The value of data keeps increasing as a corporation’s major asset, to the point that it has been labeled as ‘the new oil of the digital economy’ by Wired.com.
In order for data to go from being just a chunk of information to a building block of the company and customer interaction, data must be compiled, organized, and used in an analytics project.
The growing data stack of tools available to building a company’s infrastructure makes the usage of a singular home for a company’s information to reside in easier.
But that data still has to get extracted and pumped into operational tools like Salesforce, Iterable, Marketo, and Facebook Ads to make it useful to business teams. This is where reverse etl comes into play.
Don’t stay stuck with your data scattered in the warehouse like a junkyard pile of precious antiques buried underneath each other. Reverse ETL is the solution in analytics to make data actionable.
ETL stands for ‘extract, transform, load,’ which are the trio of processes that when used together transfer data from its many sources to one unified location. That singular source of truth for company and customer data is what we call a data warehouse, a data lake, or even a data lakehouse.
Reverse ETL completes the data loop by copying data being contained in the warehouse into systems that enable an organization’s employees to act on that data.
Take a moment to learn about the ways you are able to make the most out of your data with reverse ETL once it makes your data actionable?
1. Customer experience is key
If you are wondering what is the main reason why you need to justify why the data sitting tucked away in a data warehouse needs to become actionable, look no further than the improvement of data-driven customer experiences.
It is extremely useful to analyze data, dig deeper into the purchase and usage behaviors of your customers, and to make decisions that are informed by the available data. This will tell you what features are succeeding, and which ones need to sit lower on your priority list.
But if you are really looking to up the ante on customer experiences, reverse ESL can expand the way data I used to power customer interactions at every level. across every touchpoint. It’s an approach that requires accurate data to be available in downstream systems used by various teams to engage with customers.
2. Understand operational analytics
Operational analytics is the method of presenting data insights from analytics to business teams, all while keeping the information streamlined neatly in their usual workflow so they can make more data-informed decisions.
This doesn’t place any major reconstruction demands on your data warehouse because traditional analytics and operational analytics both rely on the same central data infrastructure.
Basically, what is happening is the information from the data warehouse can be made actionable simply by syncing to downstream SaaS tools that are being used by customer success teams, sales departments, and marketers.
3. What buyers expect
The transactional relationship between a seller and a buyer isn’t anything new. But contemporary buyers have a massive amount of options for what to buy and where to buy it from, and with that comes the responsibility of businesses to live up to high customer expectations.
In order for businesses to keep adapting to modern demands, they are in need of more efficient resources and tools to provide memorable customer experiences. This is creating an environment where personalized experiences become a necessity, and this is where customer data comes into play with the help of reverse ETL.
There is no shortage of customer data being collected and this is moving in tandem with the shrinking costs of storing data in cloud data warehouses.
Once enriched customer data is made available to an organization’s team, they will have the data that they require to improve preferred engagement tools.
4. Data on demand
It becomes a whole lot easier to provide a premium customer experience time and time again when the required data is made available in the tools like a CRM, advertising platforms, or even workplace messengers like Slack.
Some of the extremely common examples of frequent data requests that should be managed with reverse ETL are email interaction data, credit consumption on contacts and accounts, product-usage data, customer attributes that the accounting department needs, and transaction data.
Now that you have a better understanding of reverse ETL and why this growing category in the data space, go out and make the most of your data to improve your understanding of your customer activity, expand the communication abilities inside the company, and increase productivity.