Use Offline Data to Improve the Customer Experience

Online retail stores like Amazon and Warby Parker have recently established brick-and-mortar stores to provide physical presences for their online customers. Of course, one of the reasons consumers might enjoy having physical stores available to them is the convenience of being able to buy products online and return them in-store if needed. But, this isn’t the primary reason an online retailer like Amazon would create a physical presence for its customers. A brick-and-mortar store opens more possibilities for omnichannel transactions, offline data collection, improved supply chain, and ultimately, better online and offline customer experiences.

Collecting Offline Data
In addition to the data collected online, stores like Warby Parker or fashion retailer Brooks Brothers can monitor traffic patterns through their stores. For many years, Brooks Brothers had its own somewhat informal way of tracking the types of suits, shirts, or ties its customers wore. But now, more systems are available that revolve around both collecting the types of data that come from direct transactions with consumers and revealing the behaviors of intent to buy. We are now able to build the digital profile.

One type of data that marketers can collect from physical stores is dwell time. If a customer is engaged in a particular area of a store, how is he spending that time? Is the experience good, bad, or indifferent? Is he trying to move from one location to another? Is it taking him too long to find what he wants, or is there some type of engaging experience within that particular location of the store that’s driving traffic? Technology has evolved in such a way that store operators who have had the ability to use store trackers to monitor things — such as the moment somebody enters the store and how traffic flows in and out at any given time — now have more granular datasets that enable them to track how long the volume of the store is trafficked within a 60-minute timeframe or that a high percentage of customers stood in a particular department and in front of a specific display.

Challenges of Offline Data
The challenge of offline retail data is that it is transaction-rich but intent-poor. Ninety-two percent of sales go through physical retail channels, but you lose much of the intent data that you get from things like page views, search terms, and how many times somebody linked from a particular blog page.

While not used extensively due to cost and infrastructure challenges, some stores are using RFID (radio-frequency identification) technology on their products. Originally used for loss prevention, the technology enables retailers to track how many times consumers pick up a product or even whether the product was picked up, purchased, and taken from the store, giving retailers a true image of conversion — but, it still isn’t practical across all product categories. It may be feasible on a $500 jacket, but will consumers spend an additional $.011 per box of cereal? So, while the technology provides many possibilities, we have yet to overcome some of the challenges because RFID has still only been applied to a small percentage of all the products in the marketplace.

Other ways of collecting offline data include iBeacons and near-field communications (NFCs), which allow companies to track the offline behaviors of customers who have opted into a brand’s marketing-communications program. Of course, these types of technologies come with privacy concerns. Many audiences are uncomfortable with companies tracking their movements this way. Therefore, the sample of data can be limited. However, it can provide great insights into the patterns and behaviors of your best customers.

Using Data to Improve the Customer Experience
Ultimately, however, the data collected — whether online or offline — is about shaping the customer experience. Marketers can use this data to consider how they want to present products through the customer-experience flow. For example, grocery stores often put the dairy section in the back of the store because, if customers want to pick up some milk (what many people visit the grocery store for), they want them to walk through the entire store so they see all the different products before they reach the dairy section. The data collected can inform them of what they want their customers to see as they make their ways to the products they came to buy.

Convenience is another factor in the customer-experience chain. Through their Cartwheel app, Target is now giving customers the ability to create and save shopping lists. Then, the app guides customers through the store to the products on their lists, improving efficiency and helping customers get in and out of the store in less time.

From a store-operations perspective, the data can also inform and help store management and operations in terms of departmental support and staff management. Of course, this is going to be more relevant to a big-box, larger-footprint retailer.

Leveraging Offline Data for the Online Experience
Offline data can also be leveraged to enrich the online experience, where the online experience — while it has its nuances and benefits from a convenience standpoint — is challenged with regard to product interaction and shopping environment; sight, sound, and smell. Marketers need to make sure the customer experience is seamless between all channels.

For instance, if a customer visits a brick-and-mortar store on the weekend, and then visits the website or receives a communication from its email program on Tuesday morning, those interactions should complement one another. Something as simple as a postcard can drive an online/offline campaign, connecting online customers to brick-and-mortar stores and vice versa.

While offline data has traditionally been more difficult to accurately track, it’s easy to see that we are at an interesting point with technology in which we can better understand consumer behaviors in physical retail locations by combining online technology with in-store action.

The post Use Offline Data to Improve the Customer Experience appeared first on Digital Marketing Blog by Adobe.


Turning Insight into Action: The Quest for a Data-Driven Culture

Welcome to the final chapter in our three-part series taking marketers on the journey to a Data-Driven Culture. In this series, you’ll learn how to design a data-fueled marketing strategy that removes the gut check from decision making and enables hyper-relevant marketing at a global scale.


According to KPMG Capital, 99 percent of businesses consider data and analytics to be important to their organizations, yet 85 percent of executives struggle to analyze and interpret their existing data. The lesson? Most businesses now accumulate vast amounts of data, but more often than not, have no idea what to do with it.

85% of executives struggle to analyze and interpret their existing data
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Walking through the first two chapters in this series, we discussed how consolidated data is essential to creating data-driven user experiences. Without it, you’re missing the complete view of the customer. And, in this world of customer-centricity, the experience is key to survival.

But, this overarching view of the customer will only get you so far. Insights alone are meaningless unless they enable you to make decisions that have a measurable impact. The businesses challenging the status quo have connected their once isolated analytics and are using this information to drive every aspect of their organization.

What does it mean to turn insight into action?

There are several ways to make your consolidated data insights actionable. One of those used by our customers is automations. Woopra customers can segment and communicate with their users based on an interactive, multidimensional combination of behaviors and characteristics. This could include triggering a personalized message, automating a scheduled task, dynamically adding users to labels and more, in real-time. This gives them the ability to seamlessly automate what once was a manual task or not possible at all without the Woopra platform.


Another way that our customers turn insight into action is through discovery. By visualizing data sets side-by-side that previously could not be compared, questions are raised and curiosity is ignited. You can see where customers are dropping off in the purchasing funnel, identify areas for optimization, monitor campaign effectiveness across channels and instantly make changes to enhance the customer experience.

In this final chapter, I’d like to share the stories of three brands that have leveraged data transparency to boost efficiency and create impactful, timely customer experiences.

AppLovin Identifies and Solves Customer Pain Points in Real-time

AppLovin is a mobile advertising company, founded in 2012, that helps brands develop data-fueled mobile marketing campaigns. Their core product runs entirely on real-time data, so it was essential that their internal business operations were the same.

After integrating their analytics within Woopra, they set up triggers to automate actions based on the behavioral data they were collecting. For example, if a customer were browsing the AppLovin site and encountered an issue, an email alert would automatically be sent to the product team, notifying them of the bug and who encountered it.

“Woopra’s trigger feature also allows us to gather information, package it, and generate internal email alerts to sales, engineering and other internal teams,” said Andrew Karam, AppLovin’s VP of Product. “We’re able to use these triggers to proactively monitor and respond to customers who encountered issues by sending internal notifications to our team.”

By setting up automatic triggers, fueled by a combination of product data and behavioral data, the AppLovin team is able to quickly identify any potential product bugs, proactively reach out to customers having issues and optimize product workflows.

Hubstaff Uses Behavioral Data to Trigger Personalized Content

Hubstaff, a time tracking software for remote employees, was designed to meet the needs of the growing remote workforce. The company enables managers to see what their team members are working on and streamlines the time-tracking process. The result of which is time saved, more open communication and productive, efficient teams.

When Hubstaff came to Woopra, they wanted to be able to communicate with their users based on usage and behaviors taken within the Hubstaff site. After consolidating their data within the Woopra platform, they integrated with, an email automation tool, to trigger targeted campaigns based on user behavior.

“One of the things we do is send a webhook from Woopra to once we receive three or more pageviews on specific Hubstaff blog categories. For example, we publish posts on payroll, management, marketing, growth and so on. The software cannot keep track of all the pageviews, but Woopra does. So, when a lead views three posts within a category such as “payroll,” we know they’re trying to figure out how to pay their people and want specific content on how to do that,” said Dave Nevogt, Co-Founder of Hubstaff.


These users are then tagged within Woopra with the corresponding content category tag. When they’ve reached the appropriate number of views on a category, they’re automatically added to the appropriate email content campaign. This allows Hubstaff to send targeted content to users and prospects based on what they’re interested in at that time.

“Within our app, we don’t have any indicators that these users would be in this segment other than if they tried to set up payroll. But, that could lead to unqualified people,” said Dave. “We simply couldn’t do this without Woopra’s pageview tracking.”

ThingWorx Makes Data Actionable Throughout the Organization

ThingWorx, a PTC company, is a software platform that helps developers to operate, create and service the “things” in the Internet of Things (IoT) era. They partner with an extensive ecosystem to extend the capabilities of smart, connected devices for all industries.

ThingWorx found that they were able to capture massive amounts of information but struggled to make that information actionable. “We tried to capture as much as possible, but none of it was helping to tell the story that we wanted to tell,” said Marc Littman, the Director of Web Strategy and Development at ThingWorx.

“We needed to tell the story of user engagement from initial interest to the actual closing of a deal. I’ve used many different analytics tools and then found Woopra. I set up a trial account and popped the tracking pixel on our site to see what it did. It was mesmerizing! I watched as all of the analytics changed in real-time. Then, created a funnel to see where people were going and what documentation led them to drop off. I added the ability to track when somebody self-identifies, and suddenly, ThingWorx had a framework to tell stories,” he said.

We’re now getting feedback about what stories our users would like to know, rather than what we think…
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“It helped us understand how the different segments of users were using the site to serve them better,” said Marc. “We’re now getting feedback about what stories our users would like to know, rather than what we think they’d like to hear. I can watch an email campaign in real-time and see how many people are receiving it and how many convert. I can tell instantly if a Twitter or Linkedin campaign drove traffic and conversions. There are numerous questions that we didn’t even know to ask that we’re now able to ask and optimize.”


When customer data is integrated accurately and holistically in one place, employees are can see every aspect of the customer journey and take immediate action. Marketing teams can automate personalized messaging, at scale. Sales teams can identify qualified users based on website activity, product engagement and more. Product teams measure the success of features and quickly identify issues. Customer Success departments can instantly see the behaviors of a user prior to submitting support tickets. And, most importantly, the entire organization has an overarching view of the customer experience.

A human−centric approach to data that helps people and organizations become more innovative, creative…
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The customer then becomes a personalized and holistic combination actions and attributes that lead to universal knowledge and away from siloed views. It′s a human−centric approach to data that helps people and organizations become more innovative, creative and efficient.

To read the full series, check out part one –
Calling all Data: The Quest for a Data-Driven Culture and part two – Applying the Data Glue: The Quest for a Data-Driven Culture.

The post Turning Insight into Action: The Quest for a Data-Driven Culture appeared first on Woopra.


Google Machine Learning Sentence Compression Algorithms Powers Features Snippets

google machine learning

The other day, I covered at Search Engine Land a Wired article named Google’s hand-fed AI now gives answers, not just search results.

The article explains that Google is now using "sentence compression algorithms" as of this week in the desktop search results. Sentence compression algorithms is Google’s way of extracting the best answer for a query to be displayed in the featured snippets.

Of course, this is not just used for featured snippets but also for Google Home responses, Google Assistant and more. Which is why it is important that Google build a better way to get more answers.

Here is a snippet (using my own sentence compression) to pull out the core nugget from this article:

Deep neutral nets are pattern recognition systems that can learn to perform specific tasks by analyzing vast amounts of data. In this case, theyâve learned to take a long sentence or paragraph from a relevant page on the web and extract the upshotâ"the information youâre looking for.

These âsentence compression algorithmsâ just went live on the desktop incarnation of the search engine. They handle a task thatâs pretty simple for humans but has traditionally been quite difficult for machines. They show how deep learning is advancing the art of natural language understanding, the ability to understand and respond to natural human speech. âYou need to use neural networks – or at least that is the only way we have found to do it,â Google research product manager David Orr says of the companyâs sentence compression work. âWe have to use all of the most advanced technology we have."

To train Googleâs artificial Q&A brain, Orr and company also use old news stories, where machines start to see how headlines serve as short summaries of the longer articles that follow. But for now, the company still needs its team of PhD linguists. They not only demonstrate sentence compression, but actually label parts of speech in ways that help neural nets understand how human language works. Spanning about 100 PhD linguists across the globe, the Pygmalion team produces what Orr calls âthe gold data,â while and the news stories are the âsilver.â The silver data is still useful, because thereâs so much of it. But the gold data is essential. Linne Ha, who oversees Pygmalion, says the team will continue to grow in the years to come.

This kind of human-assisted AI is called âsupervised learning,â and today, itâs just how neural networks operate. Sometimes, companies can crowdsource this workâ"or it just happens organically. People across the internet have already tagged millions of cats in cat photos, for instance, so that makes it easy to train a neural net that recognizes cats. But in other cases, researchers have no choice but to label the data on their own.

I wonder if you guys noticed any changes to the featured snippets that corroborate the Wired story that this went live on desktop search at Google this week?

I asked Glenn Gabe who tracks a nice number of featured snippets and he noticed no significant changes this week with them:

Forum discussion at Twitter.


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