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The magic is in the detail. And the detail is in the data.

Data finds things that would otherwise be missed,
hidden, buried or inaccessible. It helps to direct and
perfect your program.

Clues found. Mysteries solved. Questions answered.

Things you’ve always wanted to know but have only been able to make educated guesses about, data can pinpoint and clarify. It’s a source of sought after information, answering questions like:

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Who are your prime program participants? And what makes them so?

Demographic, transactional and behavioural data help identify your most valuable employees, channel partners or customers.

Will your program participants be loyal?

Analyse past behaviours to predict future outcomes, gaining foresight into who is likely to stay and how much value they’ll bring over time.

What ROI are you getting from your loyalty and incentive programs?

By integrating performance data with participant behaviours and program costs, we help you evaluate the effectiveness of your strategies.

How do you keep people engaged and performing at their best?

Data insights and A/B testing help you optimise your program and offer personalised rewards tailored to individual preferences.

How can you identify program risk and fraud?

Data-driven algorithms can proactively monitor for suspicious patterns and unusual activities, alerting you to potential risks and allowing for quick intervention.

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Framework

From beginning to end, from hindsight to foresight.

Data can provide exceptional insights and add extraordinary value throughout a program.

At the research stage, data paints a more detailed picture of what’s gone before, what is, and what could be. Those insights all contribute to the design stage, helping to make a program more robust. During the implementation stage, data provides ongoing information that helps to update, iterate and improve the program. And at the measurement stage, data provides finer layers of information than was previously available, even predicting the future.

Data Lab Analytics

Descriptive analytics help make sense of what’s happened in the past. Examples include dashboards and ROI calculators.

Diagnostic analytics help us understand why something happened in the past. An example is the identification of performance anomalies.

Predictive analytics show what’s likely to happen in the future. Examples are predictions of employees’ intentions to resign, and future partner performance.

Prescriptive analytics recommend actions to take to influence likely outcomes. For example, AB testing, or adding incentives.

Cognitive analytics leverage artificial intelligence for deeper insight and better decision-making. An example is the automation of contact centre QA assessment.

Custom analytics tailor analytical processes to fit specific business needs. For example, dynamic dashboards.