Aurora UK launch

An e-marketing brief that became a market-entry question.

Aurora was an investment research and analysis platform preparing to enter the UK market.

I was first approached in August 2020 about helping Aurora acquire UK customers through digital marketing. The obvious answer would have been to choose a demographic, build some advertisements and start buying attention.

I proposed something more useful: work out who the product might genuinely help, understand what those people valued or worried about, develop several plausible propositions, and use the launch itself to learn which assumptions were right.

That turned an apparently straightforward marketing brief into a much broader market-entry project.

01 / Reframing the brief

The September 2020 proposal set out three phases:

1. research, demographic analysis and campaign design;
2. a relatively low-cost experimental campaign to test the assumptions;
3. a larger campaign or series of campaigns built from what we learned.

The important point was the order.

Setting up an online campaign was not the difficult part. The difficult part was understanding a new national market well enough to decide who might care about the product, which benefits mattered to them and how those ideas could be tested without pretending we already knew the answer.

Even the initial assumption that the audience should be “millennials” was open to challenge. Digital competence mattered. So did disposable income. But neither automatically made age the most useful way to describe the market.

The work therefore began with questions rather than a persona.

02 / Building a market model

The research combined UK demographic and financial-behaviour data with a closer reading of the product and the needs it might address.

The segmentation developed across several overlapping dimensions:

• geography and age;
• digital engagement;
• income and financial circumstances;
• existing financial behaviour;
• risk tolerance;
• goal-setting and planning;
• monitoring behaviour;
• price and bargain sensitivity;
• trust;
• convenience;
• perceived sophistication;
• financial worry and uncertainty.

This mattered because two people who look similar in a demographic database can have very different relationships with money and investment.

The COVID-19 period made that problem more complicated. Historical assumptions about consumer confidence, financial anxiety and spending behaviour were less dependable at exactly the point Aurora was entering the market.

Rather than ignore that uncertainty, I treated it as part of the model.

One possible proposition, for example, was that a platform which made portfolios easier to analyse and monitor might help people feel more in control during an uncertain period. But that was a hypothesis to test, not a slogan to declare true.

By late 2020 the work had developed into a set of audience and message priorities which could be taken into experiments.

03 / Turning propositions into experiments

The next stage was to design a launch which could tell us something.

I analysed the advertising structures available through Facebook and Twitter and looked for a way to make useful comparisons between them. The campaign plan separated the main kinds of variable we were dealing with: demographic and geographic targeting, psychographic differences expressed through the messages, platform interest categories, and the opaque optimisation choices made by the advertising systems themselves.

The first six broad message groups were:

• product functionality;
• value and bargain-seeking;
• saving time;
• conscientiousness and pride;
• goal-setting and planning;
• transparency and reliability.

The core testing model could generate 96 audience/message combinations inside a single campaign. Repeating the model across different campaign objectives and platforms created the possibility of several hundred controlled variants.

The point was not scale for its own sake. It was to isolate enough of the variables that a result could change the next decision.

Baseline tests would help establish the required budget and data volume. Core tests would compare the principal messages and audiences. Secondary and, if useful, tertiary tests could then be designed from the results rather than specified months in advance.

I also deliberately kept the earliest creative formats simple. If the first question was which proposition worked, elaborate video or interactive creative would introduce another variable and make the comparison harder to interpret.

04 / Connecting the campaign to the product

Advertising data alone would not answer the real question.

The plan connected acquisition activity to the rest of the customer journey: landing pages, registration routes, Google Analytics, conversion funnels and behavioural data.

I had direct access to the Aurora Analytics environment, and the implementation plan included defining registration and purchase paths, using platform tracking where appropriate, testing landing pages and analysing what happened after a user arrived.

One proposal was to create different landing pages for different message families. That would allow the promise which generated the click to continue through the page, while giving us another way to understand what users actually did.

There was also an operational principle behind the setup. Aurora should own and control its business and advertising assets, with outside operators receiving delegated access. The marketing system needed to remain the client’s system rather than becoming dependent on whoever happened to be running a campaign.

05 / From research programme to live launch

The project did not stop with the research documents.

Through early 2021 the message architecture continued to evolve, and successive versions of the Aurora DIY Investing landing page were developed. Campaign activity moved into live Facebook advertising, while the wider work expanded into SEO, content and other launch material.

By June 2021 there was a live UK-facing Aurora DIY Investing proposition with a free-trial registration route.

The customer journey extended beyond the landing page into account verification, onboarding, investment guidance, trial messaging and subscription prompts. I used the live service myself, which meant the work was being considered from inside the actual user journey rather than only through advertising dashboards.

The wider launch work also included continuing landing-page revisions, content/editorial work, an externally targeted article and a later webinar.

Not every part of that customer journey was mine to create, and Aurora’s underlying investment technology was not my product. My role was to connect market understanding, proposition, acquisition and communication around it.

06 / Working as an ongoing client programme

Aurora became a continuing engagement rather than a short campaign setup.

There were regular client calls across long stretches of 2021 and into 2022, alongside continued launch work. That ongoing rhythm matters because a launch of this kind does not become “finished” when the first adverts go live.

Messages change as they encounter real people. Landing pages expose gaps in the proposition. Analytics raise new questions. Product changes alter what can be promised. Content and search work create other routes into the same customer problem.

The useful system is the one which can keep learning.

07 / What I did

Across the engagement, my work included:

• UK market and audience research;
• synthesis of demographic and financial-behaviour data;
• demographic, psychographic, behavioural and benefit segmentation;
• proposition and message development;
• qualitative questioning and interpretation;
• translating a sophisticated investment-analysis product for individual users;
• experimental campaign architecture;
• Facebook and Twitter campaign planning and implementation;
• message grids and structured testing;
• Google Analytics and conversion-path thinking;
• landing-page and registration-flow development;
• SEO consultation;
• content and editorial work;
• wider launch communications;
• documentation detailed enough to support handoff;
• continuing client collaboration and iteration.

08 / What this work demonstrates

Aurora is useful to me as a case study because it shows a systems approach operating somewhere other than software.

An audience is also a model. A proposition contains assumptions. A campaign is capable of being an experiment. A landing page is part of a larger journey. Analytics are useful when they can change what happens next.

The underlying method was to resist simplifying the problem before I understood it, make the assumptions explicit, build ways to test them and carry the learning into implementation.

That pattern has appeared in very different forms throughout my later work.