Digital Marketing

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什麼是 Digital Marketing

Digital Marketing 又稱數碼營銷,是一種以網上平台推廣自己的產品和服務,例如Facebook ,Instagram,Youtube 等都是不同企業用以推廣自己的平台。

在現今 數碼營銷 世代,精通顧客之道繼續不斷推陳出新。智能手機的應用達到空前的盛況,社交媒體應用成為常態,聊天機器人亦不斷用在顧客體驗身上。科技巨人現正不斷改進演算法,這也意味著商家需要不斷更新他們的數碼營銷策略,以保持在市場的競爭力。

數碼營銷的種類

Digital Marketing

HKWeb+ 協助您透過不同的數碼營銷渠道:如SEO,Google Adwords 和 Facebook Ads ,內容行銷等推動您的業務發展。

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擴大您的線上營銷

今天的營銷環境與舊日已大大不同,需充分利用線上營銷已是一個不可改變的事實。我們配合網站和數碼營銷來創建具有高度針對性的營銷活動,以吸引合適的受眾,產生高質量的回响,增加潛在客戶的銷售機會,從而幫助您的企業適應這些變化。


數碼營銷

簡單3步

我們明白,時間便是金錢,為了提供優質服務給予你的客戶,你需專注你企業的服務。我們便是你可靠的伙伴,HKWeb+ 的多元化、價錢相宜的數碼營銷方案,協助有效管理你的多方業務,大大增升你企業的盈收。

數碼營銷

我們的3個簡單步驟,保證你的 數碼營銷 策略可以為你帶來至關重要的轉化。

  • 制定預算和確定數碼營銷策略

    在數碼時代,數據就是證明成功的重要測量方法,你務必要顯示出付出的回報。當你向執行人員交待你的策略時,你應該在平衡策略和顧客成本後,再考慮你能達到的的結果。你預期的結果應該要比成本低,因此跟據數據行事可以幫你爭取到更好的預算。

  • 利用搜尋引擎優化

    搜尋引擎優化日新月異,緊貼趨勢是十分重要的。尋引擎優化是最重要的策略之一,增加網站內容的曝光率等同增加你的企業收入。

  • 創造有質素的內容

    選好合適的關鍵字後,你現在就要創造有得著和用處的內容。制定創作內容策略可以改善你的內容結構,同時也幫助你達成營銷目標。


我們的營銷方案

Plan

(Price)

Email Previews

Builder

Page Testing

Interactive Testing

Spam Filter Tests

500,000 Tracking Opens

SEM

from

$1,999

/month


靈活調整預算

針對性精準營銷

快速接觸潛在客戶

增加網站流量

價錢較SEO相宜

SEO

from

$9,999

/success


優化技術性能

關鍵字/詞研究

創建業務列表

網站速度優化

提升搜尋排名

SMM

from

$2,999

/month


滲透式行銷

增加網站流量

良好客戶關係

建立品牌形象

提升搜尋排名

O2O

from

$3,999

/month


龐大評價用戶群

建立品牌信任

建立電郵模板

顯示真實評價

增加客戶信賴度


我們如何締造商機
預算優化

我們的廣告專家會最大限度地優化您的出價,以幫助您以更優惠的價格獲得更多潛在客戶。

智能廣告系列

通過針對通訊設備、地理位置和關鍵字進行優化,藉此提高廣告的轉換率,吸引更多潛在客戶消費。

跨平台配合

通過 Google 和 Facebook 上的廣告吸引更多的消費者並樹立品牌知名度。

領先市場

通過優化廣告來滿足您企業的特定需求,從而獲得更多高質量的潛在客戶。

登陸頁面內容

通過智能廣告系列,將客戶導入最合適的頁面,優化他們消費的可能性。

實時分析

通過跨平台的數據,分析您廣告系列的成效,不斷改善您的廣告策略。


讓我們一起做出令人驚嘆的事情

我們一直在尋找有潛力的客戶來合作。如果您認為我們很合適,請隨時與我們取得聯繫以快速報價。

Contact HKWeb+
網站優化方案
業務拓展
其他

內容行銷

內容行銷 如何設計出好的內容?

內容行銷 的重要性: 在現今科技發達的社

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創業失敗:10大原因

初創公司不容易做 【創業一週】美國一間風

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創業成功率

在大型企業林立、資源競爭激烈的香港 創業

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關鍵字

關鍵字 行銷 如何找出搜尋量高的關鍵字

關鍵字 的 重 要 性 在學習SEO網站

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創業:幾多人最好?

三個臭皮匠勝過一個諸葛亮? 從創立蘋果(

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Email Direct Marketing:EDM

電子郵件行銷 甚麼是 EDM 現在雖然有

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數碼營銷概談

數碼營銷概談 。喺現今呢個網絡世界,無論

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  • Generating Local Content at Scale – Whiteboard Friday
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    Posted by rjonesx.Building local pages in any amount can be a painful task. It’s hard to strike the right mix of on-topic content, expertise, and location, and the temptation to take shortcuts has always been tempered by the fact that good, unique content is almost impossible to scale. In this week’s edition of Whiteboard Friday, Russ Jones shares his favorite white-hat technique using natural language generation to create local pages to your heart’s content. Click on the whiteboard image above to open a high-resolution version in a new tab! Video Transcription Hey, folks, this is Russ Jones here with Moz again to talk to you about important search engine optimization issues. Today I’m going to talk about one of my favorite techniques, something that I invented several years ago for a particular client and has just become more and more and more important over the years.  Using natural language generation to create hyper-local content I call this using natural language generation to create hyper-local content. Now I know that there’s a bunch of long words in there. Some of you are familiar with them, some of you are not.  So let me just kind of give you the scenario, which is probably one you’ve been familiar with at some point or another. Imagine you have a new client and that client has something like 18,000 locations across the United States. Then you’re told by Google you need to make unique content. Now, of course, it doesn’t have to be 18,000. Even 100 locations can be difficult, not just to create unique content but to create uniquely valuable content that has some sort of relevance to that particular location.  So what I want to do today is talk through one particular methodology that uses natural language generation in order to create these types of pages at scale. What is natural language generation? Now there might be a couple of questions that we need to just go ahead and get off of our plates at the beginning. So first, what is natural language generation? Well, natural language generation was actually originated for the purpose of generating weather warnings. You’ve actually probably seen this 100,000 times. Whenever there’s like a thunderstorm or let’s say high wind warning or something, you’ve seen on the bottom of a television, if you’re older like me, or you’ve gotten one on your cellphone and it says the National Weather Service has issued some sort of warning about some sort of weather alert that’s dangerous and you need to take cover. Well, the language that you see there is generated by a machine. It takes into account all of the data that they’ve arrived at regarding the weather, and then they put it into sentences that humans automatically understand. It’s sort of like Mad Libs, but a lot more technical in the sense that what comes out of it, instead of being funny or silly, is actually really useful information. That’s our goal here. We want to use natural language generation to produce local pages for a business that has information that is very useful.  Isn’t that black hat? Now the question we almost always get or I at least almost always get is: Is this black hat? One of the things that we’re not supposed to do is just auto-generate content. So I’m going to take a moment towards the end to discuss exactly how we differentiate this type of content creation from just the standard, Mad Libs-style, plugging in different city words into content generation and what we’re doing here. What we’re doing here is providing uniquely valuable content to our customers, and because of that it passes the test of being quality content. Let’s look at an example So let’s do this. Let’s talk about probably what I believe to be the easiest methodology, and I call this the Google Trends method.  1. Choose items to compare So let’s step back for a second and talk about this business that has 18,000 locations. Now what do we know about this business? Well, businesses have a couple of things that are in common regardless of what industry they’re in. They either have like products or services, and those products and services might have styles or flavors or toppings, just all sorts of things that you can compare about the different items and services that they offer. Therein lies our opportunity to produce unique content across almost any region in the United States. The tool we’re going to use to accomplish that is Google Trends. So the first step that you’re going to do is you’re going to take this client, and in this case I’m going to just say it’s a pizza chain, for example, and we’re going to identify the items that we might want to compare. In this case, I would probably choose toppings for example. So we would be interested in pepperoni and sausage and anchovies and God forbid pineapple, just all sorts of different types of toppings that might differ from region to region, from city to city, and from location to location in terms of demand. So then what we’ll do is we’ll go straight to Google Trends. The best part about Google Trends is that they’re not just providing information at a national level. You can narrow it down to city level, state level, or even in some cases to ZIP Code level, and because of this it allows us to collect hyper-local information about this particular category of services or products. So, for example, this is actually a comparison of the demand for pepperoni versus mushroom versus sausage toppings in Seattle right now. So most people, when people are Googling for pizza, would be searching for pepperoni. 2. Collect data by location So what you would do is you would take all of the different locations and you would collect this type of information about them. So you would know that, for example, here there is probably about 2.5 times more interest in pepperoni than there is in sausage pizza. Well, that’s not going to be the same in every city and in every state. In fact, if you choose a lot of different toppings, you’ll find all sorts of things, not just the comparison of how much people order them or want them, but perhaps how things have changed over time. For example, perhaps pepperoni has become less popular. If you were to look in certain cities, that probably is the case as vegetarian and veganism has increased. Well, the cool thing about natural language generation is that we can automatically extract out those kinds of unique relationships and then use that as data to inform the content that we end up putting on the pages on our site. So, for example, let’s say we took Seattle. The system would automatically be able to identify these different types of relationships. Let’s say we know that pepperoni is the most popular. It might also be able to identify that let’s say anchovies have gone out of fashion on pizzas. Almost nobody wants them anymore. Something of that sort. But what’s happening is we’re slowly but surely coming up with these trends and data points that are interesting and useful for people who are about to order pizza. For example, if you’re going to throw a party for 50 people and you don’t know what they want, you can either do what everybody does pretty much, which is let’s say one-third pepperoni, one-third plain, and one-third veggie, which is kind of the standard if you’re like throwing a birthday party or something. But if you landed on the Pizza Hut page or the Domino’s page and it told you that in the city where you live people actually really like this particular topping, then you might actually make a better decision about what you’re going to order. So we’re actually providing useful information.  3. Generate text So this is where we’re talking about generating the text from the trends and the data that we’ve grabbed from all of the locales. Find local trends Now the first step, of course, is just looking at local trends. But local trends aren’t the only place we can look. We can go beyond that. For example, we can compare it to other locations. So it might be just as interesting that in Seattle people really like mushroom as a topping or something of that sort. Compare to other locations But it would also be really interesting to see if the toppings that are preferred, for example, in Chicago, where Chicago style pizza rules, versus New York are different. That would be something that would be interesting and could be automatically drawn out by natural language generation. Then finally, another thing that people tend to miss in trying to implement this solution is they think that they have to compare everything at once. Choose subset of items That’s not the way you would do it. What you would do is you would choose the most interesting insights in each situation. Now we could get technical about how that might be accomplished. For example, we might say, okay, we can look at trends. Well, if all of the trends are flat, then we’re probably not going to choose that information. But we see that the relationship between one topping and another topping in this city is exceptionally different compared to other cities, well, that might be what gets selected. 4. Human review Now here’s where the question comes in about white hat versus black hat. So we’ve got this local page, and now we’ve generated all of this textual content about what people want on a pizza in that particular town or city. We need to make sure that this content is actually quality. That’s where the final step comes in, which is just human review. In my opinion, auto-generated content, as long as it is useful and valuable and has gone through the hands of a human editor who has identified that that’s true, is every bit as good as if that human editor had just looked up that same data point and wrote the same sentences. So I think in this case, especially when we’re talking about providing data to such a diverse set of locales across the country, that it makes sense to take advantage of technology in a way that allows us to generate content and also allows us to serve the user the best possible and the most relevant content that we can. So I hope that you will take this, spend some time looking up natural language generation, and ultimately be able to build much better local pages than you ever have before. Thanks. Video transcription by Speechpad.com Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don’t have time to hunt down but want to read!

What is D i g i ta l M ar k e t i n g ?

D i g i t a l M a r k e t i n g , also known as d i g i t a l m a r k e t i n g , is an online platform to promote its products and services, such as Facebook, Instagram, Youtube, etc. are used by different companies to promote their own platforms.

In today’s d i g i t a l m a r k e ti n g generation, the mastery of the customer continues to innovate. The application of smartphones has reached an unprecedented level, social media applications have become the norm, and chat bots are constantly being used in customer experiences. Technology giants are constantly improving their algorithms, which means that businesses need to constantly update their digital marketing strategies to remain competitive in the marketplace.

Expand your online marketing

Today’s marketing environment is very different from the old days. It is an unchangeable fact to take full advantage of online marketing. We work with websites and d i g i t a l m ar k e t i n g to create highly targeted marketing campaigns to reach the right audience, generate high quality resounds, and increase sales opportunities for potential customers to help your business adapt to these changes.

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