Data science and digital marketing are two of the most important fields of work in the modern world. Digital marketing is important because nowadays any business worth its salt has an online presence. They use that presence to advertise to a larger audience than they ever could.
Data science on the other hand is the field that deals with anything related to data and its processing. Currently, data science has a lot of momentum especially paired with artificial intelligence.
In this article, we will check out how data science can help out digital marketers improve their marketing efforts.
What is the Data Science Methodology?
Data science can be applied anywhere where there is data to be collected. In digital marketing, there is a lot of data to be collected from both the customer side and the brand side. Hence, data science can be beneficial to digital marketing. But before we get into that, let’s check out what is the data science methodology.
In simple terms, there are four steps involved. They are given below:
- Data Collection
- Data Organization
- Processing
- Analysis of processed data
Data Collection
In data collection, the data scientist/engineer has to collect data from various sources. In digital marketing, we can get this data from multiple sources. The most common sources are a brand’s digital platforms. They can provide a lot of information to the data scientist. This can include behavioral data on the online store, comments on the blog, and even what kinds of pages you did not access at all.
Another source is surveys and feedback. People like to feel heard, and companies know a great way to do that. That way is to ask people for feedback on things like their service, and what kind of things they would like to see in the future. This is all data that can be collected for further processing.
Data Cleaning/Organization
The next step in the data science process is to clean the data that has been collected. You see, not all of the data is actually useful or required, however, that point is overlooked in the collection stage. That’s because, at that time, it is not known which data is useful or not. So, all sorts of things are collected.
The data collected may contain things like duplicated data, useless data, unstructured data, and even corrupt data. During cleaning, you deal with all of these issues.
This is a really time-consuming stage, but if it is not done, it can harm the rest of the process. The main things to note are:
- Duplicate data is removed
- Corrupted data is either restored or removed
- It removes data that is improperly formatted
- Data that is inconsistent is removed
This is an important step because the level of cleanliness of the data determines how accurate your predictions will be.
Data Processing
This is actually a multistep process. There is model planning involved, and then there is the actual model construction.
In model planning, the data scientist has to determine the relationship between the data they have and the outcomes they want. This is the job of a statistician because this relationship is determined using statistical formulas.
Once the model has been planned, the next step is to actually build it. The model will then do all of the processing of the data. Models are made with a combination of machine learning (ML) and statistics. Statistical algorithms teach the machine-learning model about meaningful insights. Once the ML model has learned what kind of insights there are, it can go over the entire data hoard and extract every single meaningful insight there is—a task impossible for humans.
Analysis of Processed Data
Once the entire dataset has been processed and all of the meaningful insights extracted, a data analyst has to check the results.
The analyst’s job is to see what are the insights and convert them into an understandable format such as a pie chart, bar graph, or line graph. These findings are then presented to the stakeholders who then make the business decisions based on the provided data.
And that’s it about the data science process. You can read more about it here. But for now, let’s check out some practical ways in which data science helps out digital marketing.
How does Data Science Help Digital Marketing?
In this section, we will look at some real-world applications of data science, and their benefits for digital marketing.
1. Data Science Helps Find Successful Channels for Marketing
We mentioned earlier in the article, that digital marketing has a lot of data both from the client side and the brand side. Simply using one sort of data is not good enough, and a combination of both must be used for the most effective outcomes.
Finding successful channels of marketing requires data from the brand side. In all businesses, there is a constrained budget for marketing. Marketers have to make the best use of this budget to get the best return on investment (ROI).
This can be helped with data science. Using the data from all past marketing records, a data scientist can craft a model to determine which approaches and strategies were the most successful.
Of course, this requires that a model be created and trained with the specific statistical algorithms over the provided data. A model for data analysis is usually created by data scientists themselves since premade solutions rarely exist.
Once a model is made and deployed, the data analyst will check the insights and determine which marketing methods brought in the least yield, and which ones brought in the most.
This data is then presented to stakeholders who decide whether a certain method should be either improved or completely cut off to prevent losses.
2. Data Science Helps You to Formulate Strategies within a Budget Constraint
Just like how data science can help you out with determining which marketing strategies are working or not, it can also help you to decide what strategies to apply to stay within your budget.
This may sound very similar to the previous point, but they are quite different things. In this approach, a predictive model is required rather than an analysis model. This model’s purpose is to check whether a particular marketing tactic will bring in more leads and have a better ROI compared to current tactics.
Keep in mind, that this is not a tactic that you have already applied, but are only thinking of applying. You are not determining the ROI using data you have gotten from experience. Instead, you are using data from your prospective clients and trying to predict whether a particular approach will work for them or not.
In this approach, your model also has to determine whether the new tactics will be within the budget constraints or not. It does so by predicting whether the efforts will be successful, how long will it take to break even, and when will you start generating a profit.
This method is mostly dependent on the quality of your data. If the data is high quality, the predictions will have a much smaller deviation.
3. Data Science Helps with Behavioral Targeting
Another thing that data science can help with is behavioral targeting. In digital marketing, it has been known for a long time that customers like it a lot when they feel that companies understand them. Many companies have a large budget for researching their audience.
They use the data they get from that research to segment their audience into different groups and cater to each group differently. In behavioral targeting, a company uses the data they have collected from their own platform, and observable data from third-party platforms to find out what kind of things they are interested in, and will be interested in.
Then they use things like tailored emails, and customized deals to increase the chances of “sealing the deal” i.e. convincing the customer to make a purchase.
Data science helps with this. The data collected from the companies’ own platforms, and from third-party platforms, is cleaned and processed with a model. The model extracts insights from it. Segmenting a large number of audiences, into specific categories using a metric ton of data can only be done with data science.
The analyst only determines processed data, and cannot process the data themselves. Anyhow, this is how data science can help determine what a customer likes and what approaches will help convince them.
4. Helps In SEO to Find Out Which Strategy is Working
SEO (Search Engine Optimization) is part and parcel of digital marketing. If you are a digital marketer, then you know this to be true. SEO involves things like on-page SEO and off-page SEO.
On-page SEO involves mostly technical SEO, which is very straightforward and easy to do. You just have to follow some best practices and you are done.
However, off-page SEO and the content-related part of on-page SEO are actually very vague. Google has a lot of guidelines for SEO and content. They warn you against a lot of stuff, that they label as black hat SEO tactics. A common example is keyword stuffing in which a person uses too many keywords in an attempt to fool the ranking algorithm.
However, finding out which tactics work and which don’t is a very tiresome task. It takes a lot of trial and error to find out what works for your brand. However, this can be easily bypassed with the help of data science. You can easily check if your content is being de-ranked due to problems such as plagiarism, keyword stuffing, or readability.
Then a marketer can take appropriate steps to check plagiarism, check keyword density, check readability, and tune them to acceptable levels. This prevents their content from being penalized and allows it to rank high.
With data science, you can also find out what keywords your brand is best suited for, which keywords are the easiest to rank for, and the kind of content the audience like in your niche. Fortunately, all of these services can be availed with the help of premade tools such as Ahrefs, SEMrush, and Ubersuggest.
You can also predict the success or failure of any new strategy that you wish to try out. In this way, you can utilize data science to formulate low-risk SEO strategies.
5. Helps To Identify the Best Audience for Marketing
This may sound redundant but we assure you it isn’t. In digital marketing, and actually, all kinds of marketing, figuring out who is supposed to be your target market is actually quite difficult.
You can never claim to know what other people are thinking, at most, you can make an educated guess. However, determining who is your target audience can be done with the help of data science.
This requires some prior market research and competitor analysis. From the data obtained through these endeavors, a data scientist can craft a model to analyze which demographic is best suited for marketing.
The model can analyze the competition that is similar in size and scale to your own brand. It can also analyze which type of customers frequent your closest competitors. Then it uses that data to draw insights into which kind of customers are best suited for your brand.
Then you can use behavioral targeting to attract those customers, and ensure that you get a sizeable number of leads. In this way, new businesses can utilize data science to find out to whom they should market their products and services.
6. Real-Time Analysis for Lead Conversions
Data science can work in real time in some scenarios. It is tremendously useful to see what kind of activity is happening across your digital platforms and take immediate action accordingly.
Basically, in this approach, instead of collecting data, and processing it later, the model processes it in real-time to give you quick updates on the situation.
In a digital marketing scenario, the model can be supplied with operational data as well as the data of the customer/actor interacting with your platform. The customer data includes things like the customer’s age, spending capacity, interests and wants, and all other things that are included in behavioral targeting.
The operation data include stuff like what actions they are taking on your platforms, such as adding things to a cart or removing things from the cart. Spending a lot of time on a page is also included in this data.
So, the analyzer model can quickly alert you to specific behaviors that signal interest in a purchase. You can follow that up with a small discount, or a good deal, and get the customer to commit to a purchase.
7. Data Science Helps to Create Recommendation Engines/Software
You know about this technique which was originally popularized by Amazon. When you go to a product page to buy something, you can see that there is a section below it called “frequently bought together” or “people who bought this item also viewed”. There are also sections that are titled “recommended for you.”
All of these things are the results of what we call a “recommendation engine.” This is basically software that analyzes the customer’s behavior to predict what kind of things they will be interested in based on what they are interested in currently.
This is very useful for improving the purchase rate and making more sales. As that is the ultimate goal of digital marketing it is a win-win scenario.
8. Can Analyze Sentiments
Sentiment analysis is a very useful technique for finding out whether your brand is being received well or not. It is also used to find out things that the brand can do to improve its image among the public.
Normally, sentiment analysis is done via surveys and viewing forum posts. This was done manually and that was quite time-consuming.
Nowadays, you can just get a program to scrape the web for comments. And you can conduct digital surveys. All of the data from the survey and the comments can be fed to a data analyzer which will then extract the hidden insights from them.
An analyst can use the insights to find out what is the sentiment of the public with regard to the brand, specific products and services, customer support, and other things that may be important.
This helps digital marketers with concocting new strategies, as well as rectify the existing ones. Ultimately, this leads to the creation and development of better products and services, as well as better ways of marketing them.
Sentiment analysis also teaches marketers about land mines and the red flags that they have to avoid. So, by doing sentiment analysis with the help of data science, digital marketers can keep their brand in the people’s good books.
Conclusion
Digital marketing is a vast field with many sub-divisions. The same can be said for data science. Data science requires comprehensive knowledge of statistics, algorithms, and programming. With this skill set, a data scientist can create models to analyze all sorts of data to gather insights.
In this article, we checked out eight ways in which data science can help out digital marketing. All of the methods that we discussed had some sort of massive data set that could be used for gathering insights.
The good thing about data science is that it can be outsourced to capable experts. However, big companies can create their own teams as well. It is our hope that you learned something useful from this article and that it helps you out in your marketing endeavors.