In a B2B marketing context, predictive analytics involves the study of past activity to predict future buyer behaviour. There is mounting evidence that marketers using it outperform those relying on traditional methods. It can boost success rates across retention, cross-sell and up-sell activity as well as acquisition.
This is not a new concept in itself. However, the upsurge in business data spawned by the information age has boosted the depth and breadth available for analysis. At the same time, advancements in technology have made it easier to handle and interrogate large data sets.
Back in 2010, Forbes ran an article titled Why predictive analytics is a game-changer. It included a definition that may help marketers who are just getting to grips with the discipline:
Predictive analytics can help B2B marketers target prospects displaying a tangible need for a specific offering. This enables sales and marketing efforts to focus on those organisations most likely to convert. But large swathes of data are inherently complex, so where do you start?
1. Set objectives
Don’t make the mistake of trying to do everything at once. Establish goals so that activity is focused and purposeful from the outset. Start off by deciding whether you’re looking to acquire new prospects, reactivate lapsed customers or increase lifetime value of existing customers, and take it from there.
2. Set expectations
To get the most from predictive analytics, it’s important to appreciate what it can and can’t do for your organisation. Make sure anyone involved in evaluating or contributing to its success understands this. Explain to all stakeholders that investing in technology or appointing a head of analytics is only the start of the journey. To deliver maximum impact, a significant cultural shift is required across the organisation.
3. Ensure your database is up to scratch
It goes without saying – but it’s too important not to say it. Fundamental data accuracy, completeness and reliability is essential for predictive analytics to deliver results. Get your data in order; then look for ways to enhance analytics capabilities.
4. Think before you invest in tech
Predictive analytics is complex. It involves advanced data mining, statistics and modelling across multiple data sets and many thousands of customer and prospect records. In recent years a whole host of technologies have emerged to overcome these challenges. Many of them are very good. But it is important to choose the application that best suits your specific requirements. Select a provider who can speak with you in plain English about your needs and how the technology will address them.
5. Don’t rely on technology alone
Unfortunately, no tech platform will automatically take your data analytics to new heights. Technology is a big part of the equation, but it is the human interactions with it that matter. Look to hire analysts that are as comfortable in front of a PC as they are explaining their findings to the CMO. Analytics needs to infiltrate the wider organisation – not just the marketing and IT department. This requires a cultural shift as well as technical implementation. Organisations that recognise and encourage analytical talent at various levels and across multiple departments reap the greatest rewards.
6. Achieve long-term buy-in
Predictive analytics can deliver results quickly. But to obtain a powerful ROI, ongoing commitment is required. When implementations fail, it’s usually because the business isn’t ready to use the insights, rather than due to untimely or inaccurate analysis. At a practical level, ongoing testing, adjustment and refinement is needed. And at a cultural level, change management may be required. To achieve this, it’s vital that senior management understands and supports a data-driven approach to marketing and has realistic expectations surrounding investment and returns.
7. Develop your IT ecosystem
Technologies for CRM and marketing automation need to be fully optimised and integrated with predictive analytics tools. Relevant data should be automatically captured and uploaded into the system, so that human efforts can focus on ‘brain work’ rather than data entry.
8. Set benchmarks
The attraction of predictive analytics lies in its promise to hone and accelerate sales and marketing efforts. But it’s impossible to know if that promise has been fulfilled if you don’t set tangible benchmarks for success. By setting KPIs surrounding relevant factors such as conversion, lifetime value or sales revenue you improve your ability to measure – and therefore manage – your efforts.
The most important aspect of getting started with predictive analytics is to find somewhere – anywhere – to make a start. Don’t let your organisation be paralysed by indecision. As the Forbes article concludes:
To find out more about predictive analytics, what it’s capable of and whether it’s right for you, download our new Introduction to predictive analytics. It’s free for premium members, or available to purchase. Find out about membership options here.
