How about responding to changing customer wishes before customers themselves know about them? What sounds unlikely at first is actually possible today thanks to advanced methods from AI research and machine learning. The keywords are predictive analytics and next best action. Here you will learn what they mean and how you can use predictive analysis.
Machine learning and predictive analysis are changing marketing
Companies in every industry face the challenge of handling ever-growing volumes of data. That applies especially to B2B. How do you keep making the best data-driven decisions in marketing when data volumes are growing rapidly? Artificial intelligence (AI) is the key here, because it makes data volumes manageable that have long overwhelmed any person.
But what exactly are predictive analytics and next best action?
Predictive analytics draws on historical and current data and makes predictions about future customer needs. Next best action turns this prediction into the next best step in marketing: what do you have to do next to meet your B2B customer's expectations, wishes and needs as well as possible?
Responsiveness is always a decisive factor in marketing. You want to offer customers not only the right content and offers but also at the right time.
The advantage of predictive analysis and next best action models is that you can initiate changes in marketing before the customer's behaviour actually changes.
That reduces response time practically to zero. You make the customer the right offer at the very moment they change their mind.
How does next best action differ from next best offer?
Next best offer does not refer to the next best action but to the best moment for the next offer. The analysis shows whether the customer is ready for a new offer.
Predictive intelligence compared with business intelligence
Business intelligence also deals with data management solutions in companies. Unlike predictive analysis, however, its focus is on the quantity and the quality of data and how the latter can be improved.
Business intelligence deals with collecting data and evaluating and presenting it.
Questions of data collection and presentation play a minor role or none at all in predictive analysis. It is rather about the specific algorithm you use to analyse the data. This is where machine learning comes in, an approach from AI research. These ML algorithms make it possible to draw conclusions for the future from the often huge volumes of data on customers, their behaviour and their purchase history.
The benefits of predictive analysis
Predictive analysis comes with some fundamental benefits you should know. These are the most important:
- Greater responsiveness: you can adapt your marketing activities faster to changing customer needs.
- Higher customer satisfaction: customers see that the company responds better to their needs and are more satisfied with the service.
- Better customer loyalty: next best offer helps prevent customers from leaving. If the predictive analysis reports that a customer is about to leave, you can still keep them with the right offer at the right time.
- Higher revenue: predictive analytics opens new opportunities for cross-selling and upselling.
How does predictive intelligence help my campaigns?
Predictive analysis makes it easier to predict the customer journey and to supply customers with information that resonates. It also helps increase click rates and conversions, because you understand better what your customer expects next.
How do I put predictive intelligence into practice?
The good news first: anyone can start with predictive analysis straight away, because the data needed already exists in your company. It comes, for example, from the ERP and CRM and can be used for analysis immediately. The ERP provides data from sales transactions, and the CRM supplies account information about customers and much more. You can use this data for predictive analytics and point the algorithm at it.
In practice, automation plays an important role. Individualised actions in next best action marketing can often be carried out automatically. That matters, because not only are the data volumes large, the number of customers in B2B can also make carrying out the necessary actions manually impractical.
Thanks to automation, response time falls further: the customer immediately receives the next important email, a call back from service or any other action.
For predictive analysis it does not matter whether the data is structured or unstructured. Software solutions in this field can handle very different data sources. And they now offer visualisation options that let a growing circle of stakeholders access the data.
Where do we go from here? A look ahead
For B2B companies, the shift from trade fair contacts to online leads is one of the key developments. The pandemic acted as an accelerator here. One challenge is that online leads work differently and require different approaches in marketing.
The focus today is on faster lead generation and developing these leads into paying customers. That can only work if you deliver suitable, customer-specific content at the touchpoints along the entire customer journey and can predict your B2B customers' needs precisely. Predictive analysis is likely to play an increasingly important role in exactly that.
AI makes it possible to keep improving the customer experience and to reposition marketing faster.
As the person responsible for marketing, think now about whether your teams are well prepared for handling online leads. New skills are needed to use all digital channels profitably and to implement new methods from AI research effectively.
If you take customer needs seriously, you rely on predictive analytics
For B2B marketers, becoming experts in customer needs is nothing new; it has always been a priority in B2B marketing. We already know predictive analytics and the prediction of events and trends from many areas of business. In B2C e-commerce, companies use them for self-learning recommendation systems, and machine builders use them for planning maintenance ahead. So it is time for you to rely on predictive intelligence just as consistently in your B2B business.
Conclusion: the key to B2B marketing success lies in data
Predictive intelligence gives you new methods for handling data that open a view into the future. B2B customers are demanding and sensitive to information processes that do not work smoothly from their point of view. Predictive intelligence and next best action let you anticipate changes in customer behaviour and always choose the next best action. AI offers considerable support here that no B2B marketing department should do without today.
