The growing attention of companies to the creation of an effective and responsive system of personalized recommendations is part of a context in which, in order to build a solid relationship with consumers, thus protecting them from the danger of customer churn, it is necessary not only to talk to them but to listen to them every time the opportunity arises. At the base of this listening is the ability to collect, process, and interpret all the data that concerns them.
In this post, we will discuss how brands use personalized recommendations in their marketing and communication strategies by focusing on the most effective personalization techniques today.
We will conclude with a focus on Rental, highlighting the opportunities that personalized recommendations can offer to rental companies.
How to bridge the gap between consumer expectations and brand response?
The Covid-19 pandemic has significantly accelerated the transition to a digital society. Customers have migrated to digital channels in record numbers. A survey by Eurofound, a foundation that studies the conditions of workers in the European Union, found that more than 1 in 3 workers in 2020 worked exclusively from home. That means they relied almost exclusively on digital tools to socialize, work, and access all the services they needed.
This rapid adoption, aided by long lockdown periods, produced a profound shift in consumer expectations: within a short period of time – weeks, days – consumers began to expect brands to remember who they were, where they came from, and what they preferred, regardless of the channel they were using.
And while many companies have already begun to invest in activities, technologies, and approaches to develop satisfying personalized communication, the gap between consumer expectations and brand response still seems far from closed.
According to Twilio Segment’s State of Personalization 2021, while 85% of companies surveyed believe they offer personalized experiences, only 60% of consumers seem to think so. But those same consumers overwhelmingly say they would be willing to become repeat customers after a personalized shopping experience (44% of the sample in 2017, up 16%). Even as many as a third of respondents would choose the brand that best demonstrates that they know and recognize them, even if cheaper or more convenient options are available elsewhere.
The bottom line is: brands that can’t provide true personalization today will lose customers and revenue to those who have established a personalization strategy
Co-creating unique experiences: the role of personalized recommendations
Interactions between the consumer and the company, when personalized, enable unique experiences that can in turn be used as levers to gain competitive advantage. Purchase history and, even better, the relationship gained through the various touch points in the marketing funnel influence customers’ present expectations and determine their future choices. As a result, providing input calibrated to individual user profiles, tailored content, and proactive service could break the vicious cycle of betrayed expectations and abandonment and help shape a new experience.
Going a step further in this direction we can talk about a real co-creation of experiences, which gradually take shape during the occasions where brands, consumers, influencers, and anyone who participates, in various ways, in the conversation about a given product or service, meet. The positive evaluation of the experience had in these moments defines the value perceived by the consumer.
It is here that personalized recommendations play a fundamental role: by exploiting the knowledge embedded in the data – qualitative data above all – produced within these co-creation processes, they are able to carve out a privileged space within a bidirectional communication flow. And the more they maximize the informational potential of the data, the better personalized recommendations can intercept real needs and desires.
How personalized recommendations harness the power of data
Although they rely on sophisticated digital technologies, personalized recommendations work with a rather intuitive mechanism: they display a selection of product and service suggestions at different touchpoints (strategically distributed across websites and social networks). This selection, uniquely designed for that particular visitor, is increasingly the result of an algorithm.
In other words, the more personalized recommendations leverage the power of data, the more relevant they become to the user and the more effective they are in directing the user’s purchase choices. At the heart of modern recommendation activities, supported by artificial intelligence, machine learning, and predictive analytics, are all of the data that, properly read and understood, express a specific user behavior.
It is in this sense that personal recommendations end up having a more or less direct impact on the entire customer experience.
The secret of an effective system of personalized recommendations is in continuity
A sophisticated recommendation engine based on artificial intelligence allows companies to maximize the results of personalized recommendations in terms of increased sales and revenues. Companies can exploit the information, which they acquire in greater and greater detail, to show people those items they are most likely to want to buy, starting, for example, with a homepage that seems designed specifically for each of them.
The real novelty of the approach of which Amazon is the absolute master is not resolved in the “what” – providing users with exactly what they want when they want it – but also in the “for how long”: the personalized experience cannot be exhausted, one-off, in a concluded series of interactions aimed at the purchase, but must happen continuously on a daily basis and on the basis of the real-time behavior of the customer on the platforms.
To put it another way: the thousands of companies competing in the online marketplace can’t afford to rest on a static view of personalization, which instead, continues to evolve as a process. From behavioral targeting to deep learning, from content personalization to conversion rate optimization, the imperative is to redouble efforts in an attempt to tune in to a mobile and multi-faceted concept of personalization, in a way that is carefully fine tuned and allows for continuous updating of the data on which personalized recommendations are based.
Best practices for truly effective personalized recommendations
In order to enhance their strategy and put a value on the information assets constituted by the data they possess, marketing, communication, and customer service departments can now choose between different ways with which to recommend “personalized” products. Here are a few.
- Recommendations based on the user’s browsing or purchasing history: historical data is used to offer unique related products to each visitor. This is the personalization technique from which Amazon’s transformation journey began in 2010, giving the e-commerce giant a huge leap forward (and it still works well today). According to the company, nearly 35% of its sales come from personalized recommendations, and nearly 56% of those are likely to be converted into a purchase.
- Recommendations based on a customer’s location or profile: geolocation, weather conditions, information about the visitor’s age or gender.
- Recommendations based on product affinity: recommendations based on what other similar users have done.
- Recommendations that remind shoppers of items they’ve browsed but haven’t purchased. That’s retargeting, a digital marketing feature that acts as a kind of reminder by running ads that are displayed on other websites the customer visits or sent via email. While this is a well-established technique, it can easily be disturbing or annoying if not executed with all possible caution and care. If ads are displayed too early, too frequently, or too late in the process, the risk is that the quality of the user experience and brand reputation will deteriorate. In the case of retargeting messages, it is also important to observe who responds and who doesn’t and adjust accordingly, limiting the number of retargeting actions for those who don’t interact.
- Recommendations that suggest complementary products or In order to provide something a customer might be interested in, companies now have even more sophisticated algorithms that allow, for example, the development of product recommendations that are complementary to what the buyer has already browsed or purchased.
- Recommendations built to fuel a conversation outside of the brand’s proprietary channels. This is a particularly promising tactic that, by intervening in the elements of the message (visual, structure, tone of voice, call to action) can foster content sharing within the consumer community, and in so doing:
- increase the relevance and newsworthiness of that product and brand,
- lighten the workload of customer care, which must, in any case, together with marketing and social media managers, always monitor the topics (hashtags and mentions) that concern the brand, to intervene where there is a need to reaffirm its history and values.
Focus on Rental: what data should we focus on?
Even in the sharing economy, personal recommendations play an important role because they are able to strongly influence people’s decision to participate in a sharing service. Let’s take a small step back: in this context, decisive input often comes from friends who have already had experience with a sharing service and who are consulted when deciding whether to adopt a service or which service to choose (source: Roman Netsiporuk, The Customer Experience in the Sharing Economy: A Context Specific Approach to Airbnb). But if word of mouth is still an extraordinary weapon capable of amplifying reach and of making up for the lack of access to information – or the difficulty of finding it – personal recommendations are extremely valuable.
For companies operating in the Rental sector – a sector that represents a sort of last frontier of the sharing economy – there is no doubt about the need to acquire more information about customers in order to better meet their needs and desires, the need to capture signals about their future intentions is less obvious. To formulate accurate hypotheses, requires collecting, centralizing, and interpreting all available information relating to the supply relationship in order to be quickly notified of any friction experienced by the client and intervene promptly to minimize it.
The information to be monitored can be traced to two macro categories:
“In the first, information can be acquired as part of the relationship with the client company and its needs (the relationship between the client and the rental company); in the second, the elements to be considered derive from outside the relationship and concern the client company and its market.
In the first case, information can be acquired through a level of intimacy and knowledge of customers, looking beyond sales results, trying to investigate the current motivations behind choice and purchase decisions, and the future evolution of behavior. In the second case, it is necessary to focus on the potential value of the customer, therefore, perceiving the dynamics of the sector in which it operates and building a sort of observatory that considers its growth rate, the level of competitiveness within the sector, its economic performance and relative competitive advantage, the organizational structure, and those involved in the provision of the rental service.”
In the case of Rental, a personal recommendations system that can effectively support marketing initiatives, supporting the company in achieving its business objectives, must be based on data from both these dimensions. But let’s go into more detail.
Service-specific personal recommendations: essential data
The specific quality of personalized recommendations in the case of rental companies is that they describe, comment on, recommend a service – or rather a portfolio of services. It is about data:
- that describes the customer profile: the company name, contacts, date of purchase, duration and type of rentals purchased and ancillary services, turnover, and market share;
- that describes the relationship with the brand: pricing applied, participation in promotional activities and training courses, management of complaints);
- that describes the market situation: indicators that express the strategic behavior of clients and the profitability of relationships in quantitative and measurable forms.
Based on a database of clients, it is therefore possible to obtain an articulated portfolio of services. And personalized recommendations extract value from both customer and service data, making them one of a company’s most powerful tools that can make an important contribution toward improving the costumer experience.
