3 Big Benefits of Personalisation in eCommerce & Actionable Tips for Your Business

3 Big Benefits of Personalisation in eCommerce & Actionable Tips for Your Business

Want to learn how the benefits of personalised marketing in eCommerce can help skyrocket your business? Read all about it in our guest blog post, written by personalisation and optimisation experts, Fresh Relevance.

Personalisation in retail is nothing new. Retailers and brands have been treating their customers as individuals well before the advent of online shopping, and for good reason. Personalisation works.

Personalisation helps retailers anticipate their customers’ needs, save them time, and provide offers that resonate with them.

In fact, research from Fresh Relevance shows that almost half of shoppers (41%) would drop a retailer who sends irrelevant offers, and one in four actively want to be sent offers and recommendations based on previous purchases.

But What Exactly is eCommerce Personalisation?

What is eCommerce Personalisation?

Ecommerce personalisation is when eCommerce stores deliver personalised and relevant experiences throughout their website and across email through dynamic content and product recommendations based on shopper data – such as location and past purchases.

In other words, instead of showing everyone the same generic content or products, eCommerce personalisation tailors these experiences to each individual shopper’s preferences and needs.

Fresh Relevance partners with UniFida to deepen the level of personalisation based on the UniFida Customer Data Platform.

UniFida provides identity resolution to build a single view of a customer and deeper insight and targeting, such as customer value, dormancy, or responsiveness to price reductions.

This provides real-time behaviour and insight combined with the power to act on it and enable cross-channel personalisation.

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What are the Benefits of Personalisation in Marketing?

There are numerous benefits of personalised marketing in eCommerce, and we could create a long list of them. But to save you time, we’ve narrowed it down to the top three, along with actionable tips on how to incorporate personalisation into your business strategy.

Benefit 1) Personalisation Increases Conversions

increasing conversion rate

In today’s crowded digital landscape, competition is fierce and keeping shoppers’ attention is becoming increasingly vital.

Personalisation helps eCommerce stores create seamless, tailored experiences that reduce bounce rate and friction and ultimately boost conversions.

The average Fresh Relevance client doing web personalisation sees an 8% increase in sales. What’s more, research from Epsilon shows that 80% of consumers are more likely to make a purchase when brands offer personalised experiences.

In fact, 74% of consumers actively feel frustrated when website content isn’t personalised.

Here are some conversion-boosting personalisation tactics for eCommerce:

Tip 1) Geotargeting

A shopper’s physical location has a big influence on their interests and needs when engaging with an eCommerce store. So, it makes sense to treat customers differently based on where they are located and use geotargeting tactics to ensure that they see offers most relevant to their current context.

Try adding details of the shopper’s nearest store for click-and-collect options on the cart page. Or display weather-based dynamic banners on your website and in emails to showcase appropriate products.

Tip 2) Product Recommendations Based on Personification

Every shopper has different tastes and preferences. Ecommerce stores should take note and treat each visitor to their site accordingly to ensure they resonate with everyone.

To convert visitors into customers, try displaying ‘people like you buy’ product recommendations.

This type of recommendation looks at the shopper’s past browse or purchase history and compares it with other shoppers who have viewed those products, using a machine learning algorithm to recommend the most likely eventual purchases.

It appeals to the shopper’s desire to follow the wisdom of the crowd, putting their purchase decision into the capable hands of their shopping predecessors.

Shoppers can also have different preferences when it comes to price. A tool like Fresh Relevance’s
Price Affinity Predictor uses AI to predict the price level that will appeal to each new website visitor, helping you recommend the most relevantly priced products and keep visitors on your website.

Tip 3) Triggered Emails

Triggered emails are an effective way to deliver personalised, real-time content to shoppers at the moment they are most likely to convert.

Two types of triggered emails to add to your email marketing toolkit are cart and browse abandonment emails. These messages are triggered when a shopper abandons a browsing session or their shopping basket without making a purchase.

The average Fresh Relevance client doing both cart and browse abandonment emails sees a sales uplift of 16%.

Other types of triggered emails proven to boost conversions include price drop and back-in-stock alerts.

Uncover More of Your Customer’s Data With UniFida

Benefit 2) Personalisation Improves Customer Loyalty

increasing customer loyalty

Consumers have come to expect a personalised experience from retailers and brands and are more likely to return to stores that fulfil this expectation. According to Salesforce, 70% of consumers say a company’s understanding of their personal needs influences their loyalty.

With a wealth of browse and past purchase data available at the average eCommerce store’s fingertips, it has never been more possible to foster customer loyalty through personalisation.

Here are some eCommerce personalisation tactics that improve customer loyalty:

Tip 1) Product Recommendations Based on Behavioural Data

eCommerce stores can start their loyalty campaigns straight after a customer has made a purchase, with
post-purchase emails containing product recommendations that complement the item they’ve just bought.

Making recommendations based on the customer’s frequently browsed and purchased product categories is another effective way to foster loyalty and resonate with existing customers.

Including these types of recommendations in your email newsletters helps bring customers back to your website, and displaying them on the homepage encourages customers to keep browsing and make a purchase.

Tip 2) ‘Welcome Back’ Popover

eCommerce stores can help customers pick up where they left off when they return to the website by including a popover to remind visitors of their last viewed product. Customers will appreciate the gesture, as they do not have to waste time finding the product they searched for last time.

Tip 3) Replenishment Emails

For customers who have purchased perishable products that will need replacing, such as medicine or cosmetics, triggered replenishment emails serve as a useful reminder that the time to reorder is approaching.

This type of triggered email helps boost customer loyalty and increases the likelihood of repeat purchases.

Read More: Why Email & Digital Isn’t Always the Greener Option

Benefit 3) Personalisation Encourages Customer Advocacy

happy customers

Personalised experiences lead to happy customers, and happy customers often feel compelled to recommend brands and retailers they love to their network.

A study by Forrester found that 77% of consumers have chosen, recommended, or paid more for a brand that provides a personalised service or experience.

Beyond customers becoming advocates without any prompting, thanks to a fantastic, personalised experience, eCommerce stores can use personalised post-purchase campaigns to encourage customer advocacy.

Tip 1) Utilise UGC

Try adding user-generated content (UGC) to your post-purchase emails to give customers inspiration on how to use or wear their new product, and let them know how to share their own UGC by telling them which social media channel to use and the relevant hashtag.

Tip 2) Send Post-Purchase Emails

Post-purchase emails are also an effective way to gather reviews and ratings. Frequent buyers are an ideal source for positive reviews and ratings, so be sure to encourage them to share their experiences by sending review requests.

Read More: Why Don’t Existing Customers Shop Like Prospects?

Final Thoughts: eCommerce Marketing Personalisation is Key

The eCommerce space is becoming increasingly competitive, and shoppers can switch to another brand at the click of a button. To convert and retain today’s consumers, providing personalised experiences isn’t a nice-to-have; it is essential.

A real-time personalisation and optimisation platform like Fresh Relevance helps digital marketers and eCommerce professionals drive revenue and customer loyalty.

Combined with UniFida, this enables identity resolution, single customer view and enhanced insights to enable cross-channel personalisation.

Download Fresh Relevance’s free eBook to learn more about building personalised experiences for your customers with Fresh Relevance.

This guest post was written by Fresh Relevance, a personalisation and optimisation platform for digital marketers.

Last updated: 09/12/2024

FAQs

What is the Power of Personalisation in Marketing?

Personalisation in marketing can greatly lead to improved customer experiences, increased loyalty, and higher conversions. With the wealth of data available to eCommerce stores, implementing personalised tactics can be a game-changer in the competitive online marketplace.

Customers now expect to see customised recommendations, targeted emails and promotions, and a seamless shopping experience tailored to their individual needs.

What are the Benefits of Product Personalisation?

Product personalisation allows for a more tailored shopping experience, which leads to increased satisfaction and loyalty. Customers are more likely to make repeat purchases when they feel their needs and preferences are understood and catered to.

Additionally, personalised product recommendations can help customers discover new items they may not have found on their own, leading to increased sales and revenue for the eCommerce store.

Why is Website Personalisation Important?

Personalisation on a website is essential because it creates a more individual and relevant experience for each visitor. This can lead to increased engagement, longer time spent on the site, and, ultimately, more conversions.

How Does eCommerce Personalisation Improve Customer Loyalty?

When visitors feel like a website understands their needs and preferences, they are more likely to trust the brand and make a purchase. This can lead to increased customer satisfaction and loyalty as they continue to have positive experiences with personalised content and recommendations.

How does a customer data platform work?

how does a customer data platform work?

How exactly does a customer data platform work, and help marketers leverage data to gain a better, more accurate understanding of customer behaviour?

Ingesting and integrating data

The first element in understanding this is ingesting data. CDP’s ingest customer data from multiple sources. Typically, these will include website data, paid digital, transactional, direct mail, retail, email, and call centre. All data received by a CDP will relate in some shape or form to a customer. The data is usually sent to a CDP using an API or via an SFTP site.

Customers have multiple identifiers and these change over time, such as mobile phone number, email address, cookie ID, postal address, customer reference or landline number. This data is collected, and these identifiers are used to generate a single customer view also known as ‘Identify Resolution’. For example: if someone logs into your website with their current email, but with a different cookie ID, then the new cookie ID is added to that particular customer record on the assumption that they are using a new device. Equally, if a new transaction record is received with the same customer reference, but a new address, then a new address is added on the basis they have either moved or added an extra residence.

As new data is ingested, each record goes through what is called the ‘purning’ process. This is the stage at which the record’s personal identifier(s) are matched against all other customer records that are held in the CDP until a match is or is not found. At this point the data may be matched into an existing single customer view or a new one created. Each recognised customer is given a permanent unique record number or ‘purn’.

Identity resolution

Is at the heart of a CDP and is central to all the rest of its functionality. A good CDPs’ functionality is rooted in the knowledge that people have multiple identifiers, and that these identifiers can all change. Over time many or all of these identifiers are likely to change for an individual. The CDP should keep a history for every one of every version of these, although regarding the latest versions as most likely to be current. This collection of identifiers is what it calls on to build the single customer view.

The data in a CDP is held in what is called a schema. This is the way in which the data is organised. Every organisation using a CDP will need their own schema although within an industry, schemas will have a lot of similarities.

Engineering derived data

Engineered data is important for the value it provides for selecting specific customer groups for communications or developing customer insight. It can comprise any variable that can be calculated using an algorithm or other means from the raw data in the customer data platform.

Data engineering can take many forms, from simple examples like banding variables such as age, to more complex ones like keeping a counter on customer’s total historic value. A major use of engineered data is in developing and recording scores derived from algorithms such as propensity models.

An example of an engineered data field is where we want to know what each customer has contributed to a business after the cost of acquiring them. We can then:

  • Use historic purchase data for each individual in say their first and second year since recruitment
  • Deduct the cost of acquisition which can be derived the channel they came in from
  • Deduct the cost of communications sent to them in the same period which is held in the contact history area
  • Calculate an individual customer contribution

Engineered data is updated at an individual level every time a relevant event happens; so, each new home shopping purchase, eCommerce transaction or physical retail transaction can lead to a changed score in the engineered data section. A great benefit of engineered data is that it allows you to base axis for charts or selections for campaigns on these additional variables.

Analysing customer data

A CDP is essential for gaining a full and accurate understanding of customer behaviour. For instance, without a CDP that combines web browsing history with transactions, it would not be possible to understand the relationship between the two. Again, if individual contact history is not held against a customer record then the effectiveness of campaigns that are sent to the customer, and to which the customer may respond through different channels, cannot be accurately measured.

The CDP builds the single customer view, and it is against this that customer analysis can take place. It provides the dataset that becomes the one authoritative source of information about customer behaviour for an organisation. With this in place decision makers have a firm basis on which to proceed.

There are so many aspects to the analytical tools that can be used to analyse customer data that there is little merit in trying to list them all. Some are built into the CDP and others require data to be first extracted from the CDP and then transferred to them. What matters is that they have the best possible customer data set to analyse.

So, the results from customer analysis form the basis on which key decisions about customer marketing can be made. These include such areas as:

  • Customer acquisition (targeting and channel choice)
  • Digital planning
  • Product development
  • Customer relationship management
  • Salesforce management
  • Pricing

Even corporate mergers and company valuations.

Given how important these decisions are, it makes good sense when designing a CDP to first start with a list of the kind of results that will be required from customer analysis so that for instance data is held with sufficient granularity to make these possible.

Connectivity to external systems

The CDP can support other systems in their personalisation and management of customer communications. Typical examples are:

  • Providing customer selections for email marketing systems
  • Customer segmentations for web personalisation technology
  • Names and addresses for postal marketing
  • Target audiences for social media

So just as the CDP ingests data from multiple sources it also provides selected data to external systems. These connections are usually made via an API or via transfer of data to an SFTP site.

Delivering personalised customer experiences

Within the CDP we expect to find functionality for the selection of specific customer groups either on a one-off or on a recurring basis. These groups are usually selected for output to external systems that manage the actual communications. The selections themselves can be simple based on Boolean logic rules, or they may be more complex based on propensity scores applied within the engineered data. They can also be based on triggers, such as a new customer having just been recruited.

The CDP needs to enable these different types of selection, and crucially record what contacts each individual customer has been selected for. Functionality is also required for test and control, and for including source codes with the selection.

Associated with delivering personalised customer experiences needs to be functionality for measuring the results of campaigns. This is often automated within the design of the CDP and should always include the ability to attribute results such as orders back to campaigns, even if they respond through different channels.

What are the costs for a customer data platform?

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.

Why we’re reselling Fresh Relevance’s personalisation capabilities

Why are we now reselling Fresh Relevance’s personalisation capabilities alongside UniFida’s customer data platform?

fresh relevance

One of the key uses of a customer data platform is to ensure that decisions made about how and what to communicate with each individual customer across any channel are based on one consistent single customer view.

UniFida’s customer data platform provides the single customer view, combining both online and offline data, and we have partnered with Fresh Relevance to use their personalisation capabilities for website and email personalisation.

So, what does this mean in practice?

Let us provide some quite simplistic examples (we are sure that you will be capable of creating some much better ones):

One of your dormant customers, Mr Smith, is browsing your website and we recognise him from his cookie ID, or because he provides an email address; Fresh Relevance has already been provided by UniFida with detailed information about Mr Smith’s past purchases, and can use that to remind him that he often prefers category ABC. And it does this in combination with a valuable offer should he decide to reactivate himself after not ordering for so many months.

Another customer Mrs Cook has filled her basket on several occasions but never gone as far as making a purchase online. UniFida knows that Mrs Cook is in reality a good customer who normally orders by phone. The website, powered by Fresh Relevance, then offers Mrs Cook the opportunity to chat to an agent, and to turn her basket into an order having discussed her potential purchase with one of your agents, who understands when chatting to her what she has liked to purchase in the past.

In a third case you decide to reward previously loyal customers who have not so far ordered this season. UniFida knows how much Mr Jones usually spends at this time of year, so Fresh Relevance can personalise an email that gives Mr Jones a discount based on his previous season’s spend. In this way the loyalty bonus is only offered to customers who have not ordered, and not wasted on those who have done already.

In reality there are thousands of different ways in which Fresh Relevance’s personalisation capabilities can work with a customer data platform like UniFida. The only limitation could be your fertile imagination’s capacity to come up with much smarter ideas.

What we like to do is to put these tools in your hands, so that you can experiment and find out what really works best for your customers. One thing we do know is that personalisation, when truly relevant to an individual customer, works wonders for your sales.

If you would like to talk more about personalisation and how to implement it for your business, please get in touch!

 

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.

Can marketers ignore AI and thrive?

Can marketers ignore AI and still thrive in today’s competitive market?

“Artificial intelligence (AI) is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.”

Without wanting in any way to dismiss AI, which can be incredibly useful in many different arenas like driverless cars, we believe that its role for marketers has been exaggerated. Indeed most of what we need is barely within the spectrum of technology normally defined as AI.

For most cases where marketers want to execute one to one personalisation, the area where AI could most appropriately be applied, the conventional propensity model is all that is required.

What is most often meant by personalisation is the means to carry out selections of customers for communications based on their expected response or their particular needs.

Here are some examples where personalisation is often used:

– Targeting apparently dormant customers (e.g. those who in fact have a high probability of being reactivated) with offers to reactivate them
– Making a relevant offer (e.g. based on customer characteristics that imply a higher than average probability of purchasing in a particular product category) of a specific item
– Responding to risk (e.g. predicting which customers are likely to cancel policies or stop ordering) so that they can be presented with good reasons not to abandon their policy or purchase

In each case a conventional predictive model can be built, using an historic set of customer data, where a target customer population can be distinguished from the remainder who have not evidenced reactivation, response, or reduced risk of lapsing.

The key point is that we are not asking for this kind of model to be adaptive to rapidly changing circumstances; instead it relies on past customer behaviour to inform what is likely to happen in the present or near future. And this is because human behaviour in most situations where we are reacting to propositions put to us by marketers tends to remain reasonably constant.

We have even tested propensity models on historic data going back four years and found them to work well.

propensity model on historic data

However, to build and apply these conventional propensity models there are some essential requirements:

– a single customer view to provide the greatest possible depth of customer data
– the ability to update model scores each time new data about an individual arrives
– the availability of data scientists armed with tools like R, SPSS, or SAS

A typical predictive model will take the form of an algorithm which will attribute a probability score to each member of a customer base; we judge the success of these models by the extent to which these scores are differentiated from random in the way they can be used to predict customers’ behaviour.

Looking at a recent model we built for the reactivation of dormant customers, the top customer decile had an index of 330, compared to the bottom decile’s 17.

In another case, a model for product category preference had a top decile index of 601 and bottom decile index of 11.

For most of us marketers these results will be seen as providing a huge improvement on random and quite fit for purpose. However, the methodology used does not in our opinion qualify the models to be correctly described as AI.

If you would like to talk to us about developing propensity models for you, or providing the technology for a single customer view, then please do email us back

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.

Personalisation in digital marketing: how personal do you want to get?

There is a lot of interest amongst marketers in personalisation in digital marketing. And yes, we all like to be recognised for what we are. But we suspect that people are becoming wary of trivial personalisation or ‘personalisation lite’.

For example, recognising what I last looked at on a website is reminding me of the blindingly obvious.

However, telling me about a local retail event in the near future that is connected to what I was looking at on a website is much more likely to catch my attention.

And, for a full version of personalisation, understanding that I am a longstanding and loyal customer, rewarding me for my loyalty, and telling me about something that is actually of interest to acquire, really does hit the spot.

So, what is required to make this deeper level of personalisation actually work in practice for digital marketers?

Well, the first item needed is an effective personalisation engine. A tool that decides what kind of personal experience I should receive based on the information it knows about me. These engines work by the marketer setting rules or conditions which if satisfied mean that the customer will be sent a specific version of an email, or see a particular image, or offer on a website.

But next you will need to have joined together the online and offline data worlds. That allows the personalisation engine to know both that you are a loyal customer and that you are interested in a particular kind of merchandise or customer experience.

Most personalisation engines deliver ‘personalisation light’ because they ignore the history of your relationship with the company and just focus on what you have been browsing in the last 24 hours or so.

We have teamed up with Fresh Relevance (see www.freshrelevance.com) as our personalisation engine partner to allow you to give your customers full personalisation.

We manage this by sending Fresh Relevance a nightly feed of customers’ characteristics – factors like loyalty, spend, and long-term merchandise category purchases, so that these can be used in combination with their immediate online activities and behavioural triggers.

UniFida and Fresh Relevance can deliver in combination personalisation that really makes a difference.

To find out more, please email us suggesting a time that would be good for you to have a chat.

UniFida logo

UniFida is the trading name of Marketing Planning Services Ltd, a London based technology and data science company set up in 2014. Our overall aim is to help organisations build more customer value at less marketing cost.

Our technology focus has been to develop UniFida. Our data science business comes both from existing users of UniFida, and from clients looking to us to solve their more complex data related marketing questions.

Marketing is changing at an explosive speed, and our ambition is to help our clients stay empowered and ahead in this challenging environment.