Making $47,000,000 with data ain't easy. But you can do it.

Turn Your Data Into Profit

“‘The amount of data the system can handle and the performance is fantastic.”

“Data is an enabler. … We can change the product we put in front of the customer.”

“We don’t think in terms of databases anymore, we think about what we want to achieve with the data.”

How would you like to be able to say things like this about your data?

The quotes above are from HolidayExtras CEO Matthew Pack. His company is on track to make $47,000,000 more in a year. Not because of gimmicky marketing or random chance: they’re using their own data to sell more to their best customers.

(get the whitepaper to learn how we did it)

Like many established companies, HX entered the digital market in the early 2000s. They knew they should be collecting data, so they collected data. A lot of it. But they didn’t have a data strategy or supportive data infrastructure. 

Of course, data accumulates rapidly. It just keeps growing, year after year, becoming irreplaceable and opaque.  

What HX needed was real-time streaming, on-demand analytics. They needed the ability to write business problems in code. They needed the right data architecture.

Fundamentally, Big Data represents a technical fix to a business problem. But you have to understand your business problems before you can figure out the right technical fix. Without KPIs and revenue goals, any new Data Platform will lack a driving purpose.

The first step you need to take when creating a new Data Strategy is to carry out a Data Diagnosis: observe the landscape and determine the context of the data problems.

Talk to all your executives, engineers, and other stakeholders. Get people out of their silos. Look at the systems architecture, code, and raw data. Understanding the different data silos and the paths data takes through the system.

Here are some key questions to ask:

  • What data do you collect?

  • Where does that data live?

  • What do you want to do with data?

  • What is your current infrastructure?

  • How did it get this way?

  • Who ‘owns’ the data?

  • Who can actually use the data?

  • How have you attempted to fix your data problems?

  • What are your KPIs and business goals for this year?

  • How do you measure improvement?

  • How would you use data in a dream scenario?


Your key data-supported business metrics might look like this:

  1. Evaluate campaign ROI

  2. Understand response rate

  3. Visualize marketing reach

  4. Understand how to increase customer retention rate

  5. Increase customer value

  6. Cross-sell to existing customers

  7. Migrate customers to more valuable segments


Once you know what you want to do with your data, you can start looking at the big challenges of a Data Strategy:

  • Collecting the right data

  • How to apply Machine Learning and Data Science

  • Integrating multiple data sources

  • Selecting the right architecture

  • Building out the right infrastructure




A Big Win for HX: E-mail Marketing

HX was sending the same sales emails to the same customers at the same time: spring breakers looking for cheap adventure got the same pitches as business people planning their weekly trips between London and Berlin. They knew that if they could understand and response to these different market segments, their sales would go up.

Enhanced email marketing requires a data platform that is easy to collaborate on, real-time, and painless to operate in production. Functionality includes:

  1. Real-time / stream processing at large scale (thousands of events a second)

  2. Storage of this data so that it can be accessed and queried at any time

  3. A simple way to transform and manipulate the data in batch or streaming mode

  4. Data feeds anyone can subscribe to without being a data engineer

  5. Scalable backend so no new operations hires would be needed

  6. Output to an e-mail marketing system to implement features such as automatically e-mailing abandoned carts

  7. Machine-learning based customer segmentation for hyper-targeting of campaigns


For HX, the solution ended up being drastically different than their current architecture—hosted in Google instead of AWS, streaming instead of batch, and Python/Java instead of SQL.

The core requirement was easy-to-use stream processing that non-engineers could use. Nothing like this existed in the market, so RoboticProfit created the solution from custom software and cloud services.

Get more details on how RoboticProfit led HX through their data discovery process, developed a data roadmap, and built out their platform by downloading our whitepaper.


HX is currently using their new, custom data platform in production to feed an ambitious new email campaign system. Instead of just e-mailing millions of addresses every two weeks, the platform uses data to target those customers and send them what they’re likely to buy, based on their past behavior. With a fully personalized and data-powered email, add-on, and product discovery platform, industry studies indicate that HX will see $47,000,000 in additional revenue in the next year.

Are you ready to use your data? Schedule a consultation call