The Importance of Data Analytics in Business Decision-Making

Discover why data analytics is important and how to leverage data to improve and enhance decision-making for your business. Learn more here.

Data analytics has become an essential tool for businesses to make s. In today's world, where data is generated at an unprecedented rate, data analytics provides companies with insights that enable them to stay ahead of the competition.

Data analytics involves a process of collecting, analysing, and interpreting data to uncover patterns, trends, and insights that can inform business decisions. This process helps businesses to identify opportunities and risks, as well as to optimise their operations and strategies.

What is data driven decision making?

Data-driven decision making is a process in which organisations use data and analytical techniques to inform and guide their strategic, tactical, and operational choices. It's about basing decisions on empirical evidence and insights extracted from data, rather than relying solely on intuition or experience. Data based decision making empowers organisations to make more informed, objective, and effective decisions, ultimately leading to improved outcomes.

The relationship between data-driven decision making and data analytics is essential. Data analytics plays a pivotal role in enabling data-driven decision making by providing the necessary tools and insights to extract meaningful information from the vast amounts of data that organisations generate and collect.

The importance of Data Analytics for Business Decisions Making Process

Data analytics can provide valuable data insights for business decisions making such as identifying customer needs, optimising operational efficiency, improving marketing strategies, and helping business in data driven decision making.

To understand more, here is the full explanation:

1. Identifying customer needs and preferences

Data analytics can help businesses understand their customers' needs and preferences by analysing their behaviours and interactions with the brand. For example, an eCommerce company can use data analytics to analyse their customer data including purchase history, search queries, and website interactions to gain valuable insights into what products their customers are interested in, their preferred payment methods, and the platforms they use to access the brand.

These actionable insights can help the company tailor their marketing and advertising efforts, product offerings, and user experience to better align with their customers' preferences, ultimately leading to higher customer satisfaction and loyalty.

The beauty industry was badly affected during COVID-19, and our client, a Thailand cosmetic brand was no exception. With lockdowns, malls closure, and the difficulty to reconnect with the right audience, sales plunged. We utilised data visualisation and enrichment to get a clearer picture of what our client’s potential consumers were like. From the insights gathered, we identified three personas. Based on the personas, we created multiple hook messages, background images, and end scenes. The messages had been tailored to address the pain points of WFH and some common makeup mistakes that matched each persona. As a result, brand engagement increased by 156% and cost-per-click (CPC) decreased by 27%.

2. Optimising operations

With many mega players such as Apple and Starbucks offering omnichannel experiences to their target customers, it is wise for a company to not solely focus their budget on digital-driven initiatives but also invest in offline channels such as physical shops as well. Establishing a store at the right location requires one to consider multiple factors, such as footfall, the density of the target market, as well as the competitors in the same area. This is where data analytics can be exploited to determine the ideal locations for physical store setup.

Due to the fierce competition in Indonesia’s banking industry, our client, a local commercial bank ,wanted to identify the presence and distribution of Syariah banking competitors in a few key locations before opening a new branch. With ADA’s Location Planner, the bank was able to verify the customer behaviour of a certain location. Aside from identifying the non-performing branches for relocation, the insights gathered from the solution were integrated into their 5-year branch transformation blueprint.

3. Improving marketing strategies

Data analytics can help businesses improve their marketing strategies by providing insights into the effectiveness of their campaigns. For example, with data analytics, brands can view the performance of a social media campaign at a glance. By knowing the engagement rates, click-through rates, and conversions, you can tell the type of content your audiences enjoy, thereby using similar and effective tactics to drive sales. Based on this information, the marketing team can make data-driven decisions to optimise their campaigns and achieve better results.

Our client, an American footwear company, wanted the highest-ever single-day sales on Shopee Singapore and Malaysia during Super Brand Day (SBD) campaign. With consumer insights, we gathered insights about our potential target audience and “deal-seeking” online consumers. The creative strategy is a combination of “Branding” and “Promotion” led content with best-in-class assets to entice purchases. The results? We generated more than 20 times the traffic in both markets. Sales increased by 47 times in Singapore and 30 times in Malaysia.

4. Predicting trends and market changes

Data analytics can help businesses predict trends and market changes by analysing data on customer behaviours, industry trends, and economic indicators. For example, a retailer can use data analytics to track seasonal buying patterns, monitor social media trends, and analyse economic indicators to anticipate changes in consumer behaviours and adjust their product offerings and marketing efforts accordingly. This can help the retailer stay ahead and take advantage of new opportunities as they arise.

Though with a loyal fanbase, the business growth of a quick service restaurant (QSR) chain in Thailand plateaued without any major campaigns for the past two years. They conducted a survey and discovered that Thai consumers felt that the brand was not approachable. We extracted multiple data sets with a combination of tools: Audience Explorer to track real-time data; Location Analytics to gather footfall data; Consumer Profiling to understand consumers’ attributes and behaviours. These allowed us to predict the likelihood of a consumer purchasing from the said chain. We chose audiences with an affinity for food & dining, used geolocation data to pinpoint areas with a high footfall of competitor outlets, and served ads to audiences seen in those areas. To win new customers, we excluded those who have visited the QSR chain’s website with the new menu. The campaign turned out to be a success as daily sales increased by 12%.

5. Making data-driven decisions

Data analytics enables businesses to make data-driven decisions based on quantitative insights rather than intuition. For example, a financial services company can use data analytics to monitor customer spending patterns and identify potential fraud or unauthorised transactions. Based on this information, the company can make data-driven decisions to improve their fraud prevention efforts and protect their customers' accounts. By making decisions based on data rather than intuition, businesses can reduce the risk of errors and make more informed decisions that lead to better outcomes.

As concern over the COVID-19 pandemic escalated, a transportation service leader in Indonesia, engaged with ADA to learn more about the mobility pattern and profiles of the commuters in various points of interest (POI), plus to validate several assumptions on their passenger segments. With the combination of Recency, Frequency, and Monetary (RFM) analysis, Point of Interest (POI) analysis, commuter density analysis, and data visualisation and enrichment, we discovered our client’s passengers were skewed towards business users and high affluence groups, and 13 out of the 60 POIs listed had the client’s taxi stand within reasonable reach. Data like this allowed our client to explore loyalty programmes and expansion opportunities to provide their services across the other 47 POIs, as well as optimising route planning to locate or relocate current transportation services.

How your business can become data-driven

Becoming a truly data-driven business involves a fundamental shift in how the organisation operates and makes decisions. It requires the integration of data and analytics into various aspects of the business, from strategy development to day-to-day operations. Here's a detailed roadmap for how a business can become data-driven:

1. Define Clear Objectives

Start by identifying the business goals you want to achieve through data-driven decision making. Make sure that the objectives you set match your business overall strategy.

Whether it's improving customer satisfaction, optimising supply chain operations, or increasing sales, having well-defined objectives will guide your data initiatives.

2. Cultivate Data Culture

Instil a culture where data is valued and utilised throughout the organisation. Encourage employees to seek data-driven solutions, and provide training to enhance data literacy. Ensure that decision-makers at all levels understand the benefits of data-driven approaches.

3. Data Collection and Integration

Establish robust data collection mechanisms. This includes identifying the relevant data sources, ensuring data quality, and integrating data from various systems across the organisation. At this stage, it's a good idea to start investing in data tools and technology.

4. Data Warehousing and Storage

Create a central repository (data warehouse) for storing and organising your data. This enables easy access to the data by different teams while ensuring data consistency and security.

5. Data Analytics Capability

Develop or hire a skilled data analytics team. This team should be proficient in data analysis, statistical methods, machine learning, and data visualisation. They will be responsible for extracting insights from the data to support decision-making.

6. Identify Key Performance Indicators (KPIs)

Determine the KPIs that align with your business objectives. These metrics will be used to measure progress and success. Make sure the chosen KPIs are relevant, measurable, and tied to specific business outcomes.

7. Implement Data-driven Decision Making Process

Encourage decision-makers to base their choices on data insights. This might involve regular data review meetings, where data is presented and discussed before making critical decisions.

8. Data Visualization

Use data visualisation tools to make complex data more accessible and understandable. Dashboards and reports can help stakeholders track KPIs and understand trends at a glance.

9. Continuous Improvement

Data-driven processes should be dynamic. Continuously monitor and analyse results, and use this feedback loop to refine strategies and adapt to changing conditions.

10. Leadership Support

Leadership buy-in is crucial. Ensure that top executives champion the data-driven approach and allocate resources for data initiatives.

11. Data Privacy and Security

As you collect and use data, prioritise data privacy and security. Comply with relevant regulations (e.g., GDPR, CCPA) and implement robust security measures to protect sensitive data.

12. Collaboration

Foster collaboration between different teams within the organisation. Data-driven decision making should be a cross-functional effort, involving departments like marketing, operations, finance, and IT.

13. Stay Updated

The field of data analytics is continuously evolving. Stay updated on the latest tools, techniques, and trends to ensure your data initiatives remain effective.

By following this roadmap, businesses can transition from traditional decision-making processes to a more data-driven approach, leading to improved efficiency, better customer experiences, and a competitive edge in the market.

Staying one step ahead of the competition

Data analytics is essential for businesses to make informed decisions. By analysing data on various aspects of their operations, businesses can identify opportunities and risks, optimise their operations and strategies, and stay ahead of the competition. It's important to understand the value of data analytics and to communicate its importance to your audience.

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