For any organization, aligning marketing and customer experience is a matter of critical importance. According to a report by Huffington Post, more than 34% of leading businesses said that customer journey was a top priority to them in 2020. Understanding how technology can benefit businesses before settling on the helpful ones is crucial for any organization. One of the most beneficial technologies in this era is data analytics. With this technology, you can map out various behavioural scenarios of your customers and potential clients and achieve a deeper understanding.

What is customer journey analytics?

Customer journey analytics entails processes that help organizations manage customer experience across all channels. It is software that allows tracking and measuring customer interactions across various channels. This software helps businesses to various activities in real-time and execute behaviour-driven strategies.

With customer journey analytics, companies can improve their customer experience and increase retention and lifetime value. This approach also allows them to grow their revenue. With a clear picture of the customer, you will be able to see how customers behave on your website or other platforms where you are available. It allows you to visualize how they navigate the platform and see the difficulties they encounter.

Customer journey analytics streamlines the customer experience

Customer journey analytics can come in handy in adding requested features and curating recommendations. This is crucial for the personalization of products or services. It is used by brands like Netflix and Spotify, leaders in their respective markets. According to Forrester Research, customers are 2.4 times more likely to stay with a company when the latter can resolve customer issues faster. Furthermore, they are 2.7 times more likely to spend more when there is clear communication and ten times more likely to recommend their products to others when all their questions can be answered.

Over the years, the bar for customer satisfaction has been raised. Therefore, one has always to do better. It is the only way of ensuring customers spend more to buy products or recommend them to others. Since customers provide crucial feedback that can be useful, analyzing customer journey data is the best way to find the right marketing messages and where messaging might be confusing. The analysis also allows identification of common issues or questions and employees to be trained on how to resolve the identified issues.

Rising customer expectations are leading to the growing importance of brands’ customer experience

With customer experience becoming one of the key points of competition, companies are doing all they can to ramp up their focus on this area. At the same time, marketers see the challenges brought about by the traditional approaches characterized by a lack of adequate data. Customer journey analytics paints a clear, complete, actionable and dynamic picture of the customer. It interprets millions of data points immediately and in real-time, allowing teams to find and root out the cause of different pain points.

Challenges of mapping customer journey

Although the concept of customer journey analytics is meant to improve customer experience and the entire mapping journey, the process of doing so comes with various challenges. The challenges mostly emerge when the right tools are not used. Some of the challenges in mapping customer journeys include data aggregation, timing, and complexity. Another bigger issue can come from missing the bigger picture concerning focusing on different touchpoints without looking at the entire journey.

The solution to these challenges is to ensure that journey analytics software brings everything together under one roof. Furthermore, time-stamped event data is important to know exactly when and in what sequence certain actions will occur.

Digital transformation is happening faster than anticipated a decade or two ago. This has made data an important asset for organizations of all sizes across all industries, in both public and private sectors. The factors that have led to the rising importance of data include widespread use of the internet, cloud computing adoption, and the availability of different devices and sensors that make up the internet of things (IoT) ecosystem.

With all these data sources, organizations have access to high velocity and high volume data that requires big data analytics to operate in the modern competitive landscape. While investing in big data is a critical component, more is required in terms of investing in the right equipment. Without the right data strategy, organizations will have trouble making sense of all the information. Furthermore, they risk falling behind their competitors, who are better equipped.

How businesses use data analytics

Data analytics is a term used by many organizations, professionals and business intelligence vendors to describe various functions. Various companies use it in different ways. For instance, human resource departments use data analytics to make better hiring decisions and measure the performance of their staff. Companies also use big data analytics to analyze spending patterns and prevent fraud. Online retailers use this technology to examine web traffic and marketing performance and to help in setting up targeted ad campaigns. On the other hand, the World Economic Forum uses big data analytics to predict the patterns used to fight climate change.

The competitive advantage of big data analytics

Gathering information and using it to improve services, processes, and products is a big win for businesses. Past surveys, sales reports, focus groups, and studies have identified the problems and informed business strategies. With the unprecedented amounts of data generated by businesses in every sector, the traditional approaches are no longer effective in addressing the existing problems and getting usable and accurate insights from data that leads to competitiveness. Here are some areas in which big data analytics helps deliver gains.

1. Cost reduction

Big data technologies like Hadoop or other cloud-based analytics solutions allow organizations to store massive amounts of data cost-effectively and efficiently compared to the in-house data centers. Maintaining a data center can be costly in terms of infrastructure needed and labour. Real-time data analytics allows businesses to identify and fix inefficiencies, include feedback, streamline processes and enable organizations to avoid waste, speed up work and enhance profits.

2. Improved decision making

The role of big data is to facilitate smart and faster decision-making. With big data analytics, businesses can now analyze data from different sources in near real-time and make strategic decisions based on data accuracy.

3. Enhanced products and services

Big data analytics and applications allow companies to develop better solutions and products. It allows them to identify customers, gauge satisfaction levels of their products and services and incorporate changes as demanded by the customers through collected insights. For brands, big data analytics enables them to avoid guesswork and deal with product development facts, making it easy to give customers what they want.

4. Boosting customer acquisition and retention

Customers are the most valuable asset to any business, regardless of its size. Building a solid customer base leads to success, and no organization can succeed without establishing a solid customer base. Big data analytics helps businesses understand customer behaviour. It allows them to observe various customer-related patterns and trends and build loyalty. Understanding customers will allow businesses to deliver what they want.  

While data is a crucial component, it does not provide a competitive advantage on its own. Rather, companies need to establish an effective system of collecting, storing and analyzing data from different sources and acting on valuable insights.

Data is the most important component of a business today. Although the concept of big data has been around for years, many organizations have not understood the importance of data. Most of them now understand that if they capture all data properly as it is streamed into their businesses, they can use analytics and get value. However, a good point to begin with is understanding what big data analytics is. Big data analytics is a process that involves the extraction of meaningful insights such as patterns, trends, correlations and customer preferences. It gives advantages and can be used to enhance decision-making in an organization. Modern organizations must embrace this new paradigm to compete effectively in the new business landscape. Here is why you need to get your analytics right.

Types of big data analytics

There are four types of analytics.

1. Descriptive analytics

It summarizes the past data into a form that people can easily read and understand. It helps companies create reports regarding revenue, sales and profits, among others. Furthermore, it can be used to tabulate social media metrics. Organizations can use this type of analytics to identify underutilized space and help save money. 

2. Diagnostic analytics

This analytics is done to understand what caused a given problem. Some examples include techniques like data mining, data recovery and drill-down. Companies use this type of analytics because it gives them in-depth insight into a given problem. For example, eCommerce companies can use it to find out why sales have plunged even as customers are adding products to their carts. 

3. Predictive analytics

It is analytics that uses historical and present data to predict the future; it uses data mining, AI and machine learning to analyze current data and make predictions. It predicts customer trends, market trends, and other useful trends for businesses. PayPal, for example, uses predictive analytics to determine the kind of precautions that they should take to protect their clients against fraudulent transactions. 

4. Prescriptive analytics

This analytics is used to prescribe the solution to certain problems. It works with predictive and descriptive analytics and substantially relies on AI and machine learning. A use case of this type of analytics is by an airline to maximize profit. It is used to build an algorithm that will automatically adjust the flight fares based on weather, demand, holiday, destination, and oil prices. 

Benefits and advantages of big data analytics

1. Risk management

Big data analytics is useful in managing risks. For instance, in the banking sector, it is used to identify fraudulent activities and discrepancies. Organizations use it to identify suspects of fraud and the root cause of problems that the banks experience. 

2. Innovations and product development

Big data analytics is the most useful technology when it comes to the enhancement of innovation. For instance, companies like Rolls-Royce use it to analyze the efficiency of jet engine designs for different airlines and armed forces across the world. It is also used to assess the need for improvement in those engines. 

3. Making quick and better decisions

Large companies use big data analytics to make strategic decisions to help the organization perform its work properly and compete efficiently. For example, companies like Starbucks use it to decide a location that would be suitable for a news outlet. With this technology, they can analyze different factors like population, demographics, accessibility of the new location and more and come up with a report of whether the location is suitable. 

4. Improvement of customer experience

Customer experience is important for any organization. As such, big data analytics is used to improve customer experiences by monitoring the activity of different individuals on platforms such as Twitter and Facebook regarding their products or services and addressing their concerns. By appropriately addressing these issues and giving out solutions, the company will build good customer relations.

With the closures of stores and bankruptcies caused by the pandemic, the future seems bleak for the retailers as it is for other industries. It raises the question of what steps retailers need to take to tackle the endless challenges lying ahead. This can only be answered by adopting technology. According to a study by Snowflake Computing back in 2018 and Harvard Business Review, companies that make data-driven decisions have the best chance to survive the challenges in the retail market and remain in the business longer. This makes big data a fortune-changer when it comes to the retail industry.

To understand the customers' changing needs and respond to their desires correctly, businesses must take advantage of big data and other related technologies like artificial intelligence and machine learning (AI and ML). This will help them differentiate their brands through increased personalization, customer service, and demand forecasting. With the business ecosystem that has turned out to be highly complicated, retailers will be taking advantage of opportunities offered by big data to understand the changes in the landscape and provide customers with an elevated experience.

Personalization is becoming one of the selling points for many businesses. It can only be achieved by using customer data, anticipating and shaping what the customer desires. Due to its importance in enhancing customer satisfaction, most retailers, such as those in the apparel and beauty space, are using it to help customers find their products faster. Sephora, one of the users of this technology, uses AI and big data analytics to scan the face of the customer and shade library to offer recommendations that reduce the time that can be used to search for products. 

Although the massive data that comes from daily operations can be challenging to manage, it can also be a blessing if used well by retailers. With AI-powered customer insights, customer experience can be transformed for the better. With the correct data, you can always overcome customer service challenges that often cause loss of customers and business. Your merchandising team can analyze data regarding product returns and identify the specific products or brands that are having an issue.  Armed with this knowledge, you can remove that specific product from the shelf or return it to the manufacturer to avoid issues with the customer. Data can also come in handy when you want to send personalized messages to the customer, leading to better customer retention.

According to Gartner, the volume of data will rise by over 800 percent in the coming years. This will include 80 percent of the unstructured data. As such, retailers need to have a concrete big data management solution that can help retrieve and process data from different sources. This solution can help retailers gather real-time insights for faster decision-making while generating actual value for the business.

Thanks to machine learning advancements, the internet of things, and artificial intelligence, predictive analytics help enhance accuracy and insightful data to the users. This will help businesses increase sales and make marketing teams more practical through access to precise data needed to make marketing decisions. All these are necessary for determining the lifetime value of a customer. When it comes to identifying issues and trends that may affect business operations, predictive analytics, which is one of the key elements of big data, is highly beneficial. It can help retailers identify issues with a brand and reasons why customers might leave it behind. This is critical information that can allow business owners to take action and improve the experience of the customer.

Ever since the invention of computers many developments have shaped human lives. The invention of the internet was a landmark achievement which set up the stage for more things that followed. Many would have thought that the internet was the biggest thing ever but it was only a lead-in to developments in the world of big data, AI and IoT.  Big data, AI and IoT have revolutionized the world we live in but what exactly are these terms?

AI, IoT, and big data are among the most talked about topics but still highly misunderstood. The tech jargons has been difficult to grasp for non-tech people but this article sheds a little light on the difference between the three terms, how they are related and how they differ.

The advent of social media and e-commerce led by Facebook and Amazon respectively shook the existing infrastructure. It also altered the general view of data. Businesses took advantage of this phenomenon by analyzing social media behavior through the available data and using it to sell products. Companies began collecting large volumes of data, systematically extracting information and analyzing it to discover customer trends. The word big data then became appropriate because the amount of data was orders of magnitude more than what had previously been saved. Basically, big data are extremely large sets of data which can be analyzed to reveal patterns, associations, and trends by using specialized programs. The main aim of doing so is to reveal people’s behavior and interactions, generally for commercial purposes.

Once the concept of big data had settled in and the cloud became a convenient and economical   solution for storage of huge volumes of data companies wanted to analyze it more quickly and extract value. They needed to have an automated approach for analyzing and sorting data and making decisions based on accurate information by businesses.

To achieve this, algorithms were developed to analyze data which can then be used to make more accurate predictions on which to base decisions.

Cloud’s ability to enable storage coupled with the development of AI algorithms that could predict patterns of data, meant that more data became a necessity and so was the need for systems to communicate with each other. Data became more useful as AI systems began to learn and make predictions.

The internet of things (IoT) is a collection of devices fitted with sensors that collect data and send it to storage facilities. That data is then leveraged to teach AI systems to make predictions These concepts are now making way into our homes as smart homes, smart cars, and smartwatches which are in common use..

In short, big data, AI and IoT are interrelated and feed off each other. They depend on each other for operations as AI uses the data generated by IoT. On the other hand, huge datasets would be meaningless without proper methods of collection and analysis. So yes, big data, IoT and AI are related.

The concept of big data has been around for a number of years. However, businesses now make use of big data analytics to uncover trends and gain insights for immediate actions. Big Data Analytics are complex processes involved in examining large and varied data set to uncover information such as unknown correlations, market trends, hidden patterns, and customer’s preferences in order to make informed business decisions.

It is a form of advanced analytics that involves applications with elements such as statistical algorithms powered by high-performance analytics systems.

Why Companies Use Big Data Analytics

From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, big data analytics which is driven by analytical software and systems offers benefits to many organizations.

• Analyze Structured Transaction data: Big data allows data scientists, statisticians, and other analytics professionals to analyze the growing volume of structured transaction data such as social media contents, text from customer email, survey responses, web server logs, mobile phone records and machine data captured by sensors connected to the internet of things. Examining these types of data help to uncover hidden patterns and give insight to make better business decisions.
• Boost Customer Acquisition and Retention: In every organization customers are the most important assets; no business can be successful without establishing a solid customer base. The use of big data analytics helps businesses discover customers’ related patterns and trends; this is important because customers’ behaviors can indicate loyalty. With big data analytics in place, a business has the ability to derive critical behavioral insights it needs to retain uts customer base. A typical example of a company that makes use of big data analytics in driving client retention is Coca-Cola which strengthened its data strategy in 2015 by building a digital-led loyalty program.
• Big Data Analytics offers Marketing Insights: In addition, big data analytics helps to change how business operates by matching customer expectation, ensuring that marketing campaigns are powerful, and changing the company's product line. It also provides insight to help organizations create a more targeted and personalized campaign which implies that businesses can save money and enhance efficiency. A typical example of a brand making use of big data analytics for marketing insight is Netflix. With over 100 million subscribers; the company collects data which is the key to achieving the industry status Netflix boasts.
• Ensures Efficient Risk Management: Any business that wants to survive in the present business environment and remain profitable must be able to foresee potential risks and mitigate them before they become critical. Big data analytics helps organizations develop risk management solutions that allow businesses to quantify and model risks they face daily. It also provides the ability to help a business achieve smarter risk mitigation strategies and make better decisions.
• Get a better understanding of their competitors: For every business knowing your competitors is vital to succeeding and growing. Big data algorithms help organizations get a better understanding of their competitors, know recent price changes, make new product changes, and discover the right time to adjust their product prices.

Finally, enterprises are understanding the benefits of making use of big data analytics in simplifying processes. From new revenue opportunities, effective marketing, better customer services, improved operational experience, and competitive advantages over rivals, the implementation of big data analytics can help businesses gain competitive advantages while driving customer retention.

Big data is fast gaining momentum, and so are the big data projects. Companies are increasing in size and ambition. However, the rising number of big data projects does not mean that they all succeed. Gartner estimates that the number of big data projects that fail is about 60 percent in 2016. In 2017, Gartner revised the number of big data projects that fail to be about 85 percent, which was even higher than the rate stated before. Nothing has changed since then. Even in 2021, the rate of failure still ranges at around 80 percent. Here are some of the reasons why big data projects fail.

1. Poor integration

Siloed data is a leading technological problem that causes big data failures. Since data is stored in multiple sources, integrating it into one and using it to get insights that a company needs is a big challenge. This is even bigger problem if legacy systems are involved. It costs a lot of money and often does not result in the desired outcome. According to Alan Morrison of PwC, siloes create data lakes that are just data swamps. Organizations can only access a small percentage of data with little relationships that are inadequate to find patterns and get enough knowledge. Without a graph layer that interprets all instances of data mapped underneath, you have a data lake that is a data swamp.

2. Not defining goals

Like any other project, big data projects require a proper definition of goals and objectives. Sadly, most people who undertake big data projects do not set goals that they need to achieve. Most of them think they can simply connect the structured and unstructured data and get the insight they need. As a project manager, you need to define the problem and develop the goals you want to attain. Having a clear definition of the problem and defining it in time helps achieve the desired goals accurately. However, many big data project leaders lack vision. This ends up confusing the company on big data projects and its desired objectives.

3. Shortage of skills

There has been a widespread shortage of talent in the data science industry over the past few years.  A 2018 report by LinkedIn reported a shortage of more than 150,000 individuals with data science skills. These are people such as data engineers, mathematicians, data analysts, and others. Since the field is in its initial stages, it is often hard to get people with the required skills. This slows production and ends up stalling the well-intentioned big data initiatives. Additionally, many enterprises cannot run several projects simultaneously without the right skills because they lack enough personnel.

4. Lack of transparency

Lack of transparency in big data projects can result in a disconnect between technical and business teams. For instance, while the data science teams usually focus on the accuracy of models that is often simple to measure, business teams, on the other hand, are concerned mainly with metrics like business insights, profits/financial benefits, and interoperability of the final model produced. The lack of clarity and proper alignment between the teams leads to the failure of big data projects as the different teams try to measure different metrics. This is made worse by the traditional data science initiatives that use blackbox models that lack accountability and are hard to interpret, making it difficult to scale.

The above reasons for the failure of big data projects indicate the need for proper plans when implementing big data projects. The problems can be sorted by planning ahead, working together, and setting realistic goals.

According to reports, the global big data as a service (BDaaS) industry is expected to grow significantly in the coming years. The sector was valued at $4.99 billion in 2018 but will likely reach more than$61 billion by 2026. This growth is attributed to the fast adoption of big data as a service in different industries. Other factors that are expected to drive the BDaaS industry are the rising demand for actionable insights and the increasing organizational data across businesses due to the digitization and automation of most business processes. Here are trends that you should expect in the BDaaS industry:

• The increased adoption of BDaaS by social media platforms will lead to growth

The increase in digitization and automation of business processes is the leading factor in the adoption of BDaaS and its subsequent market growth. With the ongoing deployment of the 5G infrastructure, this demand will become rapid as social media platforms such as Snapchat, Instagram, Twitter, Facebook, and YouTube, among others, embrace data as the main approach to reaching customers for growth. Consequently, social media platforms will play a crucial role in the rising global BDaaS market.

• Big companies will hold the largest share

Large multinationals continue to lead in the adoption of BDaaS solutions. With competition heating up, they are likely to continue investing in these solutions as they seek to access customer data and gather the right insights for improved decision-making. They help collect data scattered in various locations or departments to gain valuable insights through big data analysis. Large corporations are spending large amounts of money on training their employees and leveraging the benefits of BDaaS solutions as they seek to edge their competitors and know exactly what their customers want.

• Hadoop will continue in its leadership in this area

In the last year, Hadoop was a significant player in big data as a service. The Hadoop-as-a-service segment held about 31.6%, with the rest sharing the remaining 68.4%. Moving forward, this Hadoop segment is expected to grow exponentially, gaining more CAGR in the future as the craze for BDaaS continues rising. The growth will result from the continued adoption of Hadoop-as-a-service solutions among the small and medium-sized companies (SMEs) worldwide who seek to take advantage of this technology in their service provision. 

• North America will continue dominating BDaaS investments

In 2020, North America was leading in BDaaS investments with $ 6.33 billion. This region is expected to continue holding the leadership spot between now and 2026 in terms of adopting big data as a service and the revenue coming from this industry. This is due to the number of significant players that will invest in it and others such as Intel Corporation that will go on manufacturing chips that will help in the expansion of the existing storage. However, the Asia Pacific region will register a significant increase as countries such as India, China, Japan, and South Korea raise their investments.

• Large companies will embrace joint ventures to strengthen their positions in the market

Large companies that have a global presence are looking for better alternatives to stay ahead in the competition. One of the strategies includes mergers, acquisitions, partnerships, and joint ventures. In most cases, smaller companies are acquired by bigger ones, while others may strike partnership deals to compete favorably in the market. IBM is one of the companies with large big data as a service market share and has been launching solutions and building partnerships that help companies gather data of customers for use in marketing and decision-making activities.

While the coronavirus pandemic has left the world bleeding, it has also highlighted weaknesses in the global healthcare systems that were hidden before. It is evident from the response to the pandemic that there was no plan in place on how to treat an unknown infectious disease like Covid_19. Despite the challenges that the world is facing, there is hope in big data and big data analytics. Big data has changed how data management and analysis is carried out in healthcare. Healthcare data analytics is capable of reducing the costs of treatment and can also help in the prediction of epidemics’ outbreak, prevent diseases, and enhance the quality of life.

Just like businesses, healthcare facilities collect massive amounts of data from patients during their hospital visits. As such, health professionals are looking for ways in which data collected can be analyzed and used to make informed decisions about specific aspects. According to the International Data Corporation report, big data is expected to grow faster in healthcare compared to other industries such as manufacturing, media, and financial services. The report estimates that healthcare data will experience a compound annual growth of 36% by 2025.

Here are some ways in that big data will make a difference in hospitals.

1. Healthcare tracking

Along with the internet of things, big data and analytics are changing how hospitals and healthcare providers can track different user statistics and vitals. Apart from using data from wearables, that can detect the vitals of the patients, such as sleep patterns, heart rate, and exercise, there are new applications that monitor and collect data on blood pressure, glucose, and pulse, among others. The collection of such data will allow hospitals to keep people out of wards as they can manage their ailments by checking their vitals remotely.

2. Reduce the cost of healthcare

Big data has come just at the right time when the cost of healthcare appears to be out of reach of many people. It is promising to save costs for hospitals and patients who fund most of these operations. With predictive analytics, hospitals can predict admission rates and help staff in ward allocation. This reduces the cost of investment incurred by healthcare facilities and enables maximum utilization of the investment. With wearables and health trackers, patients will be saved from unnecessary hospital visits, and admissions, since doctors can easily track their progress from their homes and data collected, can be used to make decisions and prescriptions.

3. Preventing human errors

It is in records that medical professionals often prescribe the wrong medication to patients by mistake. These errors have, in some instances, led to deaths that would have been prevented if there were proper data. These errors can be reduced or prevented by big data, that can be leveraged in the analysis of patient data and prescription of medication. Big data can be used to corroborate and flag a specific medication that has adverse side effects or flag prescription mistake and save a life.

4. Assisting in high-risk patients

Digitization of hospital records creates comprehensive data that can be accessed to understand the patterns of a particular group of patients. These patterns can help in the identification of patients that visit a hospital repeatedly and understand their health issues. This will help doctors identify methods of helping such patients accurately and gain insight for corrective measures, that will reduce their regular visits.

Big data offers obvious advantages to global healthcare. Although many hospitals have not fully capitalized on the advantages brought about by this technology, the truth is that using it will increase efficiency in the provision of healthcare services.

Big data integration is a critical step in any Big Data project. However, some challenges and issues must be taken into account while integrating data. With the growing number of data consumers, big data integration can become a problem that any company needs to respond to. Although it may sound easy, big data integration is not simple as it sounds because large data sets that are structured, unstructured and semi-structured are involved. All these diverse data sets are to be stored in a data warehouse for later retrieval. Some of the challenges encountered during data integration include uncertainty in the management of data, synchronization across data sources, availability of skills and getting the right insights. Despite these challenges, managing integrated big data makes decision-making accurate and ensures the decisions arrived at are insightful.

Big data integration tools

As big data continue being appreciated across different industries, the tools for integrating big data should continue being reevaluated to identify their abilities to process ever-increasing unstructured data. Data integration technologies should have a common platform that supports data quality and profiling.

Big data integration challenges

1. Finding the personnel

With the rising adoption of big data, data scientists and analysts continue to be in high demand. There is a lack of individuals to fill the vacant positions in the big data research industry. While a typical big data expert must have experience with various big data integration tools and an understanding of data organization, coming across such people is never easy.

2. Extracting data

The process of bringing in data that come from different sources is a massive challenge that needs to be addressed appropriately. With the many sources and diversity of data, the skills required to navigate the process of extraction are needed to analyze and process it to help in decision-making.

3. Synchronizing data from different sources

After data from different sources has been extracted, it must be synchronized. This data uses different schedules and rates and can be desynchronized from the source. Synchronization provides consistency in systems while continually updating. With the traditional data management systems, extracting data migrating and transforming it promotes desynchronization. Therefore, synchronizing it will minimize variations in data.

4. Choosing the right strategy

Big data integration mostly starts with the need for information to be shared. This can be followed by the interest in breaking down the existing data silos to allow data to be analyzed. The biggest challenge for many businesses is that they often jump from one project to another without laying down an organizational plan. Therefore, a true data integration plan must be developed complete with security and compliance to meet the goals that can sometimes be difficult to achieve.

5. Security issues

Data is a new goldmine, and hackers know this quite well. Therefore, companies and data users must always ensure that big data integration is secure. Sadly, most organizations do not understand the sensitivity of data and the security challenges. Securing data can also face problems because the data sources are diverse, and data breaches can occur. Therefore, integrating data and storing them safely needs to be a key priority.

6. Demand for skilled analysts

With the rising adoption of big data and analytics across industries, there has been a rising demand for top big data and analytics professionals across the globe. The scarcity of analysts and data engineers who are the key drivers of big data projects have made big data integration difficult. Therefore, companies that intend to deploy data integration must be aware of these key challenges and try as much as they can to address them for success in their projects.

Big data analytics is the leading technology that most modern organizations are venturing into. Without this technology, most companies are blind and deaf and cannot take advantage of the massive amounts of data available in the connected world. Using big data in marketing helps uncover valuable information about the customers, allowing you to connect with them at a personal level. Unlike a few years ago, current businesses do not need to rely on shoddy market research companies to gain insights. Rather, they dig into their own datasets, get past superficial metrics such as location, age and gender of the customers, and uncover valuable information about different demographics. Under the traditional datasets, this was difficult and unattainable until recently.

Regardless of what you are trying to do in your organization, big data in marketing has proven to be one of the most important tools that any organization can rely on. It is useful in improving customer loyalty, enhancing the performance of an organization and making pricing decisions, all of which are important in marketing. These are aspects of big data, which include not only the analytics but also data ingestion, storage and integration and others, all of which are necessary to improve marketing. With big data and its related technologies, data can be filtered, curated, processed, and analyzed in vast amounts gathered during transactions.

Finding new leads

One of the key benefits of big data analytics is that it can help an organization gain insights regarding how users feel about a particular product or service that you offer. With this data, you can easily identify services or products frequently purchased. With this data at hand, you can link it to sites such as social media platforms where you can identify challenges that customers face in that particular product or service, tap into the new markets using these insights and gain access to an even bigger audience. This offers your business new leads that increase social selling.

Generating new leads

Data gathered from social networks can also be used in recommender systems. For example, Amazon has a recommender system that creates a customized homepage for each client based on their profile and history of interaction. This is good in generating repeat sales, therefore, increasing overall sales. Such is possible if the record of sales of every user is kept in a database. Since various tools in the market are available for such a job, reviewing sales reports has become easy. Marketers do not need to overburden themselves by using spreadsheets that need regular updates. 

Improved customer acquisition

Enhanced customer acquisition is another key benefit of big data to marketing. According to a McKinsey survey, intensive users of customer analytics were found to be 23 times more likely to outperform their competitors in customer acquisition. Therefore, the cloud allows organizations to collect and analyze personalized data from different sources like web, mobile application data, emails, live chats and in-store interactions.

Although using big data in marketing efforts has many advantages, challenges need to be surmounted to achieve efficiency. Some of the problems that marketers encounter include disparate data systems, which cause a disconnect, making customer personalization ineffective, lack of cross-department collaboration, and poor quality of streaming data sources.

As a marketer, the first step in big data marketing is to integrate data from different sources. Once you use big data analytics, you will understand your customers much better. This will ease your ability to connect with them with relevance and help them turn interactions into conversations with ease. Therefore, big data will play a critical role if you want to grow your small business into a larger one.

For any business, minimizing time to insight is a significant cost-saving driver. This is the reason for the increased adoption and investments in big data. As competition in different industries hots up, organizations increasingly feel the need to learn quickly and act faster. Survey indicates that 83.5% of survey respondents view factors related to speed, insight, and business agility as the main reasons for increased investment in big data. According to research, customers would be reluctant to shop with a given retailer if they receive poor customer service and a bad experience. This is revenue lost, a potential increase in shipping costs, and a risk of product returns that may never be resolved. Here are some ways you can prepare to boost your returns with big data in your e-commerce business.

1. Use e-commerce returns data to improve your business

Unlike brick-and-mortar stores, e-commerce retailers have no luxury of physical interaction with customers. This has a significant effect on product returns for e-commerce retailers, which are always higher. To mitigate this challenge and reduce the costs caused by product returns, Shopify returns management software, which automates and eases the return process, is now a proper solution for many online retailers. Although there are various solutions that help save time, most of them do not give retailers the necessary insights needed to improve their products or websites.

With big data, retailers can identify the challenges in their websites, such as misleading product photos, inaccurate product descriptions, inconsistencies in sizing conventions, and how often they drive product returns. Get feedback from shoppers concerning the problems they encounter and the leading causes of returns. This information can help improve products. Use the data to align the shoppers’ expectations and reality. Identify why certain products have high returns and spot issues by looking at the frequently used words.

2. Use the returns data to learn about your shoppers

One of the biggest challenges that retailers face in e-commerce is knowing their shoppers' interests, purchase patterns, and challenges. Although returns may have implications on the cost of running a business, it is not entirely a bad thing. It shows that a customer was close to getting what they needed. You can learn a lot about the needs of customers and their relationship with your brand without ever meeting them. The comments from customers are valuable in improving customers’ experience. Comments about returns give unique positive and negative feedback that can offer insight into the shortcomings of a product and shine a light on issues that may otherwise go unnoticed. Big data helps retailers identify acquisition and retention, which are critical for the survival of the e-commerce retail business. With big data, you learn when the customers are likely to repurchase, how customers have changed over time, and revenue coming from return customers, among other critical things necessary to run a business.

3. Use e-commerce returns data to evaluate and improve operations

The operational processes in retail have a substantial impact on customer experience. Proper operational processes enhance efficiency and allow products you order to arrive in time. For instance, if a customer orders a swimsuit for a pool party event, it should arrive in time for use in that event. If the product arrives late and the client does not receive a refund for several weeks, it would have a negative experience for the customer who will never shop on that website again. With such problematic processes, you will never compete in today’s business landscape. Blend sales and returns data to help improve your processes both in the forward supply chain and reverse supply chain. This is the only way you can compete in the modern retail landscape.

The holiday season is here again, and you do not need a crystal ball to know that the year’s biggest shopping season is here with us. According to a survey, November and December account for about half of the annual sales for many retailers. Although you might have been successful in the past holiday seasons, every holiday season is unique and often comes with its share of unique challenges. We bring fresh insights for better holiday shopping results to help you deal with this year’s challenges. With these insights, retailers are informed and inspired, and the shoppers are empowered. 

1. Online sales will continue rising

The COVID-19 pandemic has increased online sales on a scale never witnessed before. This was evident in 2020, which broke records. This year, you should expect online sales to continue growing for about 10% in the US and around 7% in the rest of the world. As we inch closer to November and December, known for increased shopping activity, online shoppers in the US will spend over $259 billion and about $1.2 trillion globally. This means that you need to have an excellent online presence as a retailer.

2. Gift cards will continue being a leading choice for consumers

Gift cards still rule the shopping world, and you are likely to get a few in the coming season. Therefore, as the new season draws closer, prepare gift cards for your customers because it is still one of the favorite discount options. Whether you are running a department store or a grocery store, gift cards might work well for your customers.

3. Rising costs will affect the shopping experience

There has been an increase in the cost of doing business across the world. This has led to the rise in labor costs and the costs associated with freight and manufacturing. As the holiday season gets closer, consumers will pay 20% more for their shopping as retailers and manufacturers raise the cost of products. This will likely affect shopping this holiday season as most people scale down their shopping.

4. There will be more swapping and shopping post-holiday

After the holidays, some people would expect a reduction in shopping activity. However, this is not always the case. Expect more shopping and swapping to go on even after the holidays as consumers plan to cash in on after-holiday bargains where the cost of products has dropped. Most consumers also expect to return gifts after the holidays, meaning shopping will continue some days after the holidays have ended.

5. Social media is still king

Social media continues to rule the marketing world, and this will not be different in the coming holiday season. As holidays get nearer, many retail companies will use hashtags to drive their sales messaging about their products. As retailers take advantage of social media platforms for marketing, you will see different hashtags. The only thing that retailers need to do is be consistent and look at the trends and what customers love. Companies will embrace concepts such as Facebook Live video to market their products.

Generally, with the pandemic still in place, there are still many challenges that make this year’s holiday experience different than ever before. Although in-store shopping will continue, online shopping is gaining momentum, and many retailers will continue embracing it. Millennial shoppers are most likely to increase their shopping and spend more money during the holiday season. On the contrary, the older shoppers tend to stick with their budget, although they will spend more compared to last year where the pandemic was at its peak. Regardless of the differences between this year’s shopping habits compared to those of the other years, you must have a concrete strategy to appeal to your customers.