In almost every walk of life, the public increasingly expects services and experiences that are tailored to their individual needs and preferences. We’ve seen it in the public services that people receive from local governments and authorities, and we’re also seeing it in the iGaming sector, where players want games, offers and messaging refined to their tastes.
Retail is one of the areas where consumer demands for personalization - to the point that they can be described as hyper-personalization - are most pressing. According to Insider Intelligence, 73% of shoppers now expect the brands they interact with to understand their unique needs and expectations. And at a time when it’s never been easier to shop around, those who can deliver on those expectations are best-placed to weather the storm when something goes wrong. Salesforce has found that 78% of consumers would be prepared to shop again with a business after an error if their customer service is excellent.
All this means that there is now great competition across the retail and eCommerce sectors to stand out from the crowd with hyper-personalization. But what is the scale of the difference it can make, and how does it work in practice?
What is personalization in retail?
First of all, let’s start with the basics. Personalization in eCommerce refers to the dynamic tailoring of messaging, sales journeys and customer experiences to suit individual customers in real time. It’s achieved through data, analytics and customer intelligence, which informs retailers of what customers like and prefer, so they can be targeted with more relevant information that is more likely to engage their interest.
As the capabilities of technology have advanced, particularly through the use of advanced algorithms, machine learning models and predictive analytics, the level of refinement possible in personalization has substantially increased. It means that more and more retailers have the practical ability to apply highly granular personalization at scale, and to a volume of customers far beyond what would be possible manually.
However, it’s important to note that the effectiveness of personalization hinges on the quality of the data used by the algorithms and tools. Without data that is both reliable and accurate, meaningful personalization can be out of reach of even the most sophisticated technology.
The benefits of hyper-personalization for retailers and shoppers
Hyper-personalization in retail, really is a win-win for the eCommerce sector. Not only does it give customers the positive, value-adding interactions that they’re looking for, enhancing the eCommerce user experience, it also gives retailers the opportunity to drive and accelerate their growth and revenue:
Benefits for retailers
Fully understanding demographics:Retailers may feel that they have a good handle on their customer base, but hard facts can remove any doubts, inaccuracies or ambiguities. Collecting and analyzing detailed customer data, including their buying habits and browsing history, can uncover insights on what they’re most interested in and even why they’re buying. This can be enormously powerful in ensuring retailers connect the right offers and messaging on the right products to the right people.
Enabling greater customer loyalty: If customers feel valued as an individual, rather than just as a ‘number’ or as a source of revenue, then they’re far more likely to build the emotional attachment that will have them shopping with a brand again and again. This can range from personalized rewards, to targeted triggers for cart abandonment that encourage customers to follow through with a purchase.
Gaining quicker insights: Data and analytics tools make the process of learning about customer behaviors and motivations far quicker, and in some cases close to real time. This enables the agility that retailers need to respond to customer drivers quickly and encourage them through the sales journey with the right messaging.
Driving competitive advantage: In a market as saturated as eCommerce, any major differentiator can have a real impact on competitive advantage. While price will always be a major factor, the level of service and experience a customer receives can also have a major influence. Targeted offers and an intuitive customer journey can really deliver on that front.
Increasing order value: According to Monetate, brands that tailor customer journeys can increase their average order value by as much as 12%. Hyper-personalization makes it easier to cross-sell and up-sell other items during the customer journey, with messaging such as ‘You may also be interested in…’
Benefits for shoppers
Higher satisfaction: A greater focus on hyper-personalization means shoppers are getting the faster, simpler, less stressful and more intuitive experiences they’re looking for - and feel more like valued customers in the process.
Delivery options that appeal: When it comes to eCommerce, stresses and complications around delivery can be one of the biggest turn-offs for customers. Hyper-personalization allows customers to be matched to their most suitable choice of delivery, whether that’s at home at a specific time, or from a delivery locker of their choosing.
Trust in retailers: Customers may be able to shop around more online, but they still want to feel that they’re dealing with a trustworthy business, and that they aren’t going to be ripped off or treated badly. Hyper-personalization can help build that connection by demonstrating to customers that they are highly thought of, and that the retailer is willing to make the time and effort for them.
Targeted communications: Being able to refine marketing messaging and comms means customers don’t feel distracted or irritated by generic emails or social posts that are basically regarded as spam. Instead, they receive content that is of genuine interest to them, and offers for products that they are more likely to purchase.
Closing the divide between eCommerce and stores
Many eCommerce operations are run in tandem with physical stores, which represents huge opportunities in combining online and offline retail. However, it’s an area that many retailers have shied away from or have struggled with. This could be for a number of reasons such as difficulty integrating the two platforms technologically, or a lack of understanding in how one channel can complement the other without cannibalizing each other’s revenue.
Hyper-personalization has a leading role to play in helping retailers bring online and offline shopping closer together, and closing off that divide. For example, a staff member in-store may guide a customer around, giving them advice on products and gaining insights on the kinds of things they’re interested in. Analytics and AI can then translate this into personalized offers, sent to the customer via email, which are most likely to resonate with them and generate an online order at a later date.
The same principle can be applied when customers buy products in-store, as that data can be used to generate recommendations of other products to buy online.
Examples of eCommerce hyper-personalization
So how does hyper-personalization work for eCommerce in practice? There are several different areas where forward-thinking retailers are already applying it to good effect, including:
Faster and easier transaction options
Customers have a huge range of payment options available to them in the modern landscape, and so expect to have the widest possible choice available to them. If a customer wants to pay using Apple Pay or Google Pay, or to access buy-now pay-later schemes, then they should be able to do so within the online sales journey without any stress or delay.
Understanding actual customer demographics
Detailed analytics and AI can help retailers gain a level of knowledge about their customers that was never previously possible. Being able to understand who they are, what they want, how they browse and how they shop, along with a huge range of other metrics and patterns, supports more informed decision-making on reaching out to them and refining individual customer journeys.
Customer insights for retargeting
Connected to the previous point, those insights are invaluable in engaging customers who have either abandoned a cart, or who haven’t shopped with the brand for a period of time. Many of these customers may have negative perceptions about the brand, which is why bringing them highly relevant offers and messaging through retargeting comms is the best possible way of getting them interested again.
Aligning search results with recommendations
Search history is a vital data point that can be used for hyper-personalization. However, companies often face significant challenges in handling data from various systems and ensuring its quality and integrity throughout the personalization process.
If a customer is known to be looking for a particular item, or type of product, then they can be targeted with future messages and recommendations, increasing the likelihood of purchase and fostering long-term customer loyalty. Amazon has long been a leader in this area, using highly sophisticated algorithms that analyze previous purchase data, browsing history and other contextual data. That way, users get suggested products and offers that are most likely to be relevant to them. But the only way that Amazon and others have been so successful is through seamless data flows, robust data governance and advanced integration capabilities.
Loyalty rewards
Simple points-based loyalty schemes are increasingly looking outdated as they lack the personalization that customers want. Instead, hyper-personalization means regular customers can earn rewards that are relevant to them, whether that’s specialized and exclusive shopping experiences, or discounts on relevant items and products.
When does personalization become a privacy breach?
Hyper-personalization requires large-scale collection, processing and analysis of detailed customer data. At the same time, the public at large is more aware of the value, sensitivity and security of their personal information than they’ve ever been before. This can make things tricky for retailers who want to generate insights and deliver hyper-personalized messaging, but without making customers feel like their privacy is being compromised - or that a retailer is spying on them.
Making this even more difficult is the fact that public opinion about data sharing for personalization is mixed. Digital Commerce 360 has found that 58% of consumers say data sharing is necessary, but 42% disagree. And of those that say it’s necessary, 87% say they should be asked permission for their personal data to be collected at the outset.
There are ways to solve this problem, and ensure customers get hyper-personalization while still having their data privacy respected. For example, customers can be incentivized to share data points such as age or gender, in exchange for offers and discounts. Enabling customers to willingly and proactively share data, instead of that data being passively collected by retailers, locking in customer consent for their data to be shared. It also ensures compliance with key privacy laws such as GDPR and CCPA.
Exploring hyper-personalization with Ciklum
The retailers that are most successful with hyper-personalization are those that have made the biggest commitment to advanced technology. Whether it’s AI and machine learning, or simply ensuring the quality and security of their data, they are constantly working to innovate and make the most of all the insights available to them.
If you want to match their success, but don’t feel that you have the in-house resources or expertise to do so, then Ciklum is your perfect partner for hyper-personalization. We develop customizations and integrations for existing personalization platforms, so that retailers like you can leverage advanced AI algorithms, optimize data flows, and address key data and integration challenges related to data quality and cross-system integration. This way, you can maximize the impact of your hyper-personalization efforts whilst simultaneously minimizing development overheads.
