A big thanks

To Think with Google APAC for hosting the Google Think Platforms event in Sydney last week at which we had our own partner booth! A great turn out for an event dedicated to data-driven marketing in today’s privacy-focused world.

Want to know more about the benefits of GCP? Read on for more here.

If you’d like to find out more about how Datisan can help you to modernise your marketing, book in for a consultation with our CEO Chris Rozic here.


Welcome to the second installation of our Cloud for Marketing Blog Series! Last time, we looked at the first step in Cloud for Marketing – Understanding the Customer Journey.  Now that that’s mastered, the second step in using the cloud to improve the customer experience is Predicting Marketing Outcomes.

Predicting Marketing Outcomes

By predicting marketing outcomes, we’re able to know ahead of time when a conversion is likely to happen – as a result, marketers can improve their conversion rate by presenting the right message, at the right time, to the right people! The ability to predict these marketing outcomes also allows for better channel planning, by providing information which allows marketers to message their target market in the best channel ahead of time.

Having the ability to predict marketing outcomes through well-connected data, and powerful analytics tools, means that marketers can better determine whether the messaging, creative and channels employed throughout a customer journey is going to be successful. It can also provide a marketer the ability to know where someone is within the customer lifecycle ahead, allowing them to move them through it more rapidly with more targeted messaging. All of this helps to streamline the process and reduce the time to conversion making for a more efficient marketing process.

So – now that we know why the ability to predict marketing outcomes is so valuable, what’s a tangible example of a problem that the cloud could be harnessed to solve?

Conversion predictions

As we know, a conversion is when a customer completes our desired goal; filling out a sign-up form, e-commerce purchase, event responses, etc. Therefore, a conversion prediction is the ability to predict the likelihood of a customer to convert!

In short, It’s an at-scale, automated way of discerning trends in your data, which allows you to observe which customers have a higher propensity to convert. Make predictions about conversion propensity for different groups of customers that can be synced into marketing and other digital platforms. This technology is otherwise known as propensity modelling, conversion prediction, lead-scoring, or visitor scoring – it is all the same tech (often with differing implementation methods). Some also refer to this as purchase prediction, however as we all know, an end goal for a business may not necessarily be a purchase but any number of actions – software downloads, event RSVP’s, database signups, etc… With this ability to notice trends between certain customers and serve them the same messaging and channels, we can target our messaging and increase the likelihood of these similar customers to convert. Even if the predicted channels or messages do not lead to a conversion, the new data can help to retrain the prediction model for even more accuracy. This, is true data-driven marketing.

Conversion Predictions by User and Product

To use an example, a large national retailer utilised this technology to analyse all customer behaviours which preceded a purchase. From this, a machine learning model was created to determine the likelihood of a customer to convert based on their online behaviour. 

During the customer journey, the customers were run through the Machine Learning prediction which, in turn, served them highly targeted messaging across multiple channels in an effort to hasten their conversion.

Without cloud warehousing, achieving this result with such a large amount of data would have been near difficult and costly. By using the cloud in this instance, the retailer was able to predict customer behaviour with higher precision, and deliver a better outcome for both the retailer and the customer. The machine learning made it easier for the customer to discover, and subsequently purchase, what they wanted, and for the client their conversions were reached more quickly.

Cloud for marketing is integral to predicting marketing outcomes, by connecting and storing all of the appropriate data in one central location. This also allows for running machine learning models over this data and at scale, providing a more efficient marketing process, and superior customer experience. 

Stay tuned for the third and final part of our Cloud for Marketing Series, where we will discuss how to leverage the cloud to personalise the customer experience.

In the interim, check out our Datisan Bytes Cloud for Marketing video with CEO Chris Rozic. If you have any further questions or want to get in touch with the team at Datisan feel free to hit us up here.