Reasons For The Rise Of Data Science In Marketing

We know that we are in an era of digital with an explosion of data. To a certain extent, it is found out that human behaviors are predictable by analysis of the data. This has led to the rise of data science, and which now is very important for business to reach, engage, and convert potential customers. The current marketing landscape is being shaped by data science as is the future of customer interactions. Here are three reasons why:

1. Marketers need data science

Data science has made marketers to make more precise hypothesis and marketing decision. Supervised machine learning enables marketers to have prediction of future trends. Pattern-matching techniques enables marketers have identification of specific purchase behaviors. When marketers are able to make good use of the available huge amount of data, they can solve marketing problems at greater scale and with more relevant business insight.

2. Mobile continues to hold great promise

Nowadays, almost everyone has a smartphone, and even more than one. We use it for getting a map, for shopping and so many other things. Advertisers are actually getting a lot of personal data from people’s smartphones and to know consumer behaviors.

Mobile apps also hold a wealth of insight, providing additional clues about a user’s interests and behaviors, which allow for more detailed customer profiles that can be used to deliver more targeted marketing

3. Big Data requires actionable insights

Data is meaningful until it gets you insights into business, and to drive sales. To achieve these insights requires analyzing a variety of algorithms as basic as cookies to as sophisticated as store visits or foot traffic.  This area in particular continues to evolve with the emergence of new technologies and platforms to provide a more detailed picture on consumer behavior.

Reference:

https://www.mediapost.com/publications/article/272154/3-reasons-for-the-rise-of-data-science-in-marketin.html

Market Segmentation

Market segmentation is a marketing concept which divides the market into different segments with consumers with a similar demand and preference. Since consumers in the same market segment are somehow alike, difference marketing tactics can be used based on the segment properties in order to maximize the effectiveness of marketing campaigns. There are 4 major ways to segment a market.

·                Geographic Segmentation

Geographic segmentation refers to the classification of market into various geographical areas. We can classify market based on continents, countries, urbanization level or climate.

Examples:

A home appliance retailer may not carry humidifier in humid countries, instead, it may carry more dehumidifier.

People in rural area may be more concern about the durability of auto vehicles and trends to buy more trucks than cars, whereas, city people prefer more stylish vehicles.

·                Demographic segmentation

This type of segmentation divides market based on people’s income, gender, age, race, occupation, family situation, etc.   

 Examples:

Marketing for super sport cars should only targets the very high income group, since other people cannot afford such an expensive car.

Feminine product should only target female customers, but not my dad.

·                Psychographic segmentation

The basis of such segmentation is the lifestyle of the individuals. The individual’s attitude, interest and lifestyle help the marketers to classify them into small groups.

Examples:

I weight the style of clothes more than the durability, while my mon is more concern about the durability.

·                Behaviouralistic Segmentation

Customers can also be classified by their behavior, such as, loyalties to a brand, reaction to discount, buying timing and frequencies.

Examples:

Promotional discount can be very effective to my mon and less effective to me.

Con’t – Data Visualization

Although there are many techniques need to be handled well for a good data visualization, it is now way easier and simpler to do it with great tools out there. However, one thing we need to bear in mind that we better not to treat data visualization like an end goal. This is one of the common mistakes people make in data reporting. 

Most data projects start off with a standard list of calculations such as finding means, medians, ranges and minimums and maximums in the data set. But after crunching the easy numbers, it can be tricky to tell which direction to explore next.It is recommended that creating some easy visualizations, like graphs and charts within the Excel program, to help spot patterns that can lead to story ideas or more questions, so that people would not be seeing numbers alone but with some exploratory visualizations and publishing them with the story as well. 

In addition, we should not overestimate the meaning of the data we have. Before even opening the file, data reporters should think carefully about the potential limitations of a data set, and what the data can and cannot tell you about a topic. People should pay attention to where the information comes from for key database fields and make sure you can trust the source.

For more common mistakes in data reporting, please click here to read:
https://www.americanpressinstitute.org/publications/data-reporting-common-mistakes/

Data Visualization and Reporting

Data is not meaningful unless people can understand it. Collecting and analyzing data are not enough, data has to be presented to concerned parties. That is the reason data visualization is such important. Here are seven key tips to help turn data into insights people will understand:

1.     Keep the audience and their information needs in mind.
We have to know who our target audience is and find out good ways to present to them. It is vital to customize any data visualization to meet the audience and their information needs. Think of who is in that audience and then think about the questions they would like answered. Knowing what we are addressing help us select right data out of tons of data and make it meaningful to audience.

2.     Choose the right chart.
There are so many different types of charts available in the data visualization tools, such as bar chart, line graph, pie chart, etc. Bar chart might be the most common chart type. However, we need to choose the right type depends on what kinds of information because not all charts are created equal. Some do a better job than others at displaying different kinds of information. We need to choose the best chart type to display the information.

3.     Go beyond Excel or PowerPoint templates. 
Speaking of data visualization, many people may first think of excel which is a very nice tool for visualizing data. But when it comes to big data, utilizing other great tools such as Tableau, Data Miner, would make the life easier. Another popular visualization tool is PowerPoint, but its built-in templates may not be doing your data any favors. Instead of trying to get fancy, keep your visualizations simple and uncluttered to be as clear as possible.

4.     Provide context.
The purpose of data visualization is not only showing the data we have in our data base, but we need to use the data to have storytelling.  Making good use of color, size and other visual cues to provide context and include short narratives that highlight the key insights. A good visualization will make the user understand what’s going go with the data, prompt the user to act on the data being presented, which may be deciding location on a new business, or may be about executing marketing campaign, or other important decisions making on company.

5.     Direct people to the most important information.
A good data reporting should be able to draw people’s attention to significant points on information and lead people to get insights from the data presented. When designing data visualizations, use sensory details like color, size, fonts, and graphics to direct attention to the most important pieces of information would be a great idea.

6.     Axis labels and numbers should be clear.
Avoid fancy labels and gauges that can get in the way of clarity. Label the axis of a graph or chart clearly and start at zero—unless you have a strong reason not to—e.g. when all the data is clustered at much higher values.

7.     Provide interactivity when appropriate.
New generations of data-visualization tools make it possible to build interactivity into many visualizations that can benefit the end user. But again, remember that this isn’t a parlor trick, and should be used when interactivity can clarify, rather than confuse, the presentation of data.

Reference:

http://data-informed.com/the-top-12-tips-for-data-visualization/

What is Data Visualization

Data visualization is not only about showing the graphs or bar charts in excel, although it is one of the parts, but is much more than that. Data visualization is graphical presentation of information. Its advantage is to enable decision makers to see analytics presented visually and to provide decision makers insights into complex data set, so they can grasp difficult concepts or identify new patterns. With data visualization, people can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed.

Not long ago, data visualization was not a necessary skill to have for managers. But nowadays, many things have gone digital, so many decisions making relies on data, data visualization becomes a must-have skills set. In this Big Data generation, data comes in overwhelming velocity and extremely huge volume that we cannot comprehend it without filtering. Data becomes the primary force behind this changing. The ability to create smart visualization become a need for managers, because it is the way to make the work they do meaningful. There are a lot of different tools out there for data visualization, such as SAS Data Miner and Tableau, which help people way easier to visualize the complex data. People can pick the tool they feel comfortable and match with the needs of company to use. However, there are some techniques in common for people who want to visualize data set and we will talk about it in the next article. 

Search Engine Optimization

The purpose of doing SEO is to use a series of methods that allow the "search engine" to understand the content of your site and then make your site rank appear in front of the natural search results and achieve high traffic. The ultimate goal of SEO is to let the site in the first page, ranking the better in front of the better.

If you are running a website, search results are important. When you have a higher ranking of a page, it will help more people find you. The key to getting a higher ranking is whether your site has a "raw material" that meets the "recipe" developed by the search engine.

In fact, many of the main "raw materials" are all we know. First, the text is important. The search engine includes all the text on the web. When a user searches for "repair shoes", the search engine can narrow the search results to only those pages related to these keywords.

Second, the title is important. Each page on the web has a formal title. But you may never have seen it because it is hidden in the code. The search engine is very important to the title, because the title is often a web page content summary, like a book title.

Third, the link between the site and the site is important. When a page is linked to another page, it is usually a recommendation that the reader is connected to the page that has good information. As a result, the search engine will be optimistic about a lot of links to get a web page. But some people will be on the Internet a lot of manufacturing or purchase fake links, connected to their own website, trying to deceive the search engine. Usually, when a website has a lot of such links, the search engine will be found. Their countermeasure is to give more links to the links from creditworthy websites.

Fourth, the text used in the link is also important. If your page mentions that "Amazon has a lot of books" and "books" are a link, then the search engine will determine that Amazon is related to the "books" website. So, when someone searches for "books", Amazon will have a good ranking.

Finally, the search engine value reputation. Continued to update high-quality content, and continue to get more links to the site, will be regarded as a network star and get a good search rankings.

How to Monetize Data

As discussed in the previous article, data may be a company’s valuable and precious asset. However, not every company is maximizing its economic benefit. It is important to figure out how to derive a profit from the data which can also help distinguish your company in the marketplace. InformationWeek talks about several ways to monetize data. Let’s look at five of those ways and see how people who want to monetize data should do for their business.

Help Decision Making and Strategy Planning

Management is responsible for setting out company’s direction and strategy. Analyzing customer data can be the solid foundation for every decision, such as, production, R&D and marketing. For example, a car manufacturer wants to set its next year production level. It will need to obtain data regarding the economic environment for next year, customers preference trending, market competition, the trend of material price and other production cost, and a lot more other data to assess the demand and supply of the market. In addition, it will also need internal data, such as financial and budgeting to determine the level of production and whether extra capacity investment will be required.

Improve Marketing ROI
This part should be the most concerned and interested part by every company. The ultimate goal of business is always gaining profit. In fact, using data to accurately target customers and improve ROI of marketing campaigns by companies is not a new thing. However, simple website clickstream analysis, though still important, is just one source of data. In today's environment, the same organizations need to understand customer behavior across channels using more data from within the enterprise and from third parties. Companies keep tracking their customers on the websites and in their stores to get a whole picture of the customers. For example, you can tell a lot about a person by looking at their credit card information, such as shopping patterns

Retain Customer Satisfaction

Retaining customer is one of the most concerned part of corporations. It is crucial part to have business successful. Having customer satisfaction for company is one of the great way to retain loyal customers. Organizations can understand customer satisfaction levels by conducting surveys and social medial sentiment analysis. For example, gathering and integrating all those collected data from different sources, restaurants are able to figure out how satisfied a customer likely is based on factors such as quality of food and quality of staff service.

Embrace a new revenue model

Data is actually changing the business models. And, data is changing the relationship between companies and their customers. Using data analytics, companies are able to provide products or services at higher levels of personalization.  For examples, new economic models are being explored, such as replacing automobile ownership with fleets of self-driving cars and supplementing traditional insurance with micro-insurance options. There is a lot of data out there for free. However, the value of data comes from marrying that data, understanding the missions of a business, and what problem that business is trying to solve.

Detect Fraud and Piracy

Data analytics is a great thing for ecommerce or online retailers. As they are probably selling their products on a lot of different sites. The sales channels often includes Amazon, eBay and online marketplaces maintained by large retailers such as Walmart and Target. Data is available online and easy to access. Selling through all of those channels is very data-intensive, because the pricing, products, and customer types often vary across channels. Sometimes the price discrepancies are so significant they signal potential fraud or piracy.

Data Monetization

How does data relate to money? Well, data could worth a lot. Revenue could be generated from available data sources by analysis, discovery, and use of that data. It is a process called monetization. Data monetization is the act of turning corporate data into currency. The currency can be money, or a bartering device or a product or service enhancement. Data monetization has been a trend like big data and the Internet of things. Corporate continue to recognize the value of data, and as the world continues to be instrumented with sensors and real time analytics, data will continue to be a booming business. This data could lead to great potential for data mining to achieve business objectives. Along with its potential, data monetization introduces challenges businesses won't be able to ignore. Businesses will need to comply with legal and regulatory constraints when selling or bartering with data. They'll also have to consider the tricky area of data privacy, which can attract or deter customers, depending on the company policy.

Cont'd - Big data in Marketing 

In last article, we got some insights into big data in marketing and how it might develop in the future. We realize the powerful and popularity of big data in today’s world. Big data has been changing in many ways in marketing. Marketing has gone to digital. Therefore, it is so important for us to understand big data and its effects in the industry. As many of us may already notice, many companies are keep expanding their marketing presence in social medial platforms.  The demand for digital marketing analytics would only be increasing and big data marketing is unavoidable in the future. It is not saying that traditional marketing will be abandoned. But marketing is no longer the same as it was in the past. It could be much more complicated and diversified for now.  Big data brings more challenges to the industry but also provides a tons of business opportunities to companies. However, as we talked before, big data is different from traditional data, which requires skills and techniques to make good use of. There are tons of information out there but it is almost impossible for us to take all and also unnecessary to use them all. We have to selectively adopt useful information align with companies’ interests and objectives.

Status and future of Big Data in Marketing

What is Big Data? Is there any difference between data and Big Data? If yes, what are the differences? And, why are the differences? Different from traditional marketing, big data is a term used to describe the collection, processing and availability of huge volumes of streaming data in real-time. The three V’s are volume, velocity and variety. Companies are combining marketing, sales, customer data, transactional data, social conversations and even external data like stock prices, weather and news to identify correlation and causation statistically valid models to help them make more accurate decisions. In the past, the database technology could not support to process huge amount of variety of data. Conversely, Big data solution could provide more accurate analyses that enable businesses to make better decisions. 

Why big data matter in marketing? The era of Big data changes today’s world. The large increase in the amount of data, such as digital photos, videos, and social media, generates significant business opportunities for sales and marketing professionals of small and midsize companies. According to Mick Hollison, there are four ways Big Data that has been changing sales and marketing since 2015. First, large enterprise would be the first to widely adopt big data and predictive analytics technologies, but more and more small and medium businesses will get on board soon thereafter and will benefit even more. Second, Marketing spend are becoming significantly more precise by leveraging insights from big data to accurately target prospects and deploy account-based marketing strategies. In addition, sales forecasting accuracy has been improving dramatically as sophisticated algorithms supplant "gut feel" as the weapon of choice for predicting sales. Moreover, real-time sales data visualization technologies have been emerging and empowering sales managers to adjust battlefield tactics based on live data feeds. 

How does big data really impact and improve in real life marketing? Here are some examples. With big data, companies can easily see exactly who is buying and tease out even more details about their customers, including things like which websites they frequent, which social media channels they use, and even which buttons they click while on a website. Also, Data can provide insights into who your customers are, where they are, what they want, how often they make a purchase, when and how they prefer to be contacted, and many other important factors. Companies also can analyze how users interact with their website – or even their physical store – to improve the user experience. Furthermore, the ROI of a blog post used to be extremely difficult to measure, but now, thanks to big data analytics, marketers can easily analyze which pieces of content are most effective at moving leads through a marketing and sales funnel. Even very small businesses can afford tools to implement content scoring, which can highlight the pieces of content that are most responsible for closing sales.


Big data is like a little Giant in marketing and almost in all other industries nowadays. It is unavoidable in today’s world. But no one knows how it will develop because it is a newly emerged term and the technology is evolving all the times that has unlimited potential. However, we can look at some predictions from the foremost experts in the field to get some ideas how likely the future of big data would be: 

1.     Data volumes will continue to grow.  considering that the number of handheld devices and Internet-connected devices is expected to grow exponentially.
2.     Ways to analyse data will improve. While SQL is still the standard, Spark is emerging as a complementary tool for analysis and will continue to grow, according to Ovum.
3.     Big data will face huge challenges around privacy, especially with the new privacy regulation by the European Union. Companies will be forced to address the ‘elephant in the room’ around their privacy controls and procedures. Gartner predicts that by 2018, 50% of business ethics violations will be related to data.
4.     More companies will appoint a chief data officer. Forrester believes the CDO will see a rise in prominence — in the short term. But certain types of businesses and even generational differences will see less need for them in the future.
5.   “Autonomous agents and things” will continue to be a huge trend, according to Gartner, including robots, autonomous vehicles, virtual personal assistants, and smart advisers.
6.   Big data staffing shortages will expand from analysts and scientists to include architects and experts in data management according to IDC.
7.   Algorithm markets will also emerge. Forrester surmises that businesses will quickly learn that they can purchase algorithms rather than program them and add their own data. Existing services like Algorithmia, Data Xu, and Kaggle can be expected to grow and multiply.
8.   “All companies are data businesses now,” according to Forrester. More companies will attempt to drive value and revenue from their data.
9.   Businesses using data will see $430 billion in productivity benefits over their competition not using data by 2020, according to International Institute for Analytics.
10. “Fast data” and “actionable data” will replace big data, according to some experts. The argument is that big isn’t necessarily better when it comes to data, and that businesses don’t use a fraction of the data they have access too. Instead, the idea suggests companies should focus on asking the right questions and making use of the data they have — big or otherwise.  

References:
https://martech.zone/benefits-of-big-data/
https://www.projectguru.in/publications/difference-traditional-data-big-data/
https://www.inc.com/mick-hollison/5-ways-big-data-will-change-sales-and-marketing-in-2015.html
http://data-informed.com/how-big-data-analytics-can-improve-your-marketing/
https://www.forbes.com/sites/bernardmarr/2016/03/15/17-predictions-about-the-future-of-big-data-everyone-should-read/#135eea6e1a32 

Big Data in Marketing

The trend in marketing industry has been changing in recent years. Marketing has gone digital, and marketing professionals in all areas – product development, advertising, distribution, research, customer relations and more. By 2016, digital marketing spending is expected to climb to $77 billion and will represent 35 percent of all advertising spending. Digital marketing involves big data which marketers have to deal with and make good use of it. Therefore, we must understand the big data trend to get us to a more effective and efficient way.

Different from the past, marketing was not as complicated and technical as it is nowadays. In the old days, companies paid for TV or radio commercials to advertise their services or companies. What they need most might be the creativity to produce commercials. Today, most companies are not only paying for traditional marketing, but also paying for online advertising. For this part, they cannot get rid of data. Data must be collected for promotion on online media channels. For example, Facebook advertisement could target on specific audience based on their collected data including users’ basic information, geographic information, interests, etc. There are a lot more examples on using big data in marketing. In next article, I will explain more on it and talk about the current trend and the future of big data in marketing.