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How To Use Data Science for Digital Marketing.

2024-06-02 | 698 Print PDF

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How To Use Data Science for Digital Marketing.

In this topic, we will explain the techniques used for data gathering and prediction, using those techniques to read our Google Analytics traffic data to decipher our Google Analytics web performance data easily.

Both data science and data analysis are interwoven in terms of definition, quoting from Wikipedia, data science is the interdisciplinary field that uses scientific methods, processes, and algorithms to extract knowledge and insights from ambiguous data, while data analysis is the systematic computational method of analyzing those data or statistics, it’s used for the discovery, for the interpretation and communication of meaningful patterns in data eg a survey on a presidential candidacy and you are taking various data information’s from peoples poll ranging from demography, gender, age, etc when you take all this information and analyze them you will find out you will get a lot of data, the best way to decipher this data is via data analysis.

In this retro scope, we will touch on “big data” terminology, which describes the use of large information data systems. E.g. telecommunication data, email leads, etc most companies that use big data query do so to profile their end users by segmenting and profiling them for best output campaign performance.

When it comes to data we have two types;

Quantitative Data

This is also called structure data; they are like the traditional data structure system you are commonly used to e.g. excel sheets, the database for the server where information is entered numerically (ID assigned), the quantitative data analysis is more in tune with statistics, mathematics, or numeric data.

Qualitative Data

This is data analysis that is based on subjective information, e.g. flow charts, which are mostly used for illustration, text, images, and transcript information. A good example of qualitative data is your survey data, the way they are prepared only denotes the concept of qualitative data analysis.

 

7 Types of Data Analysis Techniques.

1.) Regression Analysis: This is used to estimate the relationship between a set of two variables, e.g. comparing the relationship between user visits and bounce rate on your Google Analytics dashboard, or your channel traffic (medium) versus bounce rate. E.g. the qualitative type of data found on the returning versus new user visits (pie chart display on your Google Analytics dashboard), this information for an e-commerce site will differ from a blog site in the way they will interpret this information as it denotes the relationship between your customer acquisition (new user visits) and customer retention (returning customers).

Interpretation for an e-commerce website denotes that they are spending a lot in acquiring new customers and they should rather focus on returning customers for their brand.

In another instance you can use the comparison of a perishable product, eg a phone manufacturing company, providing deliverables that don’t come with reoccurrence, by pitching the sales of a newer version with improved features, catching with a trend of the latest despite the fact that the phone itself solves the issue of communication (major solution the mobile phone provides). Such techniques are not limited to phone products or electronic products same can be seen with products via augmentation eg toothpaste, the idea here is to highlight the use of data to find a way to solve offering services without reoccurrence sales like consumable products or perishables by targeting old clients (not new clients as we may think) to bait on their new features. It’s on record that most of the new features that are presented are not used by the end users, so via data gathering the electronic industries have been able to focus on users' needs and wants e.g. the new generation of iPhones are now coming in with the size of Samsung phones, from their pocket size friendly trademarks.

Another thing to note with regression analysis can be used for split testing between campaigns (A/B Split testing); with the regression analysis, you will be able to find the relationship between the two campaigns in terms of sales revenue and other metrics.

A 3/5 positive correlation implies that the more you spend on the campaign the better results you will get from a certain campaign versus the other, while a no correlation will imply that such a campaign is going to result in no sales.

With the regression analysis, you can easily spot the reason why certain variables are underperforming or work up a hypothesis to determine the cause and effect of a trend between two variables. A good example will be the interpretation of two website traffic comparisons using a constant year but a different quarter to analyze the day’s traffic in a week, in this context one will be comparing the traffic in the days of the week within months.

 

2.) Monte Carlo Simulation: It is an experimental computational algorithm that relies on repetitive random sampling to obtain numerical results (quantitative data). A good illustration of how this analysis can be used in data analysis can be seen when making decisions there is a range of possible outcomes, because you have no control of the situation, hence the merits of the monte carlo simulation, which offers different predictions. Such predictive analysis is seen when trying to place ads on Google or FACEBOOK in terms of estimated impressions or traffic you will receive depending on the amount you are paying (ie the other variable).

So when it comes to using it for your Google Analytics, you can use it to forecast your data analytics if there are certain changes in your web performance (prediction mode). A good illustration will be to predict the traffic volume of your website following the previous data you have gained from the traffic insight of before, you can easily predict the path which your current traffic is headed.

The underlying word here is to solve issues via random usage of data to determine the possible outcome of a problem, and the best way to solve this is to follow the pattern flow of the data you are analyzing to provide a futuristic answer (predictive).

 

3.) Factor Analysis: This analysis technique is used to reduce a large number of variables to a smaller number of factors; it works under the premise of multiple separate observable variables correlating with each other because they are all associated with an underline construct. For instance, when analyzing the traffic from your website you will have to take it within a sample period which is better suited for regression analysis, but for factor analysis, you are required to have more data to work with using various variables e.g. when you are trying to know which day of the week you are getting a lot of traffic from a monthly duration.

If you look at the case study we did regarding the best time and day to post on social media for traffic generation, we can easily deduce the use of factor analysis in this case using a 30days sample to find the best time to post on social media.

Such analysis can also be used to deduce the best time or days your campaigns perform better and you can easily optimize your campaign based on such data decisions in terms of days (either to pause the ad during these days) and bid cost (either to increase it or reduce it).

So factor analysis can be used to find the correlation between your KPIs (key performance index) in this regard we can use it to resolve issues to our objectives and goals.

 

4.) Cohort Analysis: To quote Wikipedia; “Cohort analysis is a kind of behavioral analytics that breaks the data in a data set into related groups before analysis. These groups, or cohorts, usually share common characteristics or experiences within a defined time span”…it can also be defined as the subset of behavioral analytics that takes data from a given data set rather than looking at all users as one unit.

It’s similar to factor analysis, whereas factor analysis is working with figures and numbers the cohort is working with behavior, and it is more related to behavioral analysis, so it is a study of a set or group of people who shares a common characteristic.

It is much more subtle for finding insights on trends, use to define a pattern, whereas others rely on patterns to define their results, cohort asks the question as to why the pattern is being developed.

Using this cohort analysis on Google Analytics can be used to define affinities, preferences, interests, and in-market segmentation data. Such analysis is also informative driven for campaigns run on Google Analytics or social media platforms like Facebook and the cohort analysis is also useful for retargeting ads and optimize what you are offering to provide more parallel centralized targeted ads.

 

5.) Times Series Analysis: Using Wikipedia's definition; “In mathematics, a time series is a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data”. It is a statistical technique used to identify trends and cycles over time, when we talk about trends we look at viral and engaging posts. These are contents that are more versatile on social media platforms, so this type of analysis is more related to social media analysis rather than Google Analytics but can still be applicable in suitable case scenarios, the points you need to look out for in this analysis are the trends and the stable linear gradual increase or decrease of the data over the period of time, another thing to look out for in a trend is seasonality that might factor in a pattern for a trend e.g. during summers you might have a rise in sales for open shirts, or during Christmas period you can find a pattern for a particular product being sold more.

Another pattern is a cycle pattern, which might denote that the reoccurrence of the data is based on a cycle

 

6.) Sentiment Analysis: Quoting from Wikipedia; “Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information”.

Sentiment analysis also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea.

Sentiment analysis is a qualitative data type and one way to study opinions, feedback, and reviews is by using survey polls to decipher how individuals weigh their emotional sentiments assigned as good, bad, or neutral. This makes sentiment analysis more suitable for social media analysis than Google analysis.

 

7.) Data Analysis: As stated by Wikipedia;” Data analysis is a process of inspecting, cleansing, transforming, and modeling data to discover useful information, informing conclusions, and supporting decision-making”.

The data analysis process is more in tune with KPIs, defining your goals and creating the means of gathering, cleaning, and providing meaning to those data to make useful and insightful data-driven decisions.

You can read more on the topic from visiting Here.

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