Optimising Revenue and Costs in Campaigns with Predictive Analytics, Deep Learning, and AI

Predictive analytics, deep learning, and AI are rapidly becoming the go-to tools for businesses looking to optimise their revenue and costs. But what are they, and how do they work together?

Predictive analytics^[1] uses historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.

Here comes the science bit: Deep Learning^[2] is a subset of machine learning. It uses artificial neural networks to simulate the human brain.

AI, or artificial intelligence, is probably the most widely known of the three - the simulation of human intelligence in machines which are programmed to think and act like humans.^[3]

This combination of technologies allows organisations to make better decisions based on real-time data. This may sound all well and good in theory, but how do these technologies work in practice? Let’s find out.

Predicting Customer Behaviour

Do you feel burnt out by the sheer volume of data your campaigns generate? You’re not alone. You know that personalization is important but, research[4] suggests that 46% of marketers spend more time preparing and segmenting data than anything else. Translation: you’re up to your eyes in spreadsheets and have very little time left over to do anything with that data.

Predictive analytics and deep learning can be used to predict customer behaviour and improve campaign targeting. With the help of these tools, an audience engine analyses customer data to identify patterns and trends that can be used to predict future behaviour. This information can then be used to develop targeted marketing campaigns that are more likely to resonate with customers and drive sales - while also delivering the personalised experiences that today’s consumers crave.

Additionally, predictive analytics and deep learning can be used to identify the most effective channels for reaching customers. This intel means you know just where to spend your marketing budget to get the biggest bang for your book. Bonus: you’ll look like a complete rock star at your next quarterly review.

Optimising Ad Targeting

Your audience engine for ecommerce means you have a good idea of how your customers are likely to behave. And why. Now, we need to improve how ad campaigns are targeted so they reach more of the right kinds of people and generate more conversions.

Questions we need to answer:

· What kinds of ads are likely to reach our target audience?

· What type of ads are most likely to provoke an action?

· What times of the day are our most valuable prospects online?

· Where should we place our adverts?

AI can interpret customer data to help answer these questions. Those answers can be used to carefully refine targeting and finely tune the ad strategy to show more of the right kinds of ads, to more of the right kinds of people.

Improving Campaign ROI

Predictive analytics is a powerful tool that can help businesses improve their campaign ROI. In short, better results by spending less money. By using data from past campaigns, predictive analytics can identify useful patterns and pinpoint trends that can help you to make more informed decisions about which campaigns to invest in and which to avoid.

What this really means is you can cut out a lot of the costly testing which would otherwise eat into your bottom line ROI and instead focus your budget on the areas where you can feel sure you can be successful.

‍Reducing Customer Acquisition Costs

There is a cost associated with every new customer you acquire. That said, you need to make an initial investment in order to generate a return. This may include allocating budget to the very tools we are talking about right now as well as paying for ad space, ad creative and so on.

Henry Ford once famously said, “A man who stops advertising to save money is like a man who stops a clock to save time.” He had a point: these costs cannot be avoided. However, they can be reduced: the trick is to get back more than what you spend.

Deep Learning algorithms, predictive analytics and AI are capable of predicting which customers are most likely to convert and are instrumental in creating effective and targeted campaigns. They’re key to achieving a higher return on investment - and drive sustainable growth in the long term.

Dynamic Pricing

Even before the days of ecommerce, price adjustments were a thing. If you owned a store and walked past a competitor advertising the same product much cheaper in their shop window, odds are you’d immediately race back and cut your price, too. The problem is that you’d have to physically visit all rival stores to do your price research.

Price adjustments today are faster paced, and with so many competitors online, keeping track of price changes in order to stay on top of the game would be a full time job in itself.^[5] And if you’re busy price-checking all day long, well, who’s running the shop?

AI is here to help. By continuously adjusting prices based on factors such as demand, competition, and customer behaviour, it can help your business always stay price competitive - with little to no effort required on your part.

Improving Customer Retention

You did it! Someone saw just the right ad, they liked the product, and you made a sale.  Mission accomplished, then? Not at all: this is but the start of the next and much bigger challenge called customer retention.

Unless you can reach an infinite number of potential customers every day (spoiler alert: you cannot), then you simply can’t rely on making a single sale to some of them and calling it a day. Repeat customers are the lifeblood of any retail business, online or offline: making your customers happy enough they will buy from you again is vital to your shop’s survival.

Retaining customers is a challenge with 30 to 40% making brand swapping a habit post-pandemic[6]. As you might have guessed at this point, this is a challenge that predictive analytics can help with. It enables companies to predict which customers are at risk of churning - which is to say, stop purchasing from you within a certain time frame^[7] - and then target them with retention campaigns to address their specific needs and concerns.

Successful Campaigns Without Discounts

Discounts have long been a popular marketing strategy to attract customers and drive sales – and it’s easy to see why. However, not all businesses can afford to host sales more than a few times a year, and therefore discounts and promotions cannot become the only incentive driving sales.

Successful campaigns can be achieved without providing discounts through the help of - you guessed it - recommendation engines! By leveraging the power of predictive analytics, Deep Learning, and AI, a recommendation engine paired with an audience engine can help a business use its available data to create better product suggestions.

An audience engine for ecommerce can be used in tandem to predict each shopper’s likely next action and step-in with targeted offers and solutions to help move them further along the path to purchase.

Get In Touch With an Expert to Find Out How QUIN Can Help Increase Your ROI Today

By investing in predictive analytics, deep learning and AI (and knowing how to integrate them), businesses can optimise their campaign costs and improve revenue. QUIN’S exceptional audience engine reacts to your audience’s needs in real time and can grow your revenue by as much as 30%. Get in touch to find out how we can help you unlock the full potential of predictive analytics, deep learning, and AI across your campaigns.

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Sources:

^[1] https://online.hbs.edu/blog/post/predictive-analytics

^[2] https://www.ibm.com/topics/deep-learning

^[3] https://www.ibm.com/topics/artificial-intelligence

^[4] https://emarsys.com/press-release/more-than-a-quarter-of-marketers-have-too-many-tools-in-their-martech-stack/

^[5] https://www.business.com/articles/what-is-dynamic-pricing-and-how-does-it-affect-ecommerce/

^[6] https://www.mckinsey.com/featured-insights/themes/how-to-drive-customer-loyalty-and-growth

^[7] https://blog.hubspot.com/service/what-is-customer-churn

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