Identity management investment can pay off, here’s how

Marketers must examine how people-based IDs differ and how quality impacts identity through activation. Learn how to evaluate your program.

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The marketing industry has been awash with articles and papers talking about marketing technology and the importance of linking identity management across an enterprise’s investments. And rightfully so. Brands should be laser focused on these topics because, simply put, they are the fundamental building blocks for establishing a meaningful, direct relationship with customers and, in turn, gaining competitive advantage.

The challenge, like many past inflection points in our industry, is how to capitalize on this. What is needed, beyond the actual physical technology and people? In my experience, the “how to activate” is often the last consideration, but really, it should be the first place to start. Let’s take a deeper look at how this impacts the need for a tactical, ground-up data plan for identity management.

Identity as a whole is impacted by the level of fidelity of your data and how it’s able to paint a clear picture of your customers, their brand interactions and the end-to-end customer journey.

Let’s use the analogy of music to help bring some clarity. I’ve always appreciated sound quality and the impact it has on my listening experience. There are multiple areas that impact the sound quality, from the environment you’re in (e.g., subway vs. home) to the device with which you’re listening (e.g., Apple earpods vs. home speakers). Most important, though, is the source. If the source file (e.g., MP3 vs. FLAC) is not high quality, your listening experience can suffer.

It’s the same with identity. Identity necessitates the highest fidelity source of data. In this case, moving from a cookie-based to a people-based world is like moving from music on cassette tapes (remember those?) to high-quality digital music files.

Today’s world of marketing is complex, with multiple ways to link customer data. These range from cookies to offline transactions IDs, all the way to people-based, one-to-one linkage. As marketers progress in adoption to 100 percent people-based marketing, they must think about why all people-based IDs are not equal and how the fidelity (i.e., the cleansed one-to-one view of a customer) impacts identity all the way through activation.

As you continue on your journey, there are many identity-related considerations, including the four key areas listed below. They illustrate the impact identity has on your people-based marketing activation, using as an example a group of customers who are top-tier loyalty members:

1. People-based platforms must be connected to activation. If an ID is not linked directly to activation, drop-off and de-duplication can occur, impacting one-to-one marketing and marketing ROI results.

Example: You want to cross-sell into this group with a new premium product by leveraging an integrated campaign with paid display and measuring the incremental impact of display on sales. To enable activation, you’ll need to turn the loyalty-based PII to anonymous IDs, such as cookies, and activate them via platforms like demand-side platforms (DSPs) for paid display targeting.

This process of turning a known loyalty audience to cookies needs to be seamless and is the point where media marketing ROI can be impacted. Industry challenges like cookie deletion and changes in devices (e.g., a new tablet) necessitates that your PII data be linked and refreshed continuously with your customers’ cookies, otherwise breakdown can occur.

If cookies are lost, it will adversely affect your ability to measure downstream engagement and the incremental effect of paid display ads on sales.

2. People-based platforms need to bring higher fidelity audience profiling capabilities from rich third-party data, leading to better insights and more precise models.

Example: Let’s say you want to use third-party data to get a deeper understanding of your audience’s interests in your new product segment. What happens if a high percentage of individuals just got a new mobile device, and they don’t authenticate for several weeks? Audience-based platforms need to continually link between known and unknown IDs; otherwise, customer insights will not be precise.

3. People-based platforms should be connected directly to offline martech PII data, enabling one-to-one resolution at the anonymous ID level.

Example: Relating to our first key area, connecting your offline PII to anonymous IDs is critical. If you have a high-value group of known customers you want to activate and cross sell, the need to speak to them one-to-one in any channel is critical. If you’re speaking to someone in a display ad and you can’t be certain it is the person you are targeting, then your ability to extend your conversion is highly limited to known channels, such as email.

4. People-based platforms should be able to easily interact/activate with offline segmentation models that incorporate a mixed set of martech data from DMPs to loyalty programs enabling seamless activation and optimization of marketing ROI insights.

Example: The adage “what’s old is new again” is a key theme in the way CRM principles are being extended to today’s ecosystem of digital marketing. Many organizations have invested a lot of time and effort into “offline” models. Whether they are credit risk models or customer segmentation across product offerings, the ability to take offline PII based-models and bring them into a digital ecosystem is critical.

While these considerations are just a starting place, I hope they help bring some food for thought in our exciting and rapidly changing marketing ecosystem. Here’s to continued success in 2019 and beyond.

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The in-housing trend is all about data

In 2019, we’ll see more brands increasingly turn to digital ad agencies and tech consultants nimble enough to act as an extension to their internal teams.

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As the advertising industry evolves, and brands look to have more ownership of their data and measurement, big media holding companies have struggled — Omnicom, IPG and WPP included. The roles of agencies, brands, tech platforms continue to shift and consultants have edged in on the agency piece. “Tech consultants are the new mad men,” declared the Wall Street Journal in November 2018.

But there’s certainly a middle ground here. Most, if not all, in-house teams simply won’t be able to be the best at everything. The speed and rate of industry change are just too high, and top talent is too difficult to retain. The result — in 2019, we’ll see more brands increasingly turn to digital advertising agencies and tech consultants nimble enough to act as an extension to their internal teams.

As Electronic Arts’ Global Head of Media Belinda Smith asserts, “Brands taking marketing, media or content in-house does not mean the apocalypse for agencies — quite the contrary.”

Advent of transparency

We believe that the conversation of in-housing is actually about transparency and ownership of data. Marketers are finding data to be more accessible, but data without a strategy is useless. In 2018, consumer privacy, fake news and brand safety were areas of concern for most CMOS. By owning data and data sources, brands can not only better understand the customer journey but can also establish more trust. Good first-party data, that is collected correctly, is the only way to capture clear insights.

Understanding customer interactions across all touchpoints is the number one challenge for today’s marketers. As WPP CEO Mark Read put it, “It’s clear that scale has moved from buying power to the power of intelligence, and the heart of that is data.” Clean data provides insights into the kind of content that works, where and to what audiences are responding but often requires a data specialist to make sense of the data. With data teams working with creatives, brands and agencies can create compelling and engaging content with better results and can deliver personalized experiences to specific audiences.

New model, new day

We agree that the agency model is transforming, but at the end of the day, data can’t replace creative. The truth is, brands need to take steps towards owning their data while not losing sight of the Yin and Yang that make up marketing teams, meaning the creative and data strategists. Yes, marketers are taking a progressive leap into the transformation that is happening in digital – but that doesn’t mean all or nothing. Data supplies the WHAT and creative delivers the WHY. But as more brands announce in-house moves, agencies need to not feel threatened but know that it is time to evolve their service offerings and help marketers to streamline data from a variety of sources. If data is not informing a buy, marketers are not data-driven, they are data-responsive. Therefore, the better teams can collaborate and leverage the “art and science” disciplines, the more effective they will be.

At the end of the day, it all comes down to visibility. Bringing data in-house just means brands own the keys to their platforms — but working with agencies and tech platform partners are still critical to delivering value and expertise towards true business outcomes. With data at the center, decision making will become easier, campaigns more predictive and return on investment will no longer be in question.

Time will only tell how these market shifts will impact the way all parties work together, but one thing is for sure: the days of data obfuscation are over. It’s time to open the possibilities on the entire marketing stack and give tech providers, agencies and brands the visibility and transparency for both media buying and measurement so that everyone is working from the same view. Those who do — internal marketers, agents, consultants — will reap the rewards.

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Cross-domain analytics tracking: Why you may not need it

Implementing a cross-domain tracking solution isn’t the answer to poorly configured websites. Here’s why.

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Over the past couple of months, I’ve seen a sharp increase in requests for setting up cross-domain tracking for a variety of different clients and websites. The process to implement cross-domain tracking can be tricky and if not done correctly can fail or cause inaccurate information to be collected in the client’s analytic tools. To those looking to implement it on their own, there are many blog posts and columns on the correct way to configure cross-domain tracking and even how to debug it (if it isn’t working as it should) on the web. There is no need to write another one, however, what these posts don’t contain is why a business should or should not implement cross-domain tracking. What are the benefits of cross-domain tracking and are there any risks associated with it? If you’ve heard about cross-domain tracking or are merely curious if your organization can benefit from it, read on.

What is cross-domain tracking?

According to Google Analytics, “Cross-domain tracking makes it possible for Analytics to see sessions on two related sites (such as an e-commerce site and a separate shopping cart site) as a single session.“ In simple terms, cross-domain allows you (a business analyst, analytics analyst, business owner, etc.) to view a website visitors session as they navigate from one domain to another as part of a single customer journey from the point of acquisition to conversion.

When and when not to implement cross-domain tracking

Implementing a cross-domain tracking solution isn’t the answer to poorly configured websites. We’ve received requests for it to solve this problem. “If you go to our site with everything is fine, but you also get there with and everything is also fine, but as you navigate the site, sometimes a user gets the www and sometimes they don’t. We want cross-domain tracking to fix this in our analytics reports.” Our answer is yes cross-domain tracking can help, but you’re better off having your admin fix it with one line in the .htacess file to either show the www or not show it every time.

Another favorite request is, “We have a few sites, and and want to see how many people navigate between them.” This may sound like a perfect reason to implement cross-domain tracking, but when you dig a bit deeper with the client and ask them, “Do you have links between your sites?” or “Are the sites related in some specific way?” and you get the answer “No!”, then what they are asking for isn’t cross-domain tracking, but rather “session stitching” which is far more complex to implement.

What cross-domain tracking, is truly intended for is connecting the data flow between related sites. For example, perhaps you have all your marketing landing pages on a sub-domain of and clicking on the call to action takes the user to a different domain (perhaps to complete a form) (i.e., and once they’ve completed this task they are then returned to your public site of with additional conversion opportunities. In this customer journey, a visitor would encounter three domains and as a business owner, you need to know which ads drove conversions and potentially if running A/B testing on landing pages which landing pages yield conversions. This is the perfect scenario to implement cross-domain tracking.

Perhaps you operated multiple domains in support of a common target audience (each one specializing in different services) that do link to each other and the services promoted on each of them. Once again, this is a perfect reason to implement cross-domain tracking as part of a roll-up analytics report.

Final use of cross-domain tracking

While a bit of a stretch, if your organization operates multiple websites that aren’t linked together you can through some custom reporting and the use of Attribution Modeling and Multi-Channel reporting, view if a user visited associated websites (including which ones) before converting on the final one. This last option can be extremely tricky to implement, expensive and fraught with holes that may limit the reliability of the data. However, to some people, a bit data is better than no data at all.

Ultimately, talk to your analytics provider and ensure that the need for cross-domain tracking is truly in line with your corporate measurement plan. Then and only then, is it going to be worth the investment of time and money to implement it.

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Case study: A tale of two insights and why they are not all created equal

Learn how one business used a virtual focus group to gather data and surfaced an unexpected insight that changed their whole campaign strategy.

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Understanding your target customer is essential to crafting messaging that results in impactful campaigns. The importance of uncovering the right insight is a struggle that all companies face. It’s a tale of two insights where not all are created equal. To articulate this point, let’s use a real example from a large, Fortune 500 company – we’ll call them “Corporation X.”

Corporation X sought to reinvent their cat litter product, which was losing market share to their competitors. To boost sales, the company was prepared to address the basics: price, packaging and/or positioning. But which factor would have the most impact on consumer purchasing behavior? It would be up to the research team to design a study to gather the data necessary to make an objective decision.

As it turned out, it wasn’t any of the traditional factors that pushed consumers to another product. It was, in fact, something completely different; something the team hadn’t even considered. Consumers found that this particular kitty litter left a dusty residue that their cat(s) would trace all over. This was the catalyst for switching to the competitor’s product.

Ask the right questions

Most marketers are familiar with traditional forms of gathering data – surveys and focus groups. In either setting, what is asked of the consumer is critical.

If Corporation X had decided to conduct a traditional survey, they would have gathered insights but not the one that would have made a significant impact. Instead, they chose to engage a large-scale virtual focus group with 131 participants in a real-time conversation that revealed the unexpected, actionable insights.

Corporation X’s methodology for gathering the data also applies to the questions asked. Structure is key: start with broad questions and then move to a more pointed line of questioning. This allows those posing the questions first to assess all the variables that may exist, and then hone in on specifics to better understand common themes that arise.

In the instance of the cat litter, Corporation X started with broad questions that sought to capture general attitudes about the product category (i.e., what phrases come to mind when you think about your experience with cat litter?). As participants’ responses came in, the questions posed became more narrowly focused (i.e., what is the biggest difference between Brand X and Brand Y?). This approach allowed Corporation X to uncover consumer-driven insights and avoid leading questions that may have skewed responses (i.e., why is lightweight litter less messy?).

Had the team at Corporation X designed research limited to understanding only consumer preference around price, packaging or brand positioning, the product issue would not have been uncovered. The team may have found out that consumers preferred the blue packaging of the competitor’s product to the purple packaging of their cat litter, but it would have been an insight that when executed against, would not have had an impact.

Do not make assumptions

Arguably the two most important factors in the data-gathering process are defining what it is you’re trying to comprehend and work with the right data set.

In the case of the cat litter, the focus on the impact of purchasing decisions rather than assuming the potential reasons and asking consumers to validate them made all the difference. The variance may be slight, but had a measurable influence on consumer behavior.

Let consumers surface truth

It can be easy to achieve a self-fulfilling prophecy in this type of exercise. If Corporation X believed that price or packaging was the main driver of its declining sales, the research and results could validate that reasoning. But keeping an open mind in the data gathering process – rather than relying on preconceived feelings or opinions – is key to surfacing the insights that matter most.

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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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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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Here’s why you should take a deep dive into data to optimize your conversions

A thorough audit of tracking tools can improve your CRO framework because knowing your platform will help you think about how to best use it for your specific business strategy.

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So, you got the basics of Conversion Rate Optimization (CRO) and why it’s essential to your marketing strategy. (To recap: a successful CRO framework increase sales and revenue while reducing the cost of paid media.) Great! But how do you get started? There are some general things everyone can focus on to improve their conversion rate, e.g. site speed. But where’s the list of best practices, you may be wondering? The unfortunate reality is that there’s no convenient checklist waiting for you. There’s quite a bit more to CRO than applying a few changes, crossing your fingers and walking away hoping for the best.

Data and analytics inform CRO

Optimizing conversions works on a case-by-case basis. Each brand, site and customer journey is different. There are millions of sites out there with varying needs, goals, traffic, designs, languages. You can’t take what works for one site and apply it to another. Your site should serve a specific purpose; both providing value and addressing the concerns of your visitors at that moment and in the future. Forget instinct: to make any kind of business decision; you need to base your judgment on the evidence. Also known as data. Otherwise, you might be making decisions that could hurt your sales.

Analytics tells you exactly what’s happening on your site, and can then guide you to investigate the bigger picture and find opportunities. Not only does analytics tell you the core journeys and behaviors that give you the best return on investment, but it also highlights friction points and areas where most people leave the website. This saves a lot of time and guesswork, letting you narrow down the improvements that need to be made to optimize conversions. The real power comes when combining this with qualitative research, to delve into the reasoning behind the key objections that result in people leaving… but also why they stay.

The tools of the trade

It’s essential to have a multi-channel overview of performance with an analytics tool like Google Analytics (GA), instead of working with siloed platforms. Rather than dipping into different tools, a unified view of performance makes it easier to see how each channel stacks up against the others. You’ll also have much richer insights from tools like GA, which is connected to millions of sites around the world. And aside from needing your analytics tool to be accurate, it’s equally important to know how to get the best out of it.

You can access tons of good stuff for free when you first get started with GA, but to get the advanced knowledge necessary for CRO you need to go deeper. These platforms are so incredibly rich in insights, but you need to know how to put them to good use. Tweak your reports. Make use of custom dimensions, filters and segmentation. The more you work with your platform, the more you get out of it. It’s 100 percent worth it to get fully trained because knowing the platform you’re using helps you think about how to use it for your specific business strategy.

Where do you get started with analytics and CRO?

Before making any changes, you need to thoroughly investigate your current setup with a thorough audit of tools and tracking. Ask yourself: are you collecting the right data? Is it robust and credible? Unless you already have a dedicated CRO and analytics team at work, it’s likely that there are some issues at play, so check your goals and events, tracking snippets, filter settings and trigger configurations.

Tagging integration is key. With tagging, you’ve got tracking, analysis and reporting all in one. Using fully synced tagging solutions, you can track everything you’d ever want to know about your site. The more you tag, the more you’ll be able to understand how your users are interacting with your web pages. If you create a new element on a page, tag it. If you’re running lots of tests or using lots of tools, make the most of your tag management for CRO. And speaking of tests… A/B testing is the bread and butter of lots of digital marketing. Whether testing between different designs or copy, A/B testing will deliver the data to assess performance. And depending on the tool you’re using, you can send the results of your A/B test treatment back into your analytics for further analysis and a deep-dive into more granular segments.

Analytics is not optional

If you’ve ever looked at a scientific research report, the basics of CRO are pretty similar: research, experiment and analyze. The beginning, the middle and the end are always about data. Specifically, good data. Before doing anything, you need to make sure that what you’re working with is a source of truth. With so many metrics to look at nowadays, there’s a danger of intentionally finding data that confirms your ideas. CRO is about minimizing this and making improvements for the right reason, which is why a good analytics setup is so important.

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Want to create better experiences and brand loyalty? Lean on your data

For 2019, marketers will need to increase efforts at data consolidation to allow truly effective multi-channel strategies.

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Marketing leaders are in the throes of 2019 planning and there’s no doubt improving consumer experiences is among the top priorities for the new year. Data shows consumer expectations (and frustrations!) are on the rise, and brands are scrambling to understand to deepen brand-to-consumer engagement. This article will explore three ways brands can make the most of data to improve consumer relationships.

Understand that consumers are evolving, and grow with them

Recent data from a global study which polled 7,000 consumers shows a third of consumers expect brands to anticipate their needs before they arise, and a whopping 70 percent of global consumers are annoyed when every company correspondence is about making a sale. Likewise, Accenture reports 75 percent of consumers are more likely to buy from a retailer that recognizes them by name, recommends options based on past purchases, or knows their purchase history.

Now more than ever, consumers expect brands to understand them and treat engagements as evidence that they are committed to knowing them better as individuals. Whether it be through recommended products, engagement channel preferences (like social media or email), more relevant content (back to school tips or downsizing advice for empty nesters) or selecting better promotions (free shipping versus bundle deals versus free sample products), brands will benefit from going the extra mile.

Glossier is an excellent brand example of evolutionary marketing. In an extensive feature on its founder, Emily Weiss, Entrepreneur Magazine wrote:

Her customers lived on social, and her products are visual by design, which meant that, with the right tools in place, sites like Instagram could become Glossier’s R&D lab and marketing platform…the company began using Instagram to build mini focus groups and quickly create products based on what they learn.  

Make data-driven marketing strategies a consistent reality

According to six months’ worth of database, customer and engagement research, Forrester Research found that “‘data-driven marketing strategies are the new standard.” According to Forrester, “B2C marketers are collaborating with CRM teams to optimize customer relationships. Customer database and engagement agencies have used this opportunity to carve out a spot for themselves on agency rosters by keeping databases secure and compliant with global regulations, as well as creating real-time insights for marketers to act on.” The industry is seeing more proof that data-led strategies are working, but that doesn’t come without its challenges.

Many marketers find critical data is spread widely across various platforms. Sixty-nine CMOs from the likes of Tacori, Fidelity Life, Office Depot, recently shared their challenges in a study conducted by The CMO Club, with 42% of them citing customer data management as the most difficult aspect of marketing technology. For 2019, marketers will need to increase efforts at data consolidation to allow truly effective multi-channel strategies.

One such success story is Opel. The 110-year-old German automobile manufacturer, which prides itself on product and service innovations, looked to marketing technology and data to strengthen its pipeline. Ultimately, by developing a data model tied to a specific goal, the company’s prospect database grew 30 percent in four months, while the number of identified profiles increased over 473 percent.

Look at data as an opportunity by working backward from a goal to determine which data sets are meaningful. Intentional, focused data collection, consolidation, and output is possible when marketers are working to uncover customer trends and opportunities. Here are a few tips:

  • Create an audit of data collection methods and management systems. This focus allows you to truly understand what systems people use and how they use them.
  • Apply industry standards for your business and create data consolidation plans accordingly. This should be a fundamental driver of systems designed to collect, manage and store data for both short term and long-term basis.
  • Know the privacy laws of your state and country. For those companies managing personal information of Californians, the California Consumer Privacy Act of 2018 (CCPA) requires that certain data be available on demand as of January 1, 2020.  Legislation like this should be incorporated into your data consolidation strategy.

Experience is everything

CMOs know how critical it is to improve relationship marketing, with 62 percent of marketers noting it is the most important (or one of the most important) functions of their team this coming year. Of course, communication and engagement is a big part of that, with two-thirds saying their top marketing automation goal for 2019 is to speak to their customers in a more relevant way. That sentiment is echoed by an Adobe report: 33 percent of retailers cite “targeting and personalization” among their top three tactical priorities for the year ahead, higher than for any other marketing tactic.

So how does a brand build both the communication channels and experience they seek?  Here are a few things to consider:

  • Ensure sales and customer service teams are properly aligned: Nothing irritates a person more than being solicited to buy a product they’ve already purchased (or hated and returned). There are many opportunities for customer feedback and service requests to inform which marketing strategies are working or not. Be sure your customer service department is a part of marketing and campaign conversations – they’ll have real-time insights that are worth hearing.
  • Lead with your values: Digital marketing can be a double-edged sword. There is enormous value in connecting via multiple channels with your customers and learning more about them through data, but brands that don’t honor consumer preferences ultimately fail because their values are being compromised in favor of potential sales.  And, let’s face it, if you don’t respect things like opt-out requests or address poor consumer experiences, customers will melt away like post-Christmas snowmen.

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