Why entities may power the future of location-based data

Businesses with physical locations need to strengthen their entities, says Columnist Adam Dorfman — doing so helps Google track customers and strengthen your findability. The post Why entities may power the future of location-based data appeared first on Search Engine Land.

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Digital Marketing News: What Marketers Think about AI, Autonomous Stores & GSC Adds Data

Infographic: What Marketers Really Think About Artificial Intelligence A new infographic shows 47% of marketers consider artificial intelligence (AI) to be over-hyped. In addition, 43% of marketers believe vendors overpromise and underdeliver when it comes to AI. AdWeek Can Autonomous Stores Catch On? Brick-and-mortar stores are testing out an automation model, functionally converting their stores to vending machines. These may increase convenience and service levels for some customers, but many remain doubtful that this will take off in a big way. MarTech Today Google Search Console Adds 16 Months of Data Can I get a heck yes?! Google has confirmed that Google Search Console will now be able to show 16 months of data versus the typical 90 days. This is currently available in their beta version for some users, with a larger rollout pending. The SEM Post The State of Video Marketing: Distribution, Topic, and Budget Trends Marketers are saying that social media brings them the highest ROI for digital video distribution, followed by email. In addition, 50% of respondents are transferring budgets from traditional media budgets to finance digital video and 37% are reallocating budgets from digital media. MarketingProfs Hulu Hits $ 1 Billion Ad Milestone In 2017, Hulu hit a record for video advertising revenue at $ 1 billion. They also saw a 40% rise in subscribers year-over-year in 2017 for video-on-demand and Live TV products. MediaPost Self-Driving Cars Have Landed at #CES2018, and Marketers Really Need to Pay Attention Self-driving cars are more than just a surreal future world pipe dream — they’re well on their way to becoming a real disruption to our typical interactions with transportation. Aside from the daily interaction, self-driving cars can also serve as a site for real-time marketing communications. HubSpot Forrester: Mobile will drive 69% of search ad growth by 2022 Mobile Marketer reports: “Mobile phones will drive most of the expansion in paid search ad spending, contributing an estimated 69% of the $ 19 billion in growth by 2022, according to Forrester research.” Mobile Marketer How Marketers Are Turning Your Car Into a Branded Experience Talking to your car isn’t as strange of a thought as it once was. But marketers and tech platforms are toying with the idea of taking this to the next level — providing helpful, timely information to consumers on-the-go. AdWeek Why Brands Will Go To Extremes — Lengthwise — With Digital Video In 2018 Marketing Dive reports: “In 2017, marketers spent 2x as much on online video than they did on TV ads. While standard 30-second ads aren’t going away, brands are increasingly experimenting with a wide array of video formats that push extremes length-wise.” Marketing Dive Google Is Sunsetting Adwords Review Extensions Next month, Google will be removing the text ad extensions that allow advertisers to highlight 3rd-party reviews within their ads. If you have used these extensions and want to keep the data, export it in AdWords this month. Search Engine Land New Data Reveals It’s Time to Change Your Headline Strategy New research from Buzzsumo revealed some surprising insights about headlines that play best on Facebook — including which word combinations get the most engagement, and which to avoid. Social Media Today On the Lighter Side: M&M’s debuts touchdown dance contest for Super Bowl – Mobile Marketer Billy Mann Discusses Video Humor as a Tool for Marketing – Small Biz Trends TopRank Marketing In the News: Debbie Friez – 2018 Digital Marketing Trends From 20+ Marketing Experts – Hot in Social Media Josh Nite – Annual Content Planning: How To Kickstart Filling Your Editorial Calendar – HeidiCohen.com Lee Odden – What’s Trending: Bring It On, 2018 – LinkedIn Marketing Solutions Lee Odden – Social Media Experts and Influencers, to Follow in 2018 – SocialChamp Lee Odden – 5 Expert Tips To Refine Your Content Marketing Strategy For 2018 – Marketing Insider Group Lee Odden – Meet the Top 21 B2B Influencers to Watch in 2018 – B2B News Network Lee Odden – How To Research and Create Evergreen Content – BuzzSumo What was the top digital marketing news story for you this week? We’ll see you next week when we’ll be sharing all new marketing news stories. Also check out the full video summary on our TopRank Marketing TV YouTube Channel.

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Online Marketing Blog – TopRank®

Connecting the Dots from Data to Better Customer Experience

Right Message Wrong Time
I love how Tom Fishburne aka marketoonist always does a great job of showcasing marketing truths. While the focus for marketers to deliver the right message to the right audience at the right time is nothing new, it’s also safe to say that we have a lot of work to do.

External Battery Charger SERP

For example, I was recently in the position of having to look for a new external battery charger. I seem to burn through these things like candy. A search on Google gave me relevant ads right on top. I clicked on a Best Buy ad and checked out the RAZORMAX Portable Power Bank.

raxormax portable power bank

It looked interesting enough and while I noticed reviews from the Best Buy site, I’m the kind of person that likes validation from 3rd party review sites. So I went back to search and looked for reviews.

portable charger review

I arrived on Tom’s Guide and what ad from Best Buy dominated the page reviewing the best portable chargers?

Best Buy 4K TV ad
An ad for Sony 4K TVs.

This seemed like a lost opportunity for Best Buy. I was hunting for something specific and leaving data trails, but the brand I was considering most wasn’t connecting those dots.

This is a scenario that extends across channels of course. As I am prone to do with tech purchases, I went to Facebook to ask my network essentially the same question I searched for on Google. I received a cornucopia of suggestions from my network of tech savvy connections. But what ads did I see?

Facebook ads

Purple mattresses and clothing ads featuring photos of guys that don’t really represent me or what I’m looking for. Of course if they were more like me, that would be scary. But I think you get the point. I was creating data crumbs of intent across channels and the dots were not being connected by the right brands when it came to my customer experience.

While brands are collecting more data than ever before, and have become more sophisticated at implementing effective campaigns on specific channels, there are disconnects in terms of meeting increasing expectations of consumers. Buyers don’t care how hard it is. They care about finding the best information that is relevant, meaningful and specific to them. Right time, right place and message.

The customer journey has evolved from what we’ve traditionally explained as a linear path (was it ever linear?) to something far more sophisticated across devices and channels. Customer expectations have evolved and are focused more on experiences as a differentiator. At the same time, the sheer volume of content being produced creates information overload.

Of course there’s no “one size fits all” customer journey, but the importance of mapping and matching content and media types to stages of the journey are more important than ever. The question is, how are brands understanding the context of these journeys to create relevant experiences?

Internet access is ubiquitous with over 50 billion connected devices expected by 2020. I’m sure some of the people reading this post are using multiple devices right now–a laptop, and phone for example. How are brands meeting the expectations of customers who have always on, everywhere access to information?

Competing with marketers’ good intentions for relevant and meaningful experiences across channels is the fact that on average, consumers in the US are consuming media for 12 hours and 7 minutes per day (eMarketer). That’s a paradox of choice at scale. At the same time, 70% of marketers are not using the insights they’re pulling from data because it’s too complex or difficult (Ensighten).

A lot of that difficulty comes from data fragmentation across tools, tactics and organizational silos. Today’s marketing mix includes more tactics than ever to meet consumers’ insatiable demands for information. Back in 2001 when Susan Misukanis started a Public Relations firm, PR and light content was it. Then we brought in SEO. And blogging. Online Advertising was added. Then Social Media, Content Marketing, Influencer Marketing and so on.

martech landscape 2017
The proliferation of marketing technology tools isn’t helping either. Infographics like the one above from Scott Brinker with over 5,000 martech tools and platforms can put marketers into shock– or “martech shock” as I like to call it. As for data, there are often silos between departments where each is creating unique data or even the same data in different contexts. Suffice it to say, data fragmentation is a problem.

So what is a solution? One of the universal truths that we’ve operated under at TopRank Marketing is about the power of information specific to customers’ that are actively searching for solutions. To be the best answer is a strategic approach to marketing that naturally empathizes with the customer journey to deliver the most relevant experiences at all the touchpoints that matter most for customers. Combined with cognitive solutions, a best answer strategy for marketing is something that can actually scale. A great start is a data-informed approach to content marketing that uses context and insights to create conversations with customers that are relevant, personalized and meaningful across channels.

You simply cannot create a best answer approach to marketing without customer insight. It’s important to ask key questions that empathize with the customer journey. Especially, “What experiences do your customers need on which channels in order to buy?” This is an essential question because when customers are seeking solutions, one of the most important jobs we have as marketers is to ensure that brand content is the best answer where ever customers are looking.

Understanding context and preferences for the customer information journey as it relates to how buyers discover solutions content is the key. Those insights about preferences for content types, topics and devices, and the triggers that motivate action, all combine to inform an effective best answer strategy.

hub spoke best answer

With those insights about content discovery, consumption preferences and triggers for action, marketers can use connected data to architect best answer content programs that are accountable to attracting the right customers, engaging them with meaningful experiences and inspiring them to take action and convert. Making sense of a best answer approach to marketing at scale means reflecting on the possible: What if you could make your marketing easier and more meaningful at the same time?

It’s been reported that customers can hit 17 touch points before they buy. Imagine if every single one those interactions delivered on your brand promise with meaningful personalization? How to be the best answer for customers with as many of those touchpoints as possible is one of the most important challenges for marketers as we move into 2018.

Ultimately, the information is there. The data is there. Customers are telling you what they want. The question is, how to connect those dots of data to understand and optimize customer experiences? Certainly, AI is part of a new era of marketing to answer that question. If you want to learn more about how AI, machine learning and cognitive solutions can help connect the dots of data for better customer experiences, keep reading.

Connecting Dots from Data to Experiences
I recorded a webinar with Michael Trapani, Product Marketing Leader at Watson Marketing on this topic where he dives a bit deeper into how marketers can connect their ecosystems with AI solutions. You can view that webinar here.


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Online Marketing Blog – TopRank®

Google Search Console beta adds 12+ months of data to performance reports

After a few years of promises, Google is finally providing longer-term data in Google Search Console — well, the beta version of Search Console. The post Google Search Console beta adds 12+ months of data to performance reports appeared first on Search Engine Land.

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Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

Microsoft adds Reddit data to Bing search results, Power BI analytics tool

Reddit posts will appear in Bing’s search results, and its data will be piped into Power BI for marketers to track brand-related comments. The post Microsoft adds Reddit data to Bing search results, Power BI analytics tool appeared first on Search Engine Land.

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Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

SearchCap: Top Google searches, more data in Search Console & Google Assistant expands

Below is what happened in search today, as reported on Search Engine Land and from other places across the web. The post SearchCap: Top Google searches, more data in Search Console & Google Assistant expands appeared first on Search Engine Land.

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Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing

Don’t Be Fooled by Data: 4 Data Analysis Pitfalls & How to Avoid Them

Posted by Tom.Capper

Digital marketing is a proudly data-driven field. Yet, as SEOs especially, we often have such incomplete or questionable data to work with, that we end up jumping to the wrong conclusions in our attempts to substantiate our arguments or quantify our issues and opportunities.

In this post, I’m going to outline 4 data analysis pitfalls that are endemic in our industry, and how to avoid them.

1. Jumping to conclusions

Earlier this year, I conducted a ranking factor study around brand awareness, and I posted this caveat:

“…the fact that Domain Authority (or branded search volume, or anything else) is positively correlated with rankings could indicate that any or all of the following is likely:

  • Links cause sites to rank well
  • Ranking well causes sites to get links
  • Some third factor (e.g. reputation or age of site) causes sites to get both links and rankings”
    ~ Me

However, I want to go into this in a bit more depth and give you a framework for analyzing these yourself, because it still comes up a lot. Take, for example, this recent study by Stone Temple, which you may have seen in the Moz Top 10 or Rand’s tweets, or this excellent article discussing SEMRush’s recent direct traffic findings. To be absolutely clear, I’m not criticizing either of the studies, but I do want to draw attention to how we might interpret them.

Firstly, we do tend to suffer a little confirmation bias — we’re all too eager to call out the cliché “correlation vs. causation” distinction when we see successful sites that are keyword-stuffed, but all too approving when we see studies doing the same with something we think is or was effective, like links.

Secondly, we fail to critically analyze the potential mechanisms. The options aren’t just causation or coincidence.

Before you jump to a conclusion based on a correlation, you’re obliged to consider various possibilities:

  • Complete coincidence
  • Reverse causation
  • Joint causation
  • Linearity
  • Broad applicability

If those don’t make any sense, then that’s fair enough — they’re jargon. Let’s go through an example:

Before I warn you not to eat cheese because you may die in your bedsheets, I’m obliged to check that it isn’t any of the following:

  • Complete coincidence – Is it possible that so many datasets were compared, that some were bound to be similar? Why, that’s exactly what Tyler Vigen did! Yes, this is possible.
  • Reverse causation – Is it possible that we have this the wrong way around? For example, perhaps your relatives, in mourning for your bedsheet-related death, eat cheese in large quantities to comfort themselves? This seems pretty unlikely, so let’s give it a pass. No, this is very unlikely.
  • Joint causation – Is it possible that some third factor is behind both of these? Maybe increasing affluence makes you healthier (so you don’t die of things like malnutrition), and also causes you to eat more cheese? This seems very plausible. Yes, this is possible.
  • Linearity – Are we comparing two linear trends? A linear trend is a steady rate of growth or decline. Any two statistics which are both roughly linear over time will be very well correlated. In the graph above, both our statistics are trending linearly upwards. If the graph was drawn with different scales, they might look completely unrelated, like this, but because they both have a steady rate, they’d still be very well correlated. Yes, this looks likely.
  • Broad applicability – Is it possible that this relationship only exists in certain niche scenarios, or, at least, not in my niche scenario? Perhaps, for example, cheese does this to some people, and that’s been enough to create this correlation, because there are so few bedsheet-tangling fatalities otherwise? Yes, this seems possible.

So we have 4 “Yes” answers and one “No” answer from those 5 checks.

If your example doesn’t get 5 “No” answers from those 5 checks, it’s a fail, and you don’t get to say that the study has established either a ranking factor or a fatal side effect of cheese consumption.

A similar process should apply to case studies, which are another form of correlation — the correlation between you making a change, and something good (or bad!) happening. For example, ask:

  • Have I ruled out other factors (e.g. external demand, seasonality, competitors making mistakes)?
  • Did I increase traffic by doing the thing I tried to do, or did I accidentally improve some other factor at the same time?
  • Did this work because of the unique circumstance of the particular client/project?

This is particularly challenging for SEOs, because we rarely have data of this quality, but I’d suggest an additional pair of questions to help you navigate this minefield:

  • If I were Google, would I do this?
  • If I were Google, could I do this?

Direct traffic as a ranking factor passes the “could” test, but only barely — Google could use data from Chrome, Android, or ISPs, but it’d be sketchy. It doesn’t really pass the “would” test, though — it’d be far easier for Google to use branded search traffic, which would answer the same questions you might try to answer by comparing direct traffic levels (e.g. how popular is this website?).

2. Missing the context

If I told you that my traffic was up 20% week on week today, what would you say? Congratulations?

What if it was up 20% this time last year?

What if I told you it had been up 20% year on year, up until recently?

It’s funny how a little context can completely change this. This is another problem with case studies and their evil inverted twin, traffic drop analyses.

If we really want to understand whether to be surprised at something, positively or negatively, we need to compare it to our expectations, and then figure out what deviation from our expectations is “normal.” If this is starting to sound like statistics, that’s because it is statistics — indeed, I wrote about a statistical approach to measuring change way back in 2015.

If you want to be lazy, though, a good rule of thumb is to zoom out, and add in those previous years. And if someone shows you data that is suspiciously zoomed in, you might want to take it with a pinch of salt.

3. Trusting our tools

Would you make a multi-million dollar business decision based on a number that your competitor could manipulate at will? Well, chances are you do, and the number can be found in Google Analytics. I’ve covered this extensively in other places, but there are some major problems with most analytics platforms around:

  • How easy they are to manipulate externally
  • How arbitrarily they group hits into sessions
  • How vulnerable they are to ad blockers
  • How they perform under sampling, and how obvious they make this

For example, did you know that the Google Analytics API v3 can heavily sample data whilst telling you that the data is unsampled, above a certain amount of traffic (~500,000 within date range)? Neither did I, until we ran into it whilst building Distilled ODN.

Similar problems exist with many “Search Analytics” tools. My colleague Sam Nemzer has written a bunch about this — did you know that most rank tracking platforms report completely different rankings? Or how about the fact that the keywords grouped by Google (and thus tools like SEMRush and STAT, too) are not equivalent, and don’t necessarily have the volumes quoted?

It’s important to understand the strengths and weaknesses of tools that we use, so that we can at least know when they’re directionally accurate (as in, their insights guide you in the right direction), even if not perfectly accurate. All I can really recommend here is that skilling up in SEO (or any other digital channel) necessarily means understanding the mechanics behind your measurement platforms — which is why all new starts at Distilled end up learning how to do analytics audits.

One of the most common solutions to the root problem is combining multiple data sources, but…

4. Combining data sources

There are numerous platforms out there that will “defeat (not provided)” by bringing together data from two or more of:

  • Analytics
  • Search Console
  • AdWords
  • Rank tracking

The problems here are that, firstly, these platforms do not have equivalent definitions, and secondly, ironically, (not provided) tends to break them.

Let’s deal with definitions first, with an example — let’s look at a landing page with a channel:

  • In Search Console, these are reported as clicks, and can be vulnerable to heavy, invisible sampling when multiple dimensions (e.g. keyword and page) or filters are combined.
  • In Google Analytics, these are reported using last non-direct click, meaning that your organic traffic includes a bunch of direct sessions, time-outs that resumed mid-session, etc. That’s without getting into dark traffic, ad blockers, etc.
  • In AdWords, most reporting uses last AdWords click, and conversions may be defined differently. In addition, keyword volumes are bundled, as referenced above.
  • Rank tracking is location specific, and inconsistent, as referenced above.

Fine, though — it may not be precise, but you can at least get to some directionally useful data given these limitations. However, about that “(not provided)”…

Most of your landing pages get traffic from more than one keyword. It’s very likely that some of these keywords convert better than others, particularly if they are branded, meaning that even the most thorough click-through rate model isn’t going to help you. So how do you know which keywords are valuable?

The best answer is to generalize from AdWords data for those keywords, but it’s very unlikely that you have analytics data for all those combinations of keyword and landing page. Essentially, the tools that report on this make the very bold assumption that a given page converts identically for all keywords. Some are more transparent about this than others.

Again, this isn’t to say that those tools aren’t valuable — they just need to be understood carefully. The only way you could reliably fill in these blanks created by “not provided” would be to spend a ton on paid search to get decent volume, conversion rate, and bounce rate estimates for all your keywords, and even then, you’ve not fixed the inconsistent definitions issues.

Bonus peeve: Average rank

I still see this way too often. Three questions:

  1. Do you care more about losing rankings for ten very low volume queries (10 searches a month or less) than for one high volume query (millions plus)? If the answer isn’t “yes, I absolutely care more about the ten low-volume queries”, then this metric isn’t for you, and you should consider a visibility metric based on click through rate estimates.
  2. When you start ranking at 100 for a keyword you didn’t rank for before, does this make you unhappy? If the answer isn’t “yes, I hate ranking for new keywords,” then this metric isn’t for you — because that will lower your average rank. You could of course treat all non-ranking keywords as position 100, as some tools allow, but is a drop of 2 average rank positions really the best way to express that 1/50 of your landing pages have been de-indexed? Again, use a visibility metric, please.
  3. Do you like comparing your performance with your competitors? If the answer isn’t “no, of course not,” then this metric isn’t for you — your competitors may have more or fewer branded keywords or long-tail rankings, and these will skew the comparison. Again, use a visibility metric.

Conclusion

Hopefully, you’ve found this useful. To summarize the main takeaways:

  • Critically analyse correlations & case studies by seeing if you can explain them as coincidences, as reverse causation, as joint causation, through reference to a third mutually relevant factor, or through niche applicability.
  • Don’t look at changes in traffic without looking at the context — what would you have forecasted for this period, and with what margin of error?
  • Remember that the tools we use have limitations, and do your research on how that impacts the numbers they show. “How has this number been produced?” is an important component in “What does this number mean?”
  • If you end up combining data from multiple tools, remember to work out the relationship between them — treat this information as directional rather than precise.

Let me know what data analysis fallacies bug you, in the comments below.

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