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8 Best Practices to Maximize ROI from Predictive Analytics

5 years ago

Back in 2010, forecasted that something new and interesting called predictive analytics was emerging as a “game changer.” Well, fast forward a handful of years, and we can easily see that the prediction was an understatement – because predictive analytics hasn’t just changed the game for marketing professionals: it has fundamentally reinvented it.

That’s because predictive analytics isn’t just a method of leveraging customer, prospect and other meaningful data to launch timely, micro-targeted communications and campaigns; on an even deeper level, it’s an engine that transcends marketing and is driving overall business strategy and vision. As Eric Siegel, Ph.D., chairman of the leading cross-vendor event for predictive analytics professionals Predictive Analytics World notes: “business is becoming a numbers game, and predictive analytics is the way to play it.”

However, while many organizations are indeed playing this game quite well — such as Macy’s, Walmart, Netflix and eBay — at the current time, there are many others that aren’t as pleased with their results. This isn’t to say that they aren’t seeing gains in some areas (e.g. uptick in customer retention rates, improved engagement scores, better sales campaign numbers, etc.). Rather, it means that they aren’t reaping the full revenue and profit potential by maximizing their ROI from predictive analytics.

What’s behind the underperformance? Typically, it’s that organizations of all sizes — from start-ups to enterprises – aren’t applying one, some, many or sometimes even all eight of these eight best practices:

1. Define A Clear Objective

Organizations need to proactively define their objective when implementing a predictive analytics platform. Are you looking to: activate prospects, reactivate lapsed customers, increase customer lifetime value or donor value for non-profits? Having a clear objective will help marketing departments craft a more concise strategy and tactical plan going forward.

2. Validate Existing Data Sets

Aside from capturing more data on customers, prospects and donors, organizations need to validate that their data is accurate and reliable. This is so when their database is full of unique contacts, and enriched with meaningful demographic, transactional, product/service and email marketing data on each contact. Click here for more information on ways marketers can capture and gather more data on their contacts.

3. Get Training and Knowledge on Analytics

While predictive analytics is not difficult to grasp — which is part of its value and popularity — it is nevertheless a distinct skillset. As such, organizations need to provide their marketing professionals and other relevant staff with training and knowledge on how to make predictive analytics work in their specific environment and marketplace, as well as on fundamental concepts such as A/B testing, data hygiene methodologies, and so on.

4. Add Necessary Staff & Resources

It is essential to have a marketing team that is primed and ready to leverage a predictive analytics platform, which includes key tasks like: collecting and governing data, creating content-in house based on predictive analytical insights, and so on.

5. Add Necessary Tools and IT infrastructure

Organizations need to ensure that their CRM, email marketing and/or marketing automation systems are reliable, optimized and integrated with their predictive analytics platform to ensure that all relevant data is captured and uploaded into the platform.

6. Get Senior Management Buy-In & Commitment

While predictive analytics can lead to some significant wins in the short-term, it is essentially a long-term commitment that will need ongoing testing, adjusting and refining. As such, it is important that senior management buys into this data-driven approach to marketing and allocates an appropriate budget for the long-term.

7. Develop Marketing Plans, Processes & Tools

If predictive analytics is the engine that drives prospect and customer engagement, then content is the fuel. As such, organizations need to develop marketing plans, process and tools – such as editorial calendars, buyer personas, etc. – that will enable them to create exceptional, relevant content and launch it to the right people, at the right time and through the most effective channel.

8. Benchmark & Measure

The ultimate value of predictive analytics is that it promises to take the guesswork out of marketing, and replace it with measurable, actionable data. However, this promise is only fulfilled when organizations effectively and regularly measure results using appropriate, pre-defined metrics or KPIs such as customer lifetime value, sales revenue or conversion rates.

The Bottom-Line

While capturing rich and relevant data is a big factor in maximizing ROI from predictive analytics, it is not the full story. It is also critical for organizations to apply all of the above-noted best practices.

Article originally appeared HERE


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