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How Successful Marketing Writers Plan Their Content

I never realized just how important it was to connect content with business goals until I had a particular conversation with a client. The client, excited to get started on blog content together, had a running list of topics for me to cover. But then something strange happened. When I asked for background information on
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SearchCap: Aivon launches blockchain-based protocol, increasing PageSpeed, memorable PPC ads & more

Below is what happened in search today, as reported on Search Engine Land and from other places across the web.



Please visit Search Engine Land for the full article.


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

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Want Your Brand to Look More Sophisticated? Think Black!

Most people never think twice about the colors that their favorite brands use. However, businesses spend a lot of time and money deciding on which hue is best for their products. This is because colors affect people’s feelings on a subconscious level. They are also associated with certain cultural or social messages.

Colors come with so many meanings. Blue has a calming influence on people. It also gives off the impression of trustworthiness and dependability. In contrast, red is associated with passion and danger. It can also give off a refined and powerful vibe, which is why red wines are considered richer than whites.

Image result for colors and emotions

[Graphic via Medium]

As a small business owner, never underestimate the impact color has on your brand. Corporations with large marketing departments, understand this all too well. For instance, most skin care companies use white in their product packaging as it symbolizes purity and cleanliness. Banks tend to use the color blue to give off an image of dependability. But what if you want to give your product a luxurious feel and classy image?

Why Black is Considered Luxurious

No other color has such a polarizing effect as black. Some western countries associate it with death and mourning. The media has also used it to portray villainy and evilness. However, the past few decades have seen the color become the epitome of luxury and class.

Despite the negative connotations, black is also associated with power, authority, class, and sophistication. Consider how high-end events are often black tie affairs, with women wearing the requisite little black dress. The color is very formal and serious and can evoke feelings of strength and intelligence.

Interestingly, black also has a slimming effect, which is why the color is used to make a product (or a person) smaller than it really is.

Look Who’s Using Black

Related image

Numerous companies have taken advantage of the way black evokes power and sophistication. For instance, makeup brands have used the color to provide their products with a sleek and classy look. Notice how even simple makeup brushes look more expensive with the color.

Ralph Lauren has even named one of their men’s scents after the color and used it as the background of their ads. Their predominantly dark ads for Polo Black target men who consider themselves to be strong and mysterious but with a depth of personality.

Brands that cater to men and luxury items have found great success in using this particular color. Rolls Royce cars are typically black, with a sleek and shiny outline. The same goes for Don Q. The rum brand uses black with splashes of gold.

There’s no denying that black is a powerful color. However, it can also be overwhelming. Use black in your packaging, your ads, and website to give off the image of luxury. But try to pair it with colors like red or gold for a striking contrast. You’ll find that your customers will be more than willing to pay extra for something that looks expensive and luxurious even if the quality is average.

[Featured image via Rolls Royce]

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Ranking the 6 Most Accurate Keyword Research Tools

Posted by Jeff_Baker

In January of 2018 Brafton began a massive organic keyword targeting campaign, amounting to over 90,000 words of blog content being published.

Did it work?

Well, yeah. We doubled the number of total keywords we rank for in less than six months. By using our advanced keyword research and topic writing process published earlier this year we also increased our organic traffic by 45% and the number of keywords ranking in the top ten results by 130%.

But we got a whole lot more than just traffic.

From planning to execution and performance tracking, we meticulously logged every aspect of the project. I’m talking blog word count, MarketMuse performance scores, on-page SEO scores, days indexed on Google. You name it, we recorded it.

As a byproduct of this nerdery, we were able to draw juicy correlations between our target keyword rankings and variables that can affect and predict those rankings. But specifically for this piece…

How well keyword research tools can predict where you will rank.

A little background

We created a list of keywords we wanted to target in blogs based on optimal combinations of search volume, organic keyword difficulty scores, SERP crowding, and searcher intent.

We then wrote a blog post targeting each individual keyword. We intended for each new piece of blog content to rank for the target keyword on its own.

With our keyword list in hand, my colleague and I manually created content briefs explaining how we would like each blog post written to maximize the likelihood of ranking for the target keyword. Here’s an example of a typical brief we would give to a writer:

This image links to an example of a content brief Brafton delivers to writers.

Between mid-January and late May, we ended up writing 55 blog posts each targeting 55 unique keywords. 50 of those blog posts ended up ranking in the top 100 of Google results.

We then paused and took a snapshot of each URL’s Google ranking position for its target keyword and its corresponding organic difficulty scores from Moz, SEMrush, Ahrefs, SpyFu, and KW Finder. We also took the PPC competition scores from the Keyword Planner Tool.

Our intention was to draw statistical correlations between between our keyword rankings and each tool’s organic difficulty score. With this data, we were able to report on how accurately each tool predicted where we would rank.

This study is uniquely scientific, in that each blog had one specific keyword target. We optimized the blog content specifically for that keyword. Therefore every post was created in a similar fashion.

Do keyword research tools actually work?

We use them every day, on faith. But has anyone ever actually asked, or better yet, measured how well keyword research tools report on the organic difficulty of a given keyword?

Today, we are doing just that. So let’s cut through the chit-chat and get to the results…

This image ranks each of the 6 keyword research tools, in order, Moz leads with 4.95 stars out of 5, followed by KW Finder, SEMrush, AHREFs, SpyFu, and lastly Keyword Planner Tool.

While Moz wins top-performing keyword research tool, note that any keyword research tool with organic difficulty functionality will give you an advantage over flipping a coin (or using Google Keyword Planner Tool).

As you will see in the following paragraphs, we have run each tool through a battery of statistical tests to ensure that we painted a fair and accurate representation of its performance. I’ll even provide the raw data for you to inspect for yourself.

Let’s dig in!

The Pearson Correlation Coefficient

Yes, statistics! For those of you currently feeling panicked and lobbing obscenities at your screen, don’t worry — we’re going to walk through this together.

In order to understand the relationship between two variables, our first step is to create a scatter plot chart.

Below is the scatter plot for our 50 keyword rankings compared to their corresponding Moz organic difficulty scores.

This image shows a scatter plot for Moz's keyword difficulty scores versus our keyword rankings. In general, the data clusters fairly tight around the regression line.

We start with a visual inspection of the data to determine if there is a linear relationship between the two variables. Ideally for each tool, you would expect to see the X variable (keyword ranking) increase proportionately with the Y variable (organic difficulty). Put simply, if the tool is working, the higher the keyword difficulty, the less likely you will rank in a top position, and vice-versa.

This chart is all fine and dandy, however, it’s not very scientific. This is where the Pearson Correlation Coefficient (PCC) comes into play.

The PCC measures the strength of a linear relationship between two variables. The output of the PCC is a score ranging from +1 to -1. A score greater than zero indicates a positive relationship; as one variable increases, the other increases as well. A score less than zero indicates a negative relationship; as one variable increases, the other decreases. Both scenarios would indicate a level of causal relationship between the two variables. The stronger the relationship between the two veriables, the closer to +1 or -1 the PCC will be. Scores near zero indicate a weak or no relatioship.

Phew. Still with me?

So each of these scatter plots will have a corresponding PCC score that will tell us how well each tool predicted where we would rank, based on its keyword difficulty score.

We will use the following table from statisticshowto.com to interpret the PCC score for each tool:

Coefficient Correlation R Score

Key

.70 or higher

Very strong positive relationship

.40 to +.69

Strong positive relationship

.30 to +.39

Moderate positive relationship

.20 to +.29

Weak positive relationship

.01 to +.19

No or negligible relationship

0

No relationship [zero correlation]

-.01 to -.19

No or negligible relationship

-.20 to -.29

Weak negative relationship

-.30 to -.39

Moderate negative relationship

-.40 to -.69

Strong negative relationship

-.70 or higher

Very strong negative relationship

In order to visually understand what some of these relationships would look like on a scatter plot, check out these sample charts from Laerd Statistics.

These scatter plots show three types of correlations: positive, negative, and no correlation. Positive correlations have data plots that move up and to the right. Negative correlations move down and to the right. No correlation has data that follows no linear pattern

And here are some examples of charts with their correlating PCC scores (r):

These scatter plots show what different PCC values look like visually. The tighter the grouping of data around the regression line, the higher the PCC value.

The closer the numbers cluster towards the regression line in either a positive or negative slope, the stronger the relationship.

That was the tough part – you still with me? Great, now let’s look at each tool’s results.

Test 1: The Pearson Correlation Coefficient

Now that we’ve all had our statistics refresher course, we will take a look at the results, in order of performance. We will evaluate each tool’s PCC score, the statistical significance of the data (P-val), the strength of the relationship, and the percentage of keywords the tool was able to find and report keyword difficulty values for.

In order of performance:

#1: Moz

This image shows a scatter plot for Moz's keyword difficulty scores versus our keyword rankings. In general, the data clusters fairly tight around the regression line.

Revisiting Moz’s scatter plot, we observe a tight grouping of results relative to the regression line with few moderate outliers.

Moz Organic Difficulty Predictability

PCC

0.412

P-val

.003 (P<0.05)

Relationship

Strong

% Keywords Matched

100.00%

Moz came in first with the highest PCC of .412. As an added bonus, Moz grabs data on keyword difficulty in real time, rather than from a fixed database. This means that you can get any keyword difficulty score for any keyword.

In other words, Moz was able to generate keyword difficulty scores for 100% of the 50 keywords studied.

#2: SpyFu

This image shows a scatter plot for SpyFu's keyword difficulty scores versus our keyword rankings. The plot is similar looking to Moz's, with a few larger outliers.

Visually, SpyFu shows a fairly tight clustering amongst low difficulty keywords, and a couple moderate outliers amongst the higher difficulty keywords.

SpyFu Organic Difficulty Predictability

PCC

0.405

P-val

.01 (P<0.05)

Relationship

Strong

% Keywords Matched

80.00%

SpyFu came in right under Moz with 1.7% weaker PCC (.405). However, the tool ran into the largest issue with keyword matching, with only 40 of 50 keywords producing keyword difficulty scores.

#3: SEMrush

This image shows a scatter plot for SEMrush's keyword difficulty scores versus our keyword rankings. The data has a significant amount of outliers relative to the regression line.

SEMrush would certainly benefit from a couple mulligans (a second chance to perform an action). The Correlation Coefficient is very sensitive to outliers, which pushed SEMrush’s score down to third (.364).

SEMrush Organic Difficulty Predictability

PCC

0.364

P-val

.01 (P<0.05)

Relationship

Moderate

% Keywords Matched

92.00%

Further complicating the research process, only 46 of 50 keywords had keyword difficulty scores associated with them, and many of those had to be found through SEMrush’s “phrase match” feature individually, rather than through the difficulty tool.

The process was more laborious to dig around for data.

#4: KW Finder

This image shows a scatter plot for KW Finder's keyword difficulty scores versus our keyword rankings. The data also has a significant amount of outliers relative to the regression line.

KW Finder definitely could have benefitted from more than a few mulligans with numerous strong outliers, coming in right behind SEMrush with a score of .360.

KW Finder Organic Difficulty Predictability

PCC

0.360

P-val

.01 (P<0.05)

Relationship

Moderate

% Keywords Matched

100.00%

Fortunately, the KW Finder tool had a 100% match rate without any trouble digging around for the data.

#5: Ahrefs

This image shows a scatter plot for AHREF's keyword difficulty scores versus our keyword rankings. The data shows tight clustering amongst low difficulty score keywords, and a wide distribution amongst higher difficulty scores.

Ahrefs comes in fifth by a large margin at .316, barely passing the “weak relationship” threshold.

Ahrefs Organic Difficulty Predictability

PCC

0.316

P-val

.03 (P<0.05)

Relationship

Moderate

% Keywords Matched

100%

On a positive note, the tool seems to be very reliable with low difficulty scores (notice the tight clustering for low difficulty scores), and matched all 50 keywords.

#6: Google Keyword Planner Tool

This image shows a scatter plot for Google Keyword Planner Tool's keyword difficulty scores versus our keyword rankings. The data shows randomly distributed plots with no linear relationship.

Before you ask, yes, SEO companies still use the paid competition figures from Google’s Keyword Planner Tool (and other tools) to assess organic ranking potential. As you can see from the scatter plot, there is in fact no linear relationship between the two variables.

Google Keyword Planner Tool Organic Difficulty Predictability

PCC

0.045

P-val

Statistically insignificant/no linear relationship

Relationship

Negligible/None

% Keywords Matched

88.00%

SEO agencies still using KPT for organic research (you know who you are!) — let this serve as a warning: You need to evolve.

Test 1 summary

For scoring, we will use a ten-point scale and score every tool relative to the highest-scoring competitor. For example, if the second highest score is 98% of the highest score, the tool will receive a 9.8. As a reminder, here are the results from the PCC test:

This bar chart shows the final PCC values for the first test, summarized.

And the resulting scores are as follows:

Tool

PCC Test

Moz

10

SpyFu

9.8

SEMrush

8.8

KW Finder

8.7

Ahrefs

7.7

KPT

1.1

Moz takes the top position for the first test, followed closely by SpyFu (with an 80% match rate caveat).

Test 2: Adjusted Pearson Correlation Coefficient

Let’s call this the “Mulligan Round.” In this round, assuming sometimes things just go haywire and a tool just flat-out misses, we will remove the three most egregious outliers to each tool’s score.

Here are the adjusted results for the handicap round:

Adjusted Scores (3 Outliers removed)

PCC

Difference (+/-)

SpyFu

0.527

0.122

SEMrush

0.515

0.150

Moz

0.514

0.101

Ahrefs

0.478

0.162

KWFinder

0.470

0.110

Keyword Planner Tool

0.189

0.144

As noted in the original PCC test, some of these tools really took a big hit with major outliers. Specifically, Ahrefs and SEMrush benefitted the most from their outliers being removed, gaining .162 and .150 respectively to their scores, while Moz benefited the least from the adjustments.

For those of you crying out, “But this is real life, you don’t get mulligans with SEO!”, never fear, we will make adjustments for reliability at the end.

Here are the updated scores at the end of round two:

Tool

PCC Test

Adjusted PCC

Total

SpyFu

9.8

10

19.8

Moz

10

9.7

19.7

SEMrush

8.8

9.8

18.6

KW Finder

8.7

8.9

17.6

AHREFs

7.7

9.1

16.8

KPT

1.1

3.6

4.7

SpyFu takes the lead! Now let’s jump into the final round of statistical tests.

Test 3: Resampling

Being that there has never been a study performed on keyword research tools at this scale, we wanted to ensure that we explored multiple ways of looking at the data.

Big thanks to Russ Jones, who put together an entirely different model that answers the question: “What is the likelihood that the keyword difficulty of two randomly selected keywords will correctly predict the relative position of rankings?”

He randomly selected 2 keywords from the list and their associated difficulty scores.

Let’s assume one tool says that the difficulties are 30 and 60, respectively. What is the likelihood that the article written for a score of 30 ranks higher than the article written on 60? Then, he performed the same test 1,000 times.

He also threw out examples where the two randomly selected keywords shared the same rankings, or data points were missing. Here was the outcome:

Resampling

% Guessed correctly

Moz

62.2%

Ahrefs

61.2%

SEMrush

60.3%

Keyword Finder

58.9%

SpyFu

54.3%

KPT

45.9%

As you can see, this tool was particularly critical on each of the tools. As we are starting to see, no one tool is a silver bullet, so it is our job to see how much each tool helps make more educated decisions than guessing.

Most tools stayed pretty consistent with their levels of performance from the previous tests, except SpyFu, which struggled mightily with this test.

In order to score this test, we need to use 50% as the baseline (equivalent of a coin flip, or zero points), and scale each tool relative to how much better it performed over a coin flip, with the top scorer receiving ten points.

For example, Ahrefs scored 11.2% better than flipping a coin, which is 8.2% less than Moz which scored 12.2% better than flipping a coin, giving AHREFs a score of 9.2.

The updated scores are as follows:

Tool

PCC Test

Adjusted PCC

Resampling

Total

Moz

10

9.7

10

29.7

SEMrush

8.8

9.8

8.4

27

Ahrefs

7.7

9.1

9.2

26

KW Finder

8.7

8.9

7.3

24.9

SpyFu

9.8

10

3.5

23.3

KPT

1.1

3.6

-.4

.7

So after the last statistical accuracy test, we have Moz consistently performing alone in the top tier. SEMrush, Ahrefs, and KW Finder all turn in respectable scores in the second tier, followed by the unique case of SpyFu, which performed outstanding in the first two tests (albeit, only returning results on 80% of the tested keywords), then falling flat on the final test.

Finally, we need to make some usability adjustments.

Usability Adjustment 1: Keyword Matching

A keyword research tool doesn’t do you much good if it can’t provide results for the keywords you are researching. Plain and simple, we can’t treat two tools as equals if they don’t have the same level of practical functionality.

To explain in practical terms, if a tool doesn’t have data on a particular keyword, one of two things will happen:

  1. You have to use another tool to get the data, which devalues the entire point of using the original tool.
  2. You miss an opportunity to rank for a high-value keyword.

Neither scenario is good, therefore we developed a penalty system. For each 10% match rate under 100%, we deducted a single point from the final score, with a maximum deduction of 5 points. For example, if a tool matched 92% of the keywords, we would deduct .8 points from the final score.

One may argue that this penalty is actually too lenient considering the significance of the two unideal scenarios outlined above.

The penalties are as follows:

Tool

Match Rate

Penalty

KW Finder

100%

0

Ahrefs

100%

0

Moz

100%

0

SEMrush

92%

-.8

Keyword Planner Tool

88%

-1.2

SpyFu

80%

-2

Please note we gave SEMrush a lot of leniency, in that technically, many of the keywords evaluated were not found in its keyword difficulty tool, but rather through manually digging through the phrase match tool. We will give them a pass, but with a stern warning!

Usability Adjustment 2: Reliability

I told you we would come back to this! Revisiting the second test in which we threw away the three strongest outliers that negatively impacted each tool’s score, we will now make adjustments.

In real life, there are no mulligans. In real life, each of those three blog posts that were thrown out represented a significant monetary and time investment. Therefore, when a tool has a major blunder, the result can be a total waste of time and resources.

For that reason, we will impose a slight penalty on those tools that benefited the most from their handicap.

We will use the level of PCC improvement to evaluate how much a tool benefitted from removing their outliers. In doing so, we will be rewarding the tools that were the most consistently reliable. As a reminder, the amounts each tool benefitted were as follows:

Tool

Difference (+/-)

Ahrefs

0.162

SEMrush

0.150

Keyword Planner Tool

0.144

SpyFu

0.122

KWFinder

0.110

Moz

0.101

In calculating the penalty, we scored each of the tools relative to the top performer, giving the top performer zero penalty and imposing penalties based on how much additional benefit the tools received over the most reliable tool, on a scale of 0–100%, with a maximum deduction of 5 points.

So if a tool received twice the benefit of the top performing tool, it would have had a 100% benefit, receiving the maximum deduction of 5 points. If another tool received a 20% benefit over of the most reliable tool, it would get a 1-point deduction. And so on.

Tool

% Benefit

Penalty

Ahrefs

60%

-3

SEMrush

48%

-2.4

Keyword Planner Tool

42%

-2.1

SpyFu

20%

-1

KW Finder

8%

-.4

Moz

-

0

Results

All told, our penalties were fairly mild, with a slight shuffling in the middle tier. The final scores are as follows:

Tool

Total Score

Stars (5 max)

Moz

29.7

4.95

KW Finder

24.5

4.08

SEMrush

23.8

3.97

Ahrefs

23.0

3.83

Spyfu

20.3

3.38

KPT

-2.6

0.00

Conclusion

Using any organic keyword difficulty tool will give you an advantage over not doing so. While none of the tools are a crystal ball, providing perfect predictability, they will certainly give you an edge. Further, if you record enough data on your own blogs’ performance, you will get a clearer picture of the keyword difficulty scores you should target in order to rank on the first page.

For example, we know the following about how we should target keywords with each tool:

Tool

Average KD ranking ≤10

Average KD ranking ≥ 11

Moz

33.3

37.0

SpyFu

47.7

50.6

SEMrush

60.3

64.5

KWFinder

43.3

46.5

Ahrefs

11.9

23.6

This is pretty powerful information! It’s either first page or bust, so we now know the threshold for each tool that we should set when selecting keywords.

Stay tuned, because we made a lot more correlations between word count, days live, total keywords ranking, and all kinds of other juicy stuff. Tune in again in early September for updates!

We hope you found this test useful, and feel free to reach out with any questions on our math!

Disclaimer: These results are estimates based on 50 ranking keywords from 50 blog posts and keyword research data pulled from a single moment in time. Search is a shifting landscape, and these results have certainly changed since the data was pulled. In other words, this is about as accurate as we can get from analyzing a moving target.

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How Facebook Messenger Can Help You Drive Sales

Facebook Messenger is only about three years old and it’s already one of the most popular messaging platforms in the world. With the right marketing strategy, the app can easily convert leads and drive sales.

Since its unveiling at the 2015 F8 conference, more than one billion messages have been sent to Facebook Messenger by businesses and their customers. Analysts expect this trend to continue and have predicted that the app will have 139.2 million users by 2020.

Number of Facebook Messenger users in the United States from 2014 to 2020

 

There are about 1.3 million people using Facebook Messenger now. That’s a number that marketers should not ignore, especially since the app is a great marketing tool.

Advantages to Using Facebook Messenger

Consumers today use messaging apps like Messenger, Viber, and WhatsApp more often than social media platforms like Instagram, Facebook, or Twitter. This means that brands who can successfully use these apps can enjoy advantages like:

  • Improved Communication With Clients: Reaching your customer is faster with Messenger since you don’t have to wait in queue or be placed on hold. One Facebook study also revealed that people nowadays prefer to send a message than to call customer service. A majority of customers also tend to purchase online from brands that they can easily reach through chat.
  • Raised Awareness of Your Products: You can raise more awareness about your products or services via Messenger. This is because you can swiftly start a dialogue regarding the products or services you offer with your prospective customer.
  • Better Customer Service: A lot of customers prefer to send Private Messages instead of publicly posting their questions in the comments section of the Support Page. With Messenger, you can quickly reply to your customers and provide them with the information or answers they need. Plus, 63 percent of consumers surveyed by Facebook were found to have developed a good impression of brands that they can easily chat with.
  • Faster Transactions: It’s also simpler to conduct transactions with the help of Messenger. You can sell your products and services directly to your customers. The application can also assist you to immediately take orders, book appointments, invite prospective clients, and send product photos, receipts, and shipping information.

 

[Graphic via Facebook IQ]

 

4 Ways to Use Messenger to Boost Sales

1. Use a Welcome Message to Initiate a Conversation

When you enter a store, you are always welcomed in by someone, whether it’s the owner, the manager or a sales personnel. You can do the same thing with your online shop. Consider setting up a welcome conversation so that when a customer sends a message to your page, you can greet them automatically. You can then proceed to inquire about what they need and assist them in finding it. A welcome conversation will make your customer feel appreciated and supported. It also provides a good experience that will entice repeat business.

2. Send a Reminder About Abandoned Carts

Buyers abandoning their carts is a big concern. Facebook Messenger has been found to be more effective than email in converting abandoned carts to sales. Set up a message that will ask the customer if they would like to complete their purchase. You can even put options like “Remind me tomorrow,” “Still thinking,” “Thanks” or even a “like” or “heart” icon. 

3. Drive Traffic With Pop-Ups

 This is one of the most popular strategies to drive traffic and accumulate opt-ins. It’s easier for users to just tap on a button to opt-in instead of typing out their whole name and email address.

4. Set Up an Auto Reply Feature 

Aside from a chatbot, companies can also utilize the Messenger’s Auto Reply feature to send a custom message to customers and prospective clients. People today don’t want to waste time listening to messages that they can just read. With Facebook’s Messenger app, you can program and send messages based on the consumer’s requirements in a quick, interactive and convenient way. This can also boost the chances of opening a conversation with the client over what you’re offering.

Your business has the potential to grow bigger if you use Facebook Messenger, especially since the company continues to develop new ways to interact with consumers. So integrate the app into your marketing campaign and give yourself an edge over your rivals.

The post How Facebook Messenger Can Help You Drive Sales appeared first on WebProNews.


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Easy Email Inbox: Reply to 3 Types of Messages (and Don’t Sweat the Rest)

When you run a content marketing platform, you’ll get other types of messages from your audience in addition to blog comments. You’ll get emails. Many people have a love/hate relationship with email. When it’s good, it’s really good — but when it’s bad, managing your inbox feels like a huge waste of time. But like
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7 Lessons Copywriters Can Learn from Simply Listening to a Really Good Conversation

The easy part of this process is following the seven lessons below. It’s much harder to find a good conversation. The sad truth is, most of us are terrible at holding even a half-decent conversation. We’re in too much of a hurry. We’re too anxious to get our own points of view across, and we
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Daily Search Forum Recap: August 10, 2018

Here is a recap of what happened in the search forums today, through the eyes of the Search Engine Roundtable and other search forums on the web…


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The Truth About Traveling Non-Stop

I’m writing you today from Castellon, a town one hour drive from Valencia in Spain. I’m here visiting my father, my 9-year-old little half-brother, and his mother, who is Spanish. This is my first time in Spain. A couple of weeks ago I was in London. Before that, I was in Lviv, Nice, Paris, Vancouver […]

The post The Truth About Traveling Non-Stop appeared first on Yaro.Blog.

Entrepreneurs-Journey.com by Yaro Starak

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7 Local Marketing Tips to Help You Make More Sales

Most small businesses want to spread the word about their product or service to as many people as possible. But after covering the cost of inventory, employee wages, utilities, taxes, and a host of other expenses, many of these businesses simply don’t have enough money left over to launch widespread advertising campaigns. The good news is…even if you’re running a business on a tight budget, you can still afford effective advertising; you’ll just need to think local.

Local marketing, or location-based marketing, is the process of optimizing your company’s website and online presence to drive traffic to localized areas. This is particularly useful now that Google’s algorithm utilizes location in its search results. People looking for goods and services online get results for nearby businesses relevant to their search request. This is a highly targeted, low-cost way for businesess—big and small—to reach customers.

7 Ways to Increase Your Revenue With Local Marketing

1. Optimize Your Website for Mobile

Small businesses need to capture the attention of mobile searchers. Increase the odds of converting these leads by optimizing your website for mobile so that whatever screen your site is accessed on, it will still be displayed seamlessly.

2. Get Your Business Listed

One of the first things you can do for your small business is to get listed on Google’s My Business or Facebook. Doing this will enable you to update and manage critical information like your business address, contact details, opening hours, and images.

3. Build Bridges With Local Businesses

Align yourself with an established shop in the area and come to a mutual marketing agreement. For instance, offer a consultancy group could offer a discount on one of its seminars to the top clients of a local accounting firm. Working with these businesses enhance your credibility and gives prospective clients a chance to learn more about your company.

4. Secure Testimonials

A lot of people rely on online reviews when they’re trying to assess whether a business is trustworthy. Try to secure a testimonial from a resident since it will carry more weight, especially if it’s from someone known in the area.

5. Use Social Media to Engage With the Locals

Social media has made connecting with people so easy. You can integrate platforms like Facebook or Instagram into your customer service and outreach efforts. For instance, you could use hyper-local keywords in your posts or hashtags to establish your current location or put the focus on local stores that you have partnered with.

6. Work With Local Publications

Work with local publications to engender more face time with the area’s residents. Find a publication that ties up with your product or business model. Check if they’re looking for ad placements or even guest blog writers. You can even ask them if they can do a write-up about your company.

7. Sponsor a Community or Charity Event

Sponsoring an event is an effective way to utilize one of the most fundamental advertising tactics—putting your name on something. Consider sponsoring a neighborhood team or donating to a known charity in exchange for putting your brand’s name on the marquee or t-shirts from the event. You’ll be giving money to a good cause while ensuring that your company’s name is seen by a captive market.

A local marketing strategy can be an effective way to generate sales for your small business without spending too much. Get your brand noticed by interacting with the community, whether it’s through local influencer, partnering with an established shop, or sponsoring an event.

[Featured image via Pixabay]

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