assortment planning take into account opportunities for millennials and their costume-wearing pets?? I assure you, pet owners with those inclinations might deliver some hefty margins. And with that one factor signaling a shift among generations, it can show how knowing the age of the customers walking in to your locations and/or surfing your websites can be important for localization. Unless you are a one-location mom-and-pop shop, a historical volume based clustering approach falls flat. Only an analytics-driven localization approach can help you cluster locations based upon product selling and local demographic information. With analytics, you gain key insights into what really is driving their purchasing decisions and how best to target this audience.

Ensuring this key data element is a part of the assortment planning process is crucial to guaranteeing your millennials can find their pet costumes, and that you're diverting your pet costume inventory away from locations where they're just not going to move as well. This concept doesn’t just impact Halloween - it can have year-round benefits, and it needs to be far more sophisticated than knowing not to ship snow shovels to your Florida stores.

The analytics help you see the patterns that are not as obvious but can have a big impact, especially when you are dealing with high-margin goods and subtleties that can change from store to store. Analytics can help you provide a better customer experience for any customer at any location - that's always the goal, right?

Think about all of the different categories that differ largely by age. Dorm accessories, school supplies, clothing choices, and so much more. This will decrease missed opportunities and excess inventory as well as improving the customers’ experience. There are already 10,835 pictures on Instagram tagged #PetCostume. Where’s yours? Check out how SAS analytics can help you localize your assortments! Let's chat about your localization strategy - whenever you like. Or if you're going to the National Retail Federation's "Big Show" in  New York this January we can talk live there.

Either way, I look forward to hearing from you!

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Want to use money wisely and reduce opt-outs? Read this.

In our wonderfully complex world of digital media, with on-line all-the-time connectivity, social media, big data opportunities (and challenges), it's never been more challenging (and at the same time easy) for marketers to use money wisely. Technology has accelerated the pace of doing business and one consequence is the common perception of saturation - and no medium is more glaring in this regard than email.

Will he open your email? Will he convert? Opt-out?

Will he open your email? Will he convert? Opt-out?

We all get too many emails, and in many places regulation drives the need to have easy opt-outs, so the price to pay for an overly active outbound marketing campaign can be quite steep.

So let me start with a confession.  As a marketing analytics manager, I have never challenged a contact policy.  By contact policy, I am talking about the rules such as ‘Don’t send more than one email a week’ or'Don’t contact a customer by telephone more than once a quarter’.  I like to describe these as the ‘X in Y’ type of contact policies.  They always seemed reasonable as the high touch and high value products had bigger windows than the low touch and low value products, so were probably in line with what customers wanted.

I support a lot of optimisation projects now, and contact policy is a central piece to the optimisation jigsaw, so I get to see how it varies across territories and industries.  But whilst the X and Y in the ‘X in Y rule’ varies significantly, the one invariable is that none of them are analytically driven.  Yet in the increasingly digital world, with its new channels, with more products to be sold and communications made, with more organisations making them, to customers with more devices…..but with less time, it seems appropriate to manage this more efficiently, doesn’t it?

Now some may say that in the digital world that contact policy is not required – after all everything can be trigger- or rules-based, and whilst that is clearly debatable, even in the digital world, I think it is beyond debate that if a customer abandons a cart 20 times in one day – they don’t need 20 SMSs or emails of phone calls to remind them of this fact….and the twentieth SMS is unlikely to get a better response than the previous nineteen messages did.

But what is the limit – and how should we derive it? Read More »

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Optimization step 5: putting everything together

We have finally arrived to our last stop in this Journey Towards Direct Marketing Optimization. If you have read the entire series of articles, you are now ready to understand what optimization means and how to perform it. For those who have not read the previous articles, I suggest you to do so before reading this last one:

In the first article, I explained what Campaign Optimization means in a direct marketing context. Then I talked about how to differentiate customers through propensity scores or response rates. Segmenting customers by any of these measures is essential in order to make decisions about the best customers to target. In the second piece, I explained how to plan campaigns in order to optimize them all together. It is very important to understand that our optimization scenario should include all the eligible customers, before current prioritization rules. After selecting the targets, we stopped to analyze what kind of goals we can set when optimizing. Having the right information to calculate the optimized value is critical. As optimization would not be necessary without constraints, I described which the most common ones are and how to handle them. So now it is time to put everything together and arrive to our Optimized destination.MO_Journey_step5


No need for complex coding

As said in the first article, to calculate an optimized scenario considering all the factors we have discussed, it is necessary to use complex optimization algorithms. However, there is no need to program them. Read More »

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#Internet of Things awakens marketers to the force

Two topics seem to be top of mind this year…the long awaited “Star Wars, The Force Awakens” and the “Internet of Things.” Being a fan of both, I think there are some striking parallels to think about. So here is a mashup of Star Wars references to describe the Internet of Things… its value… as well as some enabling technologies.

“The Force is everywhere and binds all things together”…. Interconnectedness

The internet of things is about interconnectedness.Both the ‘Force’ in Star Wars, and the ‘Internet of Things’ (IoT) deal with the concept of interconnectedness. The IoT is a network of connected devices, machines, and sensors that can generate and share streams of data. This network is rapidly expanding to include everyday objects such as appliances, wearables, electronics, autos, as well as location-aware sensors in the environment where we work, play and shop.

In their recent report on the Top 10 Technology Trends for 2016, the Gartner Group describes this as a digital mesh of devices and continuous streaming data that surrounds us and is everywhere.

 “May the Force be with you” … It’s here, right now

In fact, there is a good chance ‘Internet of Things’ is already with you… perhaps on your wrist, in your car or in your home. The IoT ecosystem also includes the mobile devices that we all carry with us. So this interconnectedness goes beyond machine to machine… to include people, places and things.

One of my favorite IoT devices is a wearable ring from a company called Ringly. The jewelry can vibrate and flash lights in different patterns to notify a woman of certain phone calls, alerts, messages. It can even tell her when her Uber has arrived! Read More »

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