Importance of data analytics in digital marketing

Learn the importance of data analytics in digital marketing, from help to understand consumer to competitive analysis, in this article!

In the age of digital marketing, data analytics has become an integral part of any successful marketing strategy. With the proliferation of digital platforms, marketers can leverage data analytics to make data-driven decisions and gain insights into consumer behaviour, market trends, and campaign performance. Here are some of the key contributions of data analytics towards digital marketing.

How analytics can help in digital marketing

Analytics has a lot to offer in digital marketing; from helping you understand your audience, to optimising your digital marketing campaigns, to measuring ROI.

Here is a detailed explanation of how analytics can help with digital marketing activities:

1. Understanding consumer behaviour

One of the most significant benefits of using analytics tools is its ability to help businesses understand consumer behaviour. By analysing customer data such as purchase history, browsing behaviour, and social media interactions, marketers can gain insights into the factors that influence their purchase decisions. This information can be used to tailor marketing messages and campaigns to the specific needs and interests of customers, resulting in more effective campaigns and higher ROI. Amazon analyses the purchase history, browsing behaviour, and search history of their customers to make personalised recommendations to them, leading to higher conversion and revenue.

2. Optimising marketing campaigns

Data analytics allows marketers to track and measure the performance of their marketing campaigns in real-time. By analysing campaign data such as click-through rates, conversion rates, and engagement rates, marketers can identify the campaigns that are performing well and the ones that need improvement. This information can then be used to optimise campaigns and properly allocate marketing budgets. Airbnb analyses campaign data such as click-through rates and conversion rates to identify which marketing channels and messages are most effective.

3. Personalisation

Personalisation is a key trend in digital marketing, and data analytics plays a crucial role in enabling it. By analysing customer data, marketers can create personalised experiences for customers across various digital channels such as email, social media, and website. Personalised experiences can increase customer engagement and loyalty, ultimately leading to higher conversion rates and revenue.

Other means of personalisation involve further understanding their users (particularly app users) by means of data enrichment. By stitching identifiers for advertisers (IFAs) of app users with our proprietary database, marketers could understand the affluence, online interest, and offline point of interest. This aids in crafting personalised and relevant messages with increased effectiveness.

Netflix analyses viewing history and search queries to make personalised show and movie recommendations to its customers. In fact, it is also able to identify the time its subscribers stream its catalogue, streaming hours, its subscribers’ content preferences and more from its recommendation system. As Netflix leveraged data to select which show to license, it bought the Primetime Emmy award-winning series, House of Cards and showcased numerous trailers and similar shows that featured the drama’s leading actors, leading to an increase in subscriber base as they pay a fee to watch shows starring their favourite artistes.

4. Competitive analysis

Data analytics can also be used to gain insights into market trends and competitor activities. By analysing competitor data such as website traffic, social media engagement, and advertising spend, marketers can identify opportunities to differentiate their brand and gain a competitive advantage. Coca-Cola utilises the data collected from social media engagement, advertising spend, and market trends to identify opportunities to differentiate its brand and gain a competitive edge.

In 2015, Coca-Cola was able to determine that its product mentions on social media were approximately once every two seconds on average. To leverage its knowledge of the demographics of its consumers, their locations, and factors that trigger them to involve in conversations about Coke, it implemented AI-powered image recognition technology to detect the moment when images of their products and competitors are shared online as well as optimised its targeted ads delivery, allowing it to enjoy a fourfold increase in the click-through rates for its ads.

5. ROI measurement

One of the most significant benefits of data analytics is its ability to measure the return on investment (ROI) of marketing campaigns. By tracking and analysing campaign data, marketers can determine which campaigns are generating the highest ROI and adjust their marketing strategies accordingly. This information can also be used to justify marketing spend to executives and stakeholders. By analysing campaign data such as website traffic, conversion rates, and engagement rates, HubSpot can determine which campaigns are generating the most ROI and adjust its marketing strategies accordingly.

How to utilise data analytics for your business

Data analytics will only be effective if your business can use it properly. Here is a manual on how you can harness the potential of data analytics for your business:

1. Set Clear Objectives

Define your analysis goals explicitly. These objectives provide direction and determine what data to collect and analyse.

2. Data Collection and Preparation

Once you have a clear objective, collect and prepare your data sources. Ensure data accuracy through cleaning and preprocessing steps. This forms the foundation for meaningful insights.

3. Choose the Right Analytics Tools

Select analytics tools aligned with your objectives and data type. Whether it's statistical analysis, machine learning, or visualisation tools, the choice should match your analysis goals.

4. Exploratory Data Analysis (EDA)

EDA is a crucial step. Visualise the data to identify initial patterns, outliers, and trends. This step often serves as a starting point for further analysis.

5. Data Analysis

Apply statistical or machine learning methods for an in-depth examination of the data. This phase involves running regression analyses, clustering, or classification based on your objectives.

6. Interpret the Results

Interpret the output of your data analysis. Translate statistical or machine learning findings into insights that relate directly to your analysis goals.

7. Identify Key Findings

Distil your analysis into key findings. These findings represent the most significant patterns, correlations, or trends in the data.

8. Contextualise Insights

To ensure actionable insights, consider the broader context of your business or industry. External factors, market conditions, and industry benchmarks can influence the implications of your findings.

9. Create Actionable Recommendations

Develop specific, actionable recommendations based on your insights. These recommendations should guide decision-making within your organisation.

10. Prioritise and Communicate

Not all insights require immediate action, so prioritise recommendations based on potential impact and feasibility. Communicate your findings effectively to stakeholders using visualisations, reports, or presentations.

11. Implement and Monitor

Put your recommendations into action. Update strategies, processes, or products based on insights and continuously monitor their impact through key performance indicators (KPIs).

12. Iterate and Refine

Data-driven decision-making is an ongoing process. Regularly revisit your data, refine your analysis, and adapt your strategies as needed to address changing circumstances.

13. Feedback Loop

Establish a feedback loop to capture insights from actions taken. This feedback informs future analyses and decision-making improvements, closing the cycle and ensuring continuous improvement within your organisation.

Exploit data analytics for digital marketing success

Data analytics is essential for any business aspiring to thrive in the digital age and is a critical component of any successful digital marketing strategy. From website traffic to social media engagement, the volume of data generated by digital channels will continue to grow and businesses that fail to embrace data analytics will be at risk of falling behind their competitors. With the right tools and strategies, those data can be harnessed to propel growth and success

Insights

Importance of data analytics in digital marketing

Insights

Importance of data analytics in digital marketing

Featured Speakers

Heading

Contents

ADA Asia

7 Advertising Budgeting Methods That Businesses Should Use

11 Factors Affecting Advertising Budget and How to Create It Effectively

8 Simple Steps on How To Do Ad Optimisation Machine Learning

Our Work

ADA garnered 4.4 million views on YouTube and Facebook for Axiata's Independence Day brand film

Acquiring high-value users for Tokocrypto with predictive analysis and lowering customer acquisition costs

We helped a leading ecommerce group increase revenue and improve profitability with advanced data analytics

ADA improves market access for biotech company's success with sales dashboards

Transform your business with us