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Qrious offers a practical guide to data-driven marketing

Generating meaningful insights from the ever-growing pool of data available to businesses has been a challenge for marketers. On hand to help is Qrious general manager Simon Conroy who shares his advice on creating value with the Qrious model.

By Graham Medcalf | February 24, 2017 | Sponsored content

The Data Strategy Whitepaper recently issued by Spark Venture's data business Qrious was the culmination of a research study conducted with 20 senior marketers.

Qrious general manager Simon Conroy, told Stoppress: “There is so much data around these days and the absence of meaningful analysis and insight to improve customer outcomes represents a massive lost opportunity for value creation.”

Conroy believes that many marketers feel overwhelmed by the sheer size and complexity of what is required, but as he says, it doesn’t need to be a huge exercise. “There is a lot of hype around data-powered marketing, and the majority of that reflects what is happening at the more advanced end of the maturity scale. Unfortunately, this creates the perception that you have to be doing real-time and segment-of-one in order to be successful. However, the reality is that there are many less complex approaches organisations can take to fundamentally step change their performance leveraging data.  

“The best way forward is to create value in the short term and then build on that. The key is to find the sweet spot – the right combination of the importance of the problem and achievability in the required time-frame.”

As referenced in the Qrious Whitepaper, small-scale, pilot projects are valuable because they allow for experimentation and testing of data processes. With fewer resources there is less risk than with larger projects. By starting small and building on success, the value to the business can be proved.

Qrious suggests each trial run from two to four weeks. Many teams pick two week sprints as they help reveal problems faster. After each major addition, the project integrity can be measured and tested so as to inform the next iteration.

The 80/20 rule is crucial for selecting pilots and maximising outcomes. It’s important to find the projects that will generate the most value to the business, while those that only provide marginal benefits can be dropped. The goal is to build capability and move to the next level of data maturity.

The Qrious model suggests taking a step-by-step approach.

Business challenge. The first step is to identify the business challenge you are trying to solve. What are your pain-points and what are the customer outcomes you want to deliver this year, next year or in five years?

What data? Conduct an audit of the data you can currently access, and then map your future data needs. Make sure you look across the entire organisation, including transactional records, digital channels, such as social and online customer surveys, to collect the data you need. Then put in place processes to capture and store any missing data.

Readiness for use. You’ll need a combination of technologies to identify and prepare a single source of verified data. A hosted data warehouse provides a neutral place to consolidate and store business data in a single, low-cost centralised repository, structured and unstructured data.

Managing the data. Plan for the most effective and efficient way to assure that the data is available when and where needed, fully in compliance with all privacy regulations and at a reasonable cost. It’s important that you have the right person in the right role to help you start breaking down barriers and making sure that everyone in the organisation is aligned to your strategy.

Data use. Data alone does not translate to data-driven marketing. The value comes from combining sources to obtain insightful or actionable information and have it available when and where it is needed. Efficient search and filtering technology is necessary to make it easy to identify the relevant data. Analytical tools are used for a wide range of business insights, ranging from automated reporting, dashboards and scorecards, through to customer lifetime value analysis, customer lifecycle management, customer segmentation analysis and ad hoc driver and modelling analysis.

Predictive analytics. The next step is to expand your capability beyond historical analysis to conduct predictive analysis about what is likely to happen tomorrow and beyond. 

  • Graham Medcalf is a writer and former editor of NZ Marketing Magazine

 

This story is part of a content partnership with Qrious. 

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