With consumers spending more time online than ever before, digital footprints have rapidly expanded to offer brands unprecedented insight into their customers. However, as consumer behaviors have evolved, so too have their expectations. Now more than ever, brands are under pressure to adapt to these changing conditions and evolve their customer relationships accordingly. Personalization remains at the heart of this evolution as brands look to leverage their data to better serve the needs of modern consumers.
However, achieving personalization is a challenge. Most companies struggle to build tools that can improve the efficiency of their marketing efforts and deliver scale to efficiently guide their communications.
Companies should not think of personalization as a tool simply for generating hype or delivering the same customer experience in a different way, but rather to build integrated and industrialized solutions, enabled by the appropriate artificial intelligence, at the service of the business objectives and with the consumer at the center of all decisions.
The challenge of improving customer experience and communication begins on the data front. Building relevant and consistent data streams is a crucial step for any business looking to create data capabilities and leverage data-driven insights. It is the foundation that ensures the relevancy and reliability of new developments.
With the appropriate data capabilities, companies can construct comprehensive customer journey data by consolidating their online and offline footprint. Artificial intelligence can then help grasp the complexity of customers’ behavior, by detecting hidden patterns within the data that humans would fail to spot.
However, personalization efforts struggle to deliver a return on investment unless they are framed and monitored through a business-first mindset. Such a framing starts from the fundamentals and advances in an iterative process to build transformative solutions.
One way of defining personalization is to be consistently communicating with customers at the right moment, with the right product and through the right channel.
To achieve this objective, one has to understand what makes customers consider a purchase and what then leads them to convert. As such, deep customer analytics allow businesses to develop relevant communication assets and supply their customers with offers that fit their expectations.
Machine Learning can also be a very powerful technique for placing customer centricity at the heart of the purchasing experience. Given a relevant and consistent data stream, it can be used to capture what the customer is looking for at each point of their journey. Carefully designed scoring algorithms that incorporate tools such as attention mechanisms, long-short term memory networks and boosting, enable granular and highly accurate segmentation of customers that bring to light the nuance of individual preferences.
Scoring involves understanding the touch-points that led a consumer to buy, and what differentiates purchasers from non-purchasers. Fundamentally, it is about understanding what drives consumers’ interest towards purchasing product A rather than product B, and using this understanding to build relevant indicators to explain their behavior and better engage with them in the future.
One such methodology starts by developing “Intention” scores that enable brands to identify the most and least likely purchasers within a certain time frame (next week, next month, next quarter…), which then allows them to intelligently determine the audiences to target depending on the time-frame, the strategy and the cost of communicating with them.
Besides the intention scores, “Product” scores allow brands to grasp the most and least likely products to be purchased for each lead within a certain time frame. This enables marketing departments, not only to push the right products at the right time, but also to strategically push specific products to the most appropriate audiences.
When assessing the outputs from an individual customer journey, business experts can often reach the same predictions as the scoring algorithm. However, the experts will need several minutes to reach their conclusion, while the algorithm will only need about a second or so. The algorithm’s advantage becomes obvious when considering that such scoring has to be applied to thousands, often millions, customer journeys on a daily basis, or even in real-time. Machine learning algorithms are thus not only a way to reach higher performance, but also a means to achieve scale.
Scale can be achieved by implementing the appropriate software, but also by using the right infrastructure as well. This is where cloud computing shines, as it allows companies to build solutions capable of generating several billions of scores daily while respecting all production and business constraints such as infrastructure costs and run-times.
Machine learning is an effective tool when supported by an exhaustive framing and continuous monitoring of the objectives, the stakes and the use-cases. However, such a tool can become obsolete if it is not industrialized and sustained to serve the company at scale. Companies should build industrialized solutions that are considered as long-term assets, and allocate sufficient and relevant resources for them to be truly transformative. This is the commitment that will benefit the business tomorrow as well as today and achieve true data science transformation.
Before designing a solution, it is highly important to ensure key outcomes are thoroughly identified. This should be crystallized through business, technological and organizational framing and monitoring.
Personalization done right has the potential to unlock new ways of engaging with customers and improving overall marketing efficiency. However if not carefully carried out, it can lead to huge costs for marginal added value. To be efficient, it requires real commitment and collaborative organizational and cultural change.
Thought Leadership
Thought Leadership
