7 min read
Back in 2012, author Geoffrey Moore tweeted that ‘without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.’
At the time, many prominent marketers believed this was a bit of an overstatement. However, fast forward a decade and big data and advanced analytics are now undoubtedly essential in the world of marketing. After all, whether you’re trying to boost customer engagement, improve loyalty rates, optimise performance, or improve decision making, you need access to data.
To help you understand exactly how big data and advanced analytics can improve your marketing activities, in this post we’ll take a look at exactly what big data is, how it works, and the benefits it provides. We’ll also explain exactly how our software can help take your data analysis to the next level.
What is big data analytics?
First coined in the mid-1990s, the term ‘big data’ was initially used to refer to the increasing amount of data that the average business was generating. But, fast forward to the 2000s, and the term became much more nuanced. Since, it has been described using the ‘3Vs of big data’. These are:
- The volume of data that’s used and stored by a business
- The variety of data that a business generates
- The velocity with which this data is created and updated
The process of analysing all this data (known as big data analytics) is complex. It involves examining a company’s data to uncover hidden patterns, correlations, market trends, and customer preferences. As a result, in the world of marketing, it’s used to help organisations make informed business decisions that are based on information rather than hunches.
Essentially, as a marketer, big data and advanced analytics will give you insights into your marketing efforts. The process captures insights into your customers, allows you to respond to real-time audience actions, and helps you drive customer behaviour in the moment.
Chiefly, marketers are interested in analysing three types of big data: customer data, financial data, and operational data. Each of these data types is typically obtained from different sources and stored in different locations. However, each is highly useful in improving your marketing efforts.
- Customer data: This is used to help improve your understanding of your target audience. Although obvious pieces of data like names, email addresses, locations, and purchase histories are important here, so are other indicators of the attitude of your audience, such as social media activities and survey responses
- Financial data: This is used to measure your performance and help you operate more effectively. Information about your sales and marketing statistics, costs, and margins fall into this category
- Operational data: All of this data is used to analyse your business processes. It may relate to shipping and logistics costs, customer relationship management systems, or feedback from hardware sensors and other sources. Analysis of this data can lead to improved performance and reduced costs
No single technology encompasses every aspect of big data analysis. In reality, several technologies work together to help you get the most from your data. The technologies involved in big data analytics are varied, but three of the most commonly-used technologies for marketing purposes are:
- Machine learning: This is a specific subset of artificial intelligence that trains machines how to learn. It makes it possible to quickly and automatically produce models that can analyse bigger, more complex data, as well as deliver faster and more accurate results on a large scale
- Data mining technology: This can be used to help you examine large amounts of data and then discover patterns within the data. With further analysis, you can then answer complex business questions
- Predictive analysis: These tools use data, statistical algorithms, and machine-learning techniques to identify the likelihood of future outcomes based on historical data
With the help of these technologies, today businesses can leverage big data and customer analytics to identify insights for immediate decisions. As a result, businesses that successfully analyse the data they have available are able to work faster and stay agile. Plus, they also gain a competitive edge.
Why is big data analytics important?
In the wider business world, big data analysis has a number of uses. However, for marketers, the primary benefits of big data and advanced analytics are:
- You can quickly analyse large amounts of data from different sources, in many different formats and types
- You can rapidly make better-informed business decisions
- You can save money, which can also result in new process efficiencies and optimisations
- You’ll gain a better understanding of your customer needs, behaviours, and sentiments. This will lead to better marketing insights and will inform future product developments or upgrades
- You can generate improved and better-informed risk management strategies
In addition to this, due to the fact that the process comprises gathering, analysing, and utilising massive amounts of digital data, big data analysis can improve a number of business operations. For example, big data and customer analytics can help with:
1. Customer acquisition and retention
When properly leveraged, customer data can help improve your marketing efforts. This is because the insights gained from it help you act on trends and increase customer satisfaction levels. By properly leveraging the data you have available, you can improve customer experiences and boost customer loyalty.
2. Improving the customer journey
When you know your customers and their preferences, you can also understand their decision-making processes. Due to this, you can focus on ways to improve the customer journey. With the help of all of this information, you can make sure that you’re showing your customers the right products at the right times and on the right platforms.
3. Increased personalisation
By analysing data about past purchases, product interactions, and viewing histories, you can generate compelling and targeted advertising campaigns. This applies both on an individual level and on a wider scale.
4. Product development
Big data analytics can provide you with an accurate view of what your customers think about your product. It can also tell you more about the problems your customers face and how they think your product can resolve them. Due to this, big data and customer analytics can provide insights that can help with future product development and potential product improvements.
5. Brand positioning
When you have the data about the growth of your brand and its customer base at your disposal, you can ensure that your brand is adequately positioned in the market. Knowing exactly why your brand is so popular with certain customers can also help you develop new marketing strategies that may also attract the attention of other desirable customers.
6. Improved decision making
When effectively organised and analysed, your data will provide you with key insights into your customers, your products, and the effectiveness of your marketing activities. Due to the speed of the process, you can use big data to make decisions quickly and accurately.
Overall, organisations use big data and customer analytics software to make data-driven decisions that improve business-related outcomes. The exact outcomes will depend on the goals of your business.
How does big data analytics work?
There are four stages to the big data analytics process. These are:
1. Data collection
In this stage of the process, data (including structured, semi-structured, and unstructured data) is collected from a number of different sources, including:
- Website data
- Webshop data
- Social media data
- Mobile applications
- Text from customer emails and survey responses
- Cloud applications
- CRM systems
2. The data is prepared and processed
After this data is collected and stored in a data warehouse, it needs to be organised, configured, and partitioned correctly so that it can be analysed. The more effectively data is prepared and processed, the higher the performance of the analytical queries will be.
3. The data is cleansed
Before the data can properly be analysed, it first needs to be cleansed so that its quality is improved. For this to happen, the data is usually ‘scrubbed’ using scripting tools or a piece of data quality software. These tools are highly useful because they can find any inconsistencies (such as duplications or formatting errors). They can also organise and tidy up the data so it’s easier to analyse and interpret.
4. The data is analysed
Once the data has been collected, processed, and cleansed, it can be analysed using analytics software. This includes tools for:
- Data mining
- (Customer) segmentation
- Predictive analysis
- Text mining
- Data visualisation
How Apteco can help take your data analysis to the next level
As you can see, the process of analysing big data is highly rewarding. However, it can also be time consuming and laborious if the process isn’t conducted correctly. Thankfully, Apteco Orbit™ and Apteco FastStats™ can help you.
With the help of our software, you can analyse millions of customer data records in seconds. In doing so, you’ll unlock the insights required to run a high-performing campaign.
Our software allows you to explore your data in greater detail than ever before. It also helps you identify patterns in transactional data, perform customer profiling analysis, predictive modelling, and basket analysis.
You can also use the software’s analysis and visualisation tools to dig deeper into your data and extract more value. Plus, integration with Python and R gives advanced analytical users access to many more modelling techniques. On top of this, our powerful software can even generate interactive charts, Venn diagrams, word clouds and profiles from your data.
Interested in learning more about how our software can help take your data analysis to the next level? Book a demo for a time slot that suits you and your team now.