Achieving the “REAL” in real-time personalization
Personalization and its impact on marketing has possibly been the most sought-after topic in the past few years. It is now an established fact that personalization, when implemented properly can bring a lot of rewards to the organization: increased sales and revenue, enhanced online conversion rates, boosted average order value, and strengthened customer loyalty and retention.
Consumers today live in an age where they are being bombarded with advertisements at every touchpoint. Most companies in a particular sector have the same offerings and therefore, what acts as a tiebreaker is how well-crafted, individualized, relevant and timely is the content reaching to the audience. Customers have a lot of options to choose from and they don’t think twice before switching to a brand that understand their needs at the right place and time. Delivering personalized customer experiences is no longer a “nice to have” — it’s rather an expectation.
Challenges facing implementing Personalization at scale
Real-time personalization is a way to dynamically deliver tailored content to different groups of visitors to the site. While it is ‘great to have’, it is equally ‘difficult to achieve’. Some of the challenges facing its implementation are:
Technologies that help achieve Real Time personalization
To achieve personalization in real time it is important that the data gets collected, processed, reprocessed, analyzed and handled within seconds. Traditional batch processing methodologies are turning redundant due to high-latency and limited bandwidth issues that businesses to channelize large datasets. However, recent advances in technology has helped overcome those challenges and achieve personalization in real-time at scale.
Cloud service providers like Amazon Microsoft Azure, Google Cloud Platforms have been the fore runners in enabling this. Unlike Hadoop and other technologies that process data in batches (thereby not providing real-time operational decisions about constantly streaming data), messaging systems like Apache Kafka, Amazon Kinesis, Google Pub-Sub, to name a few, are publish and subscribe messaging solution. Although the way the offerings are managed by these companies may vary, the use cases are more or less the same — track large streams of data records, process them and provide recommendations in real time. They are streaming analytics software solutions that perform real-time reporting and create visualizations on streaming data collected from multiple sources.
Applications connect to these systems and transfer a record onto the topic. A record can include variety of information; for example, information about an event that has happened on a website, or an event that is supposed to trigger another event. Other application may connect to the system and process or re-process records from a topic. The processed records can then be sent to dashboards, use to generate alerts, dynamically change pricing and advertising strategies, or send data to a variety of other services. The data sent is stored until a specified retention period has passed by.
These are the technologies behind Netflix’s extensive recommendation system; Spotify’s successful business model of providing customized recommendations based on factors like the time of day, what the user was doing at the time and even what other, similar users enjoy; Uber’s gathering of user, taxi and trip data in real-time to compute and forecast demand and compute surge pricing in real-time.
Some applications of real-time personalization
1. Geotargeting: Say, Peter has just landed into a new city. He receives a welcome email personalized with his name that offers him a discount on a nearby Brand store/ a local exhibition happening in the city. This type of targeting through personalized emails or push notification basis the mobile geolocation ensures Peter that the brand is thinking of him and does not want him to miss out on anything even when he is new to a place.
2. Product Recommendations: Product recommendation is a must have for an overall ecommerce experience. However, without real time personalization embedded, it might sometimes be stale and believe me when I say that customers do take notice of such things.
Real time personalization makes recommendations based on known data and the previous behavior of individuals. This is achieved by leveraging omnichannel insights which will allow the brand to deliver product recommendations across channels.
3. Account-based Marketing
Conclusion
Real time personalization is a breakthrough in the evolution of personalization. For many years now, personalization was all about segmentation which was done at the group level rather than at an individual level. True personalization is only when it is done at an individual level and reaches to the audience at a time when they are most likely to respond to it and hence move a layer deeper into the sales funnel. The beginning of this era of real time personalization is led by big titans like Google, Amazon and Microsoft. It is not long before we see it become an absolute part of our lives.
