Knowlarity to Google BigQuery – Made Easy

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Posted On: 21 Aug 2020

If you’ve come here, you are probably looking for a way to transfer data from Knowlarity to Google BigQuery quickly. In this article, we talk about why Knowlarity is essential and how you can get access to all of your Knowlarity data in a data warehouse without having to write any code.

The typical buying journey of a customer is no longer linear. They will switch between websites, compare similar products, search Google for promo codes, drift to trusted online sources for reviews, before returning to your website and finally making a purchase; perhaps using a completely different device. Thus, eCommerce vendors have to decide on what channels they want to sell and how much to spend on these channels. Understanding customer demand and problems play a critical role in the success or any business. Customer service is one of the best ways to gauge the pulse of the customer as you get feedback directly from the people buying your product or service.

An excellent Customer Service:

  • Increases the number of loyal customers who visit repeatedly
  • The loyal customer base is more open to viewing more products and trying out new ones, increasing how often they purchase from you.
  • Increases the amount of money each returning customer spends with your business.
  • Loyal customers generate positive word-of-mouth about your business and provide testimonials and reviews, which help organically increase new visitors.
  • Ensures a minimum recurring revenue, decreased marketing budgets for customer retention and increased budgets for new customer acquisition resulting in sustainable growth for the business.

Today, customer service is not limited to the traditional telephone support agent. Customers have a variety of media to interact with businesses like WhatsApp and other IM services, Social media platforms, Emails, Chat systems on your website along with phone and SMS. Many companies also offer self-service support, so customers can, to an extent, find their answers at any time of day or night.

The telephone still remains one of the most effective mediums for customer support. Personalized human touch usually results in faster resolution of problems and lesser repeat tickets, leading to more satisfied customers. 

Companies with the best customer support system

  • Track every move of their customers
  • Have sophisticated chatbots or IVR systems installed to keep customers engaged before, and the actual operator can attend the issue. Studies have shown that the most frustrating thing people have claimed when it comes to customer service long waiting times, and most people lose their patience and this ultimately results in the issue remaining unresolved and customers losing faith in the brand.
  • Listen and reply to complaints on social media and emails; this creates an image of a brand which cares about its customers.
  • Ask for regular feedback, reviews, and suggestions from the customers to gain insights into their experience with your brand even if they are not complaining or reporting things.
  • Provide support based on the activity of the customer like browsing habits, exciting products, response to marketing activities and provide tailor-made guidance to them to influence them in buying a product or a service.

Ensuring optimal customer service requires constant monitoring of the customer service team, customer queries and feedback. Hence, it involves manually generating reports from multiple data silos and analyzing them, which is where most brands falter. This compiling is a daunting task in itself, as it takes time to prepare all these reports which are then analyzed. This time-lag is one of the biggest challenges that companies face.

In a competitive digital landscape that we live in, it has become imperative that eCommerce businesses of all sizes that aspire to grow and stay profitable have to look into their data deeply and leverage this for growth.

With the increase in competition, eCommerce Companies should strive to be more data-driven for various reasons. Some of these reasons include

  • understanding the balance between demand and supply,
  • understanding customer lifetime value (LTV)
  • Segmenting customer base for effective marketing
  • finding opportunities to reduce wasteful spend
  • optimizing digital assets to maximize revenue for the same marketing spend,
  • improving ROIs on Ad campaigns and
  • offering an engaging and seamless experience for customers in every channel that the customer engages with the brand.

Data Savvy eCommerce businesses try to reduce the effort of reporting and analysis by integrating data from all these channels into a cloud data warehouse like Google BigQuery. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.

In this post, we will be looking at methods to replicate data from Knowlarity to Google BigQuery.

Before we start explaining the process involved in data transfer, let us know more about the individual platforms separately.

Knowlarity Overview

Knowlarity is the most popular cloud telephony solutions provider in India. It powers streamlined business communication on the cloud. Knowlarity will give access to a single platform in real-time for the solutions and deliver personalized customer experience on a virtual calling platform. It will empower the mobile workforce by automating business communication for quick and smooth customer experience. The unique features loved by the customers are:

  • One of the most cost-effective solutions available.
  • Can be accessed from anywhere in the world.
  • No maintenance cost is required.
  • It has a user-friendly UI.
  • Knowlarity provides the advantage of using CRM integrations.

Google BigQuery Overview

Google BigQuery is the first genuinely serverless data warehouse-as-a-service offering in the market. There is no infrastructure to manage, no patches to apply, or any upgrades to be made. The role of a database administrator in a Google BigQuery environment is to architect the schema and optimize the partitions for performance and cost. This cloud service automatically scales to fulfil the demands of any query without the need for intervention by a database administrator. Google BigQuery service also introduced an unusual pricing model that is based not on the storage capacity or the compute capacity needed to process your queries. Instead, the pricing relies on the amount of data processed by incoming queries.

The best part about Google BigQuery is that you can load data to the service and start using the data immediately. Users no longer have to worry about what runs under the hood because the implementation details are hidden from them. All you need is a mechanism to load data into Google BigQuery and the ability to write SQL queries. By making data warehousing so simple, Google BigQuery has revolutionized the cloud data warehousing space and has put the power back in the hands of the analysts.

It is good practice to understand the architecture of Google BigQuery. Understanding the architecture helps in controlling costs, optimizing query performance, and optimizing storage. The factors that govern Google BigQuery Pricing are Storage and Query Data Processed. You can read about it in more detail here.

Why Do Businesses Need to Replicate Knowlarity to Google BigQuery?

Let’s take a simple example to illustrate why data consolidation from Knowlarity to Google BigQuery can be helpful for an eCommerce business.

An e-commerce company is selling in multiple countries, across different platforms and marketplaces, for example, its website, Amazon and eBay. The company needs to have the following information in real-time.

  • How many customers were upset?
  • How many customers left with a positive impression after the assistance?
  • How many of these customers churned?
  • Are high priority customer service tickets being addressed on time?
  • Is a high-value customer treated the same as a medium-low value customer?
  • Is the team efficient with resolving issues?
  • Are issues being addressed on time?
  • Has the CLTV increased with improved customer service if not, then why?

Now, the different sources of customer query maybe through Knowlarity, responses from emails, Reviews and ratings on Amazon & eBay, SMS, phone calls, social media sites. So different data silos are being created per feedback source, per country. Compiling all of this data together is necessary to get a clear picture of the business, but it is a daunting task in itself, and it takes time to prepare reports which are then analyzed. This time lag that occurs is one of the biggest challenges that companies face since it delays the decision-making process.

Because of the lack of timely data, companies fail to address critical points like :

  • Identifying if the customer service team is trained correctly or if further training is required
  • Whether the Customer Service team is understaffed and needs reinforcing.
  • What should be the ideal frequency of monitoring the team and creating reports?
  • What type of customers is raising more tickets?
  • What are the most common issues in customer feedback analysis? Is it a technical problem or a vendor fault?
  • Is it necessary to handle customers with specific problems, or High-value customers, uniquely or differently?

Thus companies that use a cloud telephony platform like Knowlarity, typically feed all of the data coming from it and all other apps and tools to a data warehouse like Google BigQuery for easier and faster analytics.

Replicate data from Knowlarity to Google BigQuery

There are two board ways to pull data from any source to any destination. The decision is always a build vs buy decision. Let us look at both these options to see which option provides the business with a scalable, reliable, and cost-effective solution for reporting and analysis of Knowlarity data. You can also retrieve the data from Google BigQuery any time you want. To know more, click here.

Use a cloud data pipeline

Building support for APIs is not only tedious but it is also extremely time-consuming, difficult, and expensive. Engaging analysts or developers in writing support for these APIs takes away their time from more revenue-generating endeavours. Leveraging a cloud data pipeline like Daton significantly simplifies and accelerates the time it takes to build automated reporting. Daton supports automated extraction and loading of Knowlarity data into cloud data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Oracle Autonomous DB.

Configuring data replication on Daton on only takes a minute and a few clicks. Analysts do not have to write any code or manage any infrastructure but yet can still get access to their Knowlarity data in a few hours. Any new data is generated is automatically replicated to the data warehouse without any manual intervention.

Daton supports replication from Knowlarity to a cloud data warehouse of your choice, including Google BigQuery. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from Knowlarity data into Google BigQuery. Daton takes care of

  • authentication
  • rate limits,
  • Sampling,
  • historical data load,
  • incremental data load,
  • table creation,
  • table deletion,
  • table reloads,
  • refreshing access tokens,
  • Notifications

and many more important functions that are required to enable analysts to focus on analysis rather than worry about the data that is delivered for analysis.

Daton – The Data Replication Superhero

Daton is a fully-managed, cloud data pipeline that seamlessly extracts relevant data from many data sources for consolidation into a data warehouse of your choice for more effective analysis. The best part analysts and developers can put Daton into action without the need to write any code.

Here are more reasons to explore Daton:

  • Support for 100+ data sources – In addition to Knowlarity, Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms, and much more. Daton will ensure that you have a way to bring any data to Google BigQuery and generate relevant insights.
  • Robust scheduling options allow users to schedule jobs based on their requirements using simple configuration steps.
  • Support for all major cloud data warehouses including Google BigQuery, Snowflake, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL, and more.
  • Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. There is no infrastructure to manage or no code to write. 
  • Flexible loading options allow you to optimize data loading behaviour to maximize storage utilization and also easy querying.
  • Enterprise-grade encryption gives your peace of mind
  • Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus instead of analysis and insights!
  • Enterprise-grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your demands increase.

Sign up for a free trial of Daton today!

Interested in learning more about data warehouses, their architecture, and how they are priced? Check out our other articles.

Google BigQuery Google Bigquery Pricing Google BigQuery – Architecture and Key Features
Snowflake Pros and Cons of Snowflake Snowflake Architecture
AWS Redshift Amazon Redshift
Oracle Autonomous DB Oracles Autonomous Data Warehouse
For sections where we talk about manual reports and lost productivity https://sarasanalytics.com/blog/improving-data-analyst-productivity
What is a cloud data pipeline https://sarasanalytics.com/blog/what-is-a-data-pipeline

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