Marketing Analytics

The goal is to turn data into information, and information into insight."

Marketing Analytics Marketing Analytics

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Table of contents

    - Carly Fiorina

    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.

    Marketing analytics, simply put, is the practice of using models and metrics to provide marketers with helpful insight to facilitate decision-making.

    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.

    Key performance indicators (KPIs) are specific metrics related to the organisation's goals.

    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.

    Various industries use Big Data, from healthcare and education to retail and banking.

    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.

    By measuring the frequency of certain words or phrases, the analyst can find out if there are any similarities between thousands of online customer reviews and what the similarities are.

    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.

    Digital marketing analytics is analysing digital data to understand how customers behave online and how they experience digital channels (e.g. website, social media, etc.).

    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.

    Social network analysis (SNA) studies the structure, characteristics, and relationships between individuals in social systems.

    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.

    For example, LinkedIn relies on algorithms that detect social connections and structures between users.

    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.

    Chiptole has partnered with social media influencers like David Dobrik, singer Shawn Mendes, and drag star Trixie Mattel to promote its products. The company even launched a 'Chiptole Creator Class', which included 15 influencers from TikTok promoting the various food items on its menu.¹ By partnering with viral TikTok influencers, Chipotle engages a wide range of audiences and encourages all TikTok users to post about the viral dishes and food combinations they have tried, leading to increased engagement and exposure to the restaurant chain online.

    Examples of Marketing Analytics

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

    You can try this out by searching for the Google Analytics Demo Account!

    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%).

    Marketing analytics Demo analytics age StudySmarterGoogle 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.

    Marketing analytics Demo analytics gender StudySmarterGoogle 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%).

    Marketing analytics Demo analytics location StudySmarterGoogle 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.

    Marketing analytics Demo analytics traffic StudySmarterGoogle 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%.

    Marketing analytics Demo analytics page views StudySmarterGoogle Analytics Demo (Page Views), StudySmarter Originals. Source: Google Analytics Demo Account

    Marketing Analytics - Key takeaways

    • Marketing analytics uses models and metrics to provide marketers with helpful insight to facilitate decision-making.
    • There are four types of marketing analytics - predictive, prescriptive, descriptive, and diagnostic.
    • Metrics are essential in evaluating an organisation's overall success and performance. Key performance indicators (KPIs) are specific metrics related to the organisation's goals.
    • Big Data refers to enormous data sets that have to be analysed through specific software. The 7Vs of Big Data are volume, variety, velocity, veracity, variability, value, and visualisation.
    • The two analytical approaches to segmentation include factor analysis and cluster analysis.
    • There are two types of predictive models used for analytics - estimation and classification.
    • Digital marketing analytics is analysing digital data to understand how customers behave online and how they experience digital channels (e.g. website, social media, etc.).
    • Social network analysis (SNA) studies the structure, characteristics, and relationships between individuals in social systems.

    References

    1. Ruby Zheng. 10 Best Influencer Marketing Campaigns in 2021. No Good. 2021.
    Frequently Asked Questions about Marketing Analytics

    What are examples of marketing analytics?

    Marketing analytics is the practice of using models and metrics to provide marketers with helpful insight to facilitate decision-making. Examples of metrics may include customer retention, engagement, return on investment (ROI), return on ad spend (ROAS), etc.

    How is analytics used in marketing?

    Marketing analytics is a form of market research. It is a process used to help marketers and management make informed marketing decisions. Analysts must use various statistical tools, methods, metrics, and software to analyse data so as to understand customer behaviour and improve marketing strategies.

    What are the three 3 different kinds of marketing analytics?

    There are three main types of marketing analytics: descriptive analytics, predictive analytics, and diagnostic analytics.

    What is marketing analytics and its advantages?

    Overall, marketing analytics aims to understand marketing situations and use the gained insight to optimise marketing strategy. The advantages of marketing analytics include its ability to track the progression of marketing campaigns, improve marketing performance, and evaluate whether marketing goals have been achieved.

    What is the difference between marketing analytics and business analytics?

    Marketing analytics is the practice of using models and metrics to provide marketers with helpful insight to facilitate marketing decision-making. Marketing analytics is thus market-specific. On the other hand, general business analytics concern all aspects of the business, including its operational and financial performance, for instance.

    Test your knowledge with multiple choice flashcards

    ______ analytics are used to understand what has already happened (looking at the past).

    ______ analytics guide what an organisation should do in a particular situation.

    _____ analytics are used to understand why something has happened.

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