“The world is now awash in data, and we can see consumers in a lot clearer ways,” said Max Levchin, PayPal co-founder.

Simply gather data, however, doesn't bring any benefits, it's the decision-making and analytics skills that help to survive in the modern business landscape. It's not something new, but we need to know how to construct engaging customer service using the information we have at hand. Here's where Big Data analytics becomes a solution.

These days, the term Big Data is thrown around so much it seems like it is a “one-size-fits-all” solution. The reality is a bit different, but the fact remains the same — to provide well-oiled and effective customer service, adding a data analytics solution to the mix can be a decisive factor.

What is Big Data and how big is Big Data?

Big Data is extra large amounts of information that require specialized solutions to gather, process, analyze, and store it to use in business operations.

Machine learning algorithms help to increase efficiency and insightfulness of the data that is gathered (but more on that a bit later.)

Four Vs of Big Data describe the components:

Volume — the amount of data;

Velocity — the speed of processing data;

Variety — kinds of data you can collect and process;

Veracity — quality, and consistency of data.

[Source: IBM Blog]

How big is Big Data? According to the IDC forecast, the Global Datasphere will grow to 175 Zettabytes by 2025 (compared to 33 Zettabytes in 2018.) In case you're wondering what a zettabyte is, it equals a trillion gigabytes. IDC says that if you store the entire Global Datasphere on DVDs, then you'd be able to get a stack of DVDs that would get you to the Moon 23 times or circle the Earth 222 times.

Speaking regarding single Big Data projects, the amounts are much smaller. A software product or project passes the threshold of Big Data once they have over a terabyte of data.

Class Size Manage with Small < 10 Gb Excel, R Medium 10 GB - 1 TB Indexed files, monolithic databases Big > 1 TB Hadoop, cloud, distributed databases

Now let’s look at how Big Data fits into Customer Services.

Big Data Solutions for Customer Experience

Data is everything in the context of providing Customer Exerience (through CRMs and the likes), and it builds the foundation of the business operations, providing vital resources.

Every bit of information is a piece of a puzzle - the more pieces you have, the better understanding of the current market situation and target audience you have. As a result, you can make decisions which will bring you better results, and this is the underlying motivation behind transitioning to Big Data Operation.

Let’s look at what Big Data brings to the Customer Experience.

Big Data Customer Analytics — Deeper Understanding of the Customer

The most obvious contribution of Big Data to the business operation is much broader and more diverse understanding of the target audience and the ways the product or services can be presented to them most effectively.

The contribution is twofold:

First, you get a thorough segmentation of the target audience; Then you get sentiment analysis of how the product is perceived and interacted with by different segments.

Essentially, big data provides you with a variety of points of view on how the product is and can be perceived, which opens the door to many possibilities of presenting the product or service to the customer in the most effective manner according to the tendencies of the specific segment.

Here’s how it works. You start by gathering information from the relevant data sources, such as:

Your website;

Your mobile and web applications (if available);

Marketing campaigns;

Affiliate sources.

The data gets prepared for the mining process and, once processed, it can offer insights on how people use your product or service and highlight the issues. Based on this information, business owners and decision makers can decide how to target the product with more relevant messaging and address the areas for improvement.

The best example of putting customer analytics to use is Amazon. They are using it to manage the entire product inventory around the customer based on the initial data entered and then adapting the recommendations according to the expressed preferences.

Sentiment Analysis — Improved Customer Relationship



The purpose of sentiment analysis in customer service is simple — to give you an understanding of how the product is perceived by different users in the form of patterns. This understanding lays a foundation for the further adjustment of the presentation and subsequently more precise targeting of the marketing effort.

Businesses can apply sentiment analysis in a variety of ways. For example:

A study of interaction with the support team. This may involve semantic analysis of the responses or more manual filling-in of the questionnaire regarding an instance of the particular user.

An interpretation of the product use via performance statistics. This way, pattern recognition algorithms provide you with the hints at which parts of the product are working and which require some improvements.

For example, Twitter shows a lot of information regarding the ways various audience segments interact and discuss certain brands. Based on this information, the company can seriously adjust their targeting and strike right in the center.

All in all, sentiment analysis can help with predicting user intent and managing the targeting around it.

Read our article: Why Business Applies Sentiment Analysis

Unified User Models - Single Customer Relationship Across the Platforms - Cross-Platform Marketing

Another good thing about collecting a lot of data is that you can merge different sets from various platforms into the unified whole and get a more in-depth picture of how a given user interacts with your product via multiple platforms.

One of the ways to unify the user modeling is trough matching credentials. Every user gets the spot in the database and when the new information from the new platform comes in is added to the mix thus you are can adjust targeting accordingly.

This is especially important in the case of eCommerce and content-oriented ventures. The majority of modern CRM’s got this feature in their bags.

Superior Decision-Making

Knowing what are you doing and understanding when is the best time to take action are integral elements of the decision-making process. These things depend on the accurateness of the available information and its flexibility regarding the application.

In the context of customer relationship management (via platforms like Salesforce or Hubspot), the decision-making process is based on available information. The role of Big Data, in this case, is to augment the foundation and strengthen the process from multiple standpoints.

Here’s what big data brings to the table:

Diverse data from many sources (first-party & third-party) Real-time streaming statistics Ability to predict possible outcomes Ability to calculate the most fitting courses of actions

All this combined gives the company a significant strategic advantage over the competition and allows standing more firmly even in the shake market environment. It enhances the reliability, maintenance, and productivity of the business operation.

Performance Monitoring

With the market and the audience continually evolving, it is essential to keep an eye on what is going on and understand what it means for your business operation. When you have Big Data, the process becomes more natural and more efficient:

Modern CRM infrastructure can provide you with real-time analytics from multiple sources merged into one big picture.

Using this big picture, you can explore each element of the operation in detail, keeping the interconnectedness in mind.

Based on the available data, you can predict possible outcome scenarios. You can also calculate the best courses of action based on performance and accessible content.

As a direct result, your business profits from adjusted targeting on the go without experiencing excessive losses due to miscalculations. Not all experiments will lead to revenue (because there are people involved, who are unpredictable at times), but you can learn from your wins as well as from your mistakes.

Diverse Data Analytics

Varied and multi-layered data analytics are another significant contribution to decision-making.

Besides traditional descriptive analytics that shows you what you've got, businesses can pay closer attention to the patterns in the data and get:

Predictive Analytics, which calculates the probabilities of individual turns of events based on available data.

Prescriptive Analytics, which suggest which possible course of actions is the best according to available data and possible outcomes.

With these two elements in your mix, you get a powerful tool that gives multiple options and certainty in the decision-making process.

Cost-effectiveness is one of the most biting factors in configuring your customer service. It is a balancing act that is always a challenge to manage. Big Data solutions make the case of making the most out of the existing system and making every bit coming into in count.

There are several ways it happens. Let's look at the most potent:

Reducing operational costs — keeping an operation intact is hard. Process automation and diverse data analytics make it less of a headache and more of an opportunity. This is especially the case for Enterprise Resource Planning systems. Big data solutions allow processing more information more efficiently with less messing around and wasting opportunities. Reducing marketing costs — automated studies of customer behavior and performance monitoring make the entire marketing operation more efficient in its effort thus minimizing wasted resources.

These benefits don't mean that big data analytics will be cheap from the start. You need a proper architecture, cloud solutions, and many other resources. However, in the long-term, it will pay off.

Customer Data Analysis Challenges

While the benefits of implementing Big Data Solutions are apparent, there are also a couple of things you need to know before you start doing it.

Let’s look at them one by one.

Viable Use Cases

First and foremost, there is no point in implementing a solution without having a clue why you need it. The thing with Big Data solutions is that they are laser-focused at specific processes. The tools are developed explicitly for certain operations and require accurate adjustment to the system. These are not Swiss army knives — visualizing tool can’t perform a mining operation and vice versa.

To understand how to apply big data to your business, you need to:

Define the types of information you need (user data, performance data, sentiment data, etc.)

Define what you plan to do with this data (store for operational purposes, implement into marketing operation, adjust the product use)

Define tools you would need to do those processes? (Wrangling, mining, visualizing tools, machine learning algorithms, etc.)

Define how you will integrate the processed data into your business to make sure you're not just collecting information, but it is useful.

Without putting the work into the beginning stages, you risk ending up with a solution that would be costly and utterly useless for your business.

Scalability

Because big data is enormous, scalability is one of the primary challenges with this type of solutions. If the system runs too slow or unable to go under heavy pressure — you know it’s trouble.

However, this is one of the simpler challenges to solve due to one technology — cloud computing. With the system configured correctly and operating in the cloud, you don’t need to worry about scalability. It is handled by internal autoscaling features and thus uses as much computational capacity as required.

Data Sources

While big data is a technologically complex thing, the main issue is the data itself. The validity and credibility of the data sources are as important as the data coming from them.

It is one thing when you have your sources and know for sure from where the data is coming. The same thing can be said about well-mannered affiliate sources. However, when it comes to third-party data — you need to be cautious about the possibility of not getting what you need.

In practice, it means that you need to know and trust those who sell you information by checking background, the credibility of the source and its data before setting up the exchange.

Data Storage

Storing data is another biting issue related to Big Data Operation. The question is not as much “Where to store data?” as “How to store data?” and there are many things you need to sort out beforehand.

Data processing operation requires large quantities of data being stored and processed in a short amount of time. The storage itself can be rather costly, but there are several options to choose from and different types of data for each:

Google Cloud Storage — for backup purposes Google DataStore — for key-value search BigQuery — for big data analytics

This solution is not the only one available but this is what we use at the APP Solutions, and it works great.

Conclusion

In many ways, Big Data is a saving grace for customer services. The sheer quantity of available data brims with potentially game-changing insights and more efficient working processes.

Discuss with your marketing department what types of information they would like and think of the ways how to get that user data from your customers to make their journey more pleasurable and customized to their likes. And may big data analytics and processing help you along the way.