We help our clients to get the most out of their data, and to answer the tricky questions about what is or is not working, and what to do next.
We work closely with our clients to turn data into actionable insights. We have both spent over a decade in client-facing technical roles so we are well versed in presenting complex work simply, with clear, actionable recommendations.
See below for more info about the types of problems we can solve:
Transactional and member databases can be a treasure trove, but sometimes the more data you have, the harder it can become to draw actionable insights from it.
That is where we come in. We analyse transactional, member, and loyalty databases to identify key opportunities across the customer lifecycle:
Optimising Acquisition and Onboarding
Maximising value through cross-sell and up-sell
Developing data-driven Retention & Win-back strategies
The result is specific, actionable recommendations for maximising customer value across the lifecycle. We have provided these services to some of Australia's biggest retailers and service providers over the years.
“Half the money I spend on advertising is wasted; the trouble is I don't know which half.”
- John Wanamaker, Department Store owner in the 1800's.
Although this quote is from more than a hundred years ago, it is startlingly still true today for many organisations.
As digital platforms proliferate, you'd think measuring marketing outcomes would be easy, right? Sadly, this isn't always the case. Too often there is a different supplier or agency for each advertising platform, and everyone wants to claim that conversion, but:
How do you know which activity truly drove an outcome?
Was there a real uplift, or were those people more likely to convert anyway?
What is the interplay between channels or campaigns?
We can help understand the cause and effect, and design test frameworks to measure performance and ROI. By integrating BigQuery, advertising data and internal data, we help businesses understand attribution holistically to drive action and marketing efficiency.
Predictive modelling uses data about what has happened in the past to predict what is likely to happen in the future.
The methodology used depends on the type of question being asked and the data available to answer it, but generally involves applying machine learning algorithms to historical data in order to output scores or probabilities related to a given event happening in the future.
Predictive models can be used to answer questions like:
Which of these new customers are most likely to stick around and become a high-value customer?
Who is most likely to take up this marketing offer?
Which of my customers are most at-risk of leaving (and how can I stop them)?
We have used predictive modelling techniques to answer these questions many times, as well as numerous more niche challenges (such as "how long until this generator needs servicing?" and "how many units will I sell if I price this phone at $990?")
Although we work most often on the areas above, we are always up for a challenge and love using data to solve new problems.
So if you have a different kind of question, and you're looking for someone to help... just shout out to find out how we can help.