Here’s how to get the most out of your marketing analytics investment

Build organizational structure and develop analytics leaders who bridge data science with marketing strategy to improve your return on investment.

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Gartner recently published their Predicts 2019 research report, outlining several converging trends that pose a threat to CMOs and marketing organizations. The report also makes several bold predictions including that “by 2023, 60 percent of CMOs will slash the size of their marketing analytics departments by 50 percent because of a failure to realize promised improvements.”

The number one success factor for CMOs today is the ability to effectively leverage customer data and analytics. And yet, according to Gartner’s report, companies today are clearly not demonstrating consistent return on that investment, a problem which often stems from a lack of marketing analytics leaders and the organizational structure necessary to effectively translate data and insights into action.

To discuss in more detail, we chatted with one of the authors of the Gartner report, Charles Golvin, to explore what CMOs and marketing leaders can do to buck the prediction and drive stronger results for their marketing analytics investment.

Our conversation, coupled with my own experience, solidified five ways CMOs can improve return on their marketing analytics investment, while also reinforcing why it matters:

1. Build organizational structure to apply better data

Knowing how to effectively leverage customer data and analytics is the number one success factor for CMOs today. And yet, to fully leverage the power of analytics, companies need to develop organizational structure and processes to be able to identify, combine and manage multiple sources of data.

As Golvin puts it, “companies need to build a better pipeline of carrying data from its raw state to decision and action systems for data science leaders to apply insights and powerful analysis to determine the right action and right strategy.”

To build these pathways, companies need a strong methodology coupled with an approach for how data gets aggregated, digested and applied to their various marketing systems.

2. Develop analytics leaders who bridge both data science with marketing strategy

Another key success factor for companies is developing and hiring the right leaders who can bridge both data science and business strategy. Simply put, analytics leaders need to know enough about business to ask the right questions of data. Only then, can they apply data and models to yield better decisions and drive sustainable growth.

This is our philosophy at Wharton – preparing well rounded, analytically-adept business leaders who don’t ask what data can do for them, but what data is needed to increase customer lifetime value (CLV) and how to apply data and customer insights to shape brand strategy.

Gartner regularly conducts surveys about different challenges that CMOs and marketers face, and every year, the one that rises to the top is finding skilled data and analytics leaders to hire,” shares Golvin. “Companies also struggle to find those ‘unicorns,’ or people able to command both data science and business strategy.”

Golvin also pointed out that once a company does hire an analytics leader, companies need the right foundation in place to foster their success. “There’s no value to hiring a data scientist whose output leadership doesn’t understand or know how to implement.”

Too often, we see traditional marketing organizations that aren’t able to effectively apply analytics or don’t understand how to frame the questions for data scientists on their team. The reverse is also a common challenge: analytics leaders don’t grasp how to use data to shape the broader business and brand strategy.

3. Hire a Chief Analytics Officer, or up-level the importance of analytics

So how do companies up-level the importance of analytics and develop the data-driven culture, capabilities and leaders needed to successfully transform their organization? One trend we are seeing is the emergence of the Chief Analytics Officer or Chief Data Scientist across more organizations.

As Golvin notes, “we’re already starting to see the emergence of Chief Marketing Technology Officers, who are focused on deployment of the right technology, architecture and capabilities. The next trend may be marketing analytics leaders at the c-level, who are purely about analytics and understanding the data.”

When companies empower analytics leaders to lead strategy, it can transform the culture, providing a clear vision for what customer data will be used and how to reach the desired business impact. When companies fail to make this investment, it leaves high-caliber professionals in a quandary.

“Too often data science leaders end up doing grunt work such as basic data processing and preparation, rather than using their analytics mindset and abilities to drive actionable marketing strategy, separate the signal from the noise and improve marketing outcomes,” notes Golvin.

4. Focus on better data, not big data

An ongoing challenge organizations face today is what we call “better data, not big data.” Too often we see companies that are collecting data for data’s sake, rather than taking a lean approach where they only collect data when it helps to optimize the experience for their target customers or better prediction of future behaviors.

“As data becomes more integral to marketers, a ‘more is better’ attitude develops, without necessary consideration given to the downside risks,” notes Golvin. “Companies need to do a better job of being transparent about what data they use and how, as well as considering the pros/cons, and risks of incorporating that data into a profile of their customers. More data does not necessarily lead to greater business intelligence – and in many cases can expose the brand to issues that impact customer trust.”

Data collection is in no one’s interest when it’s not meaningfully tied to strategy.

5. Separate the signal from the noise to predict and optimize business outcomes

Improving ROI for marketing analytics requires constant learning and experimentation to separate the signal from noise. There’s no better way to learn about your customer than to see what works and what doesn’t.

While big data and machine learning are great to business intelligence, a well-controlled experiment can deliver far more value. Finding the most impactful experiments to run starts with asking the right questions and maintaining a test and learn mindset where you’re constantly evolving to improve the experience for customers. The iterative adaptation based on these experiments builds momentum.

Many marketers know the “Holy Grail” phrase “deliver the right product to the right person at the right time.” In the past, this was more difficult because we didn’t know where consumers were. Now when marketers use better data, they know where the customer was and is more likely to be – providing the foundation for the ultimate in contextual 1:1 marketing.

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AdStage launches Google Sheets add-on for cross-channel campaign data

Advertisers can import data from multiple search and social adverting channels with one query.

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Now AdStage, a cross-channel campaign analytics and optimization platform, is getting in the add-on game with a new data connector for Google Sheets.

What is it? AdStage for Google Sheets, which launched Thursday, is an add-on that lets users import their paid search campaigns, social campaigns and analytics data from AdStage into Google Sheets with one query. AdStage supports paid search and social networks, including Google, Bing, LinkedIn, Facebook, Twitter and Google Analytics.

AdStage for Google Sheets has been in beta for about six months. The product pricing is based on media spend and starts at $29 per month, undercutting Supermetrics – the dominant leader in this space. The license includes unlimited users and unlimited accounts, which again challenges Supermetrics’ comparable offering.

How does it work? AdStage for Google Sheets is available from the Add-ons menu in Google Sheets. Once installed, you’ll see a sidebar in your Google Sheet. The low price means the sidebar interface isn’t fancy, and is designed for somewhat technical marketers who already know how, or are willing to learn, to build queries. The query structure is straightforward, with several query templates already available to get you started. There’s also a video training series and support portal built out for it.

Why we should care. They key is getting blended data calls to pull in data from across multiple channels with just one query. You can then build reporting dashboards in Google Sheets, like the example shown above. Or, you could bring it in to Google Data Studio. AdStage for Google Sheets uses the same API as the rest of the platform, so any data you can access in AdStage should be accessible in Google Sheets with a query.

“We are using AdStage for Google Sheets to combine cost and campaign performance data for the entire company to consume and work with. Without any integration work, we were able to aggregate all of our publisher accounts and blend complex cross-channel data into a single sheet,” said beta user Arndt Voges, head of growth at space rental company Peerspace, in a statement.

Digital agency 3Q Digital, another beta user, has already created a workbook in Google Sheets to help track and visualize cross-channel budget pacing, demographics performance and more.

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

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Supermetrics for BigQuery launches on Google Cloud Platform Marketplace

Tired of manually connecting siloed data to create marketing reports? Supermetrics for BigQuery launches marketer-friendly solution for creating your own marketing data warehouse.

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Supermetrics for BigQuery enables marketers to bring together cross-channel marketing metrics in one platform.

Supermetrics has launched a connector for BigQuery, promising a “plug-and-play” solution for marketers to compile cross-channel campaign and analytics data with just a few clicks.

What it does. Supermetrics for BigQuery is designed to bring data from multiple marketing platforms into BigQuery — effectively setting up a BigQuery data warehouse without having to write code or SQL or rely on developer resources.

“This new product complements our existing offering by providing a robust, enterprise-scale data pipeline into the most powerful data warehouse out there, Google BigQuery,” said Mikael Thuneberg, founder and CEO of Supermetrics, in a statement.

Why we should care. The ultimate goal is to be able to make better decisions about marketing allocations faster. Getting data from multiple channels into one place where it can be analyzed is often a big headache for marketers. Eliminating the need to know how to code or write SQL, or rely on programmers and developers to create the data warehouse, means just about anyone on your marketing team might be able to get this going. Of course, you’ll need to be using BigQuery.

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Using technology to plan an effective people-based audience activation strategy

A well-designed identity strategy using audience analytics and insights can tie disparate forms of ecosystem data together for a global view of consumers.

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As the marketing world has grown increasingly “digital-first,” data has become more pervasive, and as it surges in scale, marketers find it more difficult to ascertain its value. One would think that with more data, understanding value would be much easier; however, many know this isn’t true. When your audiences range from first-party CRM data to third-party acquired data and everything in between, without a defined identity strategy, you will lack clarity around whom you are talking to and how they prefer to communicate.

The influences of a digital-first world are having a direct impact on advertisers. The promise of more accurate and scalable data is becoming a reality through people-based marketing but understanding how to best leverage identity across various data sources, platforms, and customer touchpoints are becoming increasingly complex.

Typical challenges faced by advertisers include:

  • How do I activate my offline CRM data for digital marketing and maintain a persistent view of my customers?
  • What is the best way to leverage CRM data in digital marketing tactics, such as display, where cookies are mixed in?
  • How can I leverage my website data to better personalize digital marketing, while maintaining knowledge of the user’s identity?
  • Is third-party data a worthwhile investment, and how do I best utilize this data for prospecting tactics?

While this list is only a small sample, it illustrates the complexity of audience activation. A well-designed identity strategy that is enabled by technology can tie disparate forms of ecosystem data together. Data Management Platforms and the newer space of Customer Data Platforms can make this a reality when combined with a persistent global view of people.

You can think of an identity strategy as a playbook, structured around insights based on audience analytics to assess value, which all ladder up to tactical business use cases. This might sound daunting, but it’s not rocket science.

Let’s discuss how you can best plan this out.

1. Start with the technology

Without getting too deep, DMPs are cookie-based data platforms that maintain first- and third-party data not tied to a known identity. CDPs, on the other hand, are good at maintaining known first-party personally identifiable information (PII), such as first name, last name, address, email, etc. You can think of a DMP as the central command center for cookie-based activation and driving connected experiences across paid media and the website. A DMP can help augment your prospecting use cases with third-party cookie data from a variety of vendors in the market.

A CDP will bring together your website data and cross-channel media data, and (most importantly) it will enable you to link this all to a known view of a customer. Whether it be someone who has raised her hand to receive an email communication or someone who purchased a product, this view of identity can be stitched together in a secure (and anonymous) way (i.e., any reference to PII is removed).

The interplay between a DMP and CDP is critical and centralizing all of this data allows you to focus on studying audience segments across various data sources to find pockets of value.

2. Understand audience use cases

Now that you have a basic understanding of the technology, you should understand use cases around audiences and how to start segmenting your data. There are general buckets of data you will come across. Everyone has different versions of these buckets; however, they represent the most common audience use cases.

  • First-party offline CRM – This is your most valuable data asset.
    • Examples: Account records, call center data, loyalty data
  • First-party online authenticated
    • Examples: Website conversion data linked to a logged-in account, email, and CRM loyalty data
  • First-party online anonymous
    • Examples: Digital media impressions, Website visitation data, and mobile data
  • Second-party
    • Examples: Direct purchases of audiences (e.g., cookies) from another data provider and publisher, where you now own the audience
  • Third-party
    • Examples: Audiences data purchased to target against and use for analytics rights; however, unlike second-party data, you do not own the rights to the audience (e.g., cookies)

3. Assess the value of this data

Now that you have a basic knowledge of different types of data and how they can be bucketed, it is time to start assessing value – a key to defining a people-based audience strategy. Value can be assessed in multiple ways, varied by industry-specific KPIs. As you will come to see, scalability is the largest driver impacting value for any data asset.

Assessing value requires building an analytics framework, which we will discuss in the next part of this series, to start evaluating your data and make recommendations on the best way to activate it across your organization.

Sunil Rao, Vice President of Analytics at Merkle, also contributed to this piece.

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Taking a page from politics: Local campaigns at scale

A national campaign director shares some lessons learned on how to uncover insights about your audience while also striking the right balance of personalization with practicality.

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Over the past two decades, the race for audiences propelled a shift away from local. Mainstream national news trumped local, national chain stores pushed out local shops and national advertising campaigns became the default over local. But this trend is changing. Now more than ever, local communities are thriving. The desire to connect with people, no matter their geography, shaped the digital landscape. Now, that desire for human connection is swinging back towards the familiar: the community and the local.

Local has become a more dominant focus for brands as well. Search, for example, is fueled by local-driven queries, with local advertisers serving as the primary driver of Google’s revenues last year. This is a fundamental shift away from traditional mass marketing, as well as an important step towards hyper-personalization. Local marketing is its unique beast – what works in Jensen Beach, Florida, will not resonate with audiences in Billings, Montana.

For many brands, local marketing is only logical at a certain scale: targeting several local communities, across a broader geographic landscape. Anything more specific requires too much additional work, from creating custom segmentations, messages and managing the campaigns (although adtech has helped significantly with the last issue).

This has been a longstanding challenge for political campaigns, particularly those of Political Action Committees, national and statewide candidates. Campaigns win by engaging voters and gaining votes which require connecting with constituents at the local level, often across many unique communities. Personalization is everything. How can you execute on a mass scale, without losing personalization?

Ethan Roeder is the director of campaigns at Forward Majority, and a former director of data for President Obama’s presidential campaign. In his current role, he focuses on state legislatures and districts where Democrats can win back elected positions before redistricting happens in 2020. This mission is cross-country, and last year the organization embarked on a six-state campaign, working in a total of 120 districts, during the 2018 election cycle.

We spoke with Roeder about lessons learned and best practices when it comes to tackling local campaigns on a massive scale:

Identify promising localities with a mission-driven focus

While Forward Majority’s overarching goal of more Democratic elected officials was a national one, their approach needed to be local. To make the most of their efforts, and budget, they identified locations which were best positioned to benefit from their involvement, both on a state and district level. Spreading resources too thin would limit the effectiveness of their work, but limiting the number of locations would minimize the organization’s impact.

“The last election cycle was a critical opportunity for us to change the tides,” explained Roeder. “To achieve that, we first focused on our selection process. We looked at states that were Republican-controlled, had large congressional delegations and were facing contentious redistricting fights post-2020. With that screening, we narrowed our focus down to six states.”

From there, Roeder and his team whittled the focus further to specific candidates. They relied on a predefined set of criteria that was mission-driven to guide this process. As an organization, they were willing to take on more risky bets, so, as Roeder explained, they decided to focus on candidates with limited monetary resources that were also under-supported from traditional party factions.

Be flexible to identify uncovered insights

In tackling 120 campaigns across six different states, Roeder and his team faced messaging needs for each local community. Messaging must resonate with each audience, and the best way to capture that understanding was through research and on-the-ground resources. As with most political campaigns, Roeder leveraged various research methods to comprehend better local sentiment and hot-button issues using that information to craft the perfect message.

Roeder’s research in Wisconsin is an example of this. At the time, a pending deal with Foxconn brokered by the state’s governor was a point of contention for many local voters. The deal was to provide generous subsidies in exchange for a $10 billion plant, with 10,000 plus jobs, in Wisconsin. When Roeder and his team tapped their local Wisconsin resources, they were advised that Foxconn was the way to win campaigns in the state.

“When we conducted our virtual focus group research, we found that while Democrats in the state were very fired up about Foxconn, Independents and Republicans saw the issue differently,” said Roeder. “It wasn’t a vote-swaying issue for the latter groups, which was our target audience.”

This information was pertinent to defining the right message for the right audience. If they went with the Foxconn angle, they would be motivating the Democratic base, but not the audiences they needed to activate to win the campaigns. It was the flexibility with which they approached the research that allowed Roeder and his team to uncover this valuable insight. They instead led with protecting health care for people with pre-existing conditions and increasing funding for Wisconsin public schools and were able to improve Democratic vote shares by 2.5 percent even in majority-Republican districts.

Strike the right balance

With so many local communities on their radar, Roeder explained it was essential to strike the right balance in their approach. The desire to be granular must be balanced with practicality.

“We created over 250 digital videos for our candidates but we had limited production capacity,” said Roeder. “The issues and themes we used were similar across similar districts, but still varied based on the candidate and local insight.”

The wisdom gained from the political trail is particularly applicable in local marketing, as it is built on personal engagement. As Roeder’s work highlights, a research-driven strategic approach provides the necessary information for executing a successful local campaign at scale.

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Here are 6 ways to build a customer-centric and data-driven culture

Delivering customer growth and value starts with creating a culture of learning.

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As every marketer today knows, the ability to collect, analyze and act on available data is increasingly vital to any brand’s success. Companies at all levels of data maturity are investing in data analytics and marketing methods to personalize and improve customer experiences at scale and in real time. And yet, transforming brand and product experiences to increase Customer Lifetime Value takes more than just a commitment to data and statistics.

CMOs today are being asked to drive sales and revenue growth, improve CX and marketing effectiveness through data analytics, all while also leading brand, product and customer marketing. Creating meaningful impact and growth across these areas starts with defining your brand strategy from the outside-in.

With that overview in mind, here are six steps companies can take to improve CLV and create a more customer-centric and data-driven culture:

1. Build an outside-in, CLV-focused strategy

Today’s most successful brands embrace the fact that every customer is different, with continually evolving tastes and preferences. A fundamental lens for this perspective is Customer Lifetime Value – a prediction of future purchases with your company based on past transactional and behavioral data, viewed through the lens of predictive analytics.

Too often, we see companies that are focusing largely on marketing execution to improve CLV, continually searching for the next piece of content or martech tool, instead of starting with a holistic strategy that’s grounded in customer and competitor insights. In other words, they’re taking an “inside-out” vs. an outside-in approach.

Brand strategy starts from the outside-in, understanding the underlying needs and motivations of customers through data (both quantitative and qualitative), in the context of competitive and market dynamics, and translating those insights to strategy – and then action.

2. Build your culture around the customer

The first thing to remember is customer centricity is not about treating all customers the same, or trying to get the highest Net Promoter Score across all customers. Rather, it is about serving your target customers.

While many organizations say they are customer-centric, the reality is the companies that are truly committed to customer-centricity stand out. They passionately believe their target customer comes first and this mentality permeates throughout the entire organization. They can make intentional trade-offs to ensure resources are focused on their strategic target customers, and try to minimize distractions from others.

To get to this level of customer-centricity, companies need to live it – literally. They have to transform their organization and gear their team to not only understand what their target and most valuable customers want but also use available first and third party data to understand their near term and future needs.

Warby Parker’s rocket ship growth is a great example. When Warby Parker founders were still at the University of Pennsylvania’s Wharton School, they conceived their company, refining their home-try-on program, arguably the key to getting people to purchase glasses online. By connect­­ing directly with their most valuable customers (MVCs), gathering data about their purchase behavior and preferences, they’re able to deliver a unique experience across customer journey, building affinity and loyalty for their brand.

3. Evolve your organization around the customer journey

At its core, CLV is a function of how a brand uniquely creates and delivers products and services for a target set of customers which causes those customers to choose and pay more for the firm’s offerings vs. a competitive alternative. Once your MVCs are identified and you’ve developed a differentiated strategy to reach them, your go-forward success depends on delivering value across the entire customer lifecycle and journey which then minimizes both acquisition costs as well as churn.

To successfully do this day in and day out, CMOs need to invest in developing a data-driven culture, competencies and best practices across their team for turning available data and insights into action.

  • What’s happening with customers at the moment and how can we improve the message, the content and the experience we’re delivering?
  • Why aren’t some of our intended target customers choosing us?
  • Are their adjacent, high-value customer segments who could be attracted to the brand or product extension?

To effectively answer these questions and improve CLV, marketers need to experiment (ideally via randomized experiments), while also separating the signal from the noise. These signals can be quantitative data (internal and external data) and should also be informed by qualitative research and feedback such as surveying front line employees, customers, partners, etc. One additional source of data is user-generated content (UGC) which allows firms to hear the “voice of the customer” using text data obtained from ratings, rankings, reviews, etc… and then tie the numerically coded text, via natural language processing methods (NLP) to consumer activity, hence CLV.

4. Identify your most valuable customers

Companies need a differentiated strategy that puts their most valuable customers at the center, or bullseye, and that strategy is a living process, one that is continually tested, verified and improved.

So how does a company go about identifying who their MVCs are? For existing customers, as Wharton Marketing professor and author Peter Fader notes, identifying your MVCs starts with asking the right questions: “Based on what a customer has done in the past, can we make a pretty accurate projection of what they are likely to do in the future?”

What segments and customers are most attractive for your brand to target and build products and services for? How do you understand the customer journeys for your most valuable customers?

Through research and analytics, companies can not only identify who their potential new and existing MVCs are but also what they care about, what motivates them and what types of differentiated products, services or offers they’re most likely to want in the future.

5. Reward learning and experimentation

There’s no better way to learn about your customer than to see what actually works and what doesn’t. While big data and machine learning are great to business intelligence, a well-controlled experiment can deliver far more value.

Finding the most impactful experiments to run starts with asking the right questions and maintaining a test and learn mindset where you’re constantly evolving to improve the experience for customers. The iterative adaptation based on these experiments builds momentum.

Before brands dive into any experimentation, they need to ask themselves the following questions:

  1. How is the org structured and how is innovation and experimentation rewarded? Do team members embrace continual experimentation and iteration?
  2. Do they have a specific team that is focused on designing and running experiments, or is it shared and expected broadly across the organization? If the latter, how are people measured and incentivized to experiment?

One of the most important aspects of successful experimentation is having transparency and visibility into what people are learning. Encourage your team to experiment, fail fast and share what they’re learning.

6. It’s about better data, not big data

An ongoing challenge organizations face today is what we call “better data, not big data.” This is a conversation that’s happening more and more at the CMO, CIO and board level.

Collecting more data doesn’t necessarily lead to greater business intelligence – and in many cases can expose the brand to issues that impact customer trust. And yet, too often we see companies collecting data for data’s sake or trying to leverage the wrong data to understand or improve the customer experience.

The key for strategic marketers is only to collect data if it allows for better prediction of future behaviors, or helps optimize the MVC’s experience with the brand. So, what data are truly needed to improve CX and CLV over time? And, what value are we delivering customers in exchange for any personal data?

Why this all matters

In today’s market, on-demand experiences are everywhere, interwoven across every facet of our daily lives, and are becoming expected. The brands that are the most successful obsess over the target customer experience. They steer every aspect of that experience by developing an outside-in strategy and anticipating and predicting the needs of their target customers through better data and analytics.

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Back to Basics: How every marketer can tame the analytics beast

Incorporating analytics into your work isn’t as challenging as you might think once you find the right resources (and many are free).

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For most marketers, analytics exists in a magic Pandora’s box, encompassing everything from CPCs to CTRs, from algorithms to artificial intelligence, from machine learning to quantum computing — with a bit of blockchain sprinkled in for good measure.

Buzzwords aside, the barriers to incorporating analytics into your life aren’t as high as analytics behemoths may make it seem. To the contrary, once you clarify a few misconceptions, you can make this seemingly enigmatic field not only relevant but also remarkably useful.

You don’t need an Excalibur

Cost is an often-cited obstacle to starting a data journey. Despite the shiny advertisements, you may see for Adobe’s Marketing Cloud (which costs upwards of $100,000 a year) and the dozens of LinkedIn messages you get from martech salespeople; you don’t need Fortune 500 money to take a stab at unlocking analytics. Google Analytics, Google Search Trends, Hotjar and HubSpot are just a few examples of industry-standard platforms that can dramatically improve your decision-making capabilities for free.

Even better, these platforms are made for data amateurs. Their interfaces are straightforward, and if you get lost, there are countless tutorials, help forums, boot camps and even classes to help you. Google also offers a certification program for Google Analytics, complete with videos and walkthroughs. It’s perfect for anyone who needs a place to start.

Don’t let the tool guide be the craftsman

Marketers often forget that data is merely a tool. Expecting a Google Analytics tag to fix your website is like throwing a hammer at your newly opened IKEA purchase and expecting a sofa to emerge.

In other words: Collecting data is the easy part. Understanding what to do with all this info is where the magic happens.

So, spend a few weeks studying how to interpret data. Bootcamps and classes are always helpful, but the secret that every engineer already knows is that Youtube and Google are your best friend. Dig out your notes from that statistics class in college and learn how to run a simple correlation in Excel. An investment of your time today learning how to interpret data will pay dividends for the rest of our career.

Keep perspective

There are no sure things in marketing. Even scientists (and yes, I mean the ones in lab coats) often need years of data collection, rigorous modeling and endless testing to prove a hypothesis. And that’s in a lab. Imagine what happens in the real world, where things are constantly changing and driven by deadlines.

In this chaos, it’s no surprise that data rarely provides a bullet-proof answer. Sure, you can add more expensive technology, but it’s important to remember that, as marketers, we’re dealing in the realm of probability, not exact certitude.

What’s more, it’s okay to be wrong. Take every failure as a badge of honor; minimizing risk does not mean avoiding it entirely. A 95 percent chance of sunshine tomorrow still means that rain is a possibility, but also, your decision to not bring an umbrella isn’t necessarily incorrect. Make peace with the risk as long as you separate logic from emotion. In the long run, your data-driven approach will result in far more wins than losses.

You’re a solver of problems, not a creator of reports

All too often, people associate analytics with reporting. While reporting is critical, it is merely a means to an end. No business has ever been transformed by a single report.

Data is meant to be used as an unbiased means to test something. Nowhere in that definition does it stipulate that you must create daily, weekly or even monthly reports.

As we’ve seen, data takes time to collect. And while you should consistently check your data, it’s up to you to find the reporting cadence that works best for your team.

Then, instead of focusing on frequency, you can focus on presentation quality. Data is like a foreign language; it’s only useful if someone else understands what you’re saying. So, make sure your reports are thoroughly readable. Be concise, use visuals and err on the side of plain language. Above all, always return to the core business problem you’re trying to solve.

Next steps in your journey

Contrary to conventional wisdom, analytics isn’t shorthand for building sophisticated statistical models. Properly understood, analytics is a philosophy that embodies something much simpler: applying the scientific method to test your educated guesses. Whether you’re running a simple paid Facebook campaign or trying to get into shape for that Bahamas cruise this summer, you can leverage data to make more targeted, meaningful choices.

The reason you’ve read this far is that we agree on a key point: every marketer needs to integrate analytics to succeed in this digital world. In an age where it’s hard to keep up with the jargon, I fully empathize with those who view “analytics” as some enormous, mystical beast. On the contrary, understand that analytics is much more like a puppy; managing your data may be a little unruly at first, but with enough consistent training and respect, the lessons you learn will last you a lifetime.

A data journey can start tomorrow with nothing but a problem to solve or a hypothesis to prove (and a laptop with an internet connection).

So tell me, what are you waiting for?

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Marketing analytics buyer’s guide for 2019

Cardinal Path’s Marketing Analytics Buyer’s Guide 2019 arose from a need to address a growing uncertainty about which digital tools, techniques, and applications will best serve the enterprise, as optimized consumer journeys—driven by data insights—become the cornerstone of an organization’s competitive advantage. This carefully curated shortlist of solutions has delivered significant business value for Fortune […]

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many charts on many computer monitorsCardinal Path’s Marketing Analytics Buyer’s Guide 2019 arose from a need to address a growing uncertainty about which digital tools, techniques, and applications will best serve the enterprise, as optimized consumer journeys—driven by data insights—become the cornerstone of an organization’s competitive advantage.

This carefully curated shortlist of solutions has delivered significant business value for Fortune 1000 organizations.

All nine solutions are tried, tested, and proven to help marketers deliver positive results against their very tangible business goals.

Visit Digital Marketing Depot to download “The Marketing Analytics Buyer’s Guide 2019.”

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The new age of ‘customer’ data

The martech ecosystem needs a new class of tools for data buyers and sellers that curates and leverages date both online and offline.

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The martech industry seems to be constantly on the hunt for the next big thing, and understandably so. In an industry where we said goodbye to a handful of platforms and brands in 2018 (R.I.P. LittleThings, Go90s, Rocketfuel), the next big thing may be what keeps us all not only chugging along, but successful. For my big bet, I think the smart money is on an evolution of 2018’s darling, the customer data platform (CDP).

A 2018 survey by The Relevancy Group found more than 80 percent of firms had already engaged a CDP vendor or planned to. The Interactive Advertising Bureau (IAB) and Winterberry Group estimated U.S. marketers spent close to $5 billion on data management and integration products in 2018, further evidence of the market’s continued emergence.

However, leading analysts noted that 2018’s customer-data fever was limited in scope. CDPs, like data management platforms (DMPs) and demand side platforms (DSPs) before them, have a narrow focus on a specific subgroup. For CDPs, it’s brands’ existing customers. For DMPs and DSPs, it’s prospective customers, based on online intent data gathered from cookies. All of these platforms almost exclusively cater to consumer brands. I believe that in the maturing era of Big Data, there’s evidence of a new world order.

New class of data buyers and sellers

In martech’s latest epoch, consumer brands are not the only companies that curate, store, leverage and sell data. As such, they’re also not the sole purchasers of marketing technology. Publishers are just one recent example of a newer breed of data buyers and sellers coming to the table.

This new class of data buyers and sellers, already engrained in data commerce, include publishers, platforms and agency holding companies. They bring unique data challenges that CDPs, DMPs and DSPs weren’t built to solve. This new class curates data both online and offline. It also requires the ability to leverage and sell data that covers existing customers, active prospects and potential prospects yet unknown.

All of these needs require tools that offer visibility and insights into this data in ways we haven’t seen before. Data owners need technology to organize, visualize and package data sets at incredible speed and scale to activate within the martech ecosystem.

AI activation for large data sets

CDPs were bankrolled, in part, because of their ability to unify customer data and make it accessible to other systems, specifically helping brands target customers across channels and devices. Platforms, publishers and other data owners require different means of organizing and understanding the data they collect. Rather than collecting data in order to reach those users again, data owners have data that they can monetize or activate for clients, which requires an additional layer of analytics, beyond centralization and organization, in order to activate it.

The technology that we’ve already developed for the targeting use case can also serve the monetization archetype. Though it’s traditionally been used for targeting, artificial intelligence is a general activation tool for deriving insights and driving activation from large data sets, and therefore can serve both needs.

Holistic view of data management

The demand and technology exist. The timing couldn’t be better. The amount of data we create and money we spend to manage it continue to grow. But in order for our new class to embrace yet another tech stack component, three principles, scale, speed and control, must be present in the form this new platform takes.

Scale is an indelible component of marketing solutions in order to justify cost, and even more so in the automated programmatic age. Speed is integral because the longer technology takes to build, implement and start driving results, the lower the ROI. Finally, as data owners leverage new technology and data to monetize data sets, they’ll require increased visibility into data accuracy, coverage, security and more. This level of transparency shepherds an in-depth understanding of data governance practices, a requisite of an age where calls for transparency continue to crescendo in the aftermath of data-related scandals, GDPR and new stateside regulation.

The eras of Big Data, digital and single customer view have long been culminating in this new generation of holistic data management. We’ve been waiting for the technology with the capacity and range to activate this data. Now that it’s here, why should brands have all the fun? There is a new class of data owners, including publishers, platforms and agency holding companies, that can leverage this technology to bring new, unique data sets into the marketplace, which can drive incremental value across the entire ecosystem.

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Embracing automation and maximizing SEO performance

Intelligent automation allows SEOs to schedule tasks and immediately activate data to inform smarter optimizations.

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Creating an automation strategy should be top of mind in 2019 – indeed, it was identified by 61 percent of marketers as the top priority for optimizing marketing automation efforts in a recent industry survey. Researchers also identified the delivery of personalized content and integration of marketing systems as the most challenging barriers to your success with marketing technology. SEO and Automation is a big part of the solution.

Automation is critical in making informed, data-driven decisions in a world in which the amount of data companies are attempting to manage is unprecedented. But we’re at the point now where, as marketers have attempted to automate various tasks, many are struggling with unwieldy stacks of different technologies all vying for resources and budget.

If you or your clients are spending more time trying to find workarounds for your tech than putting insights to work, money is being left on the table. As creative marketers with technical and analytical skills, SEOs are in a great position to lead the creation and implementation of automation strategies companies now need to succeed.

SEO, automation and the customer experience

Automation in your SEO and content process can create efficiencies and ease the burden of redundant tasks, but we’ve evolved so far past that (and quickly). Today, automation alone is not enough. SEOs must automate intelligently — not only to complete tasks but to analyze data and make decisions about which tasks to prioritize (and how to carry them out), as well.

AI is enabling the collection and analysis of datasets we simply cannot get through on our own. Layers of natural language processing and machine learning enable smarter optimizations driven by predictive analytics, pattern recognition, and evidence-based learning.

Source: Search & the Customer Experience: Utilizing AI to Drive Continuous Performance

Site audits, competitive analysis, monitoring rankings and other SEO tasks are made easier and more efficient with automation. But are you ready to take the next steps?

AI is already reshaping content marketing, all the way from ideation, planning and optimized content creation through to promotion to specific market segments. This isn’t new technology; in fact, the Associated Press has been using artificial intelligence to write business news since 2014 (and even then, the program could churn out 2,000 articles per second). We’re now at the point where automation can help identify new revenue opportunities and make recommendations on content topics, attributes, optimizations, strategic CTAs and more.

Intelligent automation also allows SEOs to schedule tasks and immediately activate data, to inform smarter optimizations. You can target specific content to searchers who interact with your chatbot, for example, depending on what led them to the interaction in the first place.

Automation – Data analysts, real-time research, content and communications

Intelligent automation is giving SEOs greater insight into – and control over – how search optimizations impact customer experience throughout every stage of the journey. Now, the insights gleaned from the real-time analysis of customer interactions can help shape every aspect of the customer experience, from discovery to conversion.

Source: PWC

How AI is driving superior search performance

Google’s dedication to AI is resulting in far more interactive search results that speak directly to searcher intent, as in these three similar queries that each produces different results:

Google is using taller organic cards, the local three-pack, Quick Answers, images and video, carousels, site links and dozens of other enhanced results to better answer searchers’ needs. The algorithm is listening to searcher cues and constantly learning to bring back the most relevant result. More and more often, that result will answer the need in such a way that the searcher doesn’t even need to click through to learn more. This is Google’s RankBrain technology at work.

Last year, research by BrightEdge (my company) revealed that 80-plus percent of queries return universal search results. Optimizing, structuring and marking up your content to show Google its relevance for queries of varying intents helps increase your visibility when and where it matters most. At the same time, you’re providing more compelling content and may even convert searches to sales without the consumer ever having visited your site.

SEO is moving further away from the static website; what you are optimizing for spans the entire search-based experience. And as Google’s ability to determine intent continuously improves, SEOs and marketers need to keep pace with AI and automation to stay on top and produce properly structured content.

  • Optimize for voice search. Use a more conversational tone in your content and incorporate longer-tail keywords. Applying a question and answer format to some content can help you rank in Instant Answers and as the best voice response. Be sure to apply proper schema markup, too. Read how visual and voice search are revitalizing the role of SEO for more.
  • Enable voice search on-site, where possible. Incorporate speech recognition in your app or on-site, if possible. You can extend the same hands-free convenience that delivered a searcher to your site by enabling some voice-free functionality.
  • Make good use of descriptive text. While many of your audience members crave visual imagery and video, some will not be able to render, watch or hear this content. AI can help in the creation of descriptive text and also with categorizing all kinds of content to improve both your accessibility and SEO.
  • Use intelligent automation to complement your skills. It’s important to understand that automation can’t ever entirely replace the creative and the strategist—they will continue to decide which technology to apply, and where. However, the Intelligent use of automation will help you do your job more effectively, so you can focus on more important and higher impact tasks.
  • Monitor regularly for new opportunities. Google is constantly testing and launching new features in the SERPs. It’s not a static space, and you cannot afford to sit still. Use automation to regularly analyze your search presence, as well as that of your most important competitors. Ensure that you have properly formatted, optimized and marked up content in place to take advantage of new SERPs features.

Embracing automation will be increasingly important to your ability to scale and succeed in all facets of digital. If you’re just getting started, try automating time-consuming, repetitive tasks like keyword research, data visualization, reporting, data collection, SERP similarity comparisons, testing JS rendering, generating content ideas, link building and technical auditing.

Next, look to AI to begin simplifying complex decision-making and prioritizing your SEO and content efforts, all with improved consumer experience in mind. SEOs who can embrace automation are making great strides in positioning themselves as the digital marketing leaders of tomorrow.

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