Analysis

From 20 year-old family business to growth machine.

October 9, 2020

What you’ll learn

How to Drive growth by leveraging personalisation at scale

Nowadays, customers expect more than an email starting with their first name—but delivering this is not easy, especially if you have decades of legacy infrastructure to deal with (ask any bank). The great thing is, if you get personalisation right you’ll see cheaper conversions, improved customer retention and better monetisation.

How to make generating insight easier and reporting faster

We help you run your first experiments from ideation through to implementation & analysis. We identify profitable channels and strategies and help you push and automate them. Once you’re comfortable, we continue to be your sounding board for new ideas, help with particularly knotty problems, or act as an extra pair of hands at crunch time.

How to build a high-Velocity experimentation culture and technology stack

Successful businesses are able to learn faster than their competitors—and the foundation of this lies in a cross-discipline growth culture. Unfortunately, this isn’t an out of the box fix. It takes time and effort to centralise data, keep your teams aligned on core business goals, and build experimentation processes. However, when pulled off, this will uncloud a whole lot of the tech and communication barriers that hold businesses back from rapid optimisation and deeper analytic insights.

About Order-In

Order In is a Sydney-based B2B marketplace offering catering, staff meal & pantry services to businesses across Australia. As a 20 year old company with dozens of staff, and hundreds of partners delivering thousands of meals each month, Order In is generating a large amount of preference data each month

Industry: Technology / Food & Beverage
Employees: 60
HQ: Sydney, Australia
Founded: 2000

What we delivered

No complicated numbers,
just results.

8H+month

Implementing a modern marketing and analytics stack allowed us to streamline reporting, and set-up marketing & sales automation systems. In turn, this lets staff focus on higher value and more interesting tasks. A win-win for efficiency and staff morale!

63%

activation

63%

retention

Delivering highly personalized marketing and sales campaigns at scale and fully automated has let us significantly improve customer retention and activation. All made possible by designing a modern customer data infrastructure.

Improvement in xyz

Together with improved analytics, tools and processes came the ability to shift culture towards hypothesis- and data-driven experimentation at increasing velocity. Only iterating quickly and learning from every experiment can deliver rapid growth.

What we delivered

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Having consistently delivered more than 20% growth year over year, one could become complacent with the status-quo—but Order In knew they could do even better. Before engaging ikaros, Order In management had identified three core areas improvement: analytics/data infrastructure, experimentation, and automation.

Without addressing these areas, Order In management knew they could not realise their vision of becoming the corporate food solution across Australia and beyond and may even risk losing their market leadership position to one of the many upstarts in the space

Analytics & Customer Data Infrastructure

Analytics & Customer Data Infrastructure Like many organisations of all shapes and sizes, Order In was relying on a mixture of analytics tools for reporting and decision making. From a marketing perspective, this was the trusted Google Analytics plus the reporting tools provided by different channels such as Facebook Business Manager, LinkedIn Insights or the Google Ads Console. Operations and sales on the other hand primarily relied on data extracted directly from their application backend and custom customer relationship management (CRM) system.

Letting their analytics and customer data ecosystem grow as an afterthought (and in many cases without a budget), is the situation a lot of companies will find themselves in. As a result, businesses commonly tend to encounter any number of issues as a result.

  • Automated reports, or reports that access backend data have to be carefully built, often by teams that actually have other day-jobs like developers or IT. This takes valuable time away from a scarce resource, creates friction and results in slowing down new analyses if they are created at all.
  • Other reports are built in Excel with data manually copied together from different sources, resulting in teams often spending more time on creating the report than interpreting the data.
  • Teams work in their own analytics bubble (if they do any analytics at all), with their own KPI definitions and data sources. Numbers don’t reconcile across different sources and teams—impacting trust in the data.
  • Data sources such as ad spend data, user behaviour on the site and CRM data are disconnected and can’t be combined. Teams can’t form a consolidated view of customer traits and behaviours across channel breaks (e.g. Facebook -> Website -> CRM).
  • Conversion data is tracked through each marketing channel as this is the only place which has spend data available—but as channels each count only their own conversions this results in overestimating effectiveness.

Together these issues make it very hard if not impossible to run an efficient experimentation process, as the success of experiments can not be measured or only be measured with significant effort. Personalisation at scale becomes impossible, as the primary source of personalisation data—the website/app platform—does not share data with other marketing systems.

Analytics & Customer Data Infrastructure

Order In started out in 2000 by sending physical catalogues to businesses and has over time morphed into a technology platform. That transformation on its own is incredibly impressive and gives them a number of advantages over younger businesses. Beyond the incredible institutional expertise the business has accumulated, they’ve built lasting client relationships and a strong brand by delivering an exceptional product and customer service.

Growth has historically been driven by organic discovery, whether through search or unprompted word of mouth and a small amount of paid advertising. Given this history, Order In hasn’t had the need to implement a structured process and culture for running growth experiments—but this has changed with management's new growth strategy.

Traditionally, businesses are often more [....] focused on [...] resulting issues:

  • Avoiding failure becomes more important than success. This leads to the modern equivalent of “no one gets fired for buying IBM”, especially in larger organisations, or even shifting goal-posts mid-campaign in order to show success. However, this minimises an organisation's opportunity for learning and improvement.
  • Long planning cycles for one-off campaigns with high production values. High cost campaigns lead to high production values which lead to higher ad spend to justify the investment—a vicious cycle. Surprisingly this even happens for performance driven campaigns.
  • Paralysis by analysis. Afraid of making mistakes, employees pass decisions up the chain, slowing down the speed with which they can be made. This is often compounded by overanalysis and goes hand in hand with “this must be perfect”, high production value campaigns.
  • Insufficient analytics infrastructure to measure test performance. The inability to measure the performance of experiments or only measure it with great effort will also slow down any experimentation culture, even if the understanding of it's necessity exists.
  • [Missing/outdated tooling / technical understanding to glue together different bits and pieces]

As a result, companies struggle to implement a build-measure-learn cycle that let’s them grow their understanding of customers, markets and trends at high speed. Start-ups don’t have the luxury of relying on institution knowledge that has been created over years or even decades—and hence are forced to learn much faster.

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