Redefining loyalty through effective Big Data and AI management
The pandemic showed retailers that an effective and adaptable system to track customer behaviour can be the key to improving sales and maintaining fidelity. Data management and AI deployment can fulfil this need by redefining the essence of loyalty programs.
Two years after the pandemic started, people continue to return to old habits. Others have found comfort in this “new normality” and have become a new type of “static” customer based in their homes. Loyalty programs need to readapt to this environment – becoming the bridge for customers to interact with retailers, and vice versa. But the concept of “loyalty” has become quite volatile with price and location enough to sway customers. Due to its direct contact with users and its tracking capabilities, retailers are now analysing how effectively an in-depth, data driven approach can tailor programs with a better understanding of customer behaviours while retaining them through AI-based solutions, resetting a new kind of relationship.
The gathering process
“Data shows us a consumer’s preferences. Whether that’s where they shop, what they buy, when they prefer to shop, or how they want to pay. By tapping into that detail and insight, retailers can better understand their consumers’ needs, which allows them to tailor their engagement and the experiences they offer,” explains Rebeka Goodale, Head of Solution & Innovation at The ai Corporation. This approach isn’t something new to retailers as interactions have long been used to design traditional data models that can build expected behaviour patterns.
These can be effective if the customer stays the same or if its behaviour changes over time. Nevertheless, Goodale emphasises how the pandemic has turn this process on its head by forcing changes overnight. Models couldn’t be adjusted at the tempo the pandemic influenced customers, who continued buying the same things but differently. She points out that retailers need to know whether any change is temporary or whether it’s likely to be a permanent change, so they can adjust their strategies accordingly.
Information is only as good as the questions asked before the gathering process. Data such as payment methods can be meaningless if not compared to another insight. “What, where, why, how… Using that data helps you to answer these questions and as a result understand your customers better,” comments Goodale. This is known as a “data-value gap”, which can be solved by achieving the right focus on the gathering process based on asking the right questions.
Apart from loyalty programs, other touchpoints can be set to gather simple data from clients. Experts recommend building an integrated data hub that can deliver meaningful customer insights. This can help reinforce certain aspects of the strategies planned or make swift changes. Information gathering can also be enriched by other data sources such as social media, the weather and online forums that can provide valuable real time information.
With great data comes great responsibility
The most meaningful step in the whole process is based on how you use the information gathered. Professionals are continuously searching for precise and impactful solutions to secure a long-term relationship with customers. Once the number of clients is large enough, how can a company act accordingly in each case?
This is when data efficiency is tested through automation by focusing on how to readapt engagement with customers. With a good amount of information gathered, an AI system can remind the user of special discounts or benefits at convenient times based on the data collected. “Automation can enable speed and predictability. Two things consumers really like. When their behaviour changes, as rapidly as it did with Covid, retailers need to be able to react quickly,” points out Goodale.
Implementing campaigns based on insights walks the fine line that divides effectiveness from frustration. Clients can like a certain product but if the platform is constantly reminding them of it without end, the strategy can be counterproductive. Goodale remarks that if data-driven experiences aren’t handled properly they can result in a “creepy factor” and marketers need to strike the right balance. The industry expert is confident that everyone agrees on the definitions around responsible use of data before implementing this kind of strategy.
Automating the future
This is only the beginning of a branching path that can lead to multiple solutions. Goodale is confident that adopting newer and better technologies will help retailers to decipher new behaviours and needs in different ways: “One of the biggest breakthroughs I see impacting our business is how quantum computing could be used to run generative machine learning models, using larger datasets than classical computers are currently able to process. That processing power will make any model more accurate and useful in a real-world setting.” What companies already know is that a more thorough and deeper understanding of clients is needed to tackle the changing landscape, and that can be achieved by redefining what their loyalty programs mean for them and their customers.