LeadSquared to Snowflake – Made Easy

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Posted On: 14 Aug, 2020
Last Updated On: 18 Mar, 2021

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If you’re reading this, you are probably looking for a way to transfer data from LeadSquared to Snowflake quickly & efficiently. In this article, we will talk about why using LeadSquared is essential and how you can get data from it and all your apps and tools together in one place 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. Understand customer demand and problems play a critical role in the success or any business.

The choice for eCommerce business when it comes to marketing and selling their merchandise is growing every day. eCommerce vendors have to decide on what channels they want to sell on, which channels they want to spend their advertising dollars. Complexity increases with the addition of every sales channel. For instance, if we consider marketing & lead nurturing channels available to support online business, you will find a choice of:

  • Social Media ads – Some platforms include Facebook Ads, Instagram, LinkedIn, Twitter, and others
  • Digital ads and remarketing – Criteo, Taboola, Outbrain, and others
  • PPC – Yahoo Gemini, Bing ads, and others
  • Email – Mailchimp, Klaviyo, Hubspot, and others
  • Podcasts
  • Affiliate – Refersion, CJ Affiliates
  • Influencer marketing
  • Offline marketing and more

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)
  • Following user journey through the conversion funnel
  • 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.

Businesses these days need to be efficient in terms of their data analysis. They are struggling to make sense of the data generated from various applications and tools used to manage different processes efficiently.

Due to the reasons highlighted above, any eCommerce business typically operates at least 10-15 different software/platforms to deliver on their customer expectations. As a result, data silos are created, which makes it more difficult to consolidate data and use the data for reporting, operations, analysis, and taking informed forward-looking decisions.

CRM platforms like LeadSquared helps companies to :

  • Manage their leads, accounts, and deals and share the data securely.
  • Trigger instant actions, stay on top of activities and follow up better with workflows
  • Streamline lead nurturing processes and make the most of every incoming lead
  • Automate every aspect of your business and plan for time-intensive, repetitive tasks
  • Give accurate lead attribution to marketing channels.

Top companies usually collect data from marketing campaigns, CRMs, e-commerce platforms, email marketing tools, social media marketing platforms, cloud telephony services. Behavioural patterns of users on like wishlists, search history, cart addition, cart abandonment data also provide great insights on product demand trends. They can use these data to project sales trends and allocate marketing and other budgets accordingly to optimize profits. This data is continuously mined and analyzed and helps a business gain insights, thereby minimizing loss and maximizing revenue.

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 Oracle Autonomous. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.

Here, we will be looking at methods to replicate data from LeadSquared to Snowflake.

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

LeadSquared Overview

LeadSquared is a customer relationship management (CRM) solution and cloud-based marketing automation for all sizes and types of businesses such as e-commerce, education, finance, health and wellness, software, real estate, and hospitality. LeadSquared allows its users to automate tasks like lead capture, marketing, sales CRM, reporting, and analytics. LeadSquared has useful features such as lead scoring, landing pages, marketing and sales insights, segmentation, and role-based user access. It can easily integrate with applications like Super-Receptionist, Ozonetel, LiveChat, Olark Connector, Zopim, and GoToWebinar. The software is available in a subscription pricing model and runs on web browsers, Android, iOS applications. Users love Leadsquared because:

  • Users get an informative dashboard of all the campaigns with crucial metrics which are easy to understand.
  • They offer 100% inbox delivery rate.
  • Their service is very customized and personalized.
  • The built-in connectors to third-party tools like a telephony system or platforms like Facebook are simple to use.
  • The API connectors & Webhooks are easy to use, and you can integrate with your core systems.

Snowflake Overview

Snowflake is a cloud-based data warehouse created by three data warehousing experts at Oracle Corporation in 2012. Snowflake Computing, the vendor behind the Snowflake Cloud Data Warehouse product, raised over $400 million over the past eight years and acquired thousands of customers. One might wonder if another data warehouse vendor is needed in an already crowded field of traditional data warehousing technologies like Oracle, Teradata, SQL Server, and cloud data warehouses like Amazon Redshift and Google BigQuery. Well, the answer is the disruption caused by cloud technologies and cloud opportunities for new technology companies. Public clouds enabled startups to shed past baggage, learn from the past, challenge the status quo, and take a fresh look at cloud opportunities to create a new data warehouse product. You can read this article to understand the core technology components that make up this modern, cloud-built data warehouse for consumers of cloud technologies.

You can register for a $400 free trial of Snowflake within minutes. This credit is sufficient to store a terabyte of data and run a small data warehouse environment for a few days.

Why Do Businesses Need to Replicate LeadSquared to Snowflake?

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

An eCommerce company selling in multiple countries is using LeadSquared to manage and nurture their leads to optimize their conversions. They run ad campaigns on various channels like Google, Facebook, Email. They have sold on platforms like Shopify, Amazon, eBay. They use separate apps to manage and optimize multiple verticals like payment gateways, inventories, logistic channels, and target audiences in each country. Decision-makers want to have an understanding of the areas of improvement and then take steps to optimize processes further. They come across the following challenges :

  • Get a complete picture of the business by analyzing all the data from the various apps and tools that they use. So all of the inventory data, customer feedback, customer behaviour data, payment gateway data need to be appropriately analyzed to develop a consolidated picture of the entire business.
  • Separate data silos mean downloading different sheets from all these multiple sources and creating detailed reports from these. To make matters worse, most of the sources have separate data silos for each country. The compilation and processing of data from multiple sources for thorough research is a considerable challenge if carried out manually, and the analysis is not very accurate.
  • While calculating profits/losses of the overall business, it becomes a nearly impossible task to pull all of these data from multiple platforms for each country separately, and then analyze all of this data together with the expense data and calculate profits. It involves a lot of working hours which drains money. The time lag involved reduces the accuracy of the analysis and its effectiveness as the data analysis does not happen in real-time.
  • LeadSquared contains data of leads, which need nurturing to get conversions. The lead nurturing is being done through email and remarketing or over the telephone. The CRM is not natively compatible with all apps and tools, so it is not possible to automate these processes based on the lead stage, leading to low conversion rates.
  • Again since the users are responding to different ad campaigns on various channels which are not compatible with the CRM. That data is not being reflected in the CRM to accurately allocate lead scores to the leads so that you can set priorities when it comes to lead engagement.
  • Since businesses use separate platforms & tools for selling, payments, logistics, it becomes impossible to track the user’s path through the conversion funnel. As a result, essential insights which can help determine when and where the user is bouncing off.

For these reasons, top companies consolidate all of their data from LeadSquared and other apps and tools into a data warehouse like Snowflake to analyze the data and to generate and automate reports at a rapid pace.

Replicate data from LeadSquared to Snowflake

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 Leadsquared data. You can also retrieve the data from Snowflake 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 Leadsquared 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 Leadsquared 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 Leadsquared to a cloud data warehouse of your choice, including Snowflake. Daton’s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from Leadsquared data into Snowflake. 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 Leadsquared , 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 Snowflake 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 allows to you optimize data loading behavior to maximize storage utilization and also easy of 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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