Presently, Big Data greatly greatly influences the way the business world operates. Its impact is not merely restricted to marketing but extends to the way the daily activities of businesses are conducted. This has made Big Data analytics in marketing a significant tool for companies across the globe that will help them stay ahead of the competition in their respective industries. Nonetheless, there are dynamic approaches to overcoming the big data challenges in marketing.
Big data is helping sales today by enhancing the quality of sales leads, boosting the performance of sales lead data, and increasing prospecting list accuracy, territory planning, win rates, and decision-maker engagement strategies.
However, merely because every business today needs Big Data solutions doesn’t imply that they can all successfully garner the benefits. The business realm has only started to gradually adopt Big Data trends and is dealing with multiple hurdles that stand in the way of successful implementation.
But how is big data influencing marketing and sales and how to use big data in marketing? There are probably some challenges. We must make use of cloud technology to curate, filter, process, and analyse the huge quantities of data we collect.
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The ever-increasing amount, pace, diversity, uncertainty, and complexity of data are referred to as “big data.” Big data is a major outcome of the new marketing environment that has emerged as a result of the virtual world we now live in for marketing organisations.
Statista defines “big data” as data sets that are either too vast or too complicated for standard data processing systems. The term is frequently used to refer to predictive analytics or other approaches to extracting value from data. Businesses rely on storage and processing capacity, as well as strong analytics capabilities and talents, to leverage big data.
The worldwide big data analytics industry is estimated to generate 68.09 billion dollars in annual revenue by 2025.
The advanced and predictive analytics software section contributes to the overall revenue by assisting in the prediction of business outcomes through data mining and predictive modelling of diverse historical data.
The term “big data” encompasses not only the data itself, but also the problems, capacities, and competencies involved with storing and analysing such massive data sets in order to enable decision-making that is more accurate and consistent.
Big Data for marketing for big corporations is revealing which content is most effective at each stage of the sales cycle, as well as how to improve investments in Customer Relationship Management (CRM) systems and strategies for increasing lead generation, prospect engagement, conversion rates, revenue, and customer lifetime value.
Big data gives insights on how to minimise Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and handle many other customer-driven metrics necessary for running a cloud-based business.
Big data in marketing has shown to be a vital tool for improving consumer loyalty and engagement, optimising performance, and making pricing decisions.
Big Data for digital marketing strategy for large companies involves the collection, analysis, and use of large volumes of digital data to enhance commercial operations.
Obtaining a comprehensive understanding of their target customers is the top-most factor in your marketing strategies. The notion of “know your customer” (KYC) offers access to information about client behaviour that was previously only available to major financial organisations. With cloud computing and big data, the benefits of KYC are now available to even small and medium-sized enterprises.
Customer engagement, or how customers perceive and interact with your brand, is a critical component of your business operations. Big data analytics gives you the insights you need to make better changes, such as upgrading existing products or boosting revenue per client.
Big data may also have a substantial influence on marketing by increasing brand awareness. “Data-driven merchants experience a bigger yearly gain in brand recognition by 2.7 times (20.1 percent vs. 7.4 percent) when compared to all others,” according to a Data-Driven Retail report.
Big data provides marketers with a 360-degree perspective of their customers, allowing them to provide customer-specific information when and where it is most successful in improving online and in-store brand image. Even if you don’t have a huge marketing budget, big data helps you to be the Band-Aid of your brand name.
Another prominent advantage that big data provides to marketing is improved client acquisition. “Intensive users of consumer analytics are 23 times more likely to significantly exceed their competition in terms of new client acquisition,” according to a McKinsey study. The cloud enables the collection and analysis of consistent and tailored data from a variety of sources, including the web, mobile apps, email, live chat, and even in-store transactions.
Big data can assist marketers in leveraging real-time data in cloud computing systems. No other technology can equal big data’s capacity to receive, process, and analyse real-time data rapidly and precisely enough to take prompt and effective action. When evaluating data from GPS, IoT sensors, website visits, or other real-time data, this is extremely important.
Big Data analytics in marketing is a crucial part of the big data ecosystem. Optimising marketing performance gives business insight that saves time and money.
Customer data, financial data, and operational data are the three types of big data that marketers care about. Each kind of data is usually gathered from several sources and kept in various locations.
Customer Data: Customer data assists marketers in gaining a better understanding of their target market. Behavioural, attitudinal, and transactional information from marketing campaigns, points of sale, websites, consumer surveys, social media, online communities, and loyalty programmes may be included in this big data category.
Operational Data: Operational data is associated with corporate processes. Objective metrics that quantify the quality of marketing processes such as marketing operations, resource allocation, asset management, budgetary controls, and so on are usually included in this big data category. This data may be analysed to enhance performance and cut expenses.
Financial Data: Financial data aids in the measurement of performance and the effective operation of your business. This big data category may contain sales, revenue, profits, and other objective data kinds that assess an organisation’s financial health. It is typically held in an organisation’s financial system.
One of the most beneficial features of big data is that it is already being used by marketers and large businesses. As a result, real-world examples of businesses using big data marketing solutions are many.
These real-world examples show us how successful businesses leverage big data in their marketing operations. Let’s look at a few of them.
Let’s start with Netflix, which has made several attempts to leverage big data to improve key aspects of its services. The most visible manifestation of this, at least to the general public, is in their data-driven recommendation system.
This has improved the company’s relationship with the audience while also saving money and influencing the types of content that hit the servers. When looking for examples of successful big data-driven marketing, look no further than Netflix.
Amazon, like Netflix, leverages big data to improve user satisfaction and personalisation. Amazon, on the other hand, uses a much more comprehensive strategy. They serve a significantly larger client base and provide a variety of services, each of which necessitates a particular set of procedures.
As it turns out, Amazon is reaping the benefits of its big data strategy, as it accounts for a significant amount of its revenues. Their machine learning synchronises with data to increase the usefulness of customer-facing features such as ratings and reviews.
Customers receive personalised direct mail coupons from Kroger thanks to big data marketing solutions. To execute this successfully, they’ll need data to figure out which users should receive certain coupons and when they should be sent.
Kruger’s coupon return rate is one of the most striking indicators that big data is successful. It has repetitively outperformed the industry average by more than 60%.
Making the best relationships with its clients is one of The Economist’s main focuses. This suggests that big data is high on their priority list since they require a better grasp of what their customers require.
The Economist added a customer data platform to their big data management, allowing them to locate the most efficient marketing offers to reach customers at important moments. This resulted in a significant increase in subscription rates.
Airbnb is one of the most prominent big data success stories, as the firm has organised so many of its operations around obtaining critical insights from data. The data science they employed after that greatly improved their marketing efficiency.
Airbnb used big data to figure out where the greatest and worst results were geographically and then used the information to draw meaningful conclusions and make beneficial improvements.
According to Statista, when data and analytics executives in Europe and the United States were asked about the major issues of leveraging data to produce business value at their firms, 41% said a lack of analytical skills among staff was the most significant challenge as of 2021. Data democratization and organizational silos were also issues when it came to data use.
Here are the top Big Data Challenges in marketing that businesses face while applying Big Data in their marketing strategy:-
[Pull Stat]- “According to the Big Data Executive Survey 2017, out of 85% companies that are implementing data-driven strategy, only 37% have been successful.”
In order to get productive insights from Big Data, the primary step is to understand its correlation with the buying journey of the customer. This becomes a challenge because the target audience typically switches between multiple channels before converting into paying customers. Moreover, it takes even longer to convert these paying customers into loyal clientele.
Therefore, from brand awareness to revenue generation, marketers need to have an in-depth understanding of the customer’s buying journey. They have to gather as much information as they can from both online and offline sources. For example, a retail store can employ data-based POS systems to collect data from different stores and combine it with the data collated from their social media profiles and website pages.
Data companies have access to an indefinite valuable stack of actionable insights. However, with the digital universe doubling in size every two years, it has become quite challenging for companies to make sense of all the data. Besides, accumulating data is not the hard part, its effective implementation is.
In a Statista survey of marketers with worldwide responsibility for media and programmatic in 2019/2020, 37 per cent of respondents said that one of the top three challenges marketers face when using data is a shortage of data science in marketing and data scientists to analyse data.
In the haste to achieve a principal position, companies gobble as much data as they can, which later results in data paralysis (data overload). To avoid this, they must consider the following steps:-
One of the basic things to do is cut down the source of data as much as possible. Look for minimal essential data sources that organisations can depend on to analyse the performance. Also, select a few data sources to analyse the crucial factors as well.
The next step is to filter out the irrelevant data that has nothing to do with business objectives. Marketers should be clear about what kind of data fits or doesn’t fit with the data analytics stream. This will ensure that no time is wasted on streaming irrelevant aspects.
Again, the bedrock of data collection should be keeping only what matters. Therefore, marketers need to identify and understand data patterns that clearly explain the company goals. Do Instagram likes are advantageous for the business? Do they drive in new leads or sales? Finding these correlations and sticking to them is very important to focus on the data pattern.
[Pull Stat]- “By the year 2020 the data created and copied yearly will hit the mark of 44 trillion gigabytes or 44 zettabytes.”
When implementing a Big Data marketing strategy, it is crucial to have a thorough segmentation process to outline and distribute leads into entitled groups. This allows the companies to have a clear perspective of the groups that can be converted into more profitable ones. This can be done in the following ways:-
Defining the goal of the segmentation is the primary step here. What will be the process of segmentation? What is its purpose? Whatever the end goal is, it should be well defined in advance to get the most effective insights into the key customer’s behaviour.
For instance, when segmenting visitors of the website, the most important parameters would be the time they spend on the website, pages they viewed for the longest duration, visitors that went through more than a single page, etc. Hence, parameters like these should be identified beforehand to ensure relevancy in the segmentation process.
The last step is to identify the process of breaking down the parameters to get the desired data insights. Typically low, medium and high are used for the granular segmentation but it varies from company to company.
[Pull Stat]- “As of 2017, around 53% of respondents said that their organisation’s lack of analytical know-how was a key challenge when employing big data technology.”
When implementing a Big Data marketing strategy, it is crucial to have a thorough segmentation process to outline and distribute leads into entitled groups. This allows the companies to have a clear perspective of the groups that can be converted into more profitable ones. This can be done in the following ways:-
Data privacy is one of the biggest challenges of implementing the data-driven approach in a company. Recently the Facebook-Cambridge Analytica Scandal involving data collection and management was in the much controversial limelight. Additionally, the General Data Protection Regulation (GDPR) has put various restrictions on how organisations can collect personal data from customers.
While the regulations are limited to the European Union, other countries are soon likely to follow the course. Besides, consumers have also become extremely cautious about how they share their personal information on the internet.
Therefore, if companies aim to get valuable data from their customers, they need to design reliable and trustworthy data collection strategies with the following key points in mind:-
In a 2020 survey conducted by Statista among data marketers mostly from North America, 34 per cent of respondents listed the fall of cookies/changes in third-party data availability as the issue that they expected to take up the majority of their focus and resources in 2020. The biggest concern, according to 62.3 per cent of respondents, was business recovery following COVID-19.
We’ve addressed the various challenges of big data analytics that your company may face, and you may have seen a common thread among them: a lack of defined procedures for collecting, managing, and analysing data.
You’ll be in the strongest position to draw valuable insights and make significant organisational changes if you have a solid data strategy that clearly describes who manages the data, where it comes from and where it goes, and how it flows through your systems. Let’s take a look at some big data best practices to keep in mind.
Here is a step-by-step guide to developing a successful big data challenge facing market strategy.
Auditing your present data management procedures is a smart step to begin. Examine all of the data-gathering tools in your software stack, such as your CRM, email marketing plan, and lead generation tool.
Some of these procedures and tools may have been established when your business was at a different stage, so they may not be a suitable fit for your current situation.
A strong big data strategy begins with data collecting or data generation. Ensure that any data entering your systems is correct and up to date.
For example, verify that your forms accept only genuine email contact information with the correct number of digits.
Also, double-check that no data is being submitted by bots (security technologies like reCAPTCHA can help with this) and that users are giving you complete consent to retain and use their data. Data protection and privacy rules must be strictly followed.
If you don’t have access to a person or team who specializes in data management, make sure your current teams who deal with it on a regular basis are aware of what to do next.
This might entail offering data management and analytics courses, hosting data management workshops, and offering intensive training on the tools you’re employing.
If you can’t afford to recruit additional people to manage data or can’t locate the right individuals, it’s critical to keep your entire staff up to date to limit the risk of human mistakes.
However, data analytics in marketing does not have to be too complicated. There are several tools and technologies available that allow anyone to effortlessly access, analyze, and make data-driven decisions.
After analyzing your present procedures, you should have a much clearer understanding of what works and what doesn’t in terms of data management for your company. Make a list of what needs to be changed and what is doing well.
In light of this, it’s time to develop a new data management approach. Your big data solution must not only meet your current demands, but also those of the future. Otherwise, when you scale, you’ll run into difficulties again.
The first step in this method is to clean up your databases. It is likely that you may need to search your databases and delete all obsolete, redundant, and invalid information.
Then, create the finest tech stack for storing and managing data, establish company-wide data input and maintenance standards, back up your data, and select integration solutions to ensure that your databases are connected and working well together.
Integrating your databases is one of the most critical things you can do to ensure you get the most out of big data. You’ll always wind up with data divisions and mismatched departments if you don’t integrate your data, no matter how fantastic your data plan is.
Furthermore, even the finest software stack in the world won’t be 100 per cent successful if it isn’t interconnected.
In addition, the most successful firms use real-time solutions that allow everyone to have an accurate, up-to-date, and 360-degree perspective of every part of the business.
Big data provides us with insights for our marketing campaigns. It provides a depth of understanding about our prospects and customers that has never been accessible before. We can react to audience activities in real-time and influence consumer behavior at the moment. Big data is revolutionizing marketing and sales in previously unimaginable ways.
From designing strategies based on trustworthy factors to precisely measuring the results, Big Data marketing strategy has changed the course of both, online and offline marketing to a great extent. Converting data into actionable insights is certainly a complicated process, however, there are many strategies that can help businesses turn their data into actionable strategies. Above mentioned are four important challenges of big data analytics in marketing faced by businesses and tips on how they can solve these challenges to head in the right direction.
Fullestop makes it simple to integrate and analyze data from almost any source, and pre-built interfaces to apps make it even easier. Our tools and techniques ensure that you’re working with the highest-quality data to provide the most reliable insights. There has never been a better opportunity for marketers to use big data. Contact us today to transform your customer experience.
