Get it today– ‘The Dummies Guide to Enterprise Customer Data Platforms’

The Enterprise Customer Data Platform for Dummies demystifies in straightforward terms customer data platforms and how they can empower marketers – to leverage valuable data and handle the segmentation that will enable better, more efficiently targeted marketing. Learn to integrate data from all sources, segment a buyer profile, create optimal buyer models, and much more. […]

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Get the guide! The Enterprise Customer Data Platform for Dummies demystifies in straightforward terms customer data platforms and how they can empower marketers – to leverage valuable data and handle the segmentation that will enable better, more efficiently targeted marketing. Learn to integrate data from all sources, segment a buyer profile, create optimal buyer models, and much more.

This guide:

  • Covers working with data that historically has been tough to unify.
  • Describes how marketers can control the data themselves without IT help.
  • Gives strategies for automation to reach buyers in optimal ways to make a brand loved by customers.

Visit Digital Marketing Depot to download your copy.

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Where is Google Attribution?

Google’s free attribution tool made a big splash in 2017. Where does it stand now?

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In May 2017, Google announced the launch of a free version of Google Attribution. In October of that year, it said the tool had rolled out to “hundreds more” advertisers. But now, more than a year and a half later, it has stayed under the radar and has yet to fully roll out.

It’s not uncommon for Google — and other tech firms — to announce something and then never release it or release it much later than anyone expected. So where does Google Attribution stand? It’s still alive, and it’s still in beta.

The company says it is continuing to collect customer feedback and does not have any updates to share at this time. Agency marketers that have had clients testing it say they have been giving Google their input. In the course of those communications, one marketer heard about a tentative timeline of late 2019 for release, but said that was not definite.

What we’re waiting for

The big selling point of Google’s free attribution tool is to help marketers make more informed bidding decisions in Google Ads campaigns by letting them capture an ad’s contribution at any point along the conversion path, and not just when it’s the last click, as highlighted in a case study of Nordic Choice Hotels from September.

It pulls in data from Google Analytics, Google Ads and Google Search Ads 360 (formerly DoubleClick Search) and applies the advertiser’s chosen attribution model, including Google’s machine learning-powered model called data-driven attribution, across channels and devices. That data can then get fed back into automated bidding strategies in Google Ads or Google Search Ads 360.

Presumably, non-brand search and display campaigns that tend to be higher funnel will be likely to get more credit when looking at the full journey, and with Google’s automated Smart Bidding strategies, bids will be adjusted accordingly.

Early concerns

Some advertisers who are testing it out said they are worried that Google may favor its own channels. Several marketers in the beta spoke to us about their thoughts on the tool on the condition of anonymity. Because there’s no way to see exactly how much credit Google is assigning various touch points, marketers are left in the dark about the weighting formulas. Google can say it treats all touch points equally, but it’s hard to tell marketers to toss aside skepticism when they can’t see the data for themselves.

Other feedback from those testing the free tool is that actionable insights aren’t easily surfaced and still require digging to find.

Another agency executive said it’s definitely a work in progress with mixed results, but that Google Attribution is still promising. That team has been providing Google with feedback on issues, and currently recommend clients use it directionally.

At the enterprise level, Google deprecated the paid Attribution 360 digital attribution beta in October. There are attribution features in beta, including Model Explorer and ROI Analysis, available in Google Analytics 360. Attribution 360 is separate from Google’s TV Attribution product, which aims to show the cross-channel (i.e. digital and search) impact of television campaigns. Google is still focusing on cross-media metrics and working on evolving TV Attribution into a holistic video measurement solution that measures both TV and online video.

Google, of course, is not the only ad seller working on attribution tools to get marketers away from last click models. Amazon has an attribution tool of its own in beta. Facebook made its attribution tool available to all advertisers in October, after beta testing started in March 2017. Marketers have reported mixed reviews.

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Riding the wave of AI: Is your marketing campaign as smart as it can be?

AI and machine learning are helping marketers in more ways than you can imagine.

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As 2019 gets underway and your marketing plan unfolds, you’ve probably set some goals for the coming year:

We’re going to break down the data silos that keep us from understanding our customers.

We’re going to improve our messaging relevance.

We’re going to target customers more accurately on their preferred channels

Sound familiar? What if you could just find the time to make any one of these resolutions a reality?

Although the promise of one-to-one marketing has been around for many years, brands still send customers too many marketing messages that are irrelevant, generic or only slightly personalized. The problem is that marketers today have too much data and not enough creative time to respond to soaring customer expectations for a personalized buying experience.

Enter artificial intelligence (AI) and machine learning-based marketing tools that are changing the nature of how marketers make decisions and deploy campaigns. For example, an AI-powered marketing assistant can help you quickly analyze campaign performance with simple verbal commands. An automated content management system can tag images, allowing you to easily create better content for your campaigns. An AI-powered software enables you to see what your customers are doing along every stage of their journey. The list goes on.

Machine-driven innovations save time and enable marketers to be creative strategists again, rather than spreadsheet jockeys. The result is that you can course-correct campaigns faster than ever, ending underperforming campaigns sooner and executing new ones that are more personalized and perform better.

Let’s take a closer look at how AI streamlines marketing processes across the customer journey and helps marketers work smarter.

Unified data across all channels

Marketers continue to be inundated with all types of data – from third-party demographics to real-time behavioral data. The challenge is making the data unified, actionable and effective when it often resides in department silos and is spread across too many systems and platforms. You spend so much time tracking it all down that you’re left with no time to make sense of it, let alone act on it.

Like many leading retailers, HSN (Home Shopping Network) relied on separate processes and systems to drive its marketing strategy for each channel. However, this approach made it complex and time-consuming to integrate data on customer interactions across different channels. It was difficult for the brand’s marketers to know which products would appeal to which customers or what kind of messages would inspire them to make purchases. To break out of its channel-by-channel mentality, HSN worked with Watson Marketing to develop an AI-driven marketing platform that would integrate data across all of its channels, including online, mobile, email and direct mail.

The goal was to use AI to create a ‘boundaryless’ experience for its customers. This new approach enabled the company to build a more complete and accurate picture of individual customer preferences based on all of their brand interactions. HSN marketing teams now craft omnichannel, multi-wave campaigns that reach customers on their favored touch points at the right times.

Improved personalization

Consumers expect personalized brand experiences, and 94 percent of companies agree that personalization is critical to their current and future success. Yet a common obstacle to deeper personalization is the ability to create multiple versions of content and determine the right combinations at the right time for thousands or millions of customers.

Growing numbers of AI-based systems can process marketing rules and directions and then create and deliver individualized content on the fly to each customer. This hyper-personalization is increasingly based on the predicted behavior of the individual rather than conforming to a statically defined segment. AI makes personalization easier for marketers by learning through each interaction and delivering the right content in the context of the customer’s previous interactions with the brand. When you know how your customers engage with your brand, it becomes much easier – and more effective – to deliver the right message at the right time.

The Georgia Aquarium sought to harness the growing popularity of digital channels to send more personalized communications to its visitors. The nonprofit’s marketers knew that increasing numbers of guests were using their smartphones and tablets to connect with the organization before, during and after their visits. But because data was stored in siloed systems, it was difficult to build a 360-degree view of guest interests and preferences.

The solution was to deploy an AI-based centralized marketing platform based on IBM Watson Campaign Automation, which would house a comprehensive range of customer data, including first names, ZIP codes, visit histories and memberships. Machine learning enabled the marketing team to segment audiences into distinct personas, such as non-purchasers, non-members, members and donors, and to execute highly personalized campaigns that were more relevant to each member of its audience. The result has been an 89 percent increase in email open rates and a 288 percent increase in engagement with those messages. More importantly, the Georgia Aquarium has experienced a 21 percent increase in revenues attributed to the digital channel.

It’s all about the [customer] journey

In fact, customers want the quickest and most intuitive path to get to what they need. As a marketer, you want to provide a better path to customer purchases and satisfaction. Seems simple, right? But as we all know, it’s not. AI can help you analyze the entire customer journey across multiple touch points, pinpointing and alerting you to friction spots so you can diagnose the issues and fix them before they affect your bottom line.

Airlines Reporting Corp. (ARC) is the leading supplier of air travel intelligence and commerce services in the U.S. The company’s martech stack included several best-of-breed platforms, but no straightforward way of connecting them to form a coherent view of individual customer journeys. ARC marketers wanted to better understand what was happening during each step of the customer journey, for example, if customers were having trouble navigating the site, finding information or signing up for services.

The company implemented IBM’s Universal Behavior Exchange to translate customer data from multiple source systems into a shared language, and combine it into a single view of the customer journey. The data is then passed into the Watson Customer Experience Analytics platform, which uses AI to automatically map out customer journeys from beginning to end — even when customers jump back and forth between channels. This approach has allowed ARC to create a rich view of customer interactions across all channels and eliminate blind spots when it comes to understanding the customer journey. Its marketers have already discovered that customers are browsing on tablet devices far more than they knew, which has led them to prioritize experience improvements on the mobile channel.

AI-powered marketing = Smart marketing in 2019

You know what you want to achieve in your marketing, you just need the time to do it. With AI, you can work smarter, and gain a holistic, real-time view of your customers and their relevant interactions throughout the entire journey. AI lets you act quickly on your data and makes it easier to focus on higher value work. Being able to get fast, actionable insights will give your team the time to focus on strategy and drive business results.

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How to create landing pages that convert

If you’re looking to gather leads for your business, you need to have a landing-page strategy. Like every other marketing tactic, this can be done well or it can be done poorly. To see the greatest return on their investment, businesses need to build effective landing pages, then test and optimize them to maximize conversion […]

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If you’re looking to gather leads for your business, you need to have a landing-page strategy. Like every other marketing tactic, this can be done well or it can be done poorly. To see the greatest return on their investment, businesses need to build effective landing pages, then test and optimize them to maximize conversion rates.

This guide from SharpSpring is written for any marketer looking to initiate or improve their landing-page strategy. It will guide you through the entire process of creating and optimizing landing pages, highlighting key points along the way.

Visit Digital Marketing Depot to download “Creating Landing Pages That Convert.”

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What is identity resolution?

What you need to know about how ID resolution works to provide omnichannel insights for personalized, people-based marketing.

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Identity resolution tools take simple analytics a step further by tying online behavior to a consumer’s unique identity, giving marketers the information they need to zero in on their target consumers with highly personalized, tailored offers that, in turn, lead to higher ROI.

Identity resolution has become increasingly important for marketers as people move across devices — mobile phones, desktops, connected TVs — throughout the day. Identity resolution can help marketers understand that Mobile User A is the same person as Desktop User B. Without that understanding, marketers aren’t able to control messaging to users as they progress through the customer journey on different devices and that’s where identity resolution can help.

It works by reconciling all available data points, which include those collected by first-, second- and/or third-parties. A composite is built that provides marketers with a cherished 360-degree view of a customer’s identity and user journey, and enables an insight-informed, data-driven “single-customer view”  — also known as people-based, or user-level, marketing.

Marketers use a number of tools and platforms to reconcile users’ identities, including simple customer relationship management (CRM) systems. In 2018, the martech landscape saw a proliferation of customer data platforms (CDPs)— tools that track user omni-channel behavior across different devices, platforms and channels.

At the center: the identity graph

To identify individual customers, data is plotted against an identity graph. Consumers give consent along their path for various pieces of marketing technology to collect, process and analyze data such as device ID, email addresses, phone numbers and cookie data, in addition to behavioral information such as purchases or website visits. That information is matched to other data in the graph using algorithims and patterns to create a likelihood, or probabilistic match.

Over time, the systems use artificial intelligence (AI) and machine learning to get smarter and make better guesses at matches. When a user takes an action that requires them to verify their identity, such as paying with a credit card, that guess then becomes deterministic — a perfect match.

It’s a mutually beneficial arrangement. In exchange for that information, brands provide customized experiences that are more relevant and useful to the consumer, a convenience that some studiessay is worth it even for those who are concerned with privacy.

Walled gardens such as Facebook have their own identity graphs, as do data management vendors, leading some of the industry’s leading demand- and supply-side platforms to formthe Advertising ID Consortium in 2017.

Data laws threaten the ID graph

Strict restrictions governing the use of personal data, such as Europe’s General Data Protection Regulation (GDPR), the soon-to-be implemented California Consumer Privacy Act (CCPA) and potential upcoming federal legislation, might throw a monkey wrench into companies’ ability to collect and use second- and third-party data.

Signal CEO Mike Sands, whose company provides such a solution, says that these laws provide “a strong incentive to invest heavily in first-party data that brands can own and operate with users’ consent.”

“Making a strategic pivot away from third-party data toward first-party data also puts brands on better footing in the fight against Amazon and other industry disruptors (e.g., direct-to-consumer upstarts) with vast troves of first-party customer insight,” Sands said, though he believes that second-party data still has a future in ID resolution.

“Second-party data is a different story,” Sands said. “Because it brings together first-party data, it is unique and high quality: not everyone can access it. While brands have chosen for decades to participate in data sharing agreements and data co-ops, emerging technologies are taking second-party data to the next level while addressing the privacy and security concerns that historically deterred many marketers from utilizing these other options.”

What’s the future of ID resolution?

Mara Chapin, digital and social media specialist at Massachusetts-based Market Mentors, says that privacy challenges could force marketers to rely on other customer analyses in addition to identity resolution.

“Due to the regulations over the past year and the reduction of second- and third-party data, a lot of the targeting options and interest-based information have been taken away from marketers when building out their marketing strategies,” Chapin said. “This has required us to learn more about consumers’ behaviors and work harder on our creative in order to accurately target the right markets and make sure the users we want to reach will be the ones who click and convert into a sale.”

“In the future,” Chapin said, “we’re going to start focusing more on user intent rather than geographic-, demographic- and interest-based targeting in order to build our audiences. Instead of having to know personal information that regulations are being built to protect, we will rely more on what people are looking for rather than what they like.”

Sands doesn’t think ID resolution is going anywhere.

“I do not envision identity resolution giving way to rival marketing solutions — if anything, it’s becoming a requisite step at all stages of the customer lifecycle, and I predict it will displace many technologies and philosophies that do not enable the same real-time, continuous engagement,” Sands said.

Chapin ultimately agreed that identity resolution is key to a good marketing strategy. “It’s important in order to reach the proper audience in your marketing messages, whether it’s through the latest digital offerings or in your traditional advertising placements,” Chapin said. “Knowing who your audience is and being able to segment your buys to those targets is the best way to use your budget and overall marketing strategies effectively.”

This story first appeared on MarTech Today. For more on marketing technology, click here.

 

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Crawl, walk, run and fly: The 4 stages of scaling website analytics

It’s time to identify where you are with your data practices and learn how to get to the next stage of your website analytics process.

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It can be intimidating to tackle the challenge of big data. While some tech-thinking companies have led the charge toward analytics, metrics and measurement, many companies are still grounded by the weight of having more questions than answers. How much data should I collect? What metrics are important to me? How do I get the best out of my investment?

If you’re ready to get up off the floor and dust off your data practices, it’s time to identify where you are and learn how companies mature to where they want to be. We will look at the four typical stages that companies go through when scaling website analytics.

Stage 1: Crawl

Every process must start somewhere – and this process should begin by asking the important questions about your organization. What are our effective key performance indicators (KPIs)? What are the driving factors that influence our business? What are the differentiators between our sectors of business that could influence our success?

Two factors to consider:

1. Important questions like those should be asked of all parts of your business. Marketing alone cannot answer these questions holistically without collaborating cross-departmentally. For example, marketing may surprise themselves with the insights and information that the shipping department or customer service team can offer for a better business understanding. A digital marketing team might think the company’s target demographic are young consumers, whereas in-store associates could tell them the reality is most shoppers are their parents. Website analytics sometimes don’t tell the whole story.

2. No answer is wrong! You must first set these benchmarks, as crazy as they may sound, to establish a philosophy that can be tested to see if it’s true. Consider yourself a researcher in your organization. Do you believe that time of year truly affects responses to marketing campaigns? Great – test these theories.

By establishing benchmarks, organizations can determine the proper way to collect the needed data to validate these guesses. To do so, find the largest white board available or create as many columns as you can in an Excel sheet – whatever your fancy – and bunker down until tough questions about your organization are answered.

Most importantly, this data should reflect aspects of your business that you can change or influence. In other words, working within your remit allows you to not only use the same process for each new test but also implement the results quicker and at scale.

Stage 2: Walk

You should now be able to determine which tools for your organization are needed – and subsequently which data points will be required – to test your theories. This can include general site metric collection such as Time Spent on Site or Bounce Rates or narrower figures like geographical distribution of high-value visitors.

Here are a few factors to keep in mind as you start this process:

  • Data collection tools are very good at giving you a lot of data, but many times it’s way more than you need. This results in companies attempting to collect as much as they can, getting overwhelmed with the results and entering in data paralysis. For example, Google Analytics can provide data about site trends, but it’s meaningless if the information is not tied to a business question.
  • The data analytics space is overly cluttered with many competing solutions with a sea of logos in Scott Brinker’s landscape, so do your due diligence to find the right tools to reflect your needs. The tool is just the start; implementing and training will lead to a larger investment of time and money than is typically planned and requires more team members than many companies account for.
  • Give yourself ample time to collect a rich data set before examining results to avoid anomalies or outside influences on your results such as the holiday season or a
    summer lull.

Stage 3: Run

Now comes the fun part – take that data and run with it. Create an organizational plan to make adjustments and changes based on your results to affect business. Did you find that your average cart size for sales goes up on the weekends? If so, then create marketing tactics to drive users on weekdays that include 2 for 1 or “Buy $100 and get $25 off” methods designed to increase total order size. Your next step will be to start a new experiment to see if these tactics worked to increase weekday sales. If they did, you now know the factors that control your business, and that is incredibly valuable to individual and organizational success.

Most importantly, now is the time when you can factor things out. Did you believe that your loyalty program was influencing return sales, but it turns out it did not? This could mean that your loyalty program needs to be overhauled, or that it cannot be the crux of marketing activities. It can be just as important to learn and confirm what is not working as what is.

Stage 4: Fly

It’s time to soar. Many marketers view their website in a bubble. The truth is that a website is just one channel that users will interact with when engaging with your brand. And not every user is made the same – some will use it for research and buy in store in commerce scenarios while certain groups will use it primarily before speaking with a sales person in other industries. Now that you can properly track and map your website using the tools and methods you have found, it’s time to expand that thinking to other touch points. Are you mapping users from your website to in-store conversion? Do your local event sponsorships lead to users joining your mailing list? Do users who have a positive experience on your support channels typically become better brand advocates on their social media channels? What method can you use to link these sales?

It can be a daunting task to connect all the dots in a sales funnel from the start, so find those easy wins by beginning with your e-commerce or dot com channel and use those powerful data tools to learn what you need before moving to the next channel. For in-store or call orders, for example, customers can be incentivized to use their online session ID to make for easier cross-channel analysis.

Are you ready for the challenge?

The technology space for web analytics tools has skyrocketed over the last few years. Buzz words like “Customer Journey Analysis” and “Machine Analytics” create intimidating spaces that marketing teams are entering into with caution flags waving. Being a member in this space myself, companies that come in with a purpose and goal before considering tactics or tools succeed far more often than those just looking for the quick fix to satisfy the C-suite.

You won’t become an expert in a day. You might idolize those companies out there like Amazon and Google who seem to know about you before you know yourself. While they might be models of data collection and analytics, they took years to get there – their own Crawl, Walk, Run and Fly process that included many of the baby steps you might be getting ready to take. Enjoy the process, take your time and you will achieve your desired level of success, one answer at a time. Keep flying, so platforms and processes provide the complete view of business success you need to compete online and off.

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Ask an SMXpert: New approaches in customization can build better analytics reports

Data-driven digital marketing expert Simon Poulton outlines opportunities in various solutions to customize dimensions for more focused analytics reporting.

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Simon Poulton SMXpert graphic

Data-driven digital marketing expert and senior director of digital intelligence at Wpromote, Simon Poulton, was one of the SMX East speakers during the “Making Your Analytics Work Harder And Smarter” session. There were a lot of questions during this popular program so Simon took the time to answer a few more. Here are five questions submitted by session attendees and his responses.

Do you have experience linking Google My Business Insights data into Data Studio in combination with Google Analytics?

Poulton: Yes, there are various solutions available here. If you’re just getting started, you can simply export the data from GMB Insights and pass this into a Google Sheet that has been synced as a data source within Data Studio.

For those looking for a more automated solution here, you can make this connection using a pre-built connector (Supermetrics released one earlier this year) or develop for the API yourself to pass this data into a sheet or a database that can be connected to Google Data Studio.

As far as I’m aware, there is no simple method to displaying this data coming from Google Analytics, and it would be challenging to tie this data to other user interactions on site, making it simpler to go with a GMB > Database (or Sheets) > Data Studio approach.

Any tips for integrating data that isn’t built into Data Studio automatically? For example, a call tracking platform that is not built into Google Data Studio.

Poulton: There are a number of platforms that do not natively connect to Data Studio – although based on how we’ve seen the number of connectors grow, I’d imagine more companies are looking to add a connection here. Many of these platforms – especially in the call tracking space, do have a native integration with Google Analytics where they pass back Event data to Google Analytics that can be tied to Client IDs and be unified with the rest of the user’s journey onsite. This is incredibly powerful and allows for the ability to connect these user actions with other data like an attributable source that can easily be visualized in Data Studio.

This is as simple as passing the Events for calls into Google Analytics from the 3rd party platform and visualizing these within Data Studio like you would with any other Event data. In general, if you can find a way to automate passing data into Google Analytics or Google Sheets, then there is a simple way to have this automatically update in Data Studio.

Can you import first-party audiences (e.g., loyalty members) into GA and/or Data Studio?

Poulton: On the surface – no, you cannot import first-party PII into Google Analytics or Data Studio. However, there is a method that you can use to append this type of data to users identified on your site using the Client ID.

Out of the box, Client ID is not an accessible dimension for matching to imported data. However, you can create this as a Custom Dimension, and work with your developers to push in the Client ID that Google Analytics has already created for you. The next step is to push this value as a hidden field with your conversions and create a list of key pairs where you identify the user within your CRM, along with the client ID and the customer state (Loyalty Member or not) – once you have this, you can upload this data using the Data Import function in Google Analytics to append this value to users. It’s important to note however, this is not retroactive and can only go back as far as you’ve been tracking Client ID as a custom dimension.

Once this has been configured, you can refresh your GA Data Source in Google Data Studio (to ensure you’re bringing in this new custom dimension) and start to bring this data into Google Data Studio. You can use it as a report itself showing the difference between loyalty and non-loyalty customers or you can use it as a Segment to isolate data for specific reports.

This is very similar to the pCLV Cohort Import example that I provided during our session on Making Your Analytics Work Harder & Smarter.

Have you tried using the “compare to” toggle for scorecard vs. blending data?

Poulton: No – and I’m not entirely sure what this would achieve. As far as I’m aware the “compare to” toggle is for time-based comparisons only. When blending data, we are using a key to join two similar data sets that make sense to be viewed together. If you’re looking at the difference between the two sources, then it likely wouldn’t make sense to blend them within a scorecard format.

 


Search Engine Land’s SMX West, the go-to event for search marketers, returns to San Jose Jan. 30-31. The agenda, packing more than 50 world-class speakers, teaches you actionable search marketing tactics you can implement immediately to drive more awareness, traffic and conversions.

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What’s a CDP? Does your company need one?

Gartner predicts that the average US adult will own more than six smart devices by 2020, making cross-device IDs and identity resolution — the ability to consolidate disparate sets of data into an individual profile — a critical need for marketing effectiveness. As the number of touch points in the customer journey expands, Customer Data […]

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Gartner predicts that the average US adult will own more than six smart devices by 2020, making cross-device IDs and identity resolution — the ability to consolidate disparate sets of data into an individual profile — a critical need for marketing effectiveness. As the number of touch points in the customer journey expands, Customer Data Platforms (CDPs) allow marketers to break down first-party customer data silos to unify and normalize contact information.

MarTech Today’s “Enterprise Customer Data Platforms: A Marketer’s Guide” examines the current market for enterprise customer data platforms (CDPs) and the considerations involved in implementing the software. This report answers the following questions:

  • What features do CDPs provide?
  • What trends are driving the adoption of CDPs?
  • Does my company need a CDP?

Also included in the report are profiles of 22 CDP vendors, pricing information, capabilities comparisons and recommended steps for evaluating and purchasing. Visit Digital Marketing Depot to get your copy.

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Back to basics: Measuring your social media efforts with unique acquisition channels

Segregating paid, organic and social activity in analytics clarifies the activity that drives which type of conversion.

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Most organizations are spending a considerable amount of money and resources on their social media marketing efforts. These efforts generally take the form of three types of effort – organic, paid and promoted (also referred to as owned, paid and earned). No matter how you label them, you should segregate them into three unique marketing acquisition channels in your analytics reports to correctly evaluate how effective your efforts are.

To segregate traffic driven to your site from various social media properties, you’ll need to configure custom channels in your analytics tool. (For a detailed explanation on how to do this in GA, see the Marketing Land article The Google Analytics Social channel is broken. Here’s how to fix it.

Defining custom social channels

Before starting any configuration changes, first decide not only the names of these new channels, but what they represent for your clients (both internal and external).

I’d recommend setting up the following three channels.

Paid social: Consists of any paid ads you are running on any social media property to drive traffic to your website.

Promoted social: All activity performed by your social media team where no additional marketing fees are required. Typical activities that fall under this channel include typical posting to your social media channels.

Organic social: Any activity that the general public (people not on your payroll) drive traffic to your site from social media. This includes a person clicking on a “Share This” icon on your blog post or perhaps just including a link to your site in a spontaneous social media post.

Once you’ve defined your social media channels, you need to define the medium definitions (for GA the utm_medium parameter value) to be used your generate a custom URL to track (see Google URL Builder). The great news is you don’t need to do anything for organic social.

Here are some typical medium definitions (required by your analytics software) or make up your own. No