To Better Understand Your Users, Learn About These 4 Categories of Marketing Analytics Tools
Customer relationship data is what it's all about. These types of tools can help.
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When entrepreneurs and early-stage founders want to make smart decisions with their customer data, they may use third-party tools such as Google Analytics, but in fact there are dozens of other software tools out there to think about.
Those choices include business intelligence software, data warehouse software, customer service help desks, CRMs, attribution tools, heat maps and behavior marketing automation software -- all powered by customer data.
In addition, when you combine some of these tools in the right way, you can harness behavioral data and user information to allow your marketing and product teams to facilitate your customers journey to conversion.
Some startups that have raised a lot of VC funding may even end up hiring whole teams of engineers to build a vast ecosystem of big-data technologies to work with custom algorithms and analytics. Oscar Health is an example.
But if you have only a few software engineers and need them to work on your app or product, you might want to be more selective about the tools you choose. Here is a list of four qualitative feedback and marketing-analytics categories of tools to help you better understand your users and make smarter product decisions.
1. Mid-funnel customer journey analytics tools
At the moment, few analytics tools can track clicks, custom events and customer sales-funnel journeys for startups but the few that are in this category include Kissmetrics, Mixpanel, Hubspot and Amplitude.
The one I would focus on is Heap Analytics. This is a cloud-based analytics SaaS solution that preconfigures the necessary integration to track every user form submission, page view, touch, swipe, gesture, tap and click-through on mobile, tablet and web devices.
How it works: Heap uses its own data lake and a data warehouse it has on Amazon Redshift. The software analyzes and collects raw web data and mobile navigational information.
Heap SQL, one of the product's features, can connect all your analytics data to your favorite business intelligence tools, such as Tableau, Looker and others. You can measure user-app sign-ups, run A/B tests (to figure out how likely it is that a user will complete a set of actions inside your product) and conduct funnel checkout tests.
You can also automatically connect the tool to customer-focused app platforms like Salesforce, Marketo, email and payment-services providers to capture every part of the sales journey. A startup like Casper has used Heap to increase conversions on certain audience segments checkouts by as much as 20 percent.
2. Business intelligence SQL analytics software
When you want to use a deeper level of businesswide analytics to improve your product market fit, or when the questions you're asking are more complex, it's time to look into business intelligence tools that connect you to data warehouse tools: These tools include Informatica and Amazon Redshift.
Business Intelligence analytics tools (BI) are software tools for capturing, categorizing and transforming raw data from customers, prospects, suppliers and internal department sources.
These tools also capture external data extracted from third-party systems, email, social media platforms or even macroeconomic data; this is data that businesses can use to make more actionable decisions to increase profits or improve operational efficiency.
BI software tools are useful for startups that want to obtain ad hoc-style reports, data discovery and data visualization with real-time access. The large number of BI vendors out there includes Domo, Tableau, Qlik, Tibco and Mode but I would focus first on Looker.
Looker has a smoother learning curve than other BI tool vendors. And while startups can do a lot with all the apps and tools available, those apps don't all talk to one other, don't all have API's or are expensive to query.
The Looker analytics platform, in contrast, aims to pull all of the data you need into a warehouse or database, then query directly from that database. With Looker, you can query and analyze information across all the apps in which your company has data, and within one platform, without complicated API calls.The process is similar to that of other distributed file systems such as Hadoop or other SQL-compliant databases. Startups such as Glossier use Looker BI analytics.
3. Data warehousing tools
Data warehousing tools were developed to help organizations build enterprise data solutions and extract insights from structured data, from among different systems. They do this in a way that streamlines response times throughout reporting tools. Data warehousing tools transform the data from these systems into a format that makes querying large, complicated datasets faster for reporting users.
Fortune 500 enterprise companies traditionally have vast data centers on their company premises, but data warehouses and their tools are helping smaller companies by moving some of that information into a cloud-based data warehouse.
Recent trends have spawned new data warehouse tools in the cloud, such as Amazon Redshift, Microsoft Azure, Teradata and Google Bigquery. These tools provide improved query functionality and are built to be cheaper and more scalable than traditional on-premises data stores or privately hosted database servers.
A good data-warehousing solution provider is Informatica. It has a popular tool called Informatica PowerCenter, which is an enterprise extract, transform and load tool (ETL) used for preparing data for analysis within enterprise data warehouses.
The PowerCenter offers access to almost any data source within one platform. These ETL tools help companies develop cloud data warehouses and perform queries faster. They are designed for developers and IT professionals to perform data-mining and analysis reports across dozens of applications, such as NetSuite, Oracle, Salesforce and SAP Concur.
Startups such as Sendgrid have used Informatica products in combination with Looker and Amazon Redshift to integrate into cloud-based data warehouse solutions from legacy systems.
4. Sales and customer-service support tools
For many early-stage startups, getting customer feedback and users' perspectives is crucial for making the correct iterations of their product or app and determining its best positioning.
In the beginning your business should be doing qualitative customer development or figuring out the best solution for a good product-market fit or a significant user base. There are all different types of sales-activation and customer-support, or CRM tools, for doing this.
These systems have out-of-the-box simple reporting solutions, but can feed into a data warehouse for complex reporting. Products like Zendesk and or Salesforce can help give your sales and customer success teams a better context about each user and enable these teams to provide more personalized support, to turn prospects into paying subscribers and brand loyalists.
Zendesk is a software solution that streamlines these communications in an easy-access configuration which can improve response times and help satisfy your startup customers. Peloton, the live-streaming cycling class startup, for example, started using Zendesk to offer better customer-experience help for general questions. This tool offers live-chat support for technical issues, as well as support tickets for any phone calls received, to improve customer loyalty.
The goal of any of these tools is to help you capture and utilize data for insights that can help you make decisions more quickly or in a more collaborative way with your teams.
Customer data is crucial to all parts of your startup's team, whether it's connected to the sales, product, marketing, design or support categories. That's why it's so important, for your company growth, to put together the best combination of tools to connect your data sources into one overall repository.