When a customer walks into a store, salespeople work to understand what the customer’s purchasing needs are and decide pretty quickly if they are window shoppers, impulse buyers, bargain hunters, researchers, regular customers, or even just wandering around and not shopping at all. How you respond in the physical world is no so different than in the digital world. But how do you respond to customer needs when there is no salesperson in the room? The answer is by using predictive modeling linked to web analytics.
Understanding the customer segmentation by propensity score
The analogy of a physical shop translates into eCommerce websites and into predictive analytics. Visitor and customer behaviour patterns can be anonymously observed by your first party web analytics data and using machine learning, you can predict who will buy.
A model can score behaviours according to propensity to buy giving a value between 0 and 1 where 1 would be 100% likelihood to buy. Customers with a value closer to 1 know what they want and are heading for till so they will tend to buy without help, so there is no need to waste effort and budget on these customers that would buy anyway. Customers with lower scores will need much more effort to actually get them closer to any meaningful conversion behaviour, this may not be the best course of action to take given how much resource may be needed to achieve that.
The greatest opportunity lies with focussing on the customers who score around 0.5, the ‘floating middle’, as their behaviour shows they are much more open to persuasion. Those that can be persuaded and influenced given the right nudge can be targeted and brought to sale. This describes how a predictive propensity scoring model ML model can help for a single conversion goal. The opportunity grows wider with more goals added – such as scoring consumer interest across a range of different product categories or offers, and using that to drive a personalised response.
Furthermore AI machine learning (ML) can also help understand how different responses to the customer can shift the likelihood of them taking a particular action, or buying a particular product. Understanding customer intent using propensity scores can guide you into offering the most targeted offer or incentive at the right time for the biggest impact.
Customised user journey and ROI efficiency
One of the main advantages of using predictive modelling is the ability to approach and engage your customer where and when it is the most efficient in order to move the user further in your online sales funnel. This approach can greatly improve and personalise the user experience by allowing your team to identify the customer intent on a case by case basis. For example you can target a customer who is looking for a particular product category with a special offer to increase the conversion potential; or you could provide help to another customer who is identified as having trouble searching for a particular category or product. Similarly, knowing the customers with the high conversion potential will give your marketing team a tool for creating immediate activation actions, like search retargeting.
Having this insight maintains the momentum nudging the customer towards the conversion stage in the decision making process which is efficient and targeted. Just like the in store salesperson.
Customer journey optimization and attribution
Another valuable outcome of predictive intent modelling is the ability to optimise your marketing channels by analysing the ROI for each feature of the website. These insights are extremely relevant for your UX and developments team and will help you to understand which features and touchpoints to focus on, or which ones should be modified or even removed.
The goal should be to develop a data-driven approach to the buying barriers and create a quick and easy sales funnel, which is extremely important for maximising the conversions. Again using the offline analogy, this would be like asking your in store sales team to use their knowledge and insight to help design your store experience so that it works best for the customers.
Dynamic prediction based on user behaviour change
There is a huge advantage to be gained by assigning a changing score to each user as they move around your site and provide you with more clues as to their interests and intent. This approach will allow you to adapt quickly to any behavioural change as the prediction model responds to the new incoming data about your website users. Moreover, various customer characteristics such as location, interests and buying patterns can be identified and linked to your sales and marketing strategy. This helps build a profile of customer preferences for future use in other digital marketing channels such as social media, search or display advertising.
Ultimately, these well timed nudges with additional marketing messages or promotions, can change behaviours and ultimately stimulate more sales and drive commercial goals. Knowing your customer has always been a crucial part of marketing strategy efficiency and data driven approach will allow business to make smarter and better informed decisions, while building intelligent and long sustained relationships with customers.
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Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: firstname.lastname@example.org
Gabriel Hughes PhD
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