Four ways Raisin uses data to sustain and enhance its business

[LookerExperts]

This content, written by Denise Duffy-Parkes, was initially posted in Looker Blog on Jun 16, 2020. The content is subject to limited support.

Since its inception in 2013, Raisin GmbH has had its sights set on operating as an efficient, data-driven company, where everyone is enabled with easily accessible data to use and obtain valuable insights to drive the business forward.

In a with the Looker team, Adolfo Grau, head of business intelligence at Raisin, speaks to how Looker has, from the start, occupied a central role in the organization’s data infrastructure and data-driven culture.

Early on, much of Raisin’s data was stored in spreadsheets, which were difficult to manage and access throughout the organization. It quickly became apparent that, to enable the business intelligence team to respond more quickly to requests and help everyone in the company get access to data, the data handling process needed to be streamlined.

To find a solution that was in keeping with its vision of a self-service data-driven culture, Raisin needed a centralized data model. This required a platform that could enable all employees to share data, included strong visualization capabilities with easy-to-use dashboards, and assurance that data was accurate and up to date. After evaluating a number of solutions, the business intelligence team knew that Looker fit the bill.

Since their Looker implementation, Grau and his team have made numerous refinements and now have a stable data technology stack in place. Their tech stack consists of Looker for business intelligence, Snowflake for data warehousing, Snowplow for data collection from multiple platforms, and Airflow as the data pipeline — all hosted on Amazon Web Services (AWS).

Four ways Raisin is using data to enhance their business

1) Marketing intelligence and outreach

The marketing team at Raisin relies on data in Looker to facilitate keyword expansion for performance marketing, as well as to determine customer acquisition costs. The team also uses Looker to understand where their customers are coming from so they can optimize marketing campaigns and improve channel efficiency.

2) Performance reporting, investor outreach, and optimizing operations

Management and finance use Looker to generate reports for regulators, servicing banks, and business partners. For example, they use Looker to analyze and report on company performance data for their quarterly supervisory board meeting. And, as Raisin is planning to expand, executives use data from Looker to pitch the business to potential investors.

Raisin’s management team believes in full transparency and shares the company’s success metrics internally. Employees receive a daily report on how the business is doing and can see the primary key performance indicators — financial assets under management — and compare the numbers from day to day and over a 30-day period.

And on the operational side of the business, the Raisin operations teams use the to receive alerts on how operations are functioning and to visualize issues they need to resolve.

3) Product feature enhancement

Raisin’s product team uses Looker for feature monitoring, funnel analysis, and to discover where they can optimize current practices in order to ensure that the business stays on track.

4) Embedded analytics

Raisin also used Looker’s offering, Powered by Looker, in its Partner Bank Portal, where participating banks can check on the number of orders they are getting and what they can expect per volume.

Raisin's tips for creating a data-driven culture with Looker

Since building the foundation for a data-driven culture and business, Grau, the business intelligence team, and the company as a whole have evolved some best practices they believe are valuable for any growing organization striving to do the same.

1) Stay agile

For growing companies, it's important to make sure that you periodically revisit your processes, technology stack, and continue finding ways to refine it. As Grau points out, circumstances will understandably change throughout periods of growth, but as long as your organization is always adapting and looking for better ways to do things, you're on the right track. Continue to re-evaluate the data model and revamp it as needed when adding new customer acquisition channels, new business lines, or partners.

2) Cater to different roles and personas

Business intelligence (BI) team members need to be sure they don’t fall into the trap of assuming that they represent the typical user. Users across every company are diverse and have unique requirements and skill levels, so BI specialists need to create different models for various types of users. In addition, it’s important that the BI team challenge users when necessary and ensure that everyone is indeed making decisions based on data, rather than on intuition.

3) Elevate skill levels through training

Self-service tools like Looker enable users to derive insights on their own and at their own pace. Providing employees with in-depth training (workshops, documentation, videos, and other resources) will empower them to do more with greater confidence. Properly trained users can then create their own models, make updates, and change or create new fields, dimensions and metrics — making their analysis more accessible and relatable to them.

4) Keep LookML organized

After working with Looker for a number of years, Grau stresses the importance of reorganizing the LookML modeling layer on a regular basis, much like keeping the code base clean and organized like you would in any tool.

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Learn more about how Raisin has successfully integrated Looker into its business. Watch the and read the .