Posts

Predict online customer intent with machine learning

When a customer walks into a store, salespeople work to understand what the customer’s purchasing needs are and decide pretty quickly if they are window shoppers, impulse buyers, bargain hunters, researchers, regular customers, or even just wandering around and not shopping at all. How you respond in the physical world is no so different than in the digital world. But how do you respond to customer needs when there is no salesperson in the room? The answer is by using predictive modeling linked to web analytics to predict online customer intent. 

Understanding the customer segmentation by propensity score 

The analogy of a physical shop translates into eCommerce websites and into predictive analytics. Visitor and customer behaviour patterns can be anonymously observed by your first party web analytics data and using machine learning, you can predict online customer intent and who will buy.

A model can score behaviours according to propensity to buy giving a value between 0 and 1 where 1 would be 100% likelihood to buy. Customers with a value closer to 1 know what they want and are heading for till so they will tend to buy without help, so there is no need to waste effort and budget on these customers that would buy anyway.  Customers with lower scores will need much more effort to actually get them closer to any meaningful conversion behaviour, this may not be the best course of action to take given how much resource may be needed to achieve that.

The greatest opportunity lies with focussing on the customers who score around 0.5, the ‘floating middle’, as their behaviour shows they are much more open to persuasion. Those that can be persuaded and influenced given the right nudge can be targeted and brought to sale. This describes how a predictive propensity scoring model ML model can help for a single conversion goal. The opportunity grows wider with more goals added – such as scoring consumer interest across a range of different product categories or offers, and using that to drive a personalised response. 

Furthermore AI machine learning (ML) can also help understand how different responses to the customer can shift the likelihood of them taking a particular action, or buying a particular product. Understanding online customer intent using propensity scores can guide you into offering the most targeted offer or incentive at the right time for the biggest impact. 

Machine learning for predicting customer intent

Customised user journey and ROI efficiency 

One of the main advantages of using predictive modelling is the ability to approach and engage your customer where and when it is the most efficient in order to move the user further in your online sales funnel and to predict online customer intent . This approach can greatly improve and personalise the user experience by allowing your team to identify the customer intent on a case by case basis. For example you can target a customer who is looking for a particular product category with a special offer to increase the conversion potential; or you could provide help to another customer who is identified as having trouble searching for a particular category or product. Similarly, knowing the customers with the high conversion potential will give your marketing team a tool for creating immediate activation actions, like search retargeting.

Having this insight maintains the momentum nudging the customer towards the conversion stage in the decision making process which is efficient and targeted. Just like the in store salesperson. 

Customer journey optimization and attribution

Another valuable outcome of predictive intent modelling is the ability to optimise your marketing channels by analysing the ROI for each feature of the website. These insights are extremely relevant for your UX and developments team and will help you to understand which features and touchpoints to focus on, or which ones should be modified or even removed.

The goal should be to develop a data-driven approach to the buying barriers and create a quick and easy sales funnel, which is extremely important for maximising the conversions. Again using the offline analogy, this would be like asking your in store sales team to use their knowledge and insight to help design your store experience so that it works best for the customers.

Dynamic prediction based on user behaviour change

There is a huge advantage to be gained by assigning a changing score to each user as they move around your site and provide you with more clues as to their interests and intent. This approach will allow you to adapt quickly to any behavioural change as the prediction model responds to the new incoming data about your website users. Moreover, various customer characteristics such as location, interests and buying patterns can be identified and linked to your sales and marketing strategy. This helps build a profile of customer preferences for future use in other digital marketing channels such as social media, search or display advertising. 

Ultimately, these well timed nudges with additional marketing messages or promotions, can change behaviours and ultimately stimulate more sales and drive commercial goals. Knowing your customer has always been a crucial part of marketing strategy efficiency and data driven approach will allow business to make smarter and better informed decisions, while building intelligent and long sustained relationships with customers. 

Get in touch with us for expert advice!

Thanks for reading – we hope you learned something through this high-level tour of marketing effectiveness methods. If you want to learn more about expert data analytics using AI, or are interested in how Metageni can help you use your data to grow online, then do get in touch with us: hello@metageni.com

Gabriel Hughes PhD


Does this article resonate with you? Check out other posts to the right of this page.

Can we help unlock the value of your analytics and marketing data?

Metageni is a London UK based marketing analytics and optimisation company offering support for developing in-house capabilities.

Please email us at hello@metageni.com

Awards winner! Metageni with AO.com win ‘Best Use Of AI’

We were absolutely thrilled that in October 2020 Metageni, as data science partners for AO.com, won the prestigious, ‘Best Use Of AI’ at the 2020 London eCommerce Awards. You can watch the announcement as it happened here.

‘Best Use Of AI’ 

Metageni and AO have been working on data analytics using AI/machine learning since 2018. Now in 2020, the combined Metageni and AO team have been selected as finalists for eCommerce awards in the categories of ‘Best Use Of AI’, specifically for AI-based personalisation and conversion optimisation solutions in the UK and Germany in the midst of upheaval and huge growth in online retail sales linked to the global pandemic.

Responding to customer signals in real-time

Online retail giant AO has a market-leading position supplying an expanding range of electricals throughout the UK and Europe, and in 2019 decided to develop solutions to provide visitors to their eCommerce site personalised shopping experiences. More specifically, they tasked Metageni to create a machine learning solution that could identify types of customers to the website in real-time based on their predicted purchase behaviour.

When a customer walks into typical bricks and mortar store there are salespeople to assist towards making a purchase. A salesperson does this by watching and talking to them, in order to understand the customer. What the salesperson is attempting to do is ‘classify’ the customer to understand what the customers purchasing needs are. Customers may be window shoppers, impulse buyers, bargain hunters, researchers, new customers or regular repeat customers, and more.  

The idea here was to use online customer data signals to predict conversion behaviour with machine learning and respond accordingly. The Metageni team used data signals from the web and sales and explored trillions of possible model and data variants using genetic algorithms to find the best models. Working with AO engineers allowed rapid deployment into live eCommerce production. Rapid iteration of the models was been possible leading to even stronger results.

Results: proven commercial success  

In recent weeks AO conducted independent robust A/B tests to measure conversion uplift due to the AI versus the underlying conversion activity which has increased due to the pandemic related shifts in customer buying –


These A/B tests showed an impressive and significant increase in the AO conversion rate over and above a matched control group in the UK. Furthermore, the result was replicated in Germany with an even big jump in conversion rate compared to the control group. Both tests are statistically significant, and while AO.com do not wish to share specific numbers for reasons of commercial interest, the results translate into long term revenue potential worth millions of pounds.  


A better outcome for AO and for AO customers 

As well as yielding great results, the approach used has allowed AO to understand more about their customers to improve the online purchase experience for everyone. The whole team at AO and Metageni have worked closely together to achieve this success – 


” Metageni have done and continue to do a fantastic job in using machine-based learning to give AO.com substantial ROI.

The last few months have been an unprecedented time for us and many retailers. In this period, Metageni has delivered again, completely retraining our custom machine learning models in both the UK and German markets, responding to the dramatic shifts in customer behaviour.

I wish the Metageni team every success with this award entry. They’re a great partner for our eCommerce function.”

David Lawson, Managing Director, AO.com


The teams at AO and Metageni continue to look for ways to use AI technology to better improve commercial and customer outcomes. 

Thank you for reading about our success. Can Metageni help unlock the value of your analytics and marketing data? Feel free to email us at hello@metageni.com for advice on how you can use data analytics and AI to grow online. 

Find insight from the user journey

You interact with your users, customers and potential customers in so many ways, through many channels and at all stages in their journey. So, how do you use data to create insights which improve their experience and grow your business?

There are three broad approaches to this problem out there. These are:

  1. the digital path approach
  2. the statistical modelling approach
  3. the user experience approach

Let’s explore these and think about how each can lead to new insight: what else we might be able to do with the user journey.

1. Digital path analysis of user journeys

At Google in 2008, a small team in London started looking at custom DoubleClick ad server platform data tracing the ad exposure and click path across digital channels to conversion on an advertiser web site (the team was headed up by yours truly). At around the same time others at Google were looking at measuring the assisting role of generic search clicks to brand search conversions. These analyses became the underpinnings of user path analysis, search funnels and attribution modelling, now built into Google Analytics.

The first task of the attribution pioneers was challenging the built-in assumption that ‘last click wins’ when measuring digital conversion journeys. Today most companies still rely on this flawed conversion tracking logic inherited from the earliest days of internet marketing. The issue is that when the customer sees an ad or searches around to research a product, they often decide what to buy first and then only after that do they make the final last click to the sale: last click does not in fact ‘win’.

More recently user path analysis based on behavioural data has become widespread and is now serviced by small army of marketing attribution technology companies. Data on marketing and web analytics has the potential to provide a complete view of the user path both off and on the website. Still many companies are only just waking up to these new ways of measuring marketing impact and a recent eConsultancy survey found that only 39% of marketers believe they understand customer journeys and adapt the channel mix accordingly.

The analytical challenge today is finding the most robust method for data driven attribution, ensuring that data on the non-converting paths are used to infer the contribution of each channel, campaign and ad. Several companies claim to do this, and clients of Google Analytics Premium can leverage Google’s own data driven modelling approach to optimise their digital marketing spend.

If you want to know what role a digital click typically plays in user journeys for your industry in your part of the world, you can even access a free data tool by Google to do this at a very high level.

In this view user journey analysis would seem to be a solved problem at least for marketing ROI, but of course it is not that simple as there are huge challenges remaining. Foremost, the data is generally incomplete: users are rarely tracked across part of the journey, even if the tracking is set up right, they use different devices and browsers which miss out the whole picture. People also have a habit of being influenced by channels you cannot so easily track in the same data set as your trackable analytics, like offline ads, call centres and not forgetting, competitor offers. In addition the data you get is usually anonymous, with limited data on who exactly you are influencing, which matters a lot because different types of people are influenced in very different ways.

So much for method one: it’s a great data story when it can tell you how to better allocate your online spend, and optimise your web site, but it is only as good as the incomplete data that goes into it.

User-journey-insight-2

2. Statistical modelling for user journeys

While digital path analysis takes the problem from the bottom up, an older method looks from the top down, tracking sales and channel influences over time, and using regression modelling to identify the role of each channel. The same techniques can be used to compare steps in a user journey on a site, tracking views and clicks over time and modelling how they influence each other in aggregate.

A great advantage of this approach is that it can look at relationships beyond what can be tracked at user level. Notably the role of offline channels can be included. The statistical techniques are well understood and applied methods like market mix modelling are taught at many business schools and universities. Given the potential to get more of a complete picture, some are advocating this type of approach (for example this post).

The downside is that while you gain in non-tracked data, you lose granularity in the tracked data. The data about each step a user takes is aggregated with the steps of all other users, leaving out the important path information. As a result the technique has somewhat fallen out of fashion, despite the advantages in linking all online and offline channels that it potentially offers. Also, if you use statistical modelling beware: a non-expert cannot tell the difference between a spurious regression model and a robust one, but the former kind is all too commonly peddled even in large organisations. So, make sure you have plenty of data and a statistician who knows what they are doing.

User-journey-insight-3

3. User experience for customer journey mapping

The third approach which many companies follow is to storyboard the customer journey across all touchpoints. The idea is to create a visual map of how different types of customer interact at all stages of the journey through to sale and ongoing engagement, across all touchpoints. Each type of customer is identified by a customer persona. Developed by UX design professionals, the map then becomes a useful way to exploring the blocking steps or friction points in the customer journeys, and for making sure that all channels are working together in synch.

The main upside of a customer journey map is the ability to summarise and communicate the user experience to key folks in product management, marketing and senior management. A good map can create empathy for the customer and focus minds on the central problem of improving their experience.

Because mapping is a communication tool, it makes for a great data story. However, of the three approaches outlined here it runs the obvious risk of being strongest on the story, but weakest on the data to back it up. To be data driven customer journey maps can work provided they are grounded in research derived customer personas and each stage in the journey is linked to metrics and KPIs that can be tracked to understand how the journey is building new and repeat business.

The true actionability of a customer map lies it the way it highlights the bottlenecks in the customer journey, so that these can be given due attention. Using market research personas, metrics and KPIs grounds the focus on the steps which matter, and guides data driven priorities. Not a bad approach, however unlike the other two, it is unlikely to yield information about the true incremental impact of any changes you make.

For more information on the customer mapping approach take a look at these this post by Adobe and this one by webdesignviews.

User-journey-insight-4

The best of all worlds

So, is it possible to get the best of all worlds? The simple but expensive answer is that you could do all the above and explore areas of agreement and inconsistency. A more manageable answer would be that if you are mainly a digital play, path analysis and attribution make sense. If you have significant offline to online interactions, and your business is high volume, you should explore statistical modelling. And whatever your business model, a high-level customer journey map can help you to understand the overall picture.

Also, if you do make changes to how you interact with your customers, do not forget to apply experimentation and AB testing to validate and optimise the journey.

Finally, if you really want to understand your customer journey, you must try going through the process yourself. After that, try the journey again but this time imagine you know nothing about your product, you are on your iPad and you are in a big hurry. You will soon find room to improve.

Gabriel Hughes


Can we help you become more data driven?
Please email us at hello@metageni.com

Pages

ConversionGeni

Award Winning AI Boosts Conversions

We developed ConversionGeni for AO.com adapting models to their Ecommerce customers. The impact was clear and in 2020 independent AB tests proved remarkable incremental lift in conversions. This led to Metageni + AO winning “Best Use of AI” in the London Ecommerce awards

We are delighted to now offer this solution to online retailers looking to boost their online conversion rates.

Patterns of visitor and customer behaviour can be detected from your first party data and using machine learning we can predict who will buy.  The model guides how you respond to customers to achieve an amazing uplift in conversions. Targeted nudges with additional marketing messages or promotions, can change behaviour and stimulate more sales.

Analyse, model, predict and then respond to your customer conversion funnel, in real time.

Key benefits

Once you use Machine Learning to predict outcomes you can respond to your customers on a case by case basis e.g. spotting when a potential customer is interested in a specific product but wavering before purchase; or another having trouble searching for a particular category. Linking predictive scoring to dynamic site features delivers AI level site personalisation.

Since you can see who is more likely to convert, you can devise ‘buy now’ stategies to pick up the borderline cases and close those less certain sales.

Each site feature and product offer helps or hinders sales. A valuable outcome of conversion prediction are ROI per feature reports so you can see what elements of the site to focus on, supporting site designers and commercial teams with data driven insight.

Our models are designed so that a specific customer can have a changing score within their visit, as the prediction responds and adapts to new behaviour data coming in.

Customer characteristics, as represented by GDPR compliant data about customer segments, locations, device use, repeat purchase patterns and product interests, can all be linked to sales through this type of model.

You can then target new customers through the major ad platforms like Facebook and Google based on what you know about their predicted likelihood to buy.

Our custom models can be deployed in live production environments whatever your tech stack.

Scores can be written back to CRM systems for activation via email and offers, or scored in real time via our API for onsite activation.

Is this for you?

  • ConversionGeni is a custom machine learning model which predicts which online visitors will take a future action, such as a purchase.
  • It is trained exclusively on your first party analytics data, predicting based on anonymous customer data and patterns of behaviour.
  • ConversionGeni models can linked to your website via an API for dynamic personalisation targeting conversion uplift.

Learn how other clients have worked with us

Interested to know more? Emails to hello@metageni.com