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Purchasing Potential: How Predictive Analytics is Shaping Modern Marketing

Predictive Analytics in Marketing

Marketers have often found themselves relying on data to help them to gain insights into the performance of their marketing campaigns.

As technology has become more advanced, so too has the quality of insights available to businesses. Today, analytics and their intelligent frameworks have evolved to be capable of effectively analysing the future. 

The path to predictive absolution has been a long one. First marketers aimed to utilise media mix modelling, a form of data-driven insight that paved the way for them to understand long-term impacts for campaigns and their influence over sales. Media mix modelling helped marketers to organise their future campaigns and focus their efforts accordingly. 

As the analytical tools continue to evolve, marketers tap into more detailed attribution models and progress towards user-level interactions.

Models like multi-touch attribution enabled marketers to better understand a consumer’s journey through a sales funnel. 

As technology has moved to accommodate more rich data, marketers have gained the ability to effectively utilise predictive analytics – and Google is the latest major organization to optimize the power of prediction, releasing a whole new batch of predictive metrics on its Google Analytics platform for businesses to sink their teeth into: 

Anticipating Visitor Actions With Predictive Analytics

The predictive features that Google has added into its Analytics setup have been designed to help businesses in connecting better with their visitors and app users alike.

The data looks to aid businesses in gaining understandings of certain defined actions that visitors are likely to take and provides recommendations for effective marketing strategies based on the predictions that it’s made. 

Steve Ganem, senior product manager at Google Analytics explained that “Analytics will now suggest new predictive audiences that you can create in the Audience Builder. For example, using Purchase Probability, we will suggest the audience ‘Likely 7-day purchasers’ which includes users who are most likely to purchase in the next seven days.”

Likely 7-day purchasers
(Image: SocialMediaToday)

This type of prediction could prove to be extremely useful for businesses. If users had a fair idea of the sort of visitors who were most likely to make a purchase soon, they could effectively be targeted with the right ads or offers to entice them into making a conversion. 

So how can Google make predictions regarding the likeliness of people buying a product? The process used takes into account Google’s advanced prediction capacity that’s built on a user’s website data. This helps Analytics to evaluate the likely response actions from each visitor. 

Elaborating on the technology, Google stated that, “for example, users who have studied product details or added items to their carts have given strong signals that they’re already taking ownership of those products.

Analytics goes beyond these simple signals and uses machine learning to find deep patterns of behavior that are unique to your property and show that a user is likely to convert.”

Google is clearly depending on machine learning technology to delve deep enough to provide insights that may not appear clear to businesses at first glance based on how prior users have behaved across your pages. This could leverage some highly effective insights into where you should prioritize your marketing efforts. 

Despite Google clearly offering some significant potential for prospective users, there are a few caveats to contend with.

Firstly, websites will need to be connected to Google’s Analytics platform and must have benchmarking enabled within their data-sharing options.

Sites will also be required to be collecting purchasing, or in-app purchase, event data. Furthermore, Google’s algorithms will need to be capable of tapping into a relevant level of activity in order to offer up predictions with a fair level of accuracy. 

In order for Google to cast actionable predictions, it’s anticipated that some 1,000 users will need to have triggered the relevant predictive condition with at least 1,000 more avoiding it.

This means that Google’s brand new feature – for all its promise – may not be applicable for many smaller and startup level businesses, but for websites with higher volumes of traffic, the insights could play a key role in highlighting subsets of users who are significantly more likely to make a purchase in the coming seven days.

In addition to this, businesses can see which visitors are more likely to avoid visiting your site. This means that marketing campaigns and re-targeting strategies can be launched to win over and cater to the right potential customers at the right time – saving fortunes on non-specific PPC campaigns. 

Also Read: Why Augmented Analytics is the Future of the Data Industry

Winning Customers in The Age of Predictive Marketing

Google may be predictive trendsetters, but various predictive analytics applications have been pioneered across marketing with impressive results. So how can more marketers take advantage of the power of predictive analytics? 

Any process or tool that can aid businesses in understanding the buying habits of customers can be a huge asset because if it’s possible to learn past purchasing trends then it’s entirely possible to anticipate new trends in the future and take decisive measures. It’s the role of predictive analytics to ensure that these measures are entirely accurate. 

Let’s take a deeper look at some of the practices available to marketers in utilizing data effectively, and monitor how predictive analytics is shaping modern marketing: 

Firstly, it’s through predictive analytics that businesses can analyze and forecast seasonal customer behavior. This is particularly effective in optimizing online sales, as successful eCommerce websites are typically those that highlight the products that visitors are most likely to want at the right time. 

(Image: Instapage)

As we can see in the example offered up by Instapage, predictive analytics can be utilized to anticipate the most popular festive products during the passages of time in which they’re most popular.

For instance, artificial Christmas tree sales typically peak a week before that of candy canes and ugly sweaters. This means that marketers could use this data to deliver Christmas tree advertisements earlier and ugly sweater ads later on.

By tapping into the form of targeting tools provided by platforms like Google Analytics, businesses can look to make spend their marketing budget wisely. 

By analysing the quality of traffic, where it’s coming from and where the traffic lands, marketers are able to create highly-targeted campaigns where most of the budget isn’t spent on low-quality traffic from low-quality sources. 

A good tool to help with this is Finteza, which as well as traffic sources, is able to identify the actual quality of the traffic, which, in return, can save thousands of dollars in wasted marketing budgets. 

Below is an example. The ones you should pay special attention to are marked in red – these, especially spam traffic, can easily drain the budget without you even knowing. 

Another high value predictive analytics method can be used through targeting the most profitable products towards the customers who are most likely to buy them.

There’s no point in creating a mailing list or a pop-up advertising campaign directed at selling motor vehicles to children. Likewise, targeting affluent customers for premium quality products is the cornerstone for effective marketing campaigns

Creating scenarios where businesses can retroactively match together successful products with consumer buying habits is another key way of casting accurate predictions in relation to demand.

For instance, if one product has been fully bought out, which customers will be likelier to buy a different product as an alternative?

While this may sound like more of a consideration for your supply chain team, it remains clear that more sales can be made if marketers can create a priority list of items to have in stock based on predictive indications. 

Finally, it’s vital to remember to prioritize customers. Be sure to bring multiple factors into play when prioritizing customers – especially their likeliness for becoming recurring customers.

It’s also important to understand what customers are likely to buy higher-margin products and which will require a smaller chunk of the advertising budget to attract – as well as who will be more likely to initiate returns. 

Also Read: 5 Location Intelligence Myths You Need To Stop Believing Right Now

The Future of Predictive Sales

The best thing about predictive analytics, as shown by the arrival of fresh Google tools, is the fact that the technology is becoming progressively cheaper for businesses to utilize.

Marketing departments no longer need to take on board a host of programmers to construct elaborate algorithms. 

(Image: MaximizeMarketResearch)

As the chart above shows, marketing can expect a period of steady growth in the field of predictive analytics following on from a slow 2020.

In this time we will undoubtedly see further growth from the predictive analytics industry’s key players like SAP, IBM and Oracle, but significantly we will also see further developments among resourceful alternative companies in the field like Marketo and Tableau offering cost-effective solutions that businesses can latch on to. 

One of the most significant variations between the large scale enterprise class brands and that of smaller but resourceful vendors isn’t necessarily down to a lack of functionality or user-friendliness, but more that the likes of SAP and Oracle tend to offer vendor uniformity, whereas IBM boasts a broad database that many users are already familiar with and thus are more likely to stick to their suite of analytical tools. 

Modern marketing has left marketers constantly on the lookout for new ways to make their campaigns more targeted and effective towards leveraging conversions.

In doing so, they can achieve better marketing ROI, more appealing levels of customer experience, and better performance in the realm of customer retention

In order to remain competitive, the data-driven marketers of today are continually set on jumping on innovations in order to develop a more unified marketing measurement.

Predictive analytics – alongside marketing analytics software, AI and machine learning – has emerged as the industry’s most exciting and one of its more effective tools in recent years. 

As eCommerce continues to steadily make its transition into a digital marketplace that’s becoming further removed from brick and mortar stores, predictive analytics can become a significant player in determining the long-term sustainability of outlets. Google’s most recent developments in the industry simply represent a small step towards a bright future. 

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