Before we start defining our customised strategy, we must first ask ourselves: Do we know who our clients are? What data do we have about them? How do they interact with our brands? What are the points of interaction?
In recent years, users have built a new paradigm for the consumption of content and information. Mobile devices have broken into people’s private lives. This physical proximity, together with the endless possibilities that mobile phones offer in terms of personalised experiences, has led users to see these devices as an extension of their personal environment. This forces brands to change their relationship with their consumers, adopting new commercial approaches that move away from those of traditional media.
The Business To Me (B2Me) emerged as a response to these circumstances, according to Retail Customer Experience. This new type of marketing is based on the behaviour of individuals, and not so much on their purchasing aspirations, their interests or their demographics. It’s not so much about selling, but about being able to develop a personalised relationship with each individual.
Personalisation as a starting point
And not only from a theoretical point of view. Relevant players in different sectors are increasingly offering end-to-end personalised experiences associated with user behaviour, both in physical and digital environments. Below are some examples of this trend in the digital sector:
- Wonderbly: personalised books. Wonderbly is a publishing company founded in 2012. In 2016, they published “Lost My Name”, a customisable children book whose plot is designed using the kids’ personal data. In less than thirty seconds and with only four clicks, users can access a preview and order their personalised book.
- Monsoon Accessorize: in-store recommendations using third party data. In 2014, the British fashion retailer pioneered the use of aggregated third party data (RichRelevance) to cross reference it with its own data, in order to customize recommendations to its customers in more than 300 shops in the UK.
- Zalon and Zalando’s AFC. In 2017, European online fashion leader Zalando introduced Zalon, its automated system connected to fashion consultants, through which it aimed to move from a garment-based sale to an outfit-based one. In 2019, Zalando presented AFC, the evolution of Zalon, in order to be able to work with scalability.
- Tik Tok’s algorithm “For your page”. With an average usage time per user that keeps increasing, TikTok is no longer just Gen Z’s favourite social network, but also one that appeals to many Millennials, and even to some Zenners. The content personalisation algorithm is responsible for this great success, as the “For you” section always shows personalised videos for each user based on their behaviour within the platform itself. In fact, when Byte Dance, TikTok’s parent company, made the algorithm public, it was praised by several computer experts.
TikTok keeps the details of its algorithm secret: all they will admit is that it is based on what users like and on who they choose to follow, as it identifies their tastes and preferences and adjusts the recommended videos accordingly.
However, through its “For your page” section, TikTok also encourages users to try other people’s content before viewing content that may have been uploaded by people they follow. In this way, brands can easily reach new audiences, and the platform acquires an irresistible appeal in the eyes of advertisers.
All these examples clearly show that the digital sector is highly committed to the personalisation of user experience. The sector taps into the unique competitive advantage offered by the digital channel: the ability to extract, analyse and learn from user behaviour data. Brands that manage to successfully take advantage of this opportunity and make data-driven decisions will see a clear improvement in their conversion and loyalty results.
When we talk about customization, we often tend to think about adaptation at the product level only, but the macro trend goes beyond that. It is about personalising the user experience at all levels, based on their tastes and interests, creating a different journey for each consumer from the very first touchpoint, and being able to infer what their next step will be. The ability to predict user behaviour based on previous data has thus become brands’ most precious treasure.
Capturing and analysing behavioural data is key
Behavioural analytics arises from the need to know how, when and why a person interacts with digital media, whether it is through a mobile app, an online game, a web application, or an eCommerce platform. In short, this discipline aims at understanding how consumers act, allowing for precise predictions about what their behaviour may be in the future.
Predictive data analysis is nothing new. In fact, it is the result of a set of projects that, since the 1960s, have sought to automate the prediction of human behavior. The Simulatics Corporation, for example, was a controversial project formed by a group of data scientists who were trying to predict voting behaviour through a data mining model, and who were closely linked to the political history of the United States. They built an electoral behaviour machine, a computer simulation of the 1960 elections that allowed them to test different scenarios in an infinitely customisable virtual population: they could ask a question about any movement a candidate might make and the machine would tell them how voters, down to the smallest segment of the electorate, would respond.
And how can we use all this data and incorporate it into our marketing strategy?
Nowadays, we are experiencing a virtuous cycle: hyperconnectivity generates huge volumes of data which, once analysed, improve machines’ ability to learn and generate value like never before. By using these tools, we can restart the process everyday.
This generates a great opportunity and a huge challenge for companies, as variability multiplies, which is why it is crucial to work with tools that allow for the integration of data into our business model.
Data integration towards Omnichannel
Companies such as Littledata, Retailnext or Google with Universal Google Analytics (and its eCommerce specialised tool, Enhanced Ecommerce) allow for the integration of offline and online information into a single dashboard.
Services such as Granify analyse data from millions of websites and variables such as scroll speed to prevent a user from leaving the page. Based on that, they generate a personalized experience to maximize the possibilities of conversion.
Nowadays, we can no longer be satisfied by just knowing what the tastes or interests of our consumers are. We must also know how they behave digitally, and ask ourselves how they look for brands, how they access content, what their consumption habits are, etc.
Today, a winning customer experience strategy recognises that the mixed channel (digital and physical) is the only possible channel, and that retailers need to leverage deep customer knowledge to understand how various user segments navigate through digital and physical channels.
In order to achieve this, it is vital to start using the right technologies allowing us to collect different types of data (transactional and digital behaviour): CRMs (Customer Relationship Management) or DMPs (Data Management Platform), are tools that are nourished as the data is obtained, and are a key element in the systems architecture of your Martech strategy.
At Good Rebels, we have been developing data intelligence projects for years. We define the data architecture and strategy, as well as the analysis methodology, in order to capture and take advantage of information that is sometimes underused, as we did for Cash Converters, where we analysed the brand’s positioning through a Data-Driven strategy.
Moreover, through different quantitative and qualitative research techniques, we also identify trends or customer insights that help us study the different archetypes and generate their customer journeys, as we did for a Swedish fashion leader that wanted to launch its eCommerce in Mexico and needed to know what its digital consumers looked like to be able to reach them.