Marketing Analytics

Marketing analytics play a key role in understanding marketing activities. However, if marketers do not know how to interpret marketing data and metrics, they are stuck with a vast pool of potentially uncorrelated quantitative and/or qualitative data. This is why it is essential to turn raw data into information that can be used as a source of actionable insight. The role of marketing analysts is not limited to looking at numbers and formulas in a spreadsheet. They must understand how to turn those metrics into helpful managerial insights to make effective marketing decisions. Read along to learn how you can transform data into effective marketing strategies!

Marketing Analytics Definition

Marketing analytics is a form of market research. It is a process used to help marketers and management make informed marketing decisions.

However, it is essential to note that marketing analytics includes measuring, analysing, and managing marketing performance. Insights gained from marketing analytics do not appear out of thin air. Analysts must use various statistical tools, methods, metrics, and software to analyse data so as to understand customer behaviour and improve marketing strategies.

As a result, there are different groups marketing analytics can fall into. The four marketing analytics types include:

1. Descriptive analytics - used to understand what has already happened (looking at the past). It is an exploratory technique used to summarise and visualise data.

2. Predictive analytics - used to understand what might happen (looking towards the future). It is a technique for forecasting a likely outcome given specific inputs.

3. Prescriptive analytics - guides what an organisation should do in a particular situation. This technique analyses available data to make recommendations and suggest improvements.

4. Diagnostics analytics - used to understand why something has happened. It uses different statistical models and hypothesis testing to explore variables' relationships.

Purpose of Marketing Analytics

Overall, marketing analytics aims to understand marketing situations and use the gained insight to optimise marketing strategy. On a micro level, marketers need to understand the role of metrics. Metrics are essential in evaluating an organisation's overall success and performance. Examples of metrics may include customer retention, engagement, return on investment (ROI), return on ad spend (ROAS), etc.

Overall, the purpose of marketing analytics metrics is to:

• Track the progression of marketing campaigns,

• Improve marketing performance,

• Monitor the marketing process,

• Detect and understand problems,

• Evaluate whether marketing goals have been accomplished.

Furthermore, the purpose of marketing analytics is to create value, not only for the organisation but also for customers. Therefore, the marketing analytics process can be viewed as a value chain, whereby the steps (for creating value) are as follows:

1. Data collection,

2. Reporting (turning data into information),

3. Analysis (turning information into insights),

4. Decision,

5. Action (creating an action plan based on the decisions that were made),

6. Value (to the firm and customers).

Different Kinds of Marketing Analytics

As outlined previously, there are different kinds of marketing analytics. Marketing analytics spreads through a wide range of industries, and various technologies can be used to gather market insight. Let's take a closer look at some of them.

Big Data analytics

Big Data refers to enormous data sets that have to be analysed through specific software as traditional software often cannot cope with its volume and complexity. Big Data is analysed to discover patterns, trends, and insights about market and consumer behaviour.

Therefore, Big Data can be used by organisations to:

• Gain consumer/market insights,

• Improve marketing processes,

• Improve operational efficiency and supply-chain management,

• Improve segmentation and targeting,

• Spark innovation.

As a result, Big Data is characterised by the following seven features (7Vs):

1. Volume - extremely large data sets.

2. Variety - the large volume of data does not follow any order/form, in other words, it is inconsistent.

3. Velocity - new data and data updates are occurring at a high rate.

4. Veracity - some data can be imprecise and biased.

5. Variability - data is always changing.

6. Value - data has to be systematised to provide value for organisations.

7. Visualisation - Big Data has to be transformed into an understandable form.

Text mining analytics

Text mining has also played a significant role in marketing analytics. The digitisation of data has recently led to an influx of digital text data in the form of customer text data (e.g. online reviews, customer chats with built-in AI chatbots, etc.) and organisational text data (e.g. social media marketing campaigns, customer communications, etc.). However, the firm must use text mining to translate the vast data pool into helpful insights.

One of the benefits of using text mining is its ability to interpret unstructured data (i.e. text data) using computer-assisted technology and transform it into actionable marketing insights.

The process used for text mining is as follows:

1. Preprocessing the data

2. Extraction

3. Converting text into text metrics

4. Assessing the validity of results

Segmentation and targeting through marketing analytics

Segmentation can be approached from an analytical standpoint. Before we discuss how this is possible, let's examine why segmentation is essential.

Market segmentation is necessary for targeting homogeneous customer groups with the organisation's marketing activities. It helps companies understand which customers have similar wants and needs and thus facilitates the creation of a tailored marketing mix (including a communications programme). Segmentation also allows marketers to identify market opportunities and threats.

The two analytical approaches to segmentation include:

1. Factor analysis - reducing a large number of variables into fewer overarching ones. It allows analysts to narrow down a large set of observable, often highly correlated variables, into fewer composite ones.

2. Cluster analysis - using data to systematically find customer groups by classifying cases into homogeneous groups (clusters).

Therefore, the segmentation process may include a factor analysis followed by cluster analysis, which can help marketers find homogeneous consumer groups (segmentation), uncover new product opportunities (positioning), and understand consumer behaviour (targeting).

Predictive marketing analytics

Predictive analytics are used in marketing situations to predict an outcome given certain factors (inputs). It is used to forecast a particular variable of interest to the marketer. There are two types of predictive models used for analytics:

1. Estimation models - used to predict the value of a variable (e.g. linear regression). For example, investigating whether a car dealership has a significant relationship between service quality and customer satisfaction.

2. Classification models - used to understand how certain variables contribute to outcomes (e.g. logistic regression). For example, investigating whether a recent purchase of women's clothes is a significant predictor of whether an individual will respond to promotion on clothing.

Digital Marketing Analytics

Digital marketing analytics is a valuable tool for marketers to understand customer behaviour.

Let's take a look at some of the key digital marketing metrics used to analyse customer behaviour on a webpage:

• Traffic metrics - which sources are bringing visitors to your website.

  • Web traffic metrics - how many users have visited the page, the time spent on the page, where the traffic is coming from (e.g. mobile or desktop), etc.

  • Web ad metrics - impression, click-through rate (CTR), impressions, etc.

• Behaviour metrics - how are visitors using your webpage. It may include metrics like:

  • Bounce rate - number of people leaving the landing page without performing any other action.

  • Checkout abandonment rate - how many people have left their digital shopping carts without actually checking out.

  • Loyalty metrics - how many times an individual has visited a page over a certain period.

• Conversion metrics - evaluating whether the marketing programme leads to the desired outcome (e.g. number of leads generated or the number of new orders placed).

• Efficiency metrics - evaluating whether the marketing activities are profitable or not (e.g. return on investment (ROI) or return on ad spend (ROAS) could be used).

Another vital tool for digital marketing analytics is social network analysis.

This form of analysis can therefore be applied to social media channels. For instance, it can be used to understand how customer reviews impact purchase decisions or how social structures are connected online.

SNA can also be used for influencer marketing. Social network analysis can help organisations predict which influencer on Instagram would be most effective for a specific marketing campaign or promotion by identifying which individual has the most influence within the social network.

Examples of Marketing Analytics

As an example of marketing analytics, let's look at Google's Merchandise Store analytics.

Demographically, a majority of users fall into the 25-34 age group (33.80%), followed by the 18-24 age group (29.53%), with the 65+ age group making up the smallest segment of users (3.04%).

Google Analytics Demo (Age), StudySmarter Originals. Source: Google Analytics Demo Account

Most users (58.95%) are male, and users are mainly interested in technology, media and entertainment, and travel.

Google Analytics Demo (Gender), StudySmarter Originals. Source: Google Analytics Demo Account

Geographically, most users are in the United States (50.10%) - with 46.67% of new users coming from the United States - followed by India (8.23%), the United Kingdom (4.86%), Canada (4.37%), and Japan (2.32%).

Google Analytics Demo (Location), StudySmarter Originals. Source: Google Analytics Demo Account

These demographic and geographic metrics could be used to identify customer segments.

On the other hand, looking at conversion traffic, traffic is mainly coming from the direct channel, followed by paid search, display, and affiliate channels.

Google Analytics Demo (Traffic), StudySmarter Originals. Source: Google Analytics Demo Account

The page has around 56,200 unique views. The average time spent on the page is 49 seconds, which is relatively low. The bounce rate (number of people leaving the landing page without performing any other action) is 46.55%, and the abandonment rate (number of people abandoning their shopping cart) is 40.91%.

Google Analytics Demo (Page Views), StudySmarter Originals. Source: Google Analytics Demo Account