11- Apr2018
Posted By: DPadmin

5 conversion boosters to optimize your PPC campaigns 

Earlier this year, Facebook broke some bad news. Organic reach is officially being choked, making it harder for brands to reach the audiences they’ve worked so hard to build.

Because of this, I believe marketers will look to SEM (search engine marketing) to recapture lost attention. The problem is, there’s already so much competition. How do you get past the noise and generate PPC (pay per click) results, and which KPIs (key performance indicators) should you be tracking to measure success?

Optimizing conversion rate (CVR) is one of the fastest ways to improve AdWords efficiency. It allows you to test new approaches and boost ROI without having to expand target keywords, campaigns or budget.

Here are five approaches to PPC that will help you generate more conversions and better results in 2018 and beyond.

1. Optimize keyword quality score

Google’s entire business model relies on providing searchers with relevant results. This goes for organic results as well as AdWords.

To do this, Google assigns your target keywords a Quality Score (QS). This QS, along with your CPC (cost per click) bid, is what then determines your “Ad Rank.”

The three elements that determine your QS are:

  1. Ad relevance (in other words, how relevant the keyword is to the ad copy you serve).
  2. Landing page experience.
  3. Expected CTR (click-through rate).

Many PPC experts consider CTR the most important factor when determining QS. Therefore, when looking to optimize your QS, start with CTR.

Analyze the keyword relevancy of your campaigns. Is your ad copy aligned with the search intent of the keyword?

It’s good practice to create separate Ad Groups for each of your keywords. Also known as Single Keyword Ad Groups, this is where you cater to the intent of specific searchers rather than a larger group.

In the example below (courtesy of ConversionXL), ASDA is the only advertiser for the term “womens red dresses” with copy tailored to that search term.

As well as relevancy, your ad copy should quickly sell the benefits of the “click.” In other words, why should the searcher pay attention? Make your headlines relevant, focusing on the desires and pain points of your audience.

By optimizing CTR, and therefore quality score, you’ll generate more qualified traffic. And high-quality traffic delivers better conversion rates.

Once you’ve optimized CTR, your landing pages should be the next target. Dynamic text replacement (DTR) can provide some quick wins. This “swaps” specific copy in your landing page based on the keyword the user searched to find you. DTR can improve quality score and therefore contribute to a higher CVR.

2. Intelligent remarketing

When it comes to AdWords, high bounce rates are a fact of life. Users who come to your landing pages are at various stages of the customer journey. For example, a call-to-action (CTA) for a demo won’t work on a searcher who is still educating themselves on different solutions.

To capture these missed opportunities, use remarketing to cross-sell and “down-sell” bounced visitors. Let’s start by expanding on the example above. If you’re offering a demo of your software to someone who is still in the awareness phase, this approach won’t be as effective as something that answers their questions.

Therefore, an e-book that teaches prospects how to overcome specific challenges is an appropriate down-sell. It would educate them on the options available to them while providing information about how your product makes the process easier.

Of course, these challenges will vary depending on personas and customer segments. Therefore, you must personalize your ad creative where necessary.

Retargeting in this way allows you to capture lead information that would have been otherwise lost, boosting the CVR and overall ROI of your campaigns. The mistake many marketers make here is to “re-sell” the demo request. Use it as an opportunity to educate them and add more value instead of forcing them further down the funnel.

Here are some tips you can apply to your remarketing ads to capture the attention of lost leads:

  • Test different lead magnets: Different personas and customer types respond to different forms of media. Split-test your remarketing ads to offer an e-book and webinar. See which generates the highest conversions and double down on those formats.
  • Name-drop influencers: If you work with well-known influencers in your space, consider including them in your remarketing ads. This association adds an element of trust like no other.
  • Use dynamic targeting: Serve specific ads to different audience segments. More on this later.

The point of remarketing is to capture lost users and retain customers. Don’t waste the opportunity by serving the same messaging. Look for ways to add value up and down the funnel.

3. Tap into the power of machine learning

AI and machine learning bring the promise of higher-performing marketing at speed. From an AdWords perspective, this would mean automated bid and budget management, using more data than a human can handle to make adjustments in real time.

To find out exactly what impact machine learning has on PPC performance, we analyzed 32,858 paid accounts using the Acquisio Turing platform to uncover the truth. Here’s what we found out about conversions and machine learning:

  1. An average increase in conversions of 71 percent.
  2. A median increase in conversions of 22 percent.

Discussions of landing page quality aside, the huge difference between average and median is explained by the fact that a certain number of accounts saw extremely high increases in number of conversions, which skews the average in a meaningful way. If we wished to exclude those extremes from the discussion, we would look at the median score, which tells us the percent increase in conversions that was observed for the 50th percentile.

The plot thickened because this increase in conversions came with an overall decrease in cost per acquisition (CPA). In fact, the median CPA had a decrease of 18 percent, with 64 percent of the group enjoying a decrease in CPA overall.

While the report above focused on the increase in conversions made possible by machine learning, our most recent study examined 50,000 campaigns to determine Google AdWords Industry Benchmarks and looked at conversion rate (CVR) with and without machine learning by industry. Here are the CVR findings segmented by business category:

Conversion rate (CVR) by industry with and without machine learning

Machine learning martech helps PPC marketers scale and optimize marketing activities efficiently, but it’s also a serious contender for conversion boosts.

Here’s the thing: Machine learning technologies get better the more they learn. In other words, results will improve as machine learning algorithms react to new findings. Check out The Marketer’s Field Guide to Machine Learning for more information.

4. Test new ad extensions

To cut through the noise, you must capture as much SERP (search engine results page) real estate as possible. This means not only standing out with your creative but also expanding how much room your ads take up.

To do this, test different ad extensions on your top-performing campaigns. Ad extensions, as defined by Google, “expand your ad with additional information — giving people more reasons to choose your business. They typically increase an ad’s click-through rate by several percentage points.”

Ad extensions come in several forms, the most popular of which are:

  • Sitelink Extensions: Provide links to other relevant pages on your website.
  • Callout Extensions: Additional information on what you’re offering, e.g., limited stock and free delivery.
  • Structured Snippets: Allows you to highlight specific elements. For example, if you’re selling “Italian vegan leather boots,” you can include a list of shoe sizes.
  • Location Extensions: Include your business address and telephone number in your ad copy.

As you’re well aware, mobile user behavior is very different from desktop users’. Indeed, 61.9 percent of all PPC clicks were from a smartphone during Q3 of 2017.

Google has reacted to this shift in behavior by adding additional extensions for ads that appear on mobile devices. These are:

  • Message Extensions: Allow users to send an SMS to your business directly from the SERPs.
  • Call Extensions: Similarly, users can dial a phone number provided within your ad copy.


As always, test different extensions on a small scale before applying them to all of your campaigns. Keep the customer’s journey and intent in mind. Are they searching for a term with several possible outcomes? Consider using a Sitelink extension. Does it look like they’re searching for your retail store on a mobile phone? Include mobile extensions.

5. Advanced segmentation with in-market audiences

Facebook Ads are popular among marketers due to the advanced targeting available. But many are still unaware of AdWords’ functionality to do the same.

Google collects a tremendous amount of data on their users. So it was only a matter of time before they allowed marketers to use it themselves.

That’s where in-market audiences come in. By using in-market audiences within your display ads targeting, you can target users based on their consumer behavior, as well as the content they have expressed an interest in online.

The data available is sorted into several market categories, including real estate, travel and telecommunication. You can then set targeting on a granular level, all the way down to specific interests and brand names:

So, how does it work? According to Google, data such as sites browsed, the proximity of visits, relevant ads clicked and conversions are all taken into account to categorize users by intent.

This means that, while this is limited to the Display network only, you’re able to serve hyper-specific ads to those who have expressed an interest. From persona segments to product categories, the options are many.

Source: 5 conversion boosters to optimize your PPC campaigns – Marketing Land

06- Apr2018
Posted By: DPadmin

How Search Engines Use Machine Learning: 9 Things We Know for Sure

Here are nine ways we know that search engines are currently using machine learning and how it relates to SEO or digital marketing.

When we first started hearing about machine learning in the early 2010s, it seemed scary at first.

But once it was explained to us (and we realized how technology is already being used to provide us with solutions), we started to get down to the practical questions:

  • How are search engines using machine learning?
  • How will it affect SEO?

Machine learning is essentially using algorithms to calculate trends, value, or other characteristics of specific things based on historical data.

Google has even declared itself a machine learning-first company.

If you want to learn more about the tactical side of this technology, Eric Enge has a great write-up on Moz explaining how machine learning impacts SEO from a mathematical standpoint.

Search engines like to always experiment with how they can use this evolving technology, but here are nine ways we know that they are currently using machine learning and how it relates to SEO or digital marketing.

1. Pattern Detection

Search engines are using machine learning for pattern detections that help identify spam or duplicate content. They plugged in common attributes of low-quality content, such as:

  • The presence of several outbound links to unrelated pages.
  • Lots of uses of stop words or synonyms.
  • Other such variables.

Being able to detect these kinds of patterns drastically cut down on the manpower it takes to review everything by actual people.

Even though there are still human quality raters, machine learning has helped Google automatically sift through pages to weed out low-quality pages without an actual human having to look at it first.

Machine learning is an ever-evolving technology, so the more pages that are analyzed, the more accurate it is (in theory).

2. Identifying New Signals

According to a 2016 podcast done with Gary Illyes from Google, RankBrainnot only helps identify patterns in queries, it also helps the search engine identify possible new ranking signals.

These signals are sought after so Google can continue to improve the quality of search query results.

Illyes also mentioned in the podcast episode that more of Google’s signals may become machine learning-based.

As search engines are able to teach technology how to run predictions and data on their own, there can be less manual labor and employees can move toward other things machines can’t do, like innovation or human-centered projects.

3. It’s Weighted as a Small Portion

However, even though machine learning is slowly transforming the way search engines find and rank websites, it doesn’t mean it has a major, significant impact (currently) on our SERPs.

In the same podcast interview, Illyes says that it’s just part of their overall ranking signal platform, and is weighted as a small portion of their overall algorithm.

Google’s end goal is to use technology to provide users with a better experience. They don’t want to automate the entire process if that means the user won’t have the experience they are looking for.

So don’t assume machine learning will soon take over all search ranking; it is simply a small piece of the puzzle search engines have implemented to hopefully make our lives easier.

4. Custom Signals Based on Specific Query

Machine learning in search engines may vary depending on the query category or phrasing, according to a July 2017 study done at the University of Washington.

Researchers used Russian search engine Yandex to analyze results for different queries. They found that the types of results displayed depended largely on the query category or phrasing.

This means that machine learning can place more weights on variables more or less heavily in certain queries over others.

Overall, it was found that personalized searches customized by machine learning increased the click-through rate (CTR) of results about 10 percent.

As the user entered more queries into Yandex, it was found that the CTR continued to increase.

This is likely because the search engine was “learning” about that specific user’s preferences and could base its information on past queries to present the most interesting information possible.

An example of this that is often used in conference presentations is a string of queries in one sitting and how the results change depending on what you last searched.

For instance, if I search “New York Football stadium” in an incognito browser, I get the answer of “MetLife Stadium.

Next, if I search in the same browser for just “jets,” Google is assuming that because my last query was about a football stadium, then this query is also about football.

google search query for foot ball

jets search query in google


As I continue my search, Google learns when I’ve turned into something else.

Searching for “Jaguars” in the same browser will bring up information about the NFL team the Jacksonville Jaguars (related to my last two searches).

But the instance I search “Zoo near San Diego” then start to type “zoo” again in the query box, Google suggests “zoos with jaguars” even though I haven’t searched jaguars a second time.

search query with google

Search history is just one component of the search experience that machine learning uses to provide better results.

5. Image Search to Understand Photos

Back in 2013, it was reported that Flickr users upload 1.4 million photos per day, 40 million are uploaded to Instagram, and Facebook users were uploading 350 million.

While these statistics have likely gone up (it was difficult to find more recent data), it shows that volume of photos that need to be cataloged and analyzed on the web daily.

This task is perfect for machine learning because it can analyze color and shape patterns and pair that with any existing schema data about the photograph to help the search engine understand what an image actually is.

This is how Google is able to not only catalog images for Google Image search results, but also powers its feature that allows users search by a photo file (instead of a text query).

Users can then find other instances of the photo online, as well as similar photographs that have the same subjects or color palette and information about the subjects in the photo, as in this example of a classic Christmas movie still:

google search for rudolf

The way the user interacts with these results can shape their SERPs in the future.

6. Identifying Similarities Between Words in a Search Query

Not only does query data get used by machine learning to identify and personalize a user’s later queries, it also helps create patterns in data that shapes the search results other users are getting.

Google Trends is a great front-facing example of this. A phrase or word that doesn’t mean anything initially (e.g. “planking” or “it’s lit”) may have nonsensical search results.

However, as its phrasing (and therefore, user searches) is used more over time, machine learning is able to display more accurate information for those queries.

As language develops and transforms, machines are better able to predict our meanings behind the words we say and provide us with better information.

7. Improve Ad Quality & Targeting for Users

According to Google U.S. patent US20070156887 and US9773256 on ad quality, machine learning can be used to improve an “otherwise weak statistical model.”

This means that Ad Rank can be influenced by a machine learning system.

“Bid amount, your auction-time ad quality (including expected clickthrough rate, ad relevance, and landing page experience), the Ad Rank thresholds, the context of the person’s search” gets fed into the system on a keyword-by-keyword basis, to determine what thresholds are considered by Google for each keyword.

8. Synonyms Identification

When you see search results that don’t include the keyword in the snippet it’s likely due to Google using RankBrain to identify synonyms.

When searching for [phd degree] you’ll see various results with the word “doctor” or “doctoral” as they can be used, for many degrees, interchangeable.

synonym usage in search

Google even highlights the synonyms in some cases, this time with “phd degrees,” further indicating that it’s recognizing the synonyms.

synonym in google search

9. Query Clarification

One of my favorite subjects is search query user intent.

Users may be searching to buy (transactional), research (informational), or find resources (navigational) for any given search. Furthermore, a keyword could be useful to one or any of these intents.

By analyzing click patterns and the content type that users engage with (e.g. CTRs by content type) a search engine can leverage machine learning to determine the intent.

An example can be seen with the query “best college” in a Google search. The results are reviews and list of colleges all in one SERP, with the universities listed at the top.

content classification using machine learning


While machine learning isn’t (and probably never will be) perfect, the more humans interact with it, the more accurate and “smarter” it will get.

This could be alarming to some – bringing visions of Skynet from the “Terminator” movies – however, the actual result is likely a better experience with technology that gives us the information and services we need, when we need it.

Source: How Search Engines Use Machine Learning: 9 Things We Know for Sure

26- Sep2017
Posted By: DPadmin

Why Machine Learning Is Key to the Search Marketing of Tomorrow

Learn how machine learning and automation are empowering search marketers today and how it can tackle digital marketing’s data problems.

Advertising has changed a lot over the years.

There was a time when machine learning, automation, and software-based marketing tech stacks weren’t a “thing.”

But now we’re past the days of just radio, outdoor, print, and a handful of channels on TV.

There are hundreds of channels across physical and print media and online at present, including social, mobile, and video. Even TV has diversified into hundreds of cable channels on your remote control. And yet, digital ad revenue has gone on to surpass that of TV.

The dominance of digital is nothing new. Paid search marketing is becoming more data-focused than ever before.

In fact, if you check the folders on your computer, I’m guessing some of you will find a few million-row spreadsheets full of cost-per-click bids, conversion rates, and return-on-ad-spend figures – along with countless other metrics for however many thousands, or millions, of keywords you manage.

So, What Do You Do with Big Data?

Because paid search is so reliant on big data – really big data, the kind that causes Excel spreadsheets to eventually crash for having too many rows – it’s my belief that the future of digital is inextricably tied to machine learning.


Is it because machine learning, automation, and software will completely replace savvy digital professionals and their creative ideas?

No. Far from it.

I believe that the future of digital will be a combination of smart marketers – like yourself – empowered by smart automation based on machine learning. As it happens, in a survey we recently ran on the subject, 97 percent of top digital marketing influencers (including speakers from AWeber, Oracle, and VentureBeat) agreed.

What Is Machine Learning & Why Is It Important?

Machine learning is the smart automation that can parse those million-row spreadsheets and pull valuable insights out of those mountains of data.

(To clarify, processing data to pull insights is something machine learning can help with… but actually taking those insights and doing things that are creative and smart with them? That’s still very much the domain of brilliant marketers like yourself, and why the ingenuity you bring to the table will continue to be so important when facing tomorrow’s digital challenges.)

As for why machine learning is important? For starters, digital advertising has a data problem. In addition, the face of marketing is changing due to the way your customers are becoming aware of, considering and purchasing your goods and services.

Digital’s Data Problem in Three Parts

Data is a challenge in modern marketing. There’s significantly more of it than there used to be, and as marketing technology matures, it becomes capable of collecting even more on top of that.

1. Overload

Data overload is a known problem. There’s too much of it – an overwhelming abundance of it already.

Yet Oracle points out that digital data growth is expected to increase globally by 4,300 percent by 2020. This problem isn’t going away anytime soon.

2. Ownership

Despite the collection of data increasing exponentially, there’s a lack of centralized ownership with big data. You and your colleagues may be collecting CPC, CTR and CVR data in spreadsheets, but is everything centralized and standardized in a way that everyone in your organization can pull the data when they need?

Veritas reports that 52 percent of all business data is “dark” (of dubious or completely unknown value), and projects that mismanaged data will cost businesses $3.3 trillion by 2020.

3. Integration

There’s also a problem with siloing. Most businesses collect data in different buckets that aren’t necessarily integrated directly with each other, or indeed, with their own in-house marketing tech stack.

Accenture reports that while three-quarters of all digital skills gaps (the gap between a team member’s current level knowledge and the level of knowledge they need to successfully use new tech and tactics) come from lack of ownership, the remaining 25 percent of digital skills gaps come from a lack of integration.

And Then There’s the Changing Customer Journey

In addition to changes in the way data is collected and used in digital advertising, customer behavior is changing.

Advertising isn’t limited to a handful of channels. There are literally thousands of ways to reach customers, and pretty much all of them can be easily tuned out by an audience of increasingly demanding and disaffected customers who expect to have exactly what they’re looking for delivered to them instantly (and who will react poorly when it isn’t).

Research firm McKinsey breaks down the all-important consideration stage of the buying journey into four parts: “initial consideration; active evaluation, or the process of researching potential purchases; closure, when consumers buy brands; and postpurchase, when consumers experience them.”

The firm also finds that two-thirds of the touchpoints in the crucial evaluation stage are customer-driven, including browsing online reviews or soliciting word-of-mouth recommendations.

More to the point for those of us in digital, the use of ad blockers has increased 30 percent in the past year. And as you’ve surely heard, Google itself will be building in an “ad filter” in a 2018 version of Chrome to filter out “irrelevant” and “annoying” ads.

Effectively, as time passes, your ads are at greater risk of being filtered out by users who aren’t buying what you’re selling at this exact point in time.

How Does Machine Learning Solve These Problems?

Machine learning can be used to rein in the challenge of data, particularly when combined with disciplines such as probability-based Bayesian statistics, regression modeling, and data science. One of its greatest strengths here is the ability to take data-driven insights and build predictive models.

These predictive models can, in turn, be used to proactively address points of peak buying interest, attrition, or other key moments observed in the customer buying journey.

Examples of Machine Learning in Action

Let’s look at some examples of the way this technology is being used.

Chatbots & Voice Assistants

You may have noticed an increase in the use of conversational interfaces from major publishers such as Google, Amazon, Microsoft, Apple and Facebook in the form of chatbots and voice assistants (Alexa, Google Assistant, Siri and Cortana among others).

TOPBOTS notes that chatbots can have uses in unique, consumer-based contexts, such as event ticketing, health-related questions and the ever-important sports scores. These interfaces create a relevant and engaging user experience by supplying conversational responses based on historically-collected data – the most commonly-used or highly-searched terms.

Predicting & Preventing Customer Churn

A significantly deeper-funnel strategy at the post-purchase stage is to use machine learning to forecast common points of customer attrition.

Microsoft Azure and Urban Airship have both built predictive analytics models to determine the approximate timeframes and buying stages at which customers tend to most frequently churn. By projecting these important points in the future, these businesses are then able to proactively address common complaints before customers churn, driving higher retention and ultimately strengthening their businesses.

Natural Language Processing (NLP) and Semantic Distance Modeling

Another method of using machine learning specifically for digital advertising is to predict accurate bidding models for low-data keywords, such as long-tail keywords with high purchase intent but little to no empirical data.

In these cases, machine learning-based digital advertising solutions can assign new keyword groups based on semantically similar keyword groups and help advertisers ramp up long-tail keyword groups and all-new ad groups with a minimum of expensive testing time.


Machine learning isn’t necessarily a threat to marketers. On the contrary, it’s a powerful ally that’s making marketers’ lives easier while empowering them to predictively engage their customers in a highly relevant way.

Now, more than ever, it’s important to deliver the right message to the right customer at the right time – and with the power of machine learning, marketers are able to more accurately accomplish this goal by relying on actual data, rather than guesswork.

Source: Why Machine Learning Is Key to the Search Marketing of Tomorrow

10- Jul2017
Posted By: DPadmin

6 Digital Marketing Strategies You Need To Adopt In 2017 


Doing business today means finding a way to market your company by harnessing the power of the internet. Still today there are companies that forgo the use of the internet thinking it is too complicated or that they will be fine without it. This kind of thinking is troubling since implementing a digital marketing strategy can do untold wonders for your business. Conversely, many companies adopt digital marketing practices without formulating a guiding strategy. Attempting to market your company or its products online without an overarching digital marketing strategy can be almost as unproductive and frustrating as having no digital marketing presence at all. The choice to implement a digital marketing strategy, therefore, is an easy one. However, since the efficacy of various marketing strategies evolve over time, it is wise to keep the marketing strategy fresh. Here are a few digital marketing strategies you should consider for 2017.

Video is Still King – People on the internet love watching videos. For this reason there is no better way to embark on a marketing campaign than to employ the use of video. With video streaming sites such as YouTube and with social media sites such as Facebook and Twitter relying heavily on video ads, launching an ad campaign based on video ads seems to be a safe bet. According to Eddie Madan, CEO of Edkent Media, video engagement rates are higher than engagement with other types of media because, as he puts it, who doesn’t love being entertained? Video ads allow users to drop their guard and willingly view a commercial. If the video is animated, the chance that they will view the ad is even higher. Video allows users to watch and listen to the ad without having to devote a large portion of their concentration to reading copy. One important note to remember is that a video ad must be fun, entertaining and visually captivating in order to attract viewers.

Interactive Emails – There is something gratifying about being able to interact with emails right in your inbox without having to open another page. With the aid of integrated HTML and CSS this is quickly becoming a much-used tool in digital marketing. Interactive emails allow customers to add items to their carts, choose between colors and styles and even play games right in the inbox. Customers will appreciate this added functionality and it may convince them to interact with the email even more. Keep in mind that even when emails are opened, they are unlikely to be clicked on. Sending out an interactive email can change your click through rate dramatically. Customers will see that your emails aren’t similar to the run-of-the-mill variety they’ve become accustomed to and will sense that your company is a step ahead of the rest.

Social Video Will Explode in Popularity – The importance of video on social media will magnify in importance in 2017. With so many users relying on social media for video entertainment, the importance of using these platforms to launch marketing campaigns will become even more important. Video uploads on just Facebook alone have grown by 75% over the last year. And with over a billion views every day, marketing managers are taking new interest in Facebook. In addition to this, with Facebook’s insistence on autoplay videos, video views are almost guaranteed. Even though Facebook allows the customer to turn off video autoplay, they are either unwilling or unable to do it. Additionally, Facebook’s introduction of live video has created another tool for marketers as it tests users’ willingness to take part in a live-streaming experience.

Mobile is Making Bigger Inroads – The days of catering primarily for desktop computer users seems to be coming to a close with the increasing use of mobile devices as a way of connecting to the internet. 2016 saw mobile traffic increasing massively and the same upward trend is expected in 2017 as well. The upsurge in mobile use means that marketing managers need to make a concerted effort to cater to users on mobile as well as desktop. Marketing managers need to ensure that all visitors will have the best experience possible when visiting the website. In addition to this, Google is beginning to demote sites that do not have adequate mobile optimization – an even greater reason why catering for mobile is so important.

Linking of Machine Learning and Marketing Automation – What good is it to have data but then fail to use it in an effective manner? The injection of machine learning into digital marketing seeks to solve this problem. Machine learning tools allow the marketing manager greater insights into the customers habits and needs. This allows companies to strategize without having to waste time wondering which tactic to employ. Since algorithm will control this messaging, marketing managers no longer have to spend time selecting message, frequency and target demographic. In 2017, look to how marketing takes on a new life with Google Home, and Amazon’s Alexa as conversation with machine learning gadgets is leveraged for marketing strategy.

Countdown Timer in Emails – Countdown timers aren’t at all a new tool used by marketers. However, marketing managers are finding new utility with the tool as they slip them into marketing emails. Countdown timers help convince would-be customers that the proverbial window is closing and that they must take action now. These tools have the added benefit of being a visual aid that the potential customer can recognize and understand in one glance. Additionally, since Gmail displays HTML by default, countdown timers are always shown in emails.

Crafting a digital marketing strategy is a matter of keeping one’s ear to the ground and staying abreast of the fresh trends that can attract new eyes. However, it is good to remember that there is no hard and fast rule when it comes to adopting digital marketing practices. Much depends on your customers, your business and what works for you. Knowing these three factors inside out will help you to make better decisions about which tactics you should employ. If tried and true strategies work for you, then there is little reason to change.

Source: 6 Digital Marketing Strategies You Need To Adopt In 2017 | HuffPost