The Power of an Audience Engine: Why Every eCommerce Business Needs One

Running an eCommerce business can be a pain in the basket. 8 in 10 visitors to your eCommerce site won’t make a purchase[1]. 69.99% of carts will be abandoned before checkout is complete[2]. Those are pretty difficult odds for any marketer to beat.

To remove these headaches, we need to dig deeper. Why are so many consumers leaving without converting? What’s turning them off? And why are they choosing to spend their money elsewhere?

Finding the answers to these questions is vital because today’s consumers are more brand agnostic than ever before. That makes life even more difficult for marketers. We know that three-quarters of shoppers switched to new products, stores, or brands during the pandemic[3]. Thankfully those days are behind us, but challenges remain. Heavy pressure on household finances is forcing many shoppers to make tough choices to save money. Often that looks like shopping around and swapping a favourite brand for lower prices and better deals.

The bottom line? As a marketer you can no longer take consumer loyalty for granted even if your service and standards are still best in class. Getting closer to your consumers, understanding their behaviours, and anticipating their needs in the moment is more important than ever.  AI can help.

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What is An Audience Engine?

An Audience Engine is a system or platform designed to identify and target specific demographics with marketing or advertising messages. Simple, right? The Audience Engine uses artificial intelligence to collect and interpret a range of data. It then creates custom audiences that can be more precisely targeted with marketing strategies and messaging.

In short, an Audience Engine helps you to better understand your audiences, without endless hours pouring over spreadsheets and analytics.

You may already have used an Audience Engine. Social networks, email marketing platforms and search engines are all examples of Audience Engines at work. They each use different methods of data collection and interpretation and depending on what you’re wanting to do, you’ll use them for different purposes. It could be that you want to build brand awareness, increase website traffic or promote certain products or services.  

When we really drill down to its essence, an Audience Engine like QUIN brings artificial intelligence and data science together. We help marketers and brands understand why consumers are taking the actions they are, and then accurately predict what their next steps will be. We do this by assessing the data in real time, so we understand each individual shopper as they’re shopping. The result? Marketing is more personalised and more targeted. You deliver more successful campaigns. You can report back on better ROI.

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Why Is An Audience Engine a Necessity For Modern Ecommerce Companies?

An Audience Engine tells you more about why your visitors are behaving as they are. It also helps you to tailor your strategy so you’re a step ahead. Is this shopper likely to abandon their cart, for example? We can offer them an incentive to stop that happening. While this happens behind the scenes, what your consumer sees is a more relevant and more personalised experience.

Today’s consumers expect a lot from the brands they interact with. More than 71% of consumers expect personalised interactions. That’s a high bar. It’s worth meeting though. 76%[4] of shoppers get frustrated when they don’t get a personalised service. That means any additional degree of personalisation you can achieve can have a direct impact on how successful your marketing strategies are.

The more personalised the experience, the more revenue you can generate. More personalised experiences also lead to a higher average order value and a better customer lifetime value[5]. It’s a win-win.

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How Does An Audience Engine Differ From A Recommendation Engine?

A recommendation engine for ecommerce is a Machine Learning method used to generate product recommendations to shoppers. Recommendation engines will often be used to populate static fields such as ‘people also purchased’ content on product pages. It does this by looking at what products are often bought together, what products are typically bought after the product that’s being browsed and so on.  

Unlike an Audience Engine, the focus of an ecommerce recommendation system is the product itself. It doesn’t consider the specific consumer or their individual behaviour. This means that it won’t help you achieve the degree of personalisation that the majority of today’s consumers expect.

In comparison, an Audience Engine understands the individual human behind the computer screen. It understands why your website visitor is acting that way, and what they are most likely to do next.

A recommendation engine is a fairly blunt tool by comparison. But, it can be made much better. How? By pairing it with an Audience Engine.

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Why Is An Audience Engine a More Strategic Choice For Growing ROI?

Audience Engines focus on behavioural patterns. They use AI and data science to predict what the visitor will do next. The engine considers an array of variables including the product the shopper is interested in, the price level of products they’re looking at and what actions they take next.

An Audience Engine will do something more valuable than simply offering an alternative product. It understands what’s likely to happen. It allows you to personalise at scale. If your visitor needs advice, such as a product recommendation, an Audience Engine understands in that moment which products, at which price levels, to suggest to that individual.

It’s able to do this thanks to deep learning. Deep learning is a subset of machine learning which extracts complex sets of non linear features by complex Neural Network  models like LSTM from massive amounts of data. Where machine learning depends on human assistance to build meaning, the deep learning algorithm does not. It’s especially useful in unstructured data environments like the internet, where massive amounts of clickstream data need to be processed. In practice, this mean’s Quin’s complex nonlinear feature extractions allow it to produce more accurate predictions.When your Audience Engine is used as the foundation layer to really leverage first-party data, you can then add in a recommendation engine for an additional level of functionality.

If the Audience Engine determines that the user is likely to leave your site because they cannot find the product they are looking for, it can trigger dynamic product content by forwarding the products that the visitor is most interested in to the recommendation engine so alternative products can be suggested.

Pairing these two tools together to drive enhanced personalisation has real business benefits. Get it right and you could see your acquisition costs reduced by as much as 50%. Your revenues could grow by 5 - 15%, and the efficiency of marketing spend could improve by 10 - 30%[6].

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How QUIN Audience Engine Works

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The QUIN Audience Engine is a customer centric tool which tracks the behaviours of each and every visitor to your website. QUIN collects and analyses data in real time to predict what that person will be next. It then triggers incentives to lead that visitor through the customer journey to completion.

Audiences created for marketing actions in eCommerce are highly generalised. They tend to focus on  past behaviour, past shopping habits and product preferences. They look to the past rather than driving towards the future. This is problematic and can lead marketers down the wrong path. After all, we can’t assume that someone who visited your site searching for sports shoes a month ago is still interested in making that purchase today.  

Regardless of whether your website visitor is a new prospect or a returning customer, QUIN understands why that consumer is visiting your site right now, and what they want to achieve today.

QUIN also understands why conversions aren’t happening and what’s behind consumer inaction. By focusing on the real-time movements of the visitor and product preferences. QUIN can offer up appropriate incentives at a relevant time, for example by offering a discount code to the shopper when a 99% probability of the visitor abandoning your site is reached.

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Get Started With QUIN

QUIN Audience Engine can help you achieve a wide range of performance and marketing goals. Whether you’re looking to grow revenue, increase ROAS in digital advertising, improve customer satisfaction levels, shorten the path to purchase or promote new products, get in touch to discover how QUIN can help.

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Sources:

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[1] https://www.quinengine.com/post/you-need-an-audience-engine

[2] https://baymard.com/lists/cart-abandonment-rate

[3] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/survey-us-consumer-sentiment-during-the-coronavirus-crisis

[4] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

[5] https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying

[6] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/marketings-holy-grail-digital-personalization-at-scale

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