Deep Learning for Hyper-Personalized Customer Experience

Today, it has become easier for digital customers to move rapidly from one brand to another and it is also true that their loyalty can no more be easily gained. One of the best ways to earn customers’ trust in your brand is by leveraging data for hyper-personalization and thereby ensuring a great customer experience as the competitive differentiator. While hyper-personalization is achieved by interacting one-on-one with individuals and by anticipating an individual’s desires at any point in time, the ground reality is, such standards are hard to be realized as it needs deep customer insights that are gained from analyzing granular data. Gaining insights quickly enough by having sufficient data can sometimes be a big challenge for brands. In such a scenario, cognitive technologies such as machine learning and deep learning come to the rescue as they enable enterprises to have faster and more effective access to various customer data points and the insights generated from them.

In the recent times, deep learning has become more popular than traditional machine learning due to its supremacy on a wide variety of tasks including speech recognition, natural language processing, and vision. In a report by Epsilon, 80 percent of consumers indicated that they are more likely to do business with a company, if it offers personalized experiences. Such reports only corroborate why leading brands like Coca-Cola and Amazon have used advanced data analytics and accurate insights to embrace more personalized marketing strategies that increase sales volumes and amplify cross-selling.

Deep learning technology allows personalization at a whole different level by using data as the voice of the customer so that you can tailor and accurately match digital experiences to your customer journeys. So, let us dive in and understand the role of deep learning in accomplishing hyper-personalized customer experience.

Developing a deep learning model to drive hyper-personalization

One of the main problems that enterprises have is a lot of old core systems that are used on a day-to-day basis to service end customers. These might be valuable systems that are built for scale and perform expected outcomes, but what they are not built for is the modern era of hyper-personalization. So, the first challenge to address is to think about how you can deliver hyper-personalization to your customers through a legacy system! The answer to this question is the Front2Back™ transformation of legacy systems. To bring this change, you need to start with your end customer and identify user journeys which are really impactful for your end customer and build the model from the front end. The way you do this is not just by building a web front-end but also by building an intelligent middle layer. This intelligent middle layer will help in producing the data and services required to fulfill your customer’s needs by setting the ground for application of deep learning technologies, which will in turn drive hyper-personalization. As a next step, incorporate techniques, such as Bayesian algorithms, collaborative filtering, neural networks and predictive analysis which are going to drive the cross-selling and bundling strategies through different channels. Later, appoint a deep learning technology expert who can maintain this upgraded model over time as this is going to be a long-term investment for you. Further, from the insights obtained through deep learning, tune in the right content about your brand tagged with a right price for your product to give a sense of personalized promotions that are dynamic and tailored to every customer.

This is what Twiddy—a vacation rental company—did! It used insights from data analysis to enable owners of its managed properties to change prices as per market conditions, seasonal trends, and size and location of a home. As a result, its portfolio increased by over 10 percent.

Having a monitoring device helps in measuring the impact of deep learning so the tweaking of the model is enabled. In summary, hyper-personalized customer experience can be attained by merging real-time customer interaction with historical trends and demographic data that paints a 360-degree, contextual picture of your customer.

Offering accurate product recommendations

By using deep learning technology, a recommender system aims to estimate the preference of your customer on a new item which he/she has not seen. The output of a recommender system will vary according to the nature of the system and the information fed as inputs. So, it can be either a rating prediction or a ranking prediction. Rating prediction aims at predicting the rating scale for an item that has not been seen by the customer by filling the misplaced entries of the item rating matrix. Whereas, ranking prediction’s objective is to predict the top items and produce a ranked list according to its similarity with the customer profile.

This is how product recommendations are made on most leading e-commerce websites. For instance, if you make purchases for a newborn on Amazon, you will immediately be shown relevant promotions for infants. Another example is Netflix, which analyzes parts of movies that were skipped or repeatedly-watched to suggest accurate movie recommendations.

Also, it is important to know that there are mainly four models used for recommendations. These models are content-based recommender system, demographic filtering, collaborative filtering, and hybrid recommender system.

For example, when deep learning models are trained, they could refer the historical transaction data, customer demographics or they can even look at the metadata of the product itself and then take all this information to provide you with a list of customer IDs for all your products that they think are relevant or they can develop a sophisticated recommendation engine for your customers by understanding their purchase intent. In addition, there are also many real-time processes in deep learning, where literally the minute someone clicks on your recommendation, it sends a signal back to you and this action can be immediately incorporated back into the next touch point.

The bottom line is greater personalization lead to better customer engagement and deep learning technology allows you to create that next best plan of action for your customers. This technology enables you to be much more targeted in your approach and economical when it comes to the budget. Furthermore, consumers are now used to hyper-personalization, if your brand is not aligned with this strength then you will be left behind.