Posts in Analytics

poker

What’s Your Customer’s Next Move?

February 22nd, 2018 Posted by Analytics, Strategy 0 thoughts on “What’s Your Customer’s Next Move?”

You know the #1 rule in marketing, right?

KNOW THY CUSTOMER

And the best marketing campaigns are the ones that are able to get into the minds of their customers. If your message can get the customer to think: “That’s what I was thinking” or “That’s exactly how I feel” or “That’s what I’ve been looking for”, you’ll likely have a winner in your hands.

Therefore, it’s typically for the first step in crafting any ad or marketing campaign is to create your customer avatar. Digital Marketer has shared a nice template, available as a free download here.

John Carlton (“The most respected and ripped off copywriter alive”) frames it as a “Bar conversation” and he talks about it in this podcast. The gist of it is that you’re at a bar, minding you’re own business, and your “Perfect Prospect” walks in and sits down on the stool next to you.

“Hey Susie, line me up a double, I’ve had an awful day.”

“Wow, what happened today?”, Susie, the bartender, replies.

What the person says next holds the keys to crafting your message.

The challenge here is that everybody is unique and may describe their challenges/emotions in different ways. It can be difficult to find the right balance between broad applicability and hyper-personalized, 1-to-1 messaging.

I’d argue that the vast majority of ad spend, particularly brand marketing, is too broad, targeting a large demographic. Companies will create focus groups and talk to a sampling of their target audience to try and have this “Bar conversation” and test the reactions to different campaign messages.

Let me switch gears for a second.

Do you play Poker? As a player, you’re trying to induce your opponent(s) into taking an action (either folding, or betting more – when you believe that you have a stronger hand). Professional poker players know all the odds (the statistical probabilities). They may have also studied their opponents’ tendencies. Poker players will stare down their opponent(s), looking for a “Tell” – an indication of which cards he/she may be holding.

In Texas Hold’em, you have information in the form of your cards, the community cards, any previous actions (bets). This is basic information.

A player could shift the odds tremendously in their favor if they just knew ONE of the cards that their opponent held. Actually, they could shift the odds by just knowing a couple cards that aren’t in play (i.e., what other players folded or what’s in the deck).

The main point is that these types of players are actively seeking additional information about their opponent(s) so that they can make smarter decisions (to induce an action).

Now, to switch back to marketing.

The poker analogy I just described explains how having more information about your customer, empowers you to make better decisions to induce them. That’s what analytics and being data-driven is all about, right? It’s using data to support a decision.

Would it help your marketing initiatives if you knew, for example:

  • Where the customer is in the buyer’s journey (awareness, consideration, decision)?
  • The probability that the customer will buy Jeans vs. a Blouse?
  • The customer is exhibiting the same behaviors as thousands before them who eventually bought something?
  • Those customers coming to the site from the “Facebook Free Trial” campaign are showing a higher propensity to subscribe?
  • The likelihood for the customer to call and ask to cancel their subscription?

 

You don’t have to create focus groups for this because your customers are talking to you every day. They are communicating to you through their actions and their behaviors. Every time someone visits your site or opens your app, they are leaving breadcrumbs, feeding your clickstream data.

Machine Learning can surface customer information in the form of Algorithmic Customer Segmentation, Product Recommendations, Propensity Scoring, Attribution Modeling, Churn Analysis, and so much more. Furthermore, it’s AI-Assisted Customer Analytics which uses Machine Learning to discover the information you need about your customers so that you can make smarter decisions for your business.

With today’s technologies, this is all low-hanging fruit. You don’t need a large team to implement AI and you can have it delivered in weeks. Yes, it’s absolutely possible.

Just don’t forget that behaviors change all the time. You can’t set it and forget it. It’s AI-Assisted so humans are required!

 


Kerry Hew

Kerry Hew is an Account Executive at Syntasa. He loves sharing ideas with other curious and growth-minded Digital Marketers and Data Scientists alike, and working together to execute on their visions faster. Connect with him on LinkedIn.

personalisation

It’s the Thought That Counts

December 8th, 2017 Posted by Analytics, Behavioral Data 0 thoughts on “It’s the Thought That Counts”

Think about the feeling you get when someone you care about gives you a gift. Surprise… delight… overall, a pleasant feeling, right? That’s because a gift is thoughtful and gives the impression that some time and effort went into providing that special gifting experience.

That’s exactly what customers are craving for from their favourite brands. Customers don’t want to feel misunderstood or overlooked. They want personalised content, offers and recommendations which surprise and delight them.

Personalisation is not some abstract concept
AI has become the standard for all winners in the online retail ecosystem.

Large, multinational, growing companies are investing more and more into personalisation each year. And the stakes couldn’t be higher during the holiday season. In 2017, Cyber Monday alone brought in $6.59 billion in online sales, surpassing all prior years. Black Friday didn’t disappoint either – there were $5.03 billion in online sales.

But these hoards of customers don’t magically appear out of a hat. In this competitive environment, retailers need to earn each customer. And a growing number of online buyers expect tailored offers and purchase recommendations from the sites they visit.

So how do you get ahead of your competition to attract customers? Two words: machine learning (also commonly referred to as ML).

Machine learning has become the standard for all winners in the online retail ecosystem: Amazon, eBay, Wal-Mart, you name it, are all utilising ML. And if you’re not (that is, if you’re looking at customer data as a static thing), you will quickly lose out.

Today it’s absolutely possible to map and predict customer behaviours based on real-time interactions. Every hover, click, scroll, and type builds a sequence of events unique to an individual user. ML, together with Data Science, helps leverage data more effectively, automatically driving insight from the data and helping organisations to understand the customer better. There are ways to use insight and model output to power the various customer experience platforms in your organization. And the earlier you implement ML, the quicker you’ll deliver relevant content and convert customers before your competition does, and the sooner you can achieve peace of mind with your marketing campaigns.

Are you getting it right with your personalisation efforts? Feel free to share your experiences with me. What’s working and what’s not? You can email me or connect with me on LinkedIn.

 

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 a 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 processes, 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 grow, 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.

 

retail online in store brick and mortar

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.

 

analytics applications

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 analytics applications. 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. And 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? More often than not, the necessary skill sets for building these kinds of applications 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

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

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.

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 in behaving that way. I also know that if you take your time browsing you’re one type of customer, whereas if you instantly 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 other customers before you, behaving similarly, 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 simple once you know that you’ve got to target your deals to your consumers based on their behavior. It’s what they want and that’s what matters.

Let’s focus on making you great again, and your customers great again. Knowing what they want will make you a winner. The best part is you can even identify who your “rotten” visitors are (and believe me there are many). It’s a huge problem for companies.

You know you gotta win. I want you to win too. You do it by knowing who you’re talking to – by segmenting. Behavioral analytics is the way to go if you want to reach the right people.

Black Friday & Cyber Monday

November 24th, 2016 Posted by Analytics 0 thoughts on “Black Friday & Cyber Monday”

While retailers lick their chops over the hordes they will attract on Black Friday, there’s a small PSA I must make.

Scrap Black Friday. No, I mean it. Burn it like that first turkey you made 20 years ago that your family will never let you live down. Just throw it all away, like what you want to do with the dull green bean casserole that your cousin insisted on bringing this year, just like he has every previous year. Heck, if I were you I would even do away with cyber Monday and treat it like the cranberry jelly that really adds nothing to the turkey-and-carbs fest, if we’re being honest.

What I mean by all this is that it doesn’t make sense to pick out one day each year to draw in customers with huge deals. If you have the right data analytics program to monitor what customers are doing every time they visit your website, you can create a Black Friday experience all year long. You can increase your conversion rates both online and in-stores, 24 hours a day, 365 days a year.

You see, a customer 360 data app allows companies to tailor how their website responds to each incoming user, based on their prior behavior on the website. That gives you the ability to influence the outcome of each visit. The algorithmic nature of today’s data apps is very powerful and it will automatically recommend the next best action that needs to be taken. You won’t even need to create gigantic sales to improve your retention rate and overall sales results.

So for this year, scrap Black Friday. And instead of stuffing your data into static analytics programs, start using dynamic behavioral analytics tools to make the most of each customer’s visit.

If you would like to find out how you can make every day of the year a Black Friday kind of day, feel free to reach out to me.

How Syntasa Came To Be

November 14th, 2016 Posted by Analytics 0 thoughts on “How Syntasa Came To Be”

Syntasa’s story began on September 11, 2001, when I was working in New York as an executive at American Express. I was only a block away from the World Trade Center when the towers came crashing down. I had witnessed the tragedy of that fateful day unfold in front of my own eyes and it shook me deeply. At the same time, it also motivated me to work in the defense sector in order to help improve the country’s defenses and prevent anything like this from ever happening again.

In 2013, I moved from New York to Washington D.C. to found ABSc, a company that would focus on providing security services to the government. I landed a project with a federal agency to help their cyber infrastructure. I started out recruiting a couple of smart, patriotic minds who had already built a name for themselves within the federal government for their work with advanced analytics. We soon grew to a staff of several hundred people. Due to budget cuts and drawing down from the war starting in 2009, our company saw ups and downs due to a decline in demand. But still, our management team was strong enough to quickly build the company back up again.

Some of our staff has been around since the company was run out of a small closet of an office. Now, we are a well-known partner of the federal agencies.

Still, my entrepreneurial bug was not going away. So, I started Syntasa in 2012, with the hopes of building a product to perform Predictive Behavioral Analytics for large commercial enterprises. This kind of software provides enterprises with insights into their customers’ behaviors, to help improve business outcomes. With behaviors analyzed in real-time, websites can be dynamic in nature so as to change content according to how the customer is interacting with a website, thereby improving the customer experience.

Syntasa’s Predictive Behavioral Analytics is at the forefront of the technological and data revolution, adapting the very latest in machine learning technology to help enterprises identify actions and outcomes. In some cases the software can be used to identify bad guys who could potentially harm our nation. There are plenty of things in the world that I’d like to work hard to solve for, in order to make the world a better place. I also realize that there are intelligent minds already at work solving these things. I just feel fortunate that I was given an opportunity to serve our nation in a small way.

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