The benefits of personalisation for ecommerce - SwiftERM

The benefits of personalisation for ecommerce. Ecommerce Personalisation is one of the most powerful tools there is to increase conversions and drive sales. If you’re not using it yet, you’re almost certainly leaving money on the table. Research by Accenture shows that businesses lost $756 billion due to lack of trust and poor personalisation.

Personalisation should be an integral part of your ecommerce strategy. When used efficiently, it will transform customer interactions and create a seamless and consistent experience that feels relevant and enjoyable. If done right, it will lead to more conversions, higher order values, returning customers, and overall improved trust in your brand.

WHAT IS ECOMMERCE PERSONALISATION?

Before we dive into the details of Ecommerce Personalisation, let’s define what it is:

Ecommerce personalisation is the process of dynamically showing each visitor individualised offers, such as product recommendations or other content, based on their previous behaviour, demographics, purchase history, and other personal data.

As online merchants seek to engage shoppers, drive sales, and increase repeat purchases, personalisation has proven to be one of the most efficient tools in the marketing toolbox. While it comes in many different forms, it’s always about providing a shopping experience that feels timely and relevant to each individual.

Related reading: Why you need a personalisation program as well

BENEFITS OF ECOMMERCE PERSONALISATION

Offering a personalised shopping experience all through the buying cycle does require some work. It takes thoughtfulness and the proper technical tooling to create experiences that wow people from the first touchpoint and through what hopefully becomes a lasting relationship. So is it worth the investment? Just a brief look at stats will give you the answer: a resounding YES.

  • Marketers report an average increase in sales by 20% when implementing personalisation
  • 77% of shoppers have recommended, chosen, or are even willing to pay more for a personalised experience
  • 80% of shoppers say they’re more likely to purchase from a company offering a customised experience

And it’s not surprising, is it? Flooded as we all are with marketing messages, it takes something special for us to notice an ad or product. Only the ones that feel spot on and relevant to our specific predicament or need, will entice us to click and potentially buy.

The benefits of personalisation for ecommerce

7 ECOMMERCE PERSONALISATION TACTICS THAT WORK

If you manage to get personalisation right, you can expect giant leaps in revenue. Gartner predicts a minimum 15% profit boost for those who succeed with personalisation in ecommerce, and there really is no ceiling for how far it can take you. Just as the number of different personalisation tactics and solutions at your disposal are almost unlimited.

To find out what personalisation tactics work best for your audience, you need to combine creativity, empathy, and the right technology with continuous testing and experimentation. Below we’ve listed 7 examples of ecommerce personalisation tactics that have a proven record of driving sales.

PERSONALISED PRODUCTS RECOMMENDATIONS

When you’re browsing an online store, you will often receive suggestions on other products you might be interested in. Those are personalised product recommendations based on your browsing or purchasing behaviour. This is a powerful personalisation tactic, and it can be done in different ways, such as:

  • Visitors who viewed this also viewed…
  • Visitors who bought this also bought…
  • Handpicked for you…
  • These items are trending now…

Choosing the best way depends on the context, but it’s definitely worth doing because it works. Research by Smart Insights shows personalised product recommendations can generate 68% of total ecommerce revenue. Not bad, right? But remember this requires the consumer to have come to your site first, as you can’t tailor their selection unless they do. Which leave the door open for your competitors doing it better, and you in the doldrums.

The solution is to use predictive personalisation email solutions. Respecting the the word “email” conjures up your existing provider, but by far the majority of providers think segmentation is personalisation, and they couldn’t be further from the truth. The distinction is about 20x more return across the board  in comparison, and that blows the other out the water. This unique distinct is commonly a stand-alone additional martech solution for your stack, such is the necessity for utilising such big-data

WEATHER-SENSITIVE PERSONALISATION

This is a fun and efficient approach to ensure visitors see items that feel most relevant to them. Because think about it – when are you most in the mood for shopping for chunky sweaters and rubber boots? When the sun is shining, or when it’s raining cats and dogs? By showing visitors clothing that matches the weather, fashion retailers can increase conversions. And it seems to work quite well. When Indow Windows implemented this personalisation tactic, it resulted in increased click-through and conversion rates as well as a 40% decrease in cost per lead.

TARGET ABANDONING VISITORS WITH PERSONALISED OFFERS

Cart abandonment is a challenge in online sales, and personalisation is one way to tackle it. By offering visitors at risk of abandoning the carts a personalised offer, you can often entice them to go back and fulfill their order; or at least sign up for your newsletter or some other valuable micro-conversion.

COMPILE LISTS OF RECENTLY VIEWED ITEMS

You know how it is when you’re browsing a website; you’re not always in the mood to buy something. Sometimes you just click around, have a closer look at some of the items you like, and then you move on. Reminding your visitors of the things they wanted next time they visit your shop increases your chances of catching them when they’re in the mood to purchase and making sure they don’t forget about your products.

BEST PRACTICES FOR ECOMMERCE PERSONALISATION

Ecommerce personalisation involves many moving parts and requires brands to collect, analyze, and activate big data in real-time. This is only possible with the proper tooling and architecture and when the necessary technological components are in place. These components include, but are not limited to:

  • Unified and accessible data
  • An open architecture
  • Automated decision logic and machine-learning

Related reading: personalisation technology whats the big deal

UNIFIED AND ACCESSIBLE DATA

Information from all touchpoints along the buying journey must be available and shared. Therefore, it’s essential to unify, streamline and integrate your marketing and ecommerce tech stack as much as possible. If not, you risk having critical data getting stuck in silos and breaking the flow of information. This can destroy the seamless and personalised experience you wish to create and ruin the customer experience.

AN OPEN ARCHITECTURE

Integrating your marketing technology, CRM, and ecommerce platform is crucial. To be agile and able to create personalised experiences quickly, you need a high level of flexibility with as little engineering time as possible. Choosing the right tools and a user-friendly setup can generate significant value by accelerating the rate of deployment.

AUTOMATED DECISION LOGIC AND MACHINE-LEARNING

Automating analysis and delivery is necessary to increase efficiency and scale operations. As the number of variations and segments increases along the experimentation roadmap, assessing the impact becomes a very data-heavy task that can only be scaled with machine learning.

Related reading: How to win at ecommerce personalisation

KEY TAKEAWAYS

People have come to want and expect a personalised brand experience, and brands who don’t offer that will miss out on sales. Numerous tactics can be used to increase conversions, sales, and the average lifetime value of your customers. Which ones are best suited to your ecommerce business depends on several factors, and the best way to find out is often through experimentation. To do that, you need a solid, technical foundation where data is unified and accessible, and the architecture of your tech is open and integrated, so you can implement automated decision logic and machine-learning at scale.

 

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