The Future of CX with Larry Ellison

Join Oracle’s CTO and Chairman Larry Ellison and industry leaders for a virtual event exploring the next generation of CX.

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The Future of CX with Larry Ellison. Adobe Cloud Virtual Summit, Thursday, October 29, 12:00 PT

Join us Thursday, October 29th at 12 p.m. PT, for the Oracle Cloud CX Virtual Summit exploring how leading organizations are re-engineering their marketing, sales, and service processes. Larry Ellison will lay out his vision for the future of customer experience, followed by leaders at Ricoh, Motorola and Hyster-Yale who will share their strategies for harnessing data, and delivering the right message to the right customer at the right time.

To make every customer interaction matter, organizations need to listen to their customers and rethink the way they market, sell and provide service across every touchpoint. If 2020 has taught customer experience leaders anything, it’s the need to be resilient in the face of constant change. Get executive viewpoints on the importance of unifying data across your business and empowering every employee to respond effectively to customer expectations.

  • Hear real-life success stories and lessons learned from Oracle CX Sales and Oracle CX Commerce customers.
  • Learn how to visualize your path to sales and commerce excellence.
  • Get the latest product news and a glimpse of what’s on the horizon.

Sign up for the free event today.

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How to make your data sing

Stop reporting absolute numbers and put your data into context with ratios to engage your stakeholders with the smaller, but important, data points.

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It is amazing; the horrible job many digital marketers do when reporting their work to clients. This includes both internal and external clients. Just think about how many marketing presentations and reports you’ve seen that simply contain screenshots from Google Analytics, Adobe Analytics, Adwords, Google Console, or reports from a backend ecommerce system. This isn’t the way to influence people with your data.

The biggest issue is that most marketers are not analytics people. Many marketers do not know how to collect all of the necessary data or how to leverage that data, and to a lesser degree, know how to present it in a meaningful way. Typically, this is the job of a data analyst. The same way purchasing a pound of nails, a hammer and a saw doesn’t make you a carpenter, gaining access to your analytics reporting tool does not make you a data analyst. This is why many reports contain those convoluted screenshots, and present data out of context, contributing little to no meaning. 

Data out of context

Many reports merely report the facts (the data) with a number and no context. Data out of context is just data. For example, simply making a statement that Adwords generated 5,000 sessions to a website last month is meaningless without context. The number 5000 is neither a good nor a bad data point without a reference point or a cost factor. It’s not until you add in other factors (open the box) that you can demonstrate whether or not your efforts were a success. If the previous month’s Adwords campaign only drove in 1,000 sessions, then yes without other data, 5000 sessions looks good. But what if the cost to drive those additional 4,000 sessions was 10 fold the previous month’s spend? What if the previous month, Adwords drove 5,000 sessions but at double the spend?

It is only by adding in the additional information in a meaningful way that marketers can turn their reporting from a subjective presentation into an objective presentation. In order to do this, stop reporting absolute numbers and put your data into context with ratios. For example, when assessing Cost per Session, toss in a 3rd factor (goal conversion, revenue, etc.) and create something similar to “Cost per Session : Revenue”.  This will put the data into context. For example, if every session generated costs $1 : $100 (Cost per session : revenue) vs. $2.25 : $100 (Cost per session : revenue) the effectiveness of a marketing spend becomes self-evident. In this example, it is clear the first result is superior to the second. By normalizing the denominator (creating the same denominator) the success or failure of an effort is easily demonstrated. 

Data is boring

Yes, presenting data is boring. Simply looking at a mega table of a collection of data will cause many to lose interest and tune out any message you might be trying to present. The best way to avoid this is to make your data sing!

Make your data sing

Just like in the marketing world, the easiest way to grab someone’s attention and make your message sing is with imagery. Take all that great data in your mega table, and turn it into an easy to understand graph, or when necessary, simplified data tables. Even better, (if you can) turn it into interactive graphs. During your presentation, don’t be afraid to interact with your data.  With some guidance, your audience can dive into the data they are most interested in.

Learn to use data visualization tools like Data Studio, Tableau, DOMO, Power BI and others. Leveraging these tools allows you to take boring data and not only give it meaning but to make the data sing, which will turn you into a data hero.

Interacting with your data

Back at the end of July 2019, my firm acquired an electric vehicle. We wanted to know if the expense was worth it. Did the cost savings of using electricity over gasoline justify the difference in the ownership cost of the vehicle (lease payments +/- insurance cost and maintenance costs).

Below is a typical data type report with all the boring detailed data. This is a mega table of data and only those truly interested in the details will find it interesting. If presented with this table most would likely only look at the right-hand column to see the total monthly savings. If presented with just this data, many will get bored, and will look up and start counting the holes in the ceiling tiles instead of paying attention.

The following graphs demonstrate many of the ways to make this data sing, by putting all of the data into context through interactive graphics.

The above graph (page 1 of the report) details the cost of operating the electric vehicle. The first question we were always curious about was how much it was costing us to operate per 100 km. By collecting data on how much electricity was used to charge the car, how many kilometers we drove in a given month and the cost for that electricity, we are able to calculate the operating cost. In the graph you can easily see the fluctuation in operating costs, with costs going up in winter months (cost of operating the heater in the car) and again in June & July (cost of running the AC). You can also see the impact of increases in electricity prices.

To truly evaluate the big question “Was acquiring an electric vehicle worth it?” we’d need to estimate how much gasoline would have been consumed by driving the same distance against the average cost for gas during the same months. On page 2 of the report the data is now starting to sing as the difference in the savings of electrical over gas becomes clear. The chart becomes interactive and allows the user to hover over any column to reveal the data details.

To make the data truly sing, we’d need to not just compare the operating costs, but the costs of ownership. Do the savings in the operating costs justify the price difference between the vehicles? We know that the difference in lease costs, insurance and annual maintenance is in the range of $85-$90/month

The above graph (page 3 of the report) demonstrates, the impact of plummeting gas prices and the reduced driving done during April 2020 due to the COVID-19 shutdown. In April 2020 a mere monthly savings of approximately $41 dollars was achieved. Therefore, there were no savings in owning a more expensive electric vehicle over an equivalent gas-powered vehicle (the difference in lease costs and insurance, etc. is in the range of $85-90/month). While it might not be sing, it definitely was screaming out when we saw it. 

Check out the entire report for yourself.  It is accessible here so you can view all the pages/charts. The report is interactive allowing you to hover given months to see data details or even change the reporting date range.

By embracing not only data visualization but the visualization of meaningful data, we as marketers can raise the bar and increase engagement with our audience. Think of the four pages of this report, which page talks most to you? Which way of presenting the data makes it sing for you? Odds are it was not the first table with all the detailed data.

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Weaving processes together: Pega’s outlook

From engaging under-served customers, to emphasizing ethical AI, center-out architectures, and addressing the distributed work environment.

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With its announcement this week of Value Finder, customer engagement platform Pega aims to help brands drive value from under-served customers. While messaging core customer segments about current products, brands can overlook the opportunity to reach large numbers of less engaged customers with relevant messages.

Selling to the 80%. Matt Nolan, Senior Director of Product Marketing, told us: “Value Finder is a new AI-driven capability inside our Customer Decision Hub. Instead of looking at campaigns and telling you which customers to target — like every other piece of marketing technology software — Value Finder looks at each of your individual customers and tells you what kinds of actions those people need.” Customer Decision Hub is Pega’s solution for determining next-best-actions based on AI modeling of individual customers’ behaviors.

“Teams can build out new offers, messages and creative that really resonate with those individuals, not just the top 20% of your customers but the other 80%,” Nolan explained. “It’s building things for the 80% rather than trying to push them into the things you regularly sell.”

Value Finder works by creating propensity scores for individual customers at scale. Serving enterprise brands, that can mean analysing millions of customers and thousands of products, Nolan said. “It breaks them down into three categories. Customers who have plenty of relevant actions (your top 20%), customers who have relevant actions but are blocked, and customers that have no relevant actions at all.” Value Finder aims at finding value in those last two categories (“blocked” customers are those that have been filtered out of a target segment by rules like low CLV, or geographic constraints: Value Finder alerts brands to those rules, which can then be reviewed and reconsidered).

Positioning for the new business environment. Although Pega offers marketing, sales, service and customer decisioning tools in its Pega Infinity platform, and serves enterprise customers like American Express, Cisco and Pfizer, its approach to customer engagement remains very different from Adobe, Oracle or Salesforce — likely because its roots are in AI-driven, case-based Business Process Management, where it remains one of the leading vendors.

We asked Nolan and Don Schuerman, CTO and VP of Product Strategy and Marketing, how Pega and its customers are responding to the challenges of 2020.

Ethical AI. On the minds of many marketers in 2020 is the need reflect who their customers are in their messaging and go-to-market strategies.Earlier this year, Pega launched Ethical Bias Check within its Customer Decision Hub, aimed at eliminating hidden biases in AI models. Brands tend to be aware of potential bias in, for example, demographic fields in their AI models — but “bias can tend to sneak in anyway,” said Nolan.

This can be evident when campaign results are reviews: “At Pega, we try to do this pro-actively,” said Nolan. “We give you a simulation capability, and if the results show you skewing in a direction that could be considered bias, we throw up an alert.”

Center-out architecture. With digital transformation raging across even business not previously digital-first, there has been a tendency towards what Schuerman calls a “front-end focused approach: “We’re going to build a mobile app, or a new campaign landing page. This tends to lead to disjointed customer experiences.” For example, a mobile app for booking appointments which, because it’s not connected to the rest of the business, is actually ill-informed about when and where appointments are available.

“Or,” Schuerman continued, “they build these really big data-heavy projects like data lakes or warehouses with service layers added in, but after going two, three or four years on a project like that, you haven’t actually changed anything in your customer’s experience. We’ve been advocating for a center-out approach.”

Instead of starting with the front- or back-end, beginning in the center means understanding not only how to engage with customers effectively, but also how to automate that work so that it can be executed at scale: “What’s the process automation and case management you need to pull together to make it happen?”

A distributed work environment. As part of digital transformation, Schuerman sees work becoming increasingly distributed, not just through remote working structures, but also through distributed micro-services architectures, and distributed partner eco-systems (he cites open banking as an example).

“With that increasing distribution, which is really powerful, you have to be able to weave it together into something that’s actually coherent for a customer or an employee,” said Schuerman. Pega launched Process Fabric as a center-out business architecture, designed to combine processes living across a variety of micro-services, some outside the business, into a unified workflow. An “interwoven” worklist allows employees to see all relevant processes in one place, regardless of which system the work is being done in.

Managing change. The concepts Schuerman describes require a rethinking of customer engagement processes, not just the layering in of new, out-of-the-box software. “We’ve been spending a lot of time on the intersection of design thinking — understanding a problem and innovating a solution for it — and low code. How do you empower people to build the software they want?”

This does require some granular analyses: “You need to understand what we call the ‘micro-journey,’ that chunk of the customer journey you want to impact, and the outcome it’s tied to. You need to understand personas, and what systems and channels they are going to use, and you need to understand the data and how it will work with your existing legacy systems. If you can get those three things nailed down, you can get a cross-functional team together and start innovating on what a process could look like, what could an experience look like.” Pega’s low code approach is designed to speed the steps from conceptual innovation to actual results.

The “as-a-Service” experience. The Pega worldview may not seem to be a fit for traditional marketing and customer engagement strategies, even with the radical changes in everyday life and the business environment. We asked Schuerman where receptive customers were to be found.

“Obviously, we engage with the enterprise,” said Schuerman. “We engage with organizations that have global scale and a large customer base, because they already have this entrenched complexity and need to figure out how to solve it. We also engage with organizations that are really service providers.” Banks, insurance providers and health-plan providers are examples. Cisco can be seen as a service provider: “Their job is not to put routers on palettes and ship them to people; their job is to connect people to networks. That’s a service.”

Companies making the “as-a-Service” transition, he said, “need to be very pro-active in how they engage with their clients. They need a digital platform which will pull together the different technologies, Pega and others, that are needed to provide that service experience.


This story first appeared on MarTech Today.



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Epsilon launches brand consideration metrics at individual level

Said to be an industry first.

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Exclusive to MarTech Today, data-based digital marketing platform Epsilon has launched what it calls an “industry first solution” to measure the impact of brand campaigns at an individual level, superseding self-reporting from small sample groups.

The solution is part of Epsilon PeopleCloud, a platform which supports personalized customer journeys. The output is a brand consideration metric, defined as the percentage of consumers who, after receiving a brand message, go on to visit the brand’s website, open a brand app or email, or research the brand on a third-party website.

Epsilon draws on over 200 million consumer profiles, incorporating demographic, behavioral and transaction data, and uses AI to serve relevant ads to consumers, based on some 200 billion daily observations of consumer behavior.

The primary focus is on brand consideration, rather than consideration of specific products and services. “However, we can use the insights to understand if people considering a brand browsed certain categories,” Matt Feczko, Epsilon’s VP Product Management told us. “In addition, we can measure the impact of lower-funnel sales and purchases of specific items or categories. Ultimately, we can understand how many people eventually bought tennis shoes (online or offline) from those who browsed the tennis shoe category.”

Given the critical role played by behavioral data in supporting this metric, we asked Feczko if the solution was vulnerable to the deprecation of third-party cookies.

“Identification is absolutely critical to both identifying consumers who are about to consider a brand and measuring actual consideration,” he said. We don’t anticipate our branding solution will be constrained by the deprecation of third-party cookies. The solution uses our people-based CORE ID and isn’t reliant on third-party cookies for messaging and measurement, meaning it can withstand changes in cookie polices and/or user permission changes. As a result, the branding solution will continue messaging, personalizing and measuring persistent CORE IDs despite all these upcoming changes.”


This story first appeared on MarTech Today.



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How connected TV measurement proves its performance marketing potential

Proper measurement on CTV not only shows its value over linear TV advertising, it showcases its performance marketing bonafides

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A lot has been written about the impact of the COVID-19 pandemic on the world of advertising. Budgets were decimated and marketers scrambled to find other ways to do more with less.

One of the less talked about themes, however, has been how the pandemic served as a major blow to linear TV consumption and as a boon to Connected TV (CTV). There are tons of stats that support this sea-change – 80% of US households now own at least one connected TV (CTV) device, and Nielsen has tracked an 81% YoY increase in CTV viewing time while linear has fallen off. Some TV networks are now even starting to prioritize their content for streaming ahead of linear TV in a nod to the new normal of user behavior.

And at this year’s virtual Upfronts, the TV advertising industry at large seemed to finally acknowledge what we’ve all noticed for some time now: streaming is no longer simply a place to park old content, it’s how and where people watch TV now.

So with the mass exodus of viewers mostly complete and the realization that CTV finally has the scale to be one of the most important advertising channels, advertisers are left wondering: how do we measure it?

The do’s and don’ts of connected TV measurement

Before we address how to approach CTV measurement, it’s best to first address how not to approach it.

Any approach that measures CTV in a similar way to linear TV is already misguided. Linear TV measurement is inherently abstract due to the limitations of broadcast TV. All of the impressive technology in the world of linear TV measurement is deployed to enable statistical modeling, not to deliver precise analytics. That methodology ultimately delivers what amounts to a best guess. 

CTV, on the other hand, is simply digital programmatic advertising but with a TV commercial as an ad unit instead of a banner ad. And its measurement functions much in the same way – it’s not saddled with linear TV’s limitations, but rather uses digital measurement that offers precision over guesstimates. 

As with any other digital marketing channel, marketers have expectations when it comes to measurement. It should give a view into the whole customer journey, it should track conversions, and it should be accountable in 3rd party analytics solutions – and CTV delivers on those needs.

How proper connected TV measurement works

A clear edge CTV has over linear TV is its inherent precision. It unlocks a level of insight that allows advertisers to run ads and know exactly how many people saw them – all the way down to the last digit. CTV also gives advertisers insight into completion rates, providing an exact understanding of how many people saw the ad from start to finish, and how many dropped out. While this is a step up from the world of linear TV advertising, it’s table stakes for CTV platforms.

Performance marketers expect more. Just like with other performance marketing channels, such as paid search and social, performance marketers want a full view into the customer journey to truly understand the impact of their CTV campaigns. That’s why at SteelHouse, our most meaningful CTV measurement kicks in after the ad is shown. Using our cross-device Verified Visits technology, we monitor traffic to the advertiser’s website after a CTV ad is shown. It’s able to identify other devices visiting the site from the same household that saw the ad – allowing us to determine site visits driven by that ad impression.

We continue to monitor the advertiser’s site to see if the users that originated from the CTV campaign eventually convert, delivering a holy grail to direct-response TV advertisers – a way of attributing purchases to the TV ads they run – while delivering an ad measurement experience familiar to all performance marketers.

Proper measurement reveals CTV campaign’s true impact

CTV’s cross-device measurement has helped prove its effectiveness as a direct-response performance channel. Case in point, a leading fine wine & spirits retailer who ran CTV campaigns with SteelHouse was able to effectively track the customer journey thanks to cross-device Verified Visits. It provided insight into who saw their ads on television, and the actions those viewers took afterward. 

The campaign proved to be a success, driving a 1.09% site visit rate, as well as 1.37% conversion rate. Cross-device measurement allowed the advertiser to truly understand the value their campaign brought them – that’s not something linear TV advertisers will ever be able to do.

Proper attribution requires third-party insights

Performance marketers find value in having their campaign data funneled into their 3rd party analytics or campaign management solution of choice. It allows them to understand the performance of their marketing efforts across disparate channels. Unfortunately, this is an area where other CTV platforms can fall short – they tend to rely on siloed measurement only available through their platform.

By reporting user visits from CTV campaigns into an analytics solution like Google Analytics, SteelHouse enables performance marketers to analyze their TV campaign performance in a familiar way. Through this integration, our performance marketing clients regularly make the realization that CTV campaigns on our platform – what we call Performance TV – routinely perform as well (if not better) than other traditional performance marketing channels.

Pick the right way to evaluate connected TV

When evaluating CTV solutions, remind yourself that CTV is simply programmatic advertising. As a performance marketer, you should expect CTV to be just as accountable, efficient, and reliable as any other performance marketing channel. And if a platform can’t deliver on any of those capabilities when running a CTV campaign, find one that can.

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Release Notes: Jivox launches purchase predictions for eCommerce

The solution generates and actions real-time purchase predictions.

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Jivox, a digital marketing company that drives engagement across paid and owned media, has announced the launch of Kairos, a new patented purchase prediction solution for the eCommerce market. Kairos will be powered by Jivox’s machine learning and artificial intelligence technology, allowing companies to enhance eCommerce marketing outreach with targeted user scores, and product advertising based on exhibited purchase intent.

According to Jivox, early testing has resulted in a 200% to 300% increase in conversions. Kairos aims to optimize conversion by combining real-time and first-party user data along with product intelligence. 

“The key to success in engaging consumers online is scalable creative, data, and smart algorithms applied in real time to tailored ad creative,” Diaz Nesamoney, CEO of Jivox. “Kairos means ‘opportune moment’ in Greek, and that’s exactly what this technology is meant to do—capture the attention of a consumer at the right time with the right message—and convert those moments into sales.”

Jivox uses the in-memory clustered IQ Personalization Hub and the first-party identity solution IQiD to identify, store and process consumer data, obtained with consent. The IQ Personalization Hub processes trillions of data signals, making data available to Kairos for real-time decisions on product recommendations, pricing and offers.

Why we care. With many brands leaning heavily into eCommerce, personalized recommendations and offers is one way to differentiate from competitors. 

This story first appeared on Search Engine Land.

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Here’s an alternative to cookies for user tracking

Instead of having your analytics toolset read a cookie, pass a unique identifier associated with the user ID. Learn how to do it and keep it privacy compliant.

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For over 20 years, website analytics has leveraged the use of persistent cookies to track users. This benign piece of code was a mass improvement over using a user’s IP address or even the combination IP and browser. Since it was first introduced, the use of these cookies has become the focus of privacy legislation and paranoia. So what alternative is there?

If your website or mobile application requires the creation of user accounts and logins, it’s time to plan to transition away from cookie-based tracking to user ID tracking. In simple terms, instead of having your analytics toolset read a cookie, you pass a unique identifier associated with the user ID and then track the user via this identifier. Typically the identifier is the login ID.

Preparing for advanced tracking

Step 1

Ensure that the user ID you’ve deployed doesn’t contain Personal Identifiable Information (PII). Too often, sites require users to use their personal email address as a login ID or event their account number. These are PII. If this is the case with your organization, then the trick is to assign a random unique client identifier to all existing accounts as well as for any future accounts as they are created. 

Step 2

Have your developers start to push the User ID to the data layer. This way, the variable will be there waiting for your analytics software to read it once you’re ready to implement the new tracking method. Check with your analytics software on the variable name for this element as it varies from analytics software to software.

Step 3

Create a new view/workspace within your analytics software and configure it to track users by their user ID. Most analytic packages will still set a temporary cookie to track user behavior prior to their login and then will connect the sessions. This way you can see what a user does on your site even prior to them logging in and what site visitors who never login do.

Benefits of tracking users by user ID

Improved accuracy

The use of cookies is flawed in many ways. If users jump between devices (from desktop, to mobile, to a tablet, or office computer to home computer) you can’t track that it was the same user. This generates inflated unique user counts.

What if a user clears their cookies (perhaps they’re utilizing antivirus software that purges all cookies every time the browser is closed)? Once again this leads to inflated user count data.

By tracking a user via their user ID, you’ll obtain a more accurate count of unique users on your site.

Cross Device Tracking

This is perhaps one of the greatest benefits of tracking users by their user ID. You can now see how users interact with your site and/or mobile app. How many use a combination of devices. Is there a specific preference for which type of device might simply be used to add to a shopping cart, only to have the order processed on another device?

Greater Analytics Insight

Armed with enhanced analytics data, new and potentially powerful insights can be harvested. With this new knowledge, you can better direct internal resources to focus and enhance the user experience and optimize the user flow for greater profits.

Real life examples

The following examples demonstrate the power of tracking users by their user ID. 

Overview – Device Overlap

The following image shows what percentage of accounts use which type of device and the percentage that use a combination of devices. For example, while 66.6% use only a desktop, 15.8% use a combination of Mobile and Desktop.

User Behavior – Device Flow

Reviewing the device flow leading up to a transaction can provide some of the greatest insights from this enhanced analytics tracking methodology.

While it might not be surprising that the two most common device (by number of Users) paths were Desktop only and Mobile only, what was surprising to me and to the client was number 3. While the device path of Desktop -> mobile -> Desktop is only experienced by approx. 3% of users, it accounts for approximately 8% of all transaction and over 9% of all revenue generated.

The minimal overall use of tablets was also a bit surprising. Of course the mix of devices does vary from client to client.

Assisted conversions

By dropping the use of cookies, the quality of the data of assisted conversions is significantly increased. For example, how many people read an email (can easily be tracked when opened and attributed to a user ID) on a mobile device, click into the site, browse around to and review the items that are being promoted (maybe add them to their shopping cart). Then think about it for a bit before logging-in later via a desktop to complete the transaction?

For example, from the above report, one can objectively assign a more accurate value to SEO efforts by examining the role Organic Search traffic played in generating sales. While a source of an immediate sale (in this case) from organic search generated traffic represents 1.3% of total revenue as an assist in the sales cycle, it played a role in over 10.4% of generated revenue.

Enhanced user insights

In this example, the client allows its customers to also have multiple logins for their account. Essentially a user ID represents a customer/client and not a single user. The client operates in the B2B world where multiple people within its clients’ organizations may require unique logins and rights (who can order, who can just view product details, who can view or add to the cart but not place an order, etc.). By leveraging both tracking by user ID and recording a unique login id within their analytics, these additional insights can be obtained.


The above report not only breaks down revenue by division, but demonstrates how within different division users use the site differently. In region 1, there is almost a 1:1 relationship between user ids and login ids. Yet in Division 3, the ration is over 4:1, this means that for every customer there is an average over 4 logins being utilized in Division 3.

How can they leverage this data for more effective marketing? By understanding that within divisions there are differences, carefully crafted email marketing can be created to target customers differently with multiple logins vs. single account/login customers. 

A further dive into the data could also reveal which login IDs are only product recommenders (only view products) from those who make specific product requests (add to the shopping cart and never place the order) from those who only process orders and from those who do it all. Each one needs to be marketed to differently with different messaging to optimize the effectiveness of the marketing effort. It’s through detailed analytics that this audience definition can be obtained.

Is tracking by user ID right for me?

Making the decision to change how you track your users is a difficult choice. First, does your site/mobile app require users to login at a reasonably early part of their journey? This is ideal for e-commerce sites and sites where the vast majority of user interaction takes place after the user logins into the site/application.

If you’re running a general website with the goal to merely share information and generate “contact us” type leads, the answer to making this switch is no.

If you have a combination of a general information site plus a registered user section, then yes you might want to consider making this change and perhaps just for the registered user section.

If you do make this change, don’t stop running your other analytics views/workspaces that use cookies. Keep them running. By operating two different views, you’ll be eventually able to reconcile the differences between the two, plus it makes it easier to explain to those who you report to, why you’ll be reporting a dramatic drop in the number of users. Of course, when you first make the switch, all users will be first-time users so expect a major increase in new visitor traffic.

If you decide to make this change, don’t forget to review the impact of the change with your legal department. They will tell you if you need to update your privacy policy.

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Release Notes: Braze boosts orchestration and personalization

Braze seeks to boost empathetic engagement through increased personalization.

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Braze, the customer engagement platform, this week announced the a series of product enhancements, some of which were already on the company’s roadmap, but in Beta or with restricted availability.

Through increased relevance of messaging, and improved orchestration of campaigns, Braze hopes to support more empathetic marketing in a time of high customer sensitivity. “This has been an extraordinary year, and brands will need to listen, understand, and act with empathy in order to survive and thrive,” said Kevin Wang, Senior Vice President of Product at Braze.

Within Braze’s customer journey tool Canvas, brands can respond to intent signals from customers via push, email or Content Cards using Native Promotion Codes, now available globally.

In the Braze Predictive Suite, Predictive Churn is now available globally, helps reduce churn by identifying customers at risk. Early Access Funnel Reports signal drop-offs in conversion from campaigns, indicating opportunities to optimize. The Reports are available to select customers, and are expected to be globally available in the near future.

Other features include in-app message previewing, inbound SMS keyword analysis, and a Huawei Push integration. Braze is also expanding its partnership with Amazon Personalize, which allows brands to leveage Amazon algorithms to support content and product recommendations.

Why we care. Brands have become adept at tracking online and in-app behavior, and resolving customer identity across devices and channels. They are still struggling to send timely, relevant and helpful messages. Braze is one of the vendors addressing that challenge.

This story first appeared on Search Engine Land.

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The importance of valuing latent orders to successful Amazon Sponsored Products management

Advertisers must consider the lag time between ad click and conversion as well as historic performance around key days to estimate shift.

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Sponsored Products is the most widely adopted Amazon search ad format, and typically accounts for more than six times as much ad spend as Sponsored Brands ads for the average Tinuiti (my employer) advertiser. As such, it’s incredibly important for advertisers to understand the full value that these ads drive.

Part of this is understanding the click-to-order period between when a user clicks on an ad and when that user ends up converting. Given how Amazon attributes orders and sales, it’s crucial that advertisers have an idea of how quickly users convert in order to value traffic effectively in real time.

Amazon attributes conversions and sales to the date of the last ad click

When assessing performance reports for Sponsored Products, advertisers should know that the orders and sales attributed to a particular day are those that are tied to an ad click that happened on that day. This is to say, the orders and sales reported are not just those that occurred on a particular day.

Advertisers viewing Sponsored Products conversions and sales in the UI are limited to only seeing those orders and sales attributed to the seven days following an ad click. However, marketers pulling performance through the API have greater flexibility and can choose different conversion windows from one to thirty days, which is how the data included in this post was assembled.

In the case of Sponsored Display and Sponsored Brands campaigns, performance can only be viewed using a 14-day conversion window, regardless of whether it is being viewed through the UI or through an API connection.

For marketers who wish to use a thirty-day conversion window in measuring Sponsored Products sales and conversions attributed to advertising, this means that it would take thirty days after the day in question in order to get a full picture of all conversions. Taking a look across Tinuiti advertisers, the first 24 hours after an ad click accounted for 77% of conversions and 78% of sales of all those that occurred within 30 days of the ad click in Q2 2020.

Unsurprisingly, the share of same-SKU conversions that happen in the first 24 hours is even higher, as shoppers are more likely to consider other products the further removed they become from an ad click.

For the average Amazon advertiser, we find that more than 20% of the value that might be attributed to ads happens more than one day after the ad click, meaning advertisers must bake the expected value of latent orders and sales into evaluating the most recent campaign performance. The math of what that latent value looks like varies from advertiser to advertiser.

Factors like price impact the length of consideration cycles

The time it takes for consumers to consider a purchase is naturally tied to the type of product being considered, and price is a huge factor. Taking a look at the share of 30-day conversions that occur more than one day after the click by the average order value (AOV) of the advertiser, this share goes up as AOV goes up. Advertisers with AOV over $50 saw 25% of orders occur more than 24 hours after the ad click in Q2 2020, whereas advertisers with AOV less than $50 saw 22% of orders occur more than 24 hours after the ad click.

Put simply, consumers usually take longer to consider pricier products before purchasing than they take to consider cheaper products, generally speaking. Other factors can also affect how long the average click-to-order cycle is for a particular advertiser.

In addition to latent order value varying by advertiser, there can also be meaningful swings in what latent order value looks like during seasonal shifts in consumer behavior, such as during the winter holiday season and around Prime Day.

Key shopping days speed up conversion process

The chart below depicts the daily share of all conversions attributed within seven days of an ad click that occurred during the first 24 hours. As you can see, one-day order share rose significantly on Black Friday and Cyber Monday as users launched into holiday shopping (and dropped in the days leading into Black Friday).

After these key days, one-day share returned to normal levels before rising in the weeks leading up to Christmas Day before peaking on December 21 at a level surpassing even what was observed on Cyber Monday. December 21 the last day many shoppers could feel confident in placing an order in time to receive it for the Christmas holiday, and it showed in how quickly the click-to-purchase path was for many advertisers.

Of course, Amazon created its own July version of Cyber Monday in the form of Prime Day, and we see a similar trend around one-day conversion share around the summer event as well.

This year’s Prime Day has been postponed, but reports indicate that the new event might take place in October.

As we head into Q4, advertisers should look at how the click-to-order window shifts throughout key times of the year in order to identify periods in which latent order value might meaningfully differ from the average.


Like any platform, advertisers are often interested in recent performance for Amazon Ads to understand how profitable specific days are. This is certainly important in determining shifts and situations in which budgets should be rearranged or optimization efforts undertaken, and that’s even more true now given how quickly performance and life are changing for many advertisers as well as the population at large.

However, in order to do so effectively, advertisers must take into consideration the lag that often occurs between ad click and conversion. Even for a platform widely regarded as the final stop for shoppers such as Amazon, more than 20% of 30-day conversions occur after the first 24 hours of the click, and this share can be much higher for advertisers that sell products with longer consideration cycles.

Further, advertisers should look to historic performance around key days like Cyber Monday and Prime Day to understand how these estimates might shift. Depending on product category, other holidays like Valentine’s Day or Mother’s Day might also cause shifts in latent order value.

Not all advertisers necessarily want to value all orders attributed to an ad over a month-long (or even week-long) attribution window equally, and particularly for products with very quick purchase cycles, it might make sense to use a shorter window. That said, many advertisers do find incremental value from orders that occur days or weeks removed from ad clicks, and putting thought into how these sales should be valued will help ensure your Amazon program is being optimized using the most meaningful performance metrics.

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What’s behind the hype about Customer Data Platforms?

How CDPs aim to solve some of marketers’ most vexing challenges.

The post What’s behind the hype about Customer Data Platforms? appeared first on Marketing Land.

The global market for customer data platforms is expected to rise dramatically over the next few years. The CDP Institute pegged industry revenue for 2019 at $1 billion and it expects the sector to reach at least $1.3 billion in 2020. Meanwhile, ResearchandMarkets predicts the industry will grow from $2. billion in 2020 to $10.3 billion by 2025, expanding at an astounding compound annual growth rate (CAGR) of 34.0% during the forecast period.

This growth is being driven by the proliferation of devices and customer touchpoints, higher expectations for marketers to orchestrate real-time personalized experiences across channels and the need to navigate complex privacy regulations. Let’s explore each of these factors in greater detail.

More devices, fragmented interactions and high expectations

Gartner predicted that the average U.S. adult would own more than six smart devices by 2020, and Cisco forecasts that the number of devices connected to IP networks globally will expand to more than three times the global population by 2023. There will be 3.6 networked devices per capita (29.3 billion overall) by 2023, says Cisco, up from 2. networked devices per capita (18. billion overall) in 2018.

Customers and potential customers are using all of these devices — several in a day, often — to interact with the companies they do business with, and they expect these brands to recognize them no matter what device they’re using at any given time.

According to a Salesforce State of the Connected Customer survey conducted April 2019, 78% of respondents prefer to use different channels to communicate with brands depending on context, but 6% expect companies’ engagements with them to be tailored based on past interactions.

This challenge isn’t going to go away anytime soon. Segmenting Salesforce’s customer data by generations reveals that younger cohorts switch devices more than older, and they’re also more likely to be adding IoT-type connected devices to their repertoire.

Meanwhile, customer data security and governance have leapt to the forefront of marketer concerns, as the alphabet soup of data regulations — from HIPAA (Health Insurance Portability and Accountability) to HITECH (Health Information Technology for Economic and Clinical Health) to GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act) and CASL (Canada Anti-Spam Legislation) — continues to grow.

Enter the Customer Data Platform, a system designed for non-IT use to streamline the flow of customer data throughout the martech stack and create a single view of the customer. High expectations, along with the proliferation of possible customer touchpoints, make cross-device IDs and identity resolution — the ability to consolidate and normalize disparate sets of data collected across multiple touchpoints into an individual profile that represents the customer or prospect — critical for helping marketers, sales and service professionals deliver the ideal total customer experience. CDPs offer this consolidation and normalization and also make the data profiles freely available to other systems.

Additionally, CDP vendors seek to help marketers address the privacy challenge by providing strong data governance protocols that are certified by third-party organizations to ensure compliance with these types of regulations, as well as other data security standards. For example, many CDP vendors are SOC (Service Organization Control), SSAE (Statement on Standards for Attestation Engagements) and/or ISO (International Standards Organization) certified. These audits confirm best practices around internal processes, data management, data privacy and security.

For more on Customer Data Platforms, including more analysis of the category and profiles of 23 different vendors, download our newly-updated buyer’s guide today!

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