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Machine-learning makes 2017 the year of Conversion Rate Optimisation

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According to Forrester, for every $100 spent driving traffic to an eCommerce site, only $1 is spent helping it convert.

And yet, if you made an improvement of just 0.2% to your conversion rate, it could increase revenue by 10%.

Just think about that for a second. That’s enough to pay for an entire marketing budget or invest some serious capital in another project. So what’s the hold up?

Why hasn’t Conversion Rate Optimisation lived up to the hopes that the eCommerce industry has for it? The term has been bandied around for years but until now nobody had a firm grip on what it actually means or how it can be done effectively.

This is because it is an incredibly complex and difficult process where the challenges aren’t as simple as they first appear. In some cases, they have been insurmountable at a real time level, until now.

Over the past 12 months things have been moving rapidly in 3 key areas. Today I want to look at these developments and explain how Conversion Rate Optimisation will finally achieve its potential in 2017 and beyond.

1. Automation opens up accessibility

Firstly with the right automation and algorithms in place, Conversion Rate Optimisation no longer has to be such a costly, time consuming and arduous process.

Between manual solutions, informal service-based approaches and claims from existing vendors that they could build it into their offering, buyers were hard-pressed to know who to trust and which areas to focus on with available budget. At the same time, the costs were still prohibitively high for most.

Now, with a machine-learning based approach, Conversion Rate Optimisation is much clearer, and within reach to even smaller retailers.

You take all your data relating to conversion rates, feed it through incredibly efficient neural-networks and then, in real time, your customer experience can be updated to maximise the amount of leads who turn into customers. So this is the first big shift – automation has enabled us to do more with less.

2. Computational power to process big data

Thanks to advances in technology, brands like Puma can now access large quantities of data to inform optimisation with no delay. This is second shift we’ve seen, thanks to massive increases in speed, the realtime web is becoming the norm.

But even though we’ve experienced a trillion fold increase in processing power over the past 60 years, it’s still not as easy as just applying extra power and taking advantage of the benefits.

Sure advancements in technology has made it much quicker to crunch numbers but it still takes intelligent design and the right tools on the back end to really tackle the problem.

The exercise is really one of organising data. For this, we are big believers in the role of neural networks to identify insights quickly and efficiently. Using machine-learning and tools like Google BigQuery, we have reached a tipping point for applying these techniques to daily challenges.

This is the tip of the iceberg. We have recently crossed the line where these options become feasible but the rate of change is not going to slow down. This time next year, we should have even more power to play with, machine-learning tools will have had even more datasets to learn from and we can expect to progress even further.

3. Cost to serve is coming down

Up until now, business models for Conversion Rate Optimisation tended to revolve around a CPM/CPA model. This simply doesn’t work to the customer advantage and result in users throwing money at a problem without seeing any tangible results.

Not only that but the CPA model means every marketing technology you use ends up competing to play their part in the customer journey. And nothing undermines a great customer experience like the technology behind it intruding without offering any further value.

Now cost to serve is coming down quickly. Alternative solutions are now presenting the possibility to simply pay a lower monthly subscription fee for the software, as you do with many other popular products. This preserves the customer experience from the tool intruding on every conversion and battling with adjacent technology just to prove its worth.

The other efficiency comes from running optimisation alongside acquisition efforts. If you can convert more of the traffic you attract, then your acquisition cost can be spread across more customers. Naturally, this means you are getting better ROI right across the chain.

A new era is upon us

Conversion Rate Optimisation no longer has to be a confusing, expanding stable of competing disciplines. It has become its own distinct area, alongside adjacent concepts like marketing automation.

But if you believe Gartner’s Hype Cycle, every technology has to go through a rollercoaster of judgement before it becomes the new normal. Conversion Rate Optimisation has many more undulations to weather before everyone is using it — but with recent advancements, it may well have already overcome the first hurdles.

James Critchley is CEO of Cloud.IQ

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