Everyone knows there’s more than one decision-maker in a complex B2B sale. But too often, companies don’t have insights into what the broader buying group is thinking and doing because they don’t have the right processes in place. As a result, revenue suffers. Join Forrester’s Kerry Cunningham and Openprise’s Allen Pogorzelski to hear how you can orchestrate your business processes to get a more robust picture of the individuals behind your target accounts, and automate those processes at scale. Register today for “Orchestrating B2B Business Processes around Buying Groups to Deliver More Revenue,” presented by Openprise.
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Are agencies going to be expected to “manage to the recommendations”? That’s among the concerns I heard from many marketers after Google announced the performance portion of the Google Partner program requirements will be evaluated based on manager account optimization scores starting in June, with added focus on Google’s automated recommendations. This change, in particular — and reaction to it — strikes at the tension between the benefits and limitations of automation and marketers’ relationship with the platforms designing the automation. There are the positives of big data insights, time-saving efficiencies. And yet, the systems don’t have the full view of an individual business, they are still training (on advertisers’ dime) and there are the inherent conflicts of interest concerns when the algorithms are designed by the very platforms that advertisers are paying to serve their campaigns. Optimization score. Google shows an optimization score at the manager account, individual account and campaign levels. It’s defined as “an estimate of how well your account is set to perform.” Google already evaluated agency performance, but it was never explicit about what criteria was being evaluated. The idea for using optimization score as an external gauge is to offer more transparency about what is being evaluated and how to improve. Agencies must have an optimization score of at least 70% for Partner status Google clarified since first announcing the change in mid-February. It also added a note stating that its internal data shows “advertisers who increased their account-level optimization score by 10 points saw a 10% increase in conversions, on average.” If agencies fall below the 70% threshold (or fail to meet the other requirements), they’ll be notified and given suggestions for meeting the requirements and have 60 days to get back in good standing before losing their badge. Agencies can regain their badge when they meet the requirements again. “Over the years, we’ve all seen various automated scorecards used by aggressive SEO or PPC agencies when they audit a company’s marketing efforts. Typically these things are meant to find fault and to achieve a specific goal [that’s] not 100% aligned with the client’s objectives,” said Andrew Goodman, founder and president of digital marketing agency Page Zero Media, which has Google Premier Partner status. “Google’s scorecards are a highly sophisticated version of the same phenomenon. Recommendations as aids to busy / stretched account managers are certainly a good idea.” His concern comes in using them to judge account or agency performance. That “is jumping the gun, IMO,” said Goodman. Bad recommendations. Google offers dozens of auto-generated recommendations in Google Ads accounts that range from keyword additions and removals to budget changes to bid strategy switches to feature adoptions. They have improved as Google’s machine learning has matured, but they’re by no means perfect. Since the announcement, I’ve spoken with numerous Google Partner agency representatives — at SMX West last week and since — about the changes, and routinely heard complaints about bad recommendations. Poor keyword suggestions; pushes to adopt dynamic search ads; and smart bidding strategies that don’t align with the business goals. “Working alongside the Google team can bring many, many benefits. From access to beta programs to knowing about updates and changed to the ad platform is a huge benefit to CMI and our clients,” said Justin Fried, EVP growth and innovation at CMI/Compas. “With that being said, it is important to remember that they are publicly traded company and while the titles of [the] team you work with may not say ‘sales,’ the team does have specific goals to get their clients to increase spend and adopt new features. Knowing this, we have to assume some of the optimization recommendations coming from the Google team are self-serving. So when we receive recommendations, we ensure anything we implement is in the best interest of our client.” For some, the optimization score criteria was the final straw. “The recommendations, for the most part, are not helpful to good agencies, or anyone that should be fully trained and at ‘Partner’ status,” said Greg Finn CMO and partner at digital marketing agency Cypress North. Finn has been outspoken about his frustrations over the change and what it says about the value of the Partner badge. The agency has dropped its badge and replaced it with an alternative “ClientPartners” badge it created for agencies to show “that you won’t put Ad Platform profit over client performance.” Finn also questions why Google is dropping the requirement for agencies to have proven experience in Google Ads of at least 12 months. “You can now set up an account… take that test…apply all recommendations and you are a partner.” Are agencies expected to manage to the recommendations? Will they be forced to either accept recommendations that will have a negligible or negative impact on performance)? That’s the big concern. Google says it understands not all recommendations will be appropriate and that the leeway in the 70% underscores this. “Optimization score is one of the best signals for partners to determine if their campaigns are reaching the right customers for their clients effectively,” a Google spokesperson told Search Engine Land. “Agencies will continue to have the control and autonomy to make the right choices from the recommendations page for their clients, while benefiting from the efficiencies the optimization score brings.” The spokesperson added, “We are committed to helping our partners who currently do not meet the necessary new requirements, including how to best use the recommendations page and maintain a 70% optimization score, with a suite of training and tools.” Analysis in the age of automation. Automation requires a healthy level of skepticism, an understanding of how the various optimizations are designed to work and savvy analytical skills to determine whether the automation is working as intended. But in the end, everything goes back to business fundamentals. Goodman points out, for example, that the 10% lift in conversions cited by Google from accepting recommendations might not benefit the bottom line. “No cost figure is cited, so this 10% increase in conversions could have come with a 10% or even 25% increase in cost. Who knows?” said Goodman. “The second flaw is simply that it could skew towards highly under-managed, clumsily-optimized accounts.” “The score is divorced from business growth and profitability metrics; we’ve retained and delighted clients precisely because we leave no stone unturned to help their businesses grow profitably, rather than optimizing to perverse or unrelated metrics.” Will it change how agencies manage campaigns? What was clear in my discussions is that agencies are not expected to manage their campaigns solely via the Recommendations tab nor that they should accept recommendations that don’t make sense for the business. Dismiss or simply ignore those that you determine aren’t good for the account. Ssome Partner agencies had already made reviewing recommendations part of their account management routines. “In an account with hundreds of campaigns, the Recommendations page gives me a place to start my optimizations. I like how easy it is to apply simple suggestions and to dismiss other recommendations that may not be relevant to my account,” said Carrie Albright, director of services at Hanapin Marketing. WPromote has done the same, seeing the recommendations as a kind of system check when making big changes to accounts. “We wanted an additional set of eyes on our account performance to roll out any changes at scale,” said Angelo Lillo, general manager of paid search, at Wpromote. Whether reviewing recommendations is part of their workflow or not, the marketers I spoke with said the new requirements won’t affect how they approach client work. “We will not change how we operate,” said Fried. “Our clients are our main priority and we will only implement optimizations that support their overall goals and move their business in a positive direction.” Most acknowledged they’ll pay closer attention to the recommendations and their optimization scores after this change, but the approach to clients won’t and shouldn’t be affected. “It is OK to put extra rigor behind our analysis of the recommendations,” said Fried. “We will continue to review the recommendations and only implement things that are in the best interest of our client.” The post Optimization scores, recommendations and their impact on Google Partner agencies appeared first on Search Engine Land. via Search Engine Land https://ift.tt/3ciqzuI Planning your paid social campaigns well ahead of your peak periods — whether seasonal, holiday or promotional — help ensure you hit your goals. Michelle Stinson Ross, marketing operations director at Apogee Results, offered tips on how advertisers can get the most out of seasonal social ads at SMX West in San Jose last week. As part of a deep focus on digital commerce marketing, speakers discussed ways social commerce and shoppable media are transforming the way online retailers approach digital marketing. Stay top of mind“Paid social sits much higher in your sales funnel than paid search does. Just think about it… Are human beings really able to search for something they’ve never heard of before? No, probably not,” said Stinson Ross. It’s the reason why paid social advertisers need to be thinking about social in the same vein as “old school” forms of mass media: newspapers, radio, or television. “These are all exposures to audiences that may have never heard of us,” she added. Social media as a marketing channel may not present an “intense moment of buying,” Stinson Ross said, but it could provide an opportunity to remind or introduce consumers to your brand and how it can benefit them. Early exposure also means you’ll be able to build up audiences to remarket to when your seasonal periods hit. “Facebook, Twitter, Instagram, Pinterest, LinkedIn – all of them have retargeting options so that [brands] can remind them – hey, there’s a sale coming up,” she said. Effective retargeting works best when advertisers can segment top-of-funnel visitors from those already familiar with your brand and tailor the messaging accordingly. Remarketing is also an opportunity to cross-sell and upsell to past customers, said Stinson. Target seasonal behaviorTiming is everything and targeting for seasonal behavior is key for online retailers. Surf gear ads in the winter and ski gear ads in the summer aren’t likely to resonate. That said, the trends and consumer behaviors may surprise you. Look at search trends data and your own analytics to be able to anticipate — and get ahead of — your seasonal upticks. Take cues from brick-and-mortar retailers, suggests Stinson Ross. “We can pay attention to competitors and other retail outlets as to when is that signal going out to customers that it’s time to start thinking about the next holiday. We can begin to target that in social.” Connecting throughout the customer journeyRetargeting becomes especially important when advertisers can return to audience pools generated through broader campaigns, Stinson Ross said. Commerce advertisers can bolster social retargeting efforts by building lists of social visitors to target with Search campaigns. When it comes to measurement, marketers should beware of measuring paid social’s impact based on last-click attribution. “While you may get them to go to that page and consider that product, they may not buy in that particular moment. But that’s where PPC can pick them up, finish the process and get them to the sale in the end,” Stinson Ross concluded. More about SMX
The post 3 tips for optimizing paid social campaigns for seasonality appeared first on Search Engine Land. via Search Engine Land https://ift.tt/3chIGB6 I have previously written about why keyword research isn’t dead. A key theme I continually make is that keyword categorization is incredibly important in order to be useful so that you can optimize towards topics and clusters rather than individual keywords. My keyword research documents often exceed 20k-50k keywords which are normally broken into two, three or sometimes more categories reflective of the site taxonomy in question. As you can see, I have categorized the keywords into 4, filterable, columns allowing you to select a certain “topic” and view the collective search volume for a cohort of keywords. What you can’t see is that there are over 8k keywords. A few years ago I used to categorize this fairly manually, using some simple formulas where I could. Took ages. So I made a keyword categorization tool to help me. It’s built using php and still pretty rudimentary but has sped the time I am able to do keyword research and categorize it from a couple of days to 12-15 hours depending on how many keywords there are. I’m a sucker for a trend. So the minute all the SEOs started shouting about how great Python is, of course I am on the bandwagon. My goal is to streamline the keyword research process even further and I’m loving learning such an adaptable language. But then I came across this video by David Sottimano where he introduced BigML into my life. Imagine an online “drag and drop” machine learning service; a system literally anyone can use. This is BigML. I am still pursuing my ultimate goal of mastering Python, but in the meantime, BigML has provided me with some very interesting insights that have already sped up my keyword categorization. The aim of this article is to give you some ideas about leveraging (free) technologies already out there to work smarter. A quick note before we delve in, BigML is a freemium tool. There is a monthly fee if you want to crunch a lot of data or want added features (like more than one person on the account at one time). However, to achieve the results in this article, the free tier will be more than enough. In fact, unless you’re a serious data scientist and need to analyze a LOT of variables, the free tier will always be enough for you. Step 1 – Getting the training dataFor this example, we’ll pretend we’re doing keyword research for River Island – a large clothing retailer in the UK for all my friends across the pond. (If you’re reading this and work for River Island, I will not be doing full keyword research.) If we look at River Island’s site taxonomy we see the following: For the purpose of this guide, we’ll just do keyword research for men and focus on these few product items: Let’s say, hypothetically, I want to group my keywords into the following categories and subcategories: Tops > Coats and Jackets > T-Shirts and vests Bottoms > Jeans > Trousers and Chinos We’ll do the “Bottoms” first. Grab the “jeans” URL for River Island and plug it into SEMRush: Filter by the top 20 keywords and export: I’ve chosen the top 20 because often, beyond that, you start to rank for some irrelevant and, sometimes, quite odd keywords. Yes, River Island ranks number 58 for this term: We don’t want these terms affecting our training model. For “jeans”, when we filter for keywords in positions 1-20 and export, we get 900 odd keywords. Drop them into a spreadsheet and add the headings “category 1” and “category 2”. You’ll then drop “bottoms” into category 1 and “jeans” into category 2 and fill down: This is the start of your machine learning “training data”. There’s probably enough data here already, but I like to be thorough so I’m also going to grab all the keywords from a company I know ranks highly for every clothing based keyword – ASOS. I’m going to repeat the process for their jeans page: After I’ve exported the resulting ranking keywords from SEMRush, added them to my spreadsheet, dropped the categories down and de-duped the list I’ve got 1,300 keywords for Bottoms > Jeans. I’m going to repeat the process for: Bottoms > Trousers and Chinos Tops > Coats and Jackets Tops > T-Shirts and Vests For these 3, I didn’t bother putting the River Island domain into SEMRush as ASOS ranked for so many keywords there will be enough data for my training model. After a quick find and replace to get rid of branded keywords: And a de-duplication, I’m left with nearly 8,000 keywords that are categorized into “Bottoms” and “Tops” at the first level, and “Jeans” and “Trousers/Chinos” at a secondary level. Tip – you may need to use the trim function to get rid of any whitespace after the find and replace as otherwise this sheet will upload with errors when we use it as our training data: Time spent so far: 5 minutes You’d of course carry on doing this for all River Islands products and into as many categories as required. If you were doing men’s and women’s, they’d likely be the first category. You’d then possibly have a fourth category which breaks things like “jackets” up further into items like “puffer jackets” and “leather jackets”. If you’re struggling to visualize the categories you may need, I will shortly be writing a post on that too. Sometimes it’s just common sense, but there is also a machine learning program to help with that too if you need it: Step 2 – Training your machine learning modelCool – we have our list of 8,000 unbranded keywords that have been categorized in 5 minutes. Save the file as a CSV and then head to BigML and get registered. It’s free. Now we’ll go through the following, incredibly simple steps, to train the machine learning program in categorizing keywords.
In most instances, the rest of the settings should be fine. If you’d like to learn more about what all the settings do, I’d recommend you watch BigML’s educational youtube channel here.
Click the “create dataset” button: Although, before you do, rename the “dataset name” to something like ML Blog Data (Category 1).
After it’s finished computing, you’ll see a decision tree like this: Again, I’m not going to go into everything you can do with this, but what it’s essentially done is created a series of if statements based on the data you’ve given it which it will use to work out the probability of a category. For example, the circle I’ve hovered over in the image is a decision path with the following attributes – if the keyword does not contain “jeans” or “trousers”, it’s likely to be a “top” with a confidence score of 85.71%. You can actually create something called an “ensemble model” which will be even more accurate. You’re also able to split the data and run a controlled test on it so you can see how accurate it’s going to be before you use it. If you’d like to learn more on this, reach out to me or read the documentation on the site. So, we’ve created a model for categorizing the keywords in category one. We now need to do the same for the second category. Head back to your sources and select your training dataset again: Repeat the steps above, but this time deselect “category 1” when you are configuring your dataset: As with before, create a one-click supervised model: Voila – your second decision tree: So now we have 2 trained models that will categorize your keywords using machine learning with a fairly high degree of accuracy. Time spent so far: 10 minutes (maybe an hour if you did every product category on River Islands website) Getting the rest of your keywordsWe only trained a model to cover 2 categories and 4 subcategories. Assuming you trained it for every product on the River Island’s website (which will likely take you an hour or two max. Maybe even get a Virtual Assistant to do it for you and put your feet up), the rest of your keyword research is going to be so easy. All I’m going to do now is plug the following competitor domains into SEMRush at domain level and export their whole site’s ranking keywords (to clarify, I’m not going to be going into each product folder as I did with the training data): https://ift.tt/2mkAmGT And I could keep going. After I’ve deduped all the keywords on these sites and got rid of branded keywords I’m left with around 100k, uncategorized keywords. I may also employ some standard keyword research techniques such as using merge words and keyword planner or Ahrefs keyword explorer to get even more keyword suggestions. The beauty is, we don’t have to spend ages making sure the keywords we are exporting are being categorized correctly. We can literally just plug in domains and seed keywords and export. You’re then going to dump this huge, ugly, uncategorized list into Google sheets: Time spent so far: 25 minutes (or an hour and 25 minutes if you got every product category from River Islands website) Using BigML’s API to categorize your keywordsGet the BigML addon on Google sheets: You’ll need to pop your username and API key in, but you’ll find these easily within your BigML dashboard and settings. Now the fun begins.
Then, click “predict” and let it go: It may take a while depending on how many keywords you have, but at least you can get on with some other tasks. You’ll notice it also gives a probability score. I tend to just filter for anything less than 50% and delete them. I’ve got 100,000 keywords, I won’t miss the odd few.
Run for as many categories as you need, and then pull in any other important data for your finalized keyword research document: Some final notesSo there we have it – an easy way to categorize 100k keywords in less than a few hours actual working time (by that I mean you’ll have to wait for the ML to go through the keywords one by one, but you won’t be working).
It’s quite basic, but surprisingly powerful and a really nice introduction to machine learning. Have fun! The post How to use machine learning (if you can’t code) to help your keyword research appeared first on Search Engine Land. via Search Engine Land https://ift.tt/2wOeNI7 Over time, Google Maps and Google My Business have increasingly encroached on Yelp’s core value proposition: local business search and reviews. And Yelp has tried to fight back with new features and capabilities to stay one step ahead of Google. Waitlist upgrades. This week is a case-in-point: the company added new capabilities to Yelp Waitlist, which allows diners to join a restaurant waitlist before physically arriving at the property. Yelp originally launched Waitlist in 2017. Google introduced similar functionality, albeit more limited, in 2019. Yelp is now rolling out its Notify Me feature to Android and the web — it launched for iPhone last September — which supports Waitlist. Notify me uses restaurant wait-time data to alert users to join Yelp Waitlist. Users provide their party size and desired dining time and then receive a notification to join the online waitlist. This helps differentiate Yelp’s Waitlist from Google’s. Better data than Google has. Note how Yelp discusses the feature in its 2019 blog post announcing Notify Me: “Yelp’s in a unique position to deliver accurate wait times for diners because restaurants across the U.S. are using Yelp Waitlist to manage their front-of-house. By using actual wait time data from the restaurant, not your location data, Yelp Waitlist will show you the best times to dine at a restaurant.” The not-so-subtext is: our data is better than Google’s. Two additional features were also just added to Waitlist:
The pitch to business owners. Waitlist is a SaaS product that allows restaurants to manage “the front of the house.” This is where the data to enable Notify Me and predictive wait times originates — directly from the restaurant. Yelp says that “guests will wait 15% longer with Yelp Waitlist” and that it creates more customer loyalty and diner frequency. Waitlist costs $249 per month for restaurants. Waitlist is a microcosm of Yelp’s larger product strategy:
Why we care. Although Google has numerous competitors, Yelp is really it in the U.S. from a broad local search perspective (Apple Maps is powered substantially by Yelp). Google Maps and GMB are increasingly transactional, matching Yelp feature for feature. Google also now has more review volume and velocity than Yelp. However, Yelp would respond that its reviews are more reliable and that it doesn’t have the fake reviews problem that Google does. Yelp has been diversifying away from advertising as a lone revenue model and introducing subscription-based products. This is good for Yelp and for business owners, provided the products are effective (e.g., Waitlist). But Yelp will need to continue to innovate, and innovate in creative and unexpected ways, to maintain user and business owner engagement. The post In feature battle with Google, Yelp improves restaurant waitlist functionality appeared first on Search Engine Land. via Search Engine Land https://ift.tt/32Baxb1 Google is experimenting with public search profile cards for individuals that would appear alongside regular search results, Android Police first reported on Thursday. The test is currently limited to India and the company currently has no plans for a wider launch, a Google spokesperson told Search Engine Land. Why we careIf this feature receives a wider rollout, personal brands will have a new way to distinguish themselves on the search results, providing prospective clients or employers a way to find out more about them. This feature also provides personal brand owners with a degree of control over what information is available to searchers. More on the news
The post Google experiments with public search profile cards appeared first on Search Engine Land. via Search Engine Land https://ift.tt/32G3627 As of Sunday, Mar. 1, Google is treating the First introduced in 2005, the Defining link attributes. Last year, Google introduced new link attributes for sponsored and user-generated content (UGC).
Why Google introduced these new attributes. Google’s Gary Illyes and Danny Sullivan, who co-authored the September announcement, have said that the new attributes serve to help Google understand the web better and allow site owners to classify the nature of their links, if they want to. But this is key: Whether you implement the attributes does not affect your site and doing so is completely voluntary. What you should be doing. “If you were using nofollow to block any sensitive areas of your site that you didn’t want crawled, it probably makes sense to go block these in a different way,” Patrick Stox, technical SEO and brand ambassador for Ahrefs, told Search Engine Land. There are various ways, such as robots.txt or meta tags, that you can use to regulate how Google crawls and indexes pages. As for the The post Google’s new treatment of nofollow links has arrived appeared first on Search Engine Land. via Search Engine Land https://ift.tt/2T96tLG Google Developers announced a new official Google Lighthouse Firefox extension Wednesday. Google’s Lighthouse is an open source software (OSS) project with a few different integration points. There has been at least one port of the reporting tool for use as a Firefox extension. Firefox had not yet reviewed the extension, so it gets automatically categorized as “not a Recommended Extension.” Firefox will likely add Google’s extension to it’s “Recommend” list after review. In the meantime, the risk level on this extension is still unknown even when it appears to be very much legit. Why we careGoogle Lighthouse is a collection of highly useful tools for practitioners across the entire spectrum of our industry. Non-technical marketing folks can easily read summary reports and have stories to tell clients, while technical folks can utilize the command line interpreter (CLI) version to insert score threshold tests ensuring performance metrics as a step in continuous integration and continuous deployment procedures. The post New Google Lighthouse extension for Firefox goes live appeared first on Search Engine Land. via Search Engine Land https://ift.tt/2I0gQvf The post SEL 20200228 appeared first on Search Engine Land. via Search Engine Land https://ift.tt/3abS1bz Yotpo has partnered with Bazaarvoice to syndicate reviews through the latter’s network, which includes nearly 2,000 global retail sites and reaches more than a billion shoppers monthly, according to the company. Yotpo is evolving from a reputation management platform to an “end-to-end platform” for direct-to-consumer (D2C) brands. The move is intended to expand the reach of Yotpo-managed reviews content for retailers. Below is an example of a Yotpo review residing on the Nordstrom website via the Bazaarvoice Network. (There’s a disclosure it came from the merchant on the reviews page.) 98% are influenced by ‘authentic’ reviews. Nobody questions the importance of reviews in building consumer trust and social proof. Indeed, a 2019 Yotpo survey of 2,000 online shoppers found that “98% consider authentic customer reviews to be the most influential factor in influencing purchase decisions.” Other surveys agree with this finding, although at different percentage levels. However, a 2019 study from CPC Strategy (part of Elite SEM) found that, on Amazon, price was the biggest factor in purchase decisions, followed by reviews. Review syndication via Bazaarvoice 4+ stars = 12X more sales. Additional Yotpo research found that products rated 4-stars or above generated “nearly 12 times more orders than those with an average rating of 3 stars.” As part of that same study, interestingly, the company discovered that people are more inclined to write reviews for more expensive products. Yotpo also says that websites featuring reviews generate more organic search traffic, from long-tail keywords, than those without reviews. Yotpo qualified its 98% review reliance finding with the word “authentic.” That’s partly because review fraud is a growing problem according to several third party analyses (Washington Post, ReviewMeta, Fakespot). One early 2019 survey found limited consumer awareness of the fake reviews problem (on Amazon). However, a more recent study, released in December, found a majority of people (70%) were looking at multiple review sites as an anti-fraud strategy. Why we care. Notwithstanding some erosion of consumer trust regarding online reviews, it’s critical for local and e-commerce merchants to have a reviews strategy and to maintain review velocity. It’s also important to have a diversified strategy as people look increasingly to multiple sites to confirm that reviews on site A are echoed by site B for the same product or property. The post Yotpo taps Bazaarvoice Network for review distribution to retailer sites appeared first on Search Engine Land. via Search Engine Land https://ift.tt/32wkhDn |
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