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How Quitting Facebook Could Change Your Life

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By Erin Corbett

Quitting Facebook can affect your life, according to a new study.

In some ways, the report, The Welfare Effects of Social Media, recalls life before the internet. That is, without Facebook, the study’s author’s found, you have more free time and spend some of it directly connecting with friends and family.

Additionally, the study learned that being unplugged from Facebook made people less knowledgeable about politics. Some were also less affected by extremes of political discourse.

The study was headed by Hunt Allcott, an associate professor of economics at New York University, and Matthew Gentzkow, a Stanford economist, and was mostly financed by the Alfred P. Sloan Foundation.

Researchers led by Alcott and Gentzkow recruited participants using Facebook ads. They were ages 18 and older and spent at least 15 minutes on the social networking platform everyday. More than 3,000 people were in the study, based on their filling out questionnaires about their daily routines, mental wellbeing, and political leanings.

Participants stopped using Facebook, except for the social media’s Messenger app.

Results of the one-month experiment showed that people scored lower on tests of their political knowledge, while political polarization dropped between 5% and 10% for some users.

“This is not a trivial finding,” David Lazer, a professor at Northeastern University, who teaches political science and computer and information science told the New York Times. “It could have gone either way. You could imagine that the other chatter and information on Facebook was crowding out news consumption.”

Participants also reported having an extra hour each day — and sometimes more for heavier Facebook users — which they spent offline with loved ones, or watching TV.

The study also found that deactivating Facebook had a positive, yet minor impact on mood. While the study did provide evidence to support the addictive nature of social media, it remained unclear whether heavy Facebook use worsened a person’s mental wellbeing, or if people with mental health disorders were heavier users.

“This is one study of many on this topic, and it should be considered that way,” a Facebook spokesperson said in a statement to the Times. The statement, quoting from the study, added that Facebook can be highly beneficial to its users, and “any discussion of social media’s downsides should not obscure the fact that it fulfils deep and widespread needs.”

Feature Image Credit: Jaap Arriens—NurPhoto via Getty Images

By Erin Corbett

Sourced from Fortune

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When it comes to agencies, brands care more about cost than transparency

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By Seb Joseph

Price, not transparency or partnerships, remains the priority for clients when it comes to their agencies.

Last year, agencies pitching for the $1.7 billion global ad account for GlaxoSmithKline were asked to make upfront guarantees on the cost of media, despite it being widely acknowledged that those guarantees can’t be made for digital media, according to an executive who was on one of the pitch teams. Most, of course, complied.

The pitch had gone on for six months and was managed and audited by ID Comms and Ebiquity. It gradually morphed into negotiations over cost savings once the procurement team took over. Before that happened, GSK’s marketers had scrutinized each agency’s operating model and probed how they would provide full transparency into the money made on the media bought.

But the longer those discussions went on, fewer business KPIs and strategic relationships came up. Finally, agency executives found themselves in a corporate box overlooking a rugby pitch, giving GSK procurement team an overview of the savings they could make, said the agency executive.

Following those talks, Publicis won the account. “GSK reviews our media agency arrangements based on a number of criteria including strategic thinking and differentiation, understanding of our business, systems and reporting, quality of talent, the cost of media and contractual terms,” said a spokesperson from GSK, when asked for comment.

Savings often trumps all else if there is no clear winner on other criteria, and even then the numbers need to be highly competitive. It can create a race to fulfill those guarantees with cheap impressions that may not be safe, viewable or real.

Consultant education
Sources said that lack of education by pitch consultants –and agencies refusing to push back on invalid processes or decisions — makes matters worse.

“Running a tender for an agency or technology partner is not the same as procuring steel or glass,” said Ruben Schreurs, managing partner at digital media consulting firm Digital Decisions. “There is so much in-depth nuance in cost structures, and even more so when you go into operating models and strategic fit, that it is simply insane to apply too much weight to the bottom-line pricing in the decision process.”

It’s the standard trajectory of how many big media accounts are handled now. Transparency issues are the reason many advertisers go to pitch, but cost pressures are often what decides the outcome. According to data from Ebiquity, out of the 100 pitches it conducted last year, 54 percent said the top criterion used was cost improvement, and 34 percent said it was “strategic vision and expertise.”

At Adidas, which had a pitch for its $300 million media budget last year, the brand spent six months planning an operating model that went to agencies in the pitch document. The pitch, which was managed by MediaSense, was won by Mediacom. Sources said it was because the agency offered good price guarantees, although the company says the decision was more layered than that.

Progressive advertisers want fair remuneration, but there is still a disconnect between the marketing and procurement teams.

“The dynamic is changing, but I don’t think we are where are we should be,” said Laetitia Zinetti, managing principal for media management at media analytics specialist Ebiquity. “It can be difficult for an advertiser to differentiate between agencies on their strategic capabilities, so they look at how efficient they’re going to be — not just on the media costs but on the remuneration and technology costs too.”

The problem is advertisers are struggling to know what they don’t know about online media, and the pitch consultants are scrambling to fill in the blanks.

“The field of pitch consultants is growing fast, and levels of expertise are not everywhere,” said the media director at a luxury advertiser on condition of anonymity. “For remuneration, we use full-time equivalent payments and an incentive element. We want our partners to be profitable, but ideally at the same rate as we are. We do, however, see a shift in the market toward outcome or performance-based remuneration.”

Clients may even respect when agencies push back: “I worked on a pitch recently where a major agency declined to pitch in a major market because they felt they couldn’t compete on price,” said the marketing procurement director for a global CPG advertiser. “I was surprised, and it was a bit inconvenient for us, but ultimately I respected their honesty — and they saved their people a lot of work for likely little reward.”

This marketer said price guarantees are still useful criteria to judge an agency’s ability to buy digital but insisted the primary focus should be on the effectiveness of that media. “Pitch consultants continue to struggle in this area,” said the marketing procurement director, who is part of a broader push at the advertiser to take both pitch management and media management in-house.

“The KPIs on which we judge agency performance in online clearly need to include price, but should be much broader and encompass how well that agency fits with our strategic priorities such as reach versus quality, viewability or value chain transparency,” said the marketing procurement director. “I have yet to see a structured approach to this from pitch consultants that doesn’t ignore pricing but enables comparison on these broader ‘softer’ metrics.”

New models
Advertisers continue to use price guarantees to award media agencies because the cost of media is easier to determine than its effectiveness. Absolut, BT and L’Oreal have all tried to adopt newer ways of paying agencies in recent years. Whether it’s driving sales, customer loyalty or selling a car, measuring the effectiveness of these outputs is still subjective, which makes it hard to see the value. Few advertisers and agencies can get to the balance of risk and reward.

When Volkswagen ran its media pitch in 2016, one agency on the account proposed a cost-per-car remuneration model, according to one executive on the pitch. The carmaker declined. It’s hard for an advertiser to commit to outcome-based models like a cost per car when advertising is one of several factors that could impact sales, and subsequently hard to attribute a value to it. Scrapping the more traditional commission-based remuneration models like price guarantees is not a simple process.

“There is often a tension in media agency reviews between strategic marketing objectives and the need to demonstrate efficiencies,” said Nick Manning, svp at consulting business MediaLink. “Bluntly put, marketing wants innovation, and procurement wants lower costs for the same media. Digital has to be handled differently. It’s about effectiveness and value, not price, and performance has to be tracked differently. You can’t benchmark digital in the same way as TV.”

By Seb Joseph

Sourced from DIGIDAY UK

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96% of top level marketers believe that tech is driving in-house. Why?

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Sourced from Banner Flow

In our recent State of In-housing report, 96% of top-level marketers believed that tech was driving the in-house movement.

Yet what is it about advertising technology that has enabled the in-house revolution in marketing?

Well, we have it figured out.

Here are our top 3 reasons:

1. Efficiency is fuelling change

Greater efficiency is a top 5 reason for brands moving their marketing in-house.

In fact, efficiency is the ‘it’ word of ad tech. All providers promise streamlined workflows, efficient processes, and enhanced productivity – something that not all agencies can deliver.

With improved workflows, brands are questioning why they should pay an external agency to do the same work they can do in-house, often a lower cost. For top-level decision makers, the adoption of ad tech is a natural choice for marketers looking to move their processes in-house.

This is not a feeling exclusive to Europe either. In a 2018 survey by the Association of National Advertisers (ANA) 12% of US respondents claimed greater speed as a key reason for moving operation in-house.

For Ville Heijari, CMO at Rovio (an early-adopter to the in-house movement) ad tech has been an essential addition: ‘We use workflow automation tools, campaign automation, and various tools that enable us to increase the speed of delivering more versions, variation and experiments with digital campaigns.’

How do you select ad tech for efficiency?

With so many solutions out there to choose from, it’s important you select the right product. A product that will not only solve existing inefficiencies but present opportunities for future growth.

Take Shutterstock for example. Their existing online production was too slow, taking weeks instead of days. Now, they not only produce advertising quickly, but they have also created some truly exceptional ads. Thanks to added analytics and optimisation features from the Bannerflow CMP.

Whatever aspect of your digital marketing you choose to make more efficient, it’s important you pick a product that will create efficiency throughout your campaign processes. Otherwise, why bother?

scaling-bannerflow-efficiency

Scaling into different versions is easy with Bannerflow’s intuitive tool.

2. Creativity is driven by effective ad tech

For 51% of respondents, lack of time is ranked as the main blocker to creativity.

Therefore, it must follow that with increased efficiency there is greater creativity?

Nowhere is ad tech’s influence more obvious than with the introduction of Dynamic Creative Optimisation (DCO) into the online advertising space. This technology, when combined with a CMP, is the perfect combination of efficiency and creativity. Allowing you to produce the right ads, at the right time, for the right person.

Be warned though, for 22% of brands from the in-housing report believed technology to be a barrier to creativity. For Lara Izlan, Director of Programmatic Trading and Innovation at AutoTrader, the pressure to adopt ad tech too fast can lead to: ‘an ad tech infrastructure that is overly complex’.

How to choose the right ad tech for creativity?

With the right product, beautiful and eye-catching designs don’t have to take weeks off your designer’s life.

In fact, if you choose a platform with a workable interface, a comprehensive onboarding programme, and an engaged customer success team your ad tech will aid, not hinder your design team.

Indeed, for Icelandic telecommunications company NOVA, increased in-house efficiency has led to some truly innovative online advertising. With the widgets provided in the Bannerflow platform making their designers want to be creative.

3. Transparency comes as standard

9 out of 10 marketers are concerned with the transparency level of media agencies.

Problems surrounding transparency have been plaguing agencies for a number of years, on a number of issues – particularly in regards to media buying. Programmatic media buying has frequently been labelled as the ‘black box’ of digital marketing. Agencies have been taking advantage of clients ignorance to take full control of clients budgets – with little or no accountability.

Ad tech has emerged as a solution to this problem. Allowing brands such as Electrolux, Philips, and Lucozade to take full control of their media buying strategy, in-house.

The right technology can provide accountability over budgets, analytics, and optimisation. As well as help tackle issues surrounding fraud, viewability, header bidding, and yield optimisation.

Although Raluca Efford, Head of Digital and Social Media Marketing at Direct Line, has her own opinion: ‘When trading desks were first set up in 2008/09 they were there to enhance transparency for clients. Since then, they have become more and more complex and have become the ‘black box’ technology that they wanted to mitigate against.’

What does transparency mean for ad tech?

In terms of media buying, it is important to choose a tool that doesn’t over complicate the process. Having your analytics in one place, and the ability to optimise in real-time will vastly improve your campaign performance.

What is more, with the Bannerflow media buyer, you have full accountability over where your money is being spent – even on third-party integrations.

bannerflow-analyse-optimise

The Bannerflow analyse and optimise feature.

Conclusion

Ad tech presents a great opportunity for brands looking to take control and bring marketing in-house.

Efficiency, creativity, and transparency are all clear benefits of an age where technology holds sway. Yet, in this oversaturated market it is important to choose a solution that will clearly deliver on these objections.

Bannerflow’s easy to use creative management platform (CMP) allows you to build, scale, publish, analyse and optimise in one. To discover more about our product get in touch with us.

Or, if you want discover more findings from our State of In-housing report you can read the full report here.

 

Sourced from Banner Flow

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The Cost of Apple News

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By Ben Thompson

Apple is, according to the Wall Street Journal, driving a hard bargain with publishers ahead of the launch of its rumored News subscription service:

Apple Inc.’s plan to create a subscription service for news is running into resistance from major publishers over the tech giant’s proposed financial terms, according to people familiar with the situation, complicating an initiative that is part of the company’s efforts to offset slowing iPhone sales. In its pitch to some news organizations, the Cupertino, Calif., company has said it would keep about half of the subscription revenue from the service, the people said. The service, described by industry executives as a “Netflix for news,” would allow users to read an unlimited amount of content from participating publishers for a monthly fee. It is expected to launch later this year as a paid tier of the Apple News app, the people said.

The rest of the revenue would go into a pool that would be divided among publishers according to the amount of time users spend engaged with their articles, the people said. Representatives from Apple have told publishers that the subscription service could be priced at about $10 a month, similar to Apple’s streaming music service, but the final price could change, some of the people said…

Another concern for some publishers is that they likely wouldn’t get access to subscriber data, including credit-card information and email addresses, the people said. Credit-card information and email addresses are crucial for news organizations that seek to build their own customer databases and market their products to readers.

Probably the most obvious way to understand this story is that it, along with the report that Apple would have a launch event on March 25, appear to be attempts to negotiate through the media. I’m reminded of the January 2010 report in the Wall Street Journal that Apple’s impending tablet would cost $1,000; when Steve Jobs announced the iPad three weeks later, the $499 starting price seemed like a bargain. Perhaps leaking a 50/50 revenue share, along with an impending deadline for negotiations, is a way to make a 60/40 or 70/30 revenue share seem like a reasonable compromise?

The Growth of Apple News

Let’s back up for a moment: Apple News has grown to be a major force in publishing, at least in terms of traffic. According to a New York Times story that Apple cooperated with, the service “is read regularly by roughly 90 million people.” That has translated into traffic for news publishers that, according to Slate, often outpaces Facebook post-last January’s algorithm change.

The problem, as Digiday explained, is that traffic — which is almost completely realized within the Apple News app, not on publisher’s web pages — comes with minuscule amounts of revenue. Yes, Apple News allows for advertising, but that advertising is either sold (poorly) by Apple or sold directly by the publisher with no allowance for either programmatic ads nor data about users.

So why do publishers bother?

Apple News and Aggregation

There are a number of factors that should ring familiar to anyone familiar with the travails of publishers on the Internet.

To start, Apple News readers visit Apple News and, for the most part, read what Apple News presents to them; the front-page may be human-selected, as Apple sought to make clear in that New York Times article, but just as is the case with algorithmic selection (which is what determines what users see for the rest of Apple News, it just happens to be called “Suggested by Siri”), no one publication is favored:


Apple News Today view

On one hand, this is obviously not good for publishers: there is limited wherewithal to build brand affinity, there is no customer data shared (for purposes of follow-up, much less ads), and as noted above, there really isn’t much money to be made.

On the other hand, what are publishers really giving up? Readers going to the Apple News app have already made the decision to not visit a particular publisher’s website directly, and, given that digital content has zero marginal cost, why not support Apple News on the off chance some article hits it big?

It should be noted that publisher pages within Apple News complicate this narrative a bit: on one hand, they are a place to build brand affinity; on the other hand, they are more likely to cannibalize direct visits to the publisher’s website. But how many Apple News users are likely to switch to a browser for a particular publisher should they leave Apple News?

What is happening is Aggregation: Apple News attracts the users, which means publishers are coming onto Apple’s platform on Apple’s terms, which makes Apple News more attractive to users, making publishers ever more reticent to leave even though they aren’t getting much out of the deal.

Apple News and Publisher Subscriptions

For suppliers, the antidote for Aggregation is to go direct to consumers; the key is to embrace the same forces that drive Aggregation. First, the addressable market should be the entire world, not just a limited geographic area. Second, the same sort of automated payment tools available to advertisers on Aggregators can be leveraged for consumers; indeed, the tools for consumers, particularly given the lower dollar amounts and decreased need for paperwork, can be as simple as Apple Pay, and they can scale indefinitely. Third, a freemium approach to content means that social networks can be used for user-generated marketing.

Apple News as currently construed is actually somewhat helpful in this regard: publishers can push subscription-only content (as well as free content) into Apple News, and give users the option to subscribe using the App Store. For example, the Wall Street Journal elected to make the piece that triggered this Article free:

Free Wall Street Journal article in Apple News

However, the next story over, about Google Cloud, requires a subscription:

Subscription story in Apple News

It’s not perfect: clicking on that subscription link means the publisher has to pay Apple 30% the first year and 15% after that, and they don’t get any customer data (unless the customer creates an account in order to use their subscription on other platforms). Still, to my mind it is somewhat less egregious than Apple’s restrictions on in-app purchase; Apple News is driving the customer to a publisher’s content and charging accordingly (as opposed to taking a tax simply because there is no alternative to the App Store), but at the end of the day the publisher is still establishing a direct paying relationship with a subscriber.

The Spotify of News

What Apple is reportedly building now, though, is decidedly different. The so-called “Netflix of News” — although, given that Apple will pay out on a marginal basis as opposed to buying content, a better descriptor would be the Spotify of News — would entail customers paying one monthly fee to Apple which Apple distributes to publishers based on what subscribers read.

Publishers should be very clear about the implications of this model: it is not a direct-to-consumer model. Rather, it is an Aggregation model that happens to monetize via subscriptions instead of ads. That means it has all of the same problems for publishers that are posed by Aggregators:

  • Publishers do not form a direct connection with users; that connection is with Apple News
  • Publishers get no meaningful data (including no email addresses); there is no means to increase engagement or monetization down the road
  • Publishers must compete with every other publisher for attention

That last point is the most important, and should weigh heavily on publishers that have committed to the subscription model. What makes subscriptions work is an alignment between editorial and business model: the former is incentivized by quality and differentiation because the payoff is a customer with a high lifetime value; the New York Times put this succinctly in their 2020 Report:

We are, in the simplest terms, a subscription-first business. Our focus on subscribers sets us apart in crucial ways from many other media organizations. We are not trying to maximize clicks and sell low-margin advertising against them. We are not trying to win a pageviews arms race. We believe that the more sound business strategy for The Times is to provide journalism so strong that several million people around the world are willing to pay for it. Of course, this strategy is also deeply in tune with our longtime values. Our incentives point us toward journalistic excellence.

The proposed Apple News model, on the other hand, which pays out according to reader engagement, pushes in the opposite direction — the Facebook direction. The motivation is “to maximize clicks” and “win a pageviews arms race”, with some “time-spent” variables mixed in; sure, the driver isn’t low-margin advertising, but shifting the means of monetization doesn’t change the ends as far as incentives go.

The Cost of Apple News

It is absolutely worth noting what a great deal for consumers an Apple News subscription bundle would be: I totally get the idea of subscription fatigue, and having one place to get all of the best journalism would be amazing. That, though, doesn’t mean that Apple News wouldn’t be an Aggregator: that confirms it! Aggregators win because consumers prefer them, leaving publishers no choice but to go where the consumers are.

To that end, I am sure that a significant number of publications will sign up for Apple’s offering; clearly the company is confident enough to leak a date. And, frankly, many publications should: most publishers are already locked into the volume game when it comes to their editorial direction, and Apple News subscription payouts will be additive to the bottom line.

Publishers that have truly committed to subscriptions, though, should say no: not only will it be difficult to make up revenue that will be cannibalized lower per-customer payouts from Apple News, but more importantly a reversion to a model predicated on page views will hurt their business in the long run. This is especially the case if Apple News becomes a major revenue driver; yes, digital content can be distributed with zero marginal cost, but the incentive cost should not be discounted — it works directly against the quality imperative that is the critical factor in making the Aggregator-avoiding direct-to-consumer business model work.

I wrote a follow-up to this article in this Daily Update.

 

By Ben Thompson

Sourced from Stratechery

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Study Finds Tech is Number One Industry for Workplace Drinking

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By Adam Rowe

Workplaces and alcohol have a long and tipsy history together, from 60s-era Mad Men culture to a co-working space hot-button topic today. It’s no secret that the tech industry is pretty drink-friendly, with a beer fridge often nestled between the ping-pong table and a snack bar on the list of attractive workplace perks.

But, in recent years, we’ve started side-eyeing tech’s drinking habits. The #MeToo movement has impacted drinking policies at major companies – most prominently, WeWork dropped its unlimited beer tap option last October following sexual assault allegations at WeWork events.

Now, a new study has pulled tech drinking culture back into the spotlight. The tech world, the study found, is the single industry most likely to allow or sponsor alcohol consumption in the workplace.

Tech’s Drinking Culture

In a survey with more than 1,000 respondents, Niznik Behavioral Health found that the tech industry took home the prize for the most drinking-friendly workplaces. The marketing and advertising industry came in third, and the construction industry came in second

Specifically, a little over half (53%) of the tech industry was found by the study to include alcohol at the team bonding events. A further 35% of tech firms offer company-sponsored happy hours.

Moreover, about half (49%) of the employees across all industries (not just tech) thought that drinking with a coworker or their boss would improve their relationship. However, moderation is still in order – most said that two drinks was the right amount.

The Downsides

Granted, it makes sense that a little light drinking can improve team bonding. But, there are plenty of potential downsides, and the negative impact of too much drinking was highlighted by the study:

“Heavy drinking can lead to regrettable disclosures, and work events were no exception to this trend. Respondents were most likely to report revealing secrets to colleagues after drinking at a work event or complaining about work issues in a way they later regretted. Another 9 percent simply said they embarrassed themselves by becoming clearly intoxicated. In such cases, some would propose a proactive approach: Rather than waiting for others to broach the subject, express your mortification and move on.”

Over-indulgence is an issue likely to crop up at any drinking event. However, it’s an expected downside, rather than an existential threat to the concept of workplace drinking itself.

A larger issue is the cultural pressure on those who won’t drink at all.

Abstainers Will Self-Exclude

Of the 35% of respondents who prefer not to drink at a work event, 22.3% said they would give an excuse just to avoid the work event entirely. That was the most common response, followed by drinking anyway (15.8%). A remarkable 11.5% of respondents said they would pretend to drink at such an event.

In other words, even moderate workplace drinking can have a concerning effect on the one thing it’s supposed to encourage: employee bonding.

Workplace drinking in tech

There are plenty of anecdotes to back this up online, from one tech employee’s account of the variety of experiences being “the sober one” (hey, at least hearing secrets beats dealing with vomit), to tech startups alleged to have excluded non-drinkers because they weren’t a “culture fit.”

Needless to say, you don’t want a full third of your workplace to feel uncomfortable at your party. If you’re committed to drinking, make sure that alcohol-free options are easily available and try to fight any stigma or pressure as best you can.

Some recommend including an equal amount of non-alcoholic to alcoholic drinks at every tech event (and advertising as much beforehand). This actually might be easier than you think. Plenty of alcohol producers have been adding an increasing amount of non-alcoholic options over the past few years.

According to a recent CB Insights report, the world’s largest brewer, AB InBev, has made seven acquisitions or investments in the space, and believes “low-alcohol and non-alcoholic business [will] represent 20% of global sales by 2025, up from 8% today.”

Workplace drinkingAre Dry Events the Answer?

Dropping the alcohol at a workplace event isn’t likely to upset too many people, with almost a third of the survey’s respondents saying that they’d even prefer a healthy alternative such as massages or exercise.

“Dry activities also appealed to a significant portion of people who liked to drink at work events,” the study found. “At least 30 percent of this group would trade drinking events for sponsored lunches, massages, fitness training, and permission to bring pets to work.”

Dry events may not quite replace drinking ones in the tech industry. But, there’s a sliding scale for how inappropriate workplace drinking can be.

Sponsored happy hours may feel fine, but team bonding exercises will make abstaining team members a little more uncomfortable if non-alcoholic beverages aren’t included. Drinking at after-work events can feel better than bringing beers directly into the nine-to-five itself, but the pressure to conform to a tech work culture can remain.

The bottom line: moderation is a must. And the tech industry’s status as the number one top drinking industry indicates that it might need a tad more moderation yet.

By Adam Rowe

Adam is a writer with an interest in a variety of mediums, from podcasts to comic books to video essays to novels to blogging — too many, basically. He’s based out of Seattle, and remains a staunch defender of his state’s slogan: “sayWA.” In his spare time, he recommends articles about science fiction on Twitter, @AdamRRowe

Sourced from TECH.CO

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Predictive Analytics For Retail: What Lies Ahead?

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By Dave Wendland

Although I’m not certain that they all were “Robert Wendland originals,” my late father had many witticisms to which I credit him. With impeccable timing, a simple, pithy phrase would be spoken that was not only appropriate for the moment but also stuck with me for a lifetime. One in particular that I reference often suggests that “You can’t drive forward if you spend your time only looking in the rear-view mirror.”

The rate of change occurring in our lives and across virtually every industry is unprecedented. Oddly, the smartphone was first introduced less than a decade-and-a-half ago, and yet its ubiquity makes it unfathomable to think of going through life without one. The same could be said of self-check-in at airports. (Remember the days of paper tickets, travel agents and full-service?) And, in retail, where our company Hamacher Resource Group works across the retail supply chain, everything I remember from my childhood seems like ancient history.

I am personally fascinated by predictive analytics and the ability to combine data elements to create an approximate idea of what the future may hold. Although data modeling has been used quite extensively in certain industries (e.g., weather predictions based on specific historic models and key indicators, insurance estimates and actuarial tables, etc.), the concept of applying similar science to retail holds unrealized potential. Given technology advancements and data-mining tools, potential future predictions — in other words, answering the question “What lies ahead?” — seems far more attainable than ever before. Considering big data already captured within the retail sector, it becomes mind-boggling.

Virtually all industries have pools of information that could be used for predictive purposes. This reminds me of a presentation I sat through nearly three decades ago by a then-IBM executive who suggested that the future will be owned by those who have the keys to data and the intelligence that it generates. And, as recently as this month’s National Retail Federation event, the power of data was repeatedly emphasized.

According to a recent blog post from McKinsey, “winning decisions are increasingly driven by analytics more than instinct, experience, or merchant ‘art’; what succeeded in the past is now a poor predictor of the future, and analytics is helping to inform and unlock new pockets of growth.”

So, what types of data do we have available in the retail class of trade? Here’s a partial list:

• Point-of-sale transaction-level data

• Retail pricing strategies

• Consumer-based loyalty information (shopper insights)

• Physical store size and demographic characteristics

• Department sizes and product assortment (planogram data)

• Store navigation intelligence (traffic flow)

• Social media metrics

• Competitive intelligence

• Seasonality and other external trends (e.g., weather)

If one takes the time to imagine connecting discrete data elements and begins creatively combining them in unique ways, amazing predictions can be formed. Whether informing promotional campaigns, personalizing customer offers, improving employee training, building customer retention or simply forecasting demand and optimizing assortment and stocking levels, using big data and some gut instinct can conjure up interesting insights. Here are a couple on my mind.

Imagine aligning store navigation intelligence, shopper loyalty data and neighborhood demographics to consider how to attract others within the area by enhancing navigation and department placement to match their needs. Once again, continuing to do the same thing time and time again may not be positioning the retailer to best capture additional sales.

As an example, say a grocery store was initially designed to attract stay-at-home moms with two kids. The store was arranged to cater to her needs, but the neighborhood was now comprised of older adults with limited mobility and fixed incomes. How could the data predict their navigation pattern, category preferences and better cater to their overall shopping occasion? The hypothesis I would look to prove is whether shelves should be lower and aisles wider, whether certain categories (e.g., sugary cereals and baby diapers) should be downsized and how to rearrange the checkout to be less confined and more staff-centric. Predictive analytics could be used to model the potential result of such changes and allow the retailer to assess whether such an investment would be justified by the return.

Another scenario fueled by predictive analytics could look at the success ratio of certain product launches within a retail operation. Examining planogram and assortment data alongside point-of-sale transaction details and customer loyalty intelligence, future predictions can be created to fuel decisions about placement, promotion and timing.

Today a delicate balance exists between staid and proven stock keeping units (SKUs) and potential innovators and disrupters. Developing analytic modeling that can better predict performance could greatly improve buying decisions and bolster the performance of the category. This would certainly help in honing the process of new item evaluation and potentially reduce the number of new products shelved that do not perform to the expectations of the retailer.

In essence, only looking in the rear-view mirror and continually employing the same process will merely produce the same results. On the other hand, using learnings from the past and combining new, enriched data elements could generate a true breakthrough that drives new results.

Feature Image Credit: Getty

By Dave Wendland

Vice President, Strategic Relations at Hamacher Resource Group, Inc., passionate about optimizing results across the retail supply chain.

Sourced from Forbes

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How Tableau aims to make data analytics more accessible

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Sourced from IT Brief

Tableau Software announced the general availability of Ask Data, which leverages the power of natural language processing to enable people to ask data questions in plain language and instantly get a visual response right in Tableau.

This patent-pending capability makes it easier for people, regardless of skill set, to engage with data and produce analytical insights they can share with others without having to do any setup or programming. Ask Data is available as part Tableau’s newest release, Tableau 2019.1.

Customers can simply type a question such as, “What were my sales this month?,” and Tableau will return an interactive visualisation that they can continue to explore without limits, either with iterations, new questions, or drag and drop gestures.

There is no need to have a deep understanding of the data structure, no setup required and no programming skills necessary. Ask Data uses sophisticated algorithms that are driven by an understanding of the person’s intent, not keywords, which helps Tableau return more relevant results.

Ask Data technology translates simple questions into analytical queries

Ask Data supposedly uses sophisticated algorithms that are driven by an understanding of the person’s intent, not keywords, which helps to understand a person’s question, anticipate needs, and allow for smart visualisation selection.

For example, when someone types in “APAC furniture” for their sales data, they want to filter “Product Name” to “Furniture,” and “Region” to “Asia Pacific.” Ask Data combines statistical knowledge about a data source with contextual knowledge about real-world concepts: “Furniture” is a common value for the “Product Name” field and “APAC” is an acronym of “Asia Pacific.”

Additionally, Ask Data’s parser automatically cuts through ambiguous language, making it easy for people to ask questions in a natural, colloquial way. This means, if a question could be interpreted multiple ways, Ask Data will combine knowledge about the data source with past user activity and present a number of valid options to choose from, with the ability to refine the results if needed.

Tableau expands platform with prep conductor in new data management package

Tableau also announced today the general availability of Prep Conductor, a new product that enables organisations to schedule and manage self-service data preparation at scale with no programming or complicated setup. Tableau Prep Conductor is part of a new subscription package called Tableau Data Management.

Prep Conductor supposedly automates flows created in Prep Builder (the renamed Tableau data prep product).  Prep Builder is already in use by more than 11,000 customer accounts, and Prep Conductor will help organisations use it more broadly to ensure that clean and analysis-ready data is always available.

It also gives people greater confidence in their data by providing added visibility and detail behind cleaning history and data connections, as well as alerts when processes are not operating as scheduled.

Tableau Prep Conductor gives IT the ability to monitor and set up automatic cleaning processes (flows) for their data across the entire server. It also allows customers to build permissions specifically around data flows and data sources in order to maintain control and meet data compliance standards and policies.

Feature Image Credit: Getty

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How to Create Quality Content That Ranks

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By Usman Raza    

Quality content does not always rank. The advantage of Author credibility and how up to date the content is can sometimes be irrelevant to how it ranks. That isn’t to say those factors are useless, but content creators tend to miss an important factor that controls ranking: Relevancy.

What is Quality Content?

This is a simple definition. Quality content is determined by its success. If it has achieved a goal, drives traffic, conversions, or Google search rankings, it is seen as quality content.

The quality of content is not determined by what is put into it as much as its effect on a particular audience or search query.

The definition of quality content has shifted from the past, where it was defined by its relevance to a search query. However today, it is formed by relevance to the people who search for it.

What’s the Difference?

When creating content that is relevant, it is important to realize the difference between content that is relevant to a search engine, and relevant to the person searching. Content should be designed to meet the needs of the customer, first and foremost. That is, producing content that meets the needs of the person searching, instead of matching keywords to a search query.

How to Be Relevant

That is not to say using keywords are bad. Instead, evolve your experience with keywords to prioritize those people. People with problems to solve and questions to answer make content Relevant.

Know your Audience

When creating relevant content, it is important to know your target audience. The best way to do this is with tools like Google Analytics. It is designed to analyze different aspects of a website, from viewer location, most and least searched content and best to worst content.

When creating content based on analysis of your website, keep in mind the nature of your target audience. These questions here are perfect to consider what your target audience is all about.

  • Who are they?
  • How old? What gender?
  • What Social status/ background?
  • What do they like?
  • What are there problems?
  • Where do they live?

Now that you have figured out just what your target audience is like, it’s time to produce content that is based on that information. Make sure to write at a level your audience can easily comprehend.

Finding out the nature of your audience is an immensely experimentative path. However, deciding and sticking to a particular niche is the best way to normalize your audience.When posting content on social media, make sure to utilize analytics tools to determine the state of your content in relation to your audience. Audience analysis helps content creators adapt to their needs to keep content relevant to the audience. This is more officially known as data-driven marketing.

Backlinking

Backlinking is a great way to ensure your content is relevant. Web pages rank because websites link back to them. The reason they are linked to the web page is because they add useful information. Giving and receiving backlinks are truly one of the most powerful ways to rank content.

The benefits of backlinks for content ranking are:

Ranking is Organic – Backlinks drive organic traffic to your website. Organic traffic ranks content and web pages a lot higher than paid traffic. Websites and content that ranks organically are also more likely to be clicked.

Organic traffic is ideal for establishing trust with consumers over time while PPC traffic is great for producing short-term conversions.

Index Speed – Backlinked pages can be found more quickly by a search engine’s bots which in turn index’s it much more effectively. The quicker a page is indexed by a search engine, the faster it can start ranking.

Referral Traffic/ Low Bounce Rate – When producing blog content, the aim is to have a low bounce rate. This is the rate at which people come to your website and the time they spend on the website. If the content does not constantly grab the attention of the readers, then high bounce rates are sure to come as a result.

When receiving or giving backlinks, referral traffic is distributed from the referer to the authority content. Referral traffic is good because it usually has a low bounce rate. Since referral traffic is comprised of users looking for extra information relevant to the primary search query, it has lower bounce rates.

Also, referral traffic is trackable in Google Analytics, making it a useful way to monitor the strength of a backlink.

How Backlinks Affect Relevance

Backlinks should come from pages that are relevant to the content. This is how backlinks create relevancy.

When backlinking content to a web page, you create referral traffic to the backlinked website. Although this does not inherently create more traffic to your webpage, it does, however, increase its ranking on a search engine and strengthens the credibility of the page if the backlink is relevant to the page’s topic.

When receiving a backlink from another website, users are referred to you. This improves the relevance of your page and drives referral traffic to your page. Since that traffic is referred, there will be a low bounce rate for users who have searched that topic. This also establishes your page as a form of authority because your source supports related content.

When creating content, It is important to know your audience and seek the support of backlinks. When users view content that is relevant to their needs and supported with information that is also relevant, the content will rank highly in search engines. Use keywords that relate to your target audience and seek the aid of data to drive content that highlights your audience’s needs.

By Usman Raza    

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10 High-Paying Jobs That Require a Knowledge of Data Analytics

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By Charlie Custer

There’s never been a better time to learn data analytics and enter the workforce as a data scientist. The job landscape is promising, opportunities span multiple industries and the nature of the job often allows for remote work flexibility and even self-employment.

Plus, many data analytics experts boast a high median salary, even at entry-level positions.

With technology reaching new heights and a majority of the population having access to an internet connection, there’s no denying that Big Data and data analytics have become hot topics in recent years – and a growing need. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.

Demand for knowledgeable data analytics professionals currently outweighs the supply, meaning that companies are willing to pay a premium to fill their open job positions.

But the skillset and job opportunities within data science go beyond the tech and digital spaces. Let’s take a look at what you need to know as a data scientist – and what you’ll learn when you take our courses.

What Skills Are Required for a Job in Data Analytics?

As you delve into the 10 jobs we have here and start applying for positions in the data analytics field, you’ll notice many of them require the same foundational skills. Make sure you’ve mastered these before you start sending your cover letter and portfolio to potential employers.

And, if you find a skill that you still need to learn, remember that you can take an affordable, self-paced data science course that will help you learn everything you need to know for a successful career in data science.

Python

Python is currently one of the most commonly used programming languages.

Having a solid understanding of how to use Python for data analytics will probably be required for many roles. Even if it’s not a required skill, knowing and understanding Python will give you an upper hand when showing future employers the value that you can bring to their companies.

If you’re ready to advance your programming language proficiency, learn how to manipulate and analyze data, understand the concept of web scraping and data collection, and start building web applications, consider enrolling in our Python for Data Science: Fundamentals Course.

SQL (Structured Query Language)

Working with data sources is a necessary aspect of data analytics.

Early in your career, you’ll need at least a basic understanding of SQL. SQL (pronounced sequel) is often a major component of these positions. When you go to interview, listen for hiring managers’ mentions of this programming language when asking about your work with databases.

The experience you’ll get in our SQL courses will give you a good foundation. Like Python, SQL is a relatively easy language to start learning. Even if you are just getting started, a little SQL experience goes a long way.

Knowing the basics of SQL will give you the confidence to navigate large databases, and to obtain and work with the data you need for your projects. You can always seek out opportunities to continue learning once you get your first job.

Data Visualization Skills

Knowing how to visualize data and communicate results is a huge competitive edge for job seekers.

On the job market, these skillsets have high demand (and high pay)! Regardless of the career path you’re looking into, being able to visualize and communicate insights related to your company’s services and bottom line is a valuable skillset that will turn the heads of employers.

In this way, data scientists are a bit like data translators for other people in the organization that aren’t sure what conclusions to draw from their datasets.

At Dataquest, students are equipped with specific knowledge and skills for data visualization in Python and R using data science and visualization libraries.

10 Jobs that Require a Knowledge of Data Analytics

Before you take the time to learn a new skillset, you’ll likely be curious about the earning potential of related positions. Knowing how your new skills will be rewarded gives you the proper motivation and context for learning.

Lots of employers are hiring for these positions, both remote and onsite, worldwide. Here are a few positions worth looking into – and their median incomes, according to popular job search websites.

1. IT Systems Analyst

Systems analysts use and design systems to solve problems in information technology.

The required level of technical expertise varies in these positions, and that creates opportunities for specialization by industry and personal interests. Some systems analysts use existing third-party tools to test software within a company, while others develop new. proprietary tools from their understanding of data analytics and the business itself.

A typical systems analyst in the US makes around $68,807.

2. Healthcare Data Analyst

Healthcare data analysts have the opportunity to improve the quality of life for many people by helping doctors and scientists find answers to the questions and problems they encounter on a daily basis.

The amount of data coming from the healthcare industry is growing rapidly, be it with the increased popularity of wearables like Apple Watch or through enhanced medical testing in clinics, hospitals and labs. Plus, with a rise in regulations and restrictions on how that data can be stored, retrieved, and processed, demand for proficient data analysts is on the rise as well.

The median salary for a Healthcare Data Analyst is $61,438.

3. Operations Analyst

Operations analysts are usually found internally at large companies, but may also work as consultants.

Operations analysts focus on the internal processes of a business. This can include internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations.

It’s more important for professionals in these roles to have general business savvy, and they often have technical knowledge of the systems they’re working with. Operations analysts are found in every type of business, from large grocery chains, to postal service providers, to the military and can make upwards of $75,000 annually. Due to the versatile nature of this data analytics job and the many industries you may find employment in, the salary can vary widely.

4. Data Scientist

Much like analysts in other roles, data scientists collect and analyze data and communicate actionable insights. Data scientists are often a technical step above of data analysts, though. They are the ones who are able to understand data from a more informed perspective to help make predictions. These positions require a strong knowledge of data analytics including software tools, programming languages like Python or R, and data visualization skills to better communicate findings.

These positions are challenging – and rewarding, with an average salary of $91,494. The demand for data analytics experts with technical backgrounds is at an all-time high.

Dataquest has multiple learning paths that are tailored to provide you with everything you need to hone your technical skills, including the Data Scientist Path that will help you become a certified data scientist.

5. Data Engineer

Data engineers often focus on larger datasets and are tasked with optimizing the infrastructure surrounding different data analytics processes.

For example, a data engineer might focus on the process of capturing data to make an acquisition pipeline more efficient. They may also need to upgrade a database infrastructure for faster queries. These high-level data analytics professionals are also well-paid, with median salaries being comparable to data scientists at $90,963.

6. Quantitative Analyst

A quantitative analyst is another highly sought-after professional, especially in financial firms. Quantitative analysts use data analytics to seek out potential financial investment opportunities or risk management problems.

The median salary for quantitative analysts is $82,879. They may also venture out on their own, creating trading models to predict the prices of stocks, commodities, exchange rates, etc. Some analysts in this industry even go on to open their own firms.

7. Data Analytics Consultant

Like many of these positions, the primary role of an analytics consultant is to deliver insights to a company to help their business. While an analytics consultant may specialize in any particular industry or area of research, the difference between a consultant and an in-house data scientist or data analyst is that a consultant may work for different companies in a shorter period of time.

They may also be working for more than one company at a time, focusing on particular projects with clear start and end dates.

These positions are best for those who like change, and those who have a narrowed interest and expertise in a field of study. Analytics consultants are also in a great position to work remotely, another enticing factor about this role to consider.

Compensation varies widely by industry, but $78,264 is a representative salary for the role.

8. Digital Marketing Manager

Digital marketing also requires a strong knowledge of data analytics. Depending on your other complementary skills and interests, you could find yourself in a specific analytics role within a company or agency, or simply applying your data science expertise as a part of a larger skillset.

Marketers often use tools like Google Analytics, custom reporting tools and other third party sites to analyze traffic from websites and social media advertisements. While these examples require a basic understanding of data analytics, a skilled data scientist has the ability to create a long-term career in marketing.

A lot of money could be wasted on campaigns that do not drive traffic, so marketing professionals will continue to need analysts to make smart decisions about how to leverage existing resources.

While digital marketing positions have a wide range, the higher end salary for an advanced digital marketing manager is $97,000.

9. Project Manager

Project managers use analytics tools to keep track of a team’s progress, track their efficiency, and increase productivity by changing processes.

Project managers need at least a working understanding of data analytics, and often more.

These positions are found internally at large corporations, and frequently in management consulting. Another example of a career trajectory for project managers could be moving into product and supply chain management, which companies rely on to maintain profit margins and smooth operations.

A typical salary for a project manager is around $73,247.

10. Transportation Logistics Specialist

A transportation logistics specialist optimizes transportation of physical goods, and could be found in large shipping companies, like Amazon, UPS, naval transport companies, airlines and city planning offices.

A data analytics background is especially helpful in this job because transportation logistics specialists need to reliably identify the most efficient paths for products and services to be delivered. They must look at large amounts of data to help identify and eliminate bottlenecks in transit, be it on land, sea or in the air.

With seasoned professionals in this industry making around $79,000 per year, a transportation logistics specialist is an appealing career path for individuals who are detail-oriented, technical and forward thinkers.

A data analytics background also helps transportation logistics specialists, among others, to focus on the most important issues, seeing potential problems and solutions in context and communicating those effectively.

Data Analytics Opportunities Around The Globe

These are just a few of the many high-paying jobs which require knowledge of data analytics. Specific figures from this article are for the median salaries in the United States, all cities included.

Salaries in each city may vary and reflect local demand and general cost-of-living expenses. Boston, Portland, and Denver, for example, have become hotspots for data analytics positions.

While the numbers included in this article represent a typical salary in the United States, opportunities for data analytics professionals can be found all around the globe. Many of them can even be done remotely, allowing you the highly-desired opportunity to work from anywhere in the world on a competitive US salary.

Whether your goal is to get a full-time job in a new industry, advance in your existing career, or work for yourself in the data analytics field, Dataquest can prepare you for the opportunity. With the portfolio-building missions and projects in Dataquest’s Data Analyst path, a community of mentors, and a strong alumni network, you’ll have all you need to become a certified data analyst and be set up to get the job of your dreams.

By Charlie Custer

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How to Streamline Reporting with Google Tag Manager & Google Analytics

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By Brett McHale

One area that seems to elude many digital marketers is the relationship between conversion tracking and website analytics. Often, when businesses get started with online advertising, they have established each piece of the puzzle separately, with Google Analytics monitoring site traffic, and paid channels (like Facebook and Google Ads) tracking conversions individually within their respective dashboards.

This set-up may be effective on a small scale, but it will inevitably cause issues when your efforts expand. Having the proper tracking to view granular paid channel performance in Google Analytics will allow you to add another layer of attribution to hold each channel accountable for what’s really happening on your website.

In addition to Analytics, Google offers tools to streamline the process of managing website pixels and conversion tracking for each channel. With the combination of clean conversion tracking and reliable analytics, you should be able to scale your paid programs without having to worry about whether the information you’re looking at is accurate.

In this post, I’ll walk you through how to use Google Tag Manager and Google Analytics to improve your paid channel performance reporting in four steps.

Step #1: Implement Google Tag Manager

Google Tag Manager allows you to implement and manage the tags on your site in one place. This means you only have to place a snippet of code once across your site as opposed to manually inserting a tag from every channel individually. After the GTM tag is implemented, you will be able to add and manage tags all in one place. The reason I start here is that many advertisers will manually place each paid channel’s tracking pixel individually on the pages of their website. This can create confusion and clutter within your website’s code. To simplify the process of adding multiple pixels and tracking codes to your site, I highly recommend setting up Google Tag Manager as soon as you can. This will also make the life of your future web developer much easier if your team and operations scale.

To get started with tag manager you’ll have to create an account.

getting started with Google Tag Manager

Once there, you’ll want to follow the steps for establishing your account information. Name the account after your business and continue to set up the “container.” The container will be your website address:

set up Google Tag Manager account

You’ll want to select “web” before proceeding. Google will then generate two tags for you to place across your site.

install Google Tag Manager

The top tag will be implemented within the <head> and the second after the opening <body> tag. It’s a good idea to copy and paste these tags and save them in a document so that you can easily access them at any time. If you use a platform like HubSpot to manage your site, it’s rather simple to apply these tags across all web pages quickly.

Keep in mind that if you use a third party for landing pages, you’ll want to have this code injected there as well.

Before you implement the GTM tag across your website, you’ll want to remove tags from any paid channels that had been previously manually installed. You will be able to transfer these tags to GTM afterwards, but removing them in the meantime will ensure that those tags aren’t firing multiple times or causing issues once the GTM tag is in place. It’s also a good idea to save these pre-existing pieces of code in a document – I’d suggest the same one that you have the GTM tags in for safe keeping. If you are managing accounts for clients, make sure that they give you publishing permissions in their GTM. This will allow you to create new tags and publish them on your own.

For Facebook, there is a quick way to implement the pixel into GTM. After you finish setting up your Tag Manager account and remove pre-existing paid channel tags from your site, go to Facebook Business Manager and select “Pixels” from the main drop-down menu:

Facebook Pixel

Select “Set Up Pixel”:

select "Set Up Pixel"

Then select “Use an Integration or Tag Manager”:

Google Tag Manager pixel options

This should then allow you to choose Google Tag Manager from the list and follow the walkthrough to easily implement the pixel. In Google you can add your conversions by going to Tools > Conversion > and then selecting the specific conversion you would like to add. At the bottom of the page there should be an option for “Tag Setup.” There you will be able to select the Google Tag Manager:

choose to install using Google Tag Manager

This will provide you the information you need to create the conversion in Google Tag Manager. This includes the conversion ID and conversion label.

To create a new tag for any channel, select “New Tag” from the Google Tag Manager home screen and then choose the channel you want to set up:

Google Tag Manager home

When doing so you will be prompted to choose how each tag fires or when it is “triggered.” For Tags like LinkedIn, Facebook, and Google Remarketing, you’ll want to set them to fire on “All Pages”:

LinkedIn Insight

For something like Google Ads conversion tracking, you’ll want to have the tag fire only on specific page URLs:

choose specific pages for trigger configuation

Here, you can specify which pages you want the conversion to fire. For example, “thank you” pages or any destination page associated with the conversion. After doing so, you also have the option to add a conversion linker to the pageview event. Conversion linkers are used to help tags measure click data so that conversions are measured effectively. To add a conversion linker tag, simply select “New Tag” and “Conversion Linker” from the tag configuration menu:

Google Tag Manager configuration menu

Then you’ll want to have the linker trigger to “All Pages.”

Once you add in your tags from each respective channel, select the “Submit” button at the top of the page. This will push the changes you made to Tag Manager Live:

push tag manager live

To ensure that your GTM is firing properly, I suggest downloading the Google Chrome extension Tag Assistant. This plug-in will allow you to see which tags are live on specific web pages.

You can also get the Facebook version of this tool to ensure those tags are firing as well.

To use these tools, go to your website and enable the Tag Assistant extension:

Tag Assistant extension

Here you can see the two tags are in place and firing. This is a good way to ensure you removed old tags properly without having to dig through each page’s code.

Step #2: Set Your Google Analytics Goals

The essential piece of the reporting puzzle is tying Google Analytics Goals back to your efforts in paid media. In case you haven’t established Google Analytics Goals yet, I’ll walk you through the process.

Sign into your Google Analytics account and select the proper view for your website. On the left-hand side, select “Admin” (FYI, you’ll need to have admin privileges in order to create goals in Analytics).

go to Google Analytics Admin

After that you’ll want to select “Goals” from the desired view:

select Goals in Google Analytics

From there you can view existing goals or create new ones. You’ll want to ensure that the goals that exist or the ones that you create are synonymous with your paid advertising conversions, like demo requests, ebook download, free trial start, purchase, or add to cart. You have a few options when it comes to creating the types of goals you want, including goals from a template, custom goals, and smart goals. I recommend creating custom goals because they allow you the flexibility of specifying destination URLs:

select goal type in Google Analytics

The idea here is to create goals that are identical to the conversions you have in place within your paid channels. For example, if you are driving traffic to an ebook landing page on Facebook, then you will want to have ebook “thank you” pages established as goals within Analytics. In a scenario like that, you’ll want to select “Destination.” From there you will be able to specify the destination landing page for the conversion event in the same way you would in your paid platforms. Assuming Tag Manager is properly established, you will be recording conversions within each individual channel respectively – with goals set up in Analytics for those conversions, you can now use Analytics to compare and ensure that the results are comparable.

Step #3: Start Using Analytics URL tracking

In order to see the specific source of traffic in relation to the goal completes, you can use Google’s URL campaign builder.

Here, you can create unite URL UTM parameters for each specific paid channel campaign. Under “Campaign Source,” you can put the respective channel (Facebook, Google, LinkedIn, etc). After having everything set up and your campaigns running, you can then go to “Goal Flow” underneath the “Conversions” section in Analytics:

select Goal Flow in Google Analytics

From there, you can then select one of your custom goals in the top right and view the amount of goal completes from each respective channel. For example, ebook downloads:

example of goal flow in Google Analytics

There are other ways to get channel visibility like this, but I found this to be the simplest way to do it.

Step #4: Keep Track of Your Reporting

After you have Google Tag Manager and Google Analytics set up for your desired conversions for each channel, you’re almost done. Because you’ll be able to see what channels are driving which goals, you’ll want to make sure you’re keeping on top of regular reporting to make the most of this information.

If you’re seeing numbers that are not aligning to your reports in Google Analytics from Facebook, for example, you can go into your custom conversions and see if there is an issue there (on the Facebook level). The idea is to make Google Analytics your source of truth when tying back costs from your paid efforts. You will also be able to use Analytics to monitor page traffic and the associated metrics for pageviews and bounce rate (this comes in handy as Facebook’s click data is usually far from accurate). You will also be able to get insights into how users are interacting with the rest of your site after entering through a paid channel. For example, a user lands on your site through and ad but navigates to another page and converts. You will be able to see this information and tie it back to the overall paid ROI.

If you want to consolidate all of this data into one simple dashboard, consider using a tool to connect your accounts and create reports automatically. Some good options include Databox, OpenStack, and WordStream Advisor.

With Google Tag Manager and Google Analytics goals working in harmony, your paid channel reporting will become streamlined and a lot more accurate. If you ever wondered what results you actually received from paid advertising, having these systems set up is the first step towards clarity.

By Brett McHale

Brett McHale is the founder of Empiric Marketing, a digital marketing agency dedicated to scaling startups through paid search and social.

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