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eric siegel

Eric Siegel on the State of Predictive Analytics

September 14th, 2017 Posted by Interviews, Predictive Analytics 0 thoughts on “Eric Siegel on the State of Predictive Analytics”

When you think of predictive analytics, which person comes to mind?

Think about it for a moment and then hold that thought…

For me, it most definitely is Eric Siegel. If you don’t already know, Eric Siegel, Ph.D., is the founder of the Predictive Analytics World conference series, the Executive Editor of Predictive Analytics Times, and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Revised and Updated).

So when I got the opportunity to pick someone’s brain for a new interview series we’re doing, who else but Eric? Here’s what he had to share about the state of predictive analytics today.

What value does predictive analytics deliver to companies in their digital marketing efforts?

ERIC: Predictive analytics (aka machine learning) targets content, product recommendations, fraud detection, and retention efforts — in all cases, rendering these processes more effective. To get an idea of your possible upside, start with others’ case studies and then do scratch calculation to forecast your own win. For the first of these two steps, the central insert of my book “Predictive Analytics” is a compendium of 182 mini-case studies divided into nine industry groups, including examples from BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, Match.com, MTV, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more.

Where are we in the adoption curve of predictive analytics, as it relates to digital marketing?

ERIC: We don’t have the complete industry data to properly answer that question, but I would informally estimate that we’re about 5-10% where it could and eventually will be, as far as adoption, implementation, and effective deployment. Despite my low estimate, I would say the deployment of machine learning is well beyond early “Innovator” or “Early Adopter” stages. The concepts and technology/solutions are fully developed and proven. But the process to commercially integrate and deploy is not just a technical one – it is an organizational process. This is quite different from most technologies. You need to not only crunch data and derive predictive scores per individual, you then need to actually change the preexisting operational process to make use of the predictive scores, thus fundamentally changing “business as usual”.

What do you see as the biggest challenges in adopting predictive analytics?

ERIC: The greatest pitfall is an organizational/process one. The deployment of predictive analytics is not turnkey or plug-and-play. You don’t just “install” it. Rather, it is a change to organizational process, priorities, and basic system operations. The per-individual predictions generated by this technology – such as whether an individual will click, buy, defect, commit fraud, or unsubscribe from an email list – are only valuable when acted upon (i.e., integrated into existing systems, thus actively changing “business as usual”). To that end, the project must be conceived up to and including the executive level, and there must be broad organizational buy-in, commitment, and coordination.

What are you most excited about when it comes to the future of predictive analytics?

ERIC: While core technology and software solutions are evolving in exciting ways, I’m most excited about the breadth of business applications, both across digital marketing and beyond (sectors such as financial credit risk and healthcare deploy the same core analytical technology in analogous ways). As the awareness, understanding, and comfort with deploying predictive models grows, so does its organic integration into more and more processes.


Do you know someone else working in predictive analytics? They could be featured in a future post. Tweet at meconnect with me, or email me to let me know.

Predictive Analytics World is coming to New York, London, and Berlin this fall. Don’t miss out!

You can also find Eric Siegel on Twitter and LinkedIn.

 

Retailers Can Have Their Bricks and Click Them Too

August 30th, 2017 Posted by Analytics, Behavioral Data 0 thoughts on “Retailers Can Have Their Bricks and Click Them Too”

Despite what you might have read, retail is not dying. Sure, brick-and-mortar retailers today face significant competition from their digital counterparts. But with the right omni-channel strategy, they can outperform online-only stores by providing the best of both worlds – the convenience of online e-commerce along with the human experience of physical stores.

Last week, Macy’s announced its new President will be Hal Lawton, a former eBay and Home Depot executive who is credited with building Home Depot’s stellar interconnected retail experience. Macy’s knows that the path to sustainability involves a unified online and in-store strategy and it has plans to expand its data analytics and consumer insights.

That’s because today retail stores are sitting on an enormous mound of customer and enterprise data, which includes point-of-sale receipts, online visits and purchases, warehouse inventory, and so on. And all of these data points are extremely valuable with the right data analytics strategy and technology in place.

In particular, predictive behavioral analytics has allowed retailers to know when to do what and where. As a result, a store can maintain optimal inventory levels and anticipate what a customer will want to look at on their next visit. It can also pair up a customer’s in-store and online activities to ensure a seamless customer experience and optimal conversion rate with each visit. Imagine, a sales representative having the most up-to-date customer information at their fingertips to help the customer determine the next best action.

It is these kinds of capabilities that will allow companies to stay relevant and win big.

If any of this resonates with you, tweet at me or email me to share your thoughts and experience with analytics.

 

Data That Stays Together, Works Together

July 31st, 2017 Posted by Analytics, Behavioral Data, Strategy 0 thoughts on “Data That Stays Together, Works Together”

When it comes to data analytics, marketers are missing the forest for the trees. If you think your company’s most marketable data source lies in your enterprise data, think again. Your company is sitting on a gold mine of customer data, siloed in different departments, just waiting to be integrated and activated.

According to eConsultancy’s 2017 Digital Intelligence Briefing on Digital Trends, 59% of marketers who have an intermediate or advanced understanding of the customer journey stated that they had trouble unifying different data sources.

On the front end, you may have your clickstream data, which can include activity from ad displays, social media and email campaigns. Some companies even have data on the voices of their customers and that’s a real trove for piecing together customer demographic profiles. Companies also have loads of data on the back end, waiting to be mined, and this includes margin data, CRM product data, and enterprise resource planning, among others. Combined, both front end and back end data can turbocharge your data analytics system.

But in order for this to happen, the data needs to be removed from its silo and made accessible in a central behavioral data repository. Everything under one roof and one program to rule them all.

A predictive behavioral analytics platform can take almost any type of data sitting in your data lake and turn it into gold. It does not require you to manually identify every single data point across different departments because machine learning algorithms do the work for you. An individual data point teaches the model something completely new, regardless if it is tied to other data points in the dataset or not. As the number of data points coming in to the central behavioral data repository grows, the algorithm’s predictions on user behaviors become more and more accurate. Therefore, companies which have started activating their data are gaining the edge needed to secure their spot as a market leader for tomorrow. The sooner you “compound”, the greater the benefit.

Are you already doing something similar? Tweet at me or email me to share your experiences.

 

Are Homemade Predictive Behavioral Analytics Applications Better?

July 17th, 2017 Posted by Analytics, Strategy 0 thoughts on “Are Homemade Predictive Behavioral Analytics Applications Better?”

Homemade apple pie beats the Entenmann’s variety, right? Well, only if there’s a good cook at the helm. The same principle holds true when building a data application. Your team may be perfectly capable of building an infrastructure and enterprise application. But what happens when you have to build for a more specialized function, such as predictive behavioral analytics? With an advanced analytics solution, IT teams can, for example, tailor their websites to a visitor in-real-time, delivering the right content at the right time. However, predictive behavioral applications require specialized skill sets that may fall outside the scope of your team.

You’ll know at the time of deployment just how tall this order will be for your company’s IT coder. They will declare victory after they have created a number of scripts, yet still there is no product. That’s why large organizations – including government agencies – get an advanced behavioral analytics solution to sit on top of their open-source big data stack. It’s that lattice top that every homemade apple pie needs. In this way, organizations have complete control over their data, proprietary models and implementation. There is no confidential or proprietary data that ever needs to be shared with an outside vendor. And with an open-source framework, your data scientists have the freedom to build models on top of it if they so wish.

So, the question is which applications should you build in-house and which you should buy. Enterprise analytics applications are designed for enterprise IT to build in-house, whereas predictive behavioral analytics applications are acquired, as long as it is the right one – one that can sit on your data lake and is not a “black box” of proprietary technology.

Do you have a similar experience? Tweet at me or email me.

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Rules-Based Attribution Models Are Made to Be Broken

June 13th, 2017 Posted by Analytics 0 thoughts on “Rules-Based Attribution Models Are Made to Be Broken”

Love it or hate it, the political establishment has been thoroughly shaken up by President Trump’s total disregard for rules and protocols. While they may have some legitimate purposes in the realm of policy, when it comes to data analytics, rules are a knock-off version of truly, data-driven models.

Rules-based attribution models give you rudimentary insights into your multichannel marketing mix. They’re also labor-intensive, costly and rigid as they depend on a user to constantly push out new rules. It’s like placing a kitten in a glass jar. I know, bonsai kittens aren’t a real thing, but you get the idea. No matter how well the jar is designed, it would never be able to accommodate a breathing, growing creature.

Innovative companies looking for a truly comprehensive view of all touchpoints, from first to last, have discovered algorithmic attribution. These models are agile and manage the complexity of customer behaviors for you. They also get smarter as they process more and more data, and deliver the next best content to consumers.

The difference in capabilities is enormous because your data is always growing and changing. You already automate most of your marketing processes… are you doing the same for attribution models?

Tweet at me and let me know what you think.

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Learn How Lenovo & Others Make Adobe & Enterprise Data Actionable in Hadoop

April 1st, 2017 Posted by Analytics 0 thoughts on “Learn How Lenovo & Others Make Adobe & Enterprise Data Actionable in Hadoop”

To understand your audience, it’s vital to have a complete view of the entire customer journey. However, when an organization fails to integrate all of its data, it’s robbing itself of the knowledge needed to achieve this and be successful digital marketers.

In this video, find out how integrating enterprise and clickstream data in Hadoop extends the capabilities of Adobe Marketing Cloud and hear stories from business leaders activating their data to target customers effectively and gain more conversions.

WATCH THE VIDEO

Hey, Media: Do You Know Who Is Consuming Your Content?

January 30th, 2017 Posted by Analytics 0 thoughts on “Hey, Media: Do You Know Who Is Consuming Your Content?”

Fake news always seems to be in the news these days.

Media companies are working themselves into a frenzy wondering why so many disaffected readers have turned towards click-baity headlines and conspiracy- monging websites, instead of opting for their own tried and true content.

So, how can a website hold onto an increasingly divided audience when there are so many other – more ideologically tailored — options to choose from?

I think the answer is simple. Mainstream news outlets have done enough solid reporting throughout the presidential campaign to earn the trust of the broader American electorate. But the content needs to be placed in front of the right pair of eyes.

If The Washington Post wants more Trump supporters to visit its website, it should place its vigorous reporting on the failings of Hillary Clinton as a candidate front and center when they visit the website.

If it wants liberals to remain loyal readers, it should send them constant updates on its investigations into Trump’s nonprofit organization and controversial past.

By clustering their audience in a clever way, media companies can hold onto their readership – and even grow it. Even in these contentious times.

And media consumers are rarely uni-dimensional. Once they’re in, they will move beyond the content that drew them in. They will check out other verticals. (Cat videos, perhaps). And eventually, they will warm up to the stories that contradict their world view.

It’s more important than ever for media companies to place an emphasis on targeting readers with the right content.

This should begin with the use of sophisticated tools like behavioral analytics, which allows websites to cluster visitors based on how they act, what they read, how much time they spend on each story, do they read the entire story or skip to the end, do they watch the embedded video, do they watch the entire advertisement or skip etc.  rather than the demographic category they belong to. Knowing these behaviors and more will help companies in the business of content to personalize and monetize their content by serving them advertisements that are relevant to their behavior.

Netflix did this brilliantly, and guess what happened? They are no longer remembered for their “Be Kind, Rewind” days. They rose overnight to become one of the preeminent media companies of our day. Youtube gives you video recommendations without you even knowing that they are recommendation.

It’s time for newspapers and other traditional media companies to shed their old ways. Once we get people on both sides of the political divide to trust real news, then we will start having constructive debates over the future of our country.

IoT and Behavioral Analytics: A Perfect Marriage of Big Data

January 18th, 2017 Posted by Analytics 0 thoughts on “IoT and Behavioral Analytics: A Perfect Marriage of Big Data”

Big data is about to get even bigger.

As the Internet of Things (IoT) grows – connecting everything from our cars to our FitBits and inventory pallets to coordinated networks – so does the need for sophisticated data analytics processes like behavioral analytics.

First, it is important to note that the Internet of Things places the onus on dynamic analysis. What do we like about self-driving car? They process information instantaneously to produce the most efficient, and safe, outcome possible for the rider.

The days of gathering data to inform business decisions to be made in the next quarter are over. Companies now need a direct input-analysis-output vector to satisfy customers or make strategic decisions. And on the other hand, customers also expect quick results. The days of waiting for customer’s next visit to the site are over – companies need to capture their attention and sell a product during their first digital touch.

Additionally, machines are better at predicting not only large-scale outcomes – like the state of the traffic on your local interstate – but also individual human behavior.

That’s what a team of MIT professors proved last year in an experiment that compared how a computer system fared in creating predictive algorithms for unfamiliar dataset.

The computer finished ahead of 615 human teams out of 906 – and worked exponentially faster. It even produced better results in predicting human behavioral outcomes, like dropout rates, by selecting more relevant data than its human competitors.

This is all good news, because not only will IoT make behavioral analytics processes stronger by increasing the data pool by several orders of magnitude. It will also make it more valuable.

Take your FitBit. Imagine, after the company has gathered ten years of data on millions of users, what a sophisticated algorithm will be able to infer from your heart rate, monitored hour after hour, day after day.

FitBit will have enough information to create advanced counterpart identification models to not only diagnose users’ health problems – but also estimate what they are at risk for.

Tesla Motors has over 1 billion miles of customer driving behavior data so they can design a better car and feed the data into a smarter autonomous car.

That’s the beauty of behavioral analytics, synced to IoT. It should make your heart race with excitement.

How Trump Beat the Pollsters, and What You Can Learn From It

December 21st, 2016 Posted by Analytics 0 thoughts on “How Trump Beat the Pollsters, and What You Can Learn From It”

After Donald Trump was elected president in a historic upset last month, many pollsters had to eat their words.

In fact, one prominent expert, Princeton professor Sam Wang, went on live TV to follow through on a pledge he had made pre-election: that he would eat a bug if Trump won.

“After all I was wrong. A lot of people were wrong,” said Wang, before munching on a cricket.

Bets aside, the election results were a jarring reminder of the limitations of polling as a science.

Polling is a small numbers game. In order to predict the outcome of an election, pollsters divide up voters into demographic categories — age, geographic location, ethnicity, socioeconomic status, and so on.

Based on responses from a small subset of people in each group, they infer what the population at large is going to decide.

In the era of big data, that sounds a little backwards.

If your company is basing its online sales strategies on similar calculations, it’s got it all wrong.

The best way to determine the outcome of a consumer’s visit on a website is to watch what he or she does, and map that onto past behaviors observed on the site. The more data you have on prior customer behavior, the more accurate the result will be.

This type of demographics-agnostic calculus is what we call behavioral analytics. Try it, and you’ll never have to eat a bug on live TV.

This is Going to be “Yuge”

December 15th, 2016 Posted by Analytics 0 thoughts on “This is Going to be “Yuge””

Behavioral analytics is great, let me tell you. I am firm believer in behavioral analytics. This is going to be Yuge!

If you want to make your business great again, you gotta know who your customer is. It’s a huge problem when you don’t use behavioral analytics to know your customers. We need to take our customer back to being great again. It’s gonna be massive.

Your customers are good guys. But when they’re shopping online, they want good deals. They want you to give them good deals.

Now, I know what customers want. Trust me, I do. The reason why I know what customers want is that I get them.

I get them because when they behave in a certain way on your site, I’ll know they’re not alone. It’s a movement. And I know if you take your time you’re one type of customer, and if you immediately click on a certain type of product you’re another type of customer.

And once I know what type of customer you are, I’ll know what you want. Because of what the other customers before you have wanted.

Now, this is not about identity. Black, white, young, old, rich, poor. When you go onto that website and you know what you want, you’re going to want the same deals, no matter who you are.

It’s so easy. For me, it is. It’s so simple, once you know that you gotta target your deals to your consumers based on their behavior.

It’s what they want, that’s what matters. We are all winners and we will make you great again. Knowing what they want will make you a winner. The best part is we’ll even tell you who your “rotten” visitors are and believe me there are many. It’s a ‘uge problem for companies.

And I’ve got so many wins already, I want you to win too.

How do you win? By knowing who you’re talking to. By segmenting. And trust me, behavioral analytics is the way to go if you want to reach people.

Now I’m a businessman, you’re a businessman too. You gotta be successful. That’s why you gotta use behavioral analytics. Let me tell you.

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