Boost Your Customer Experience with Predictive Analytics

In a world where product differentiators are minimal, customer experience is becoming the decisive factor. In a report by PwC, 73% of respondents listed customer experience as important, yet companies are still not leveraging this opportunity enough. Organizations should also consider that 42% of the same respondents said that they are ready to pay more if that guarantees a better experience.

But first, what does customer experience (CX) mean after all, and how can technology help? Not surprisingly, CX stands for efficiency, convenience, easy payments, friendly service, and personalization. It boils down to offering clients what they need and when they need it, with a human touch.

Predictive analytics can help with each of these goals through targeted ads, discounts, and friendly recommendations of products customers are most likely to want. This is possible by comparing customers’ past actions with those of similar customers, with the help of a recommendation engine.

We are creatures of habit, and our needs stay the same over time.
By using data of previous purchases, a predictive app could make a very good estimation about what a particular client will purchase in the future, even if they are looking for something completely different from their usual purchases.

For example, when shopping for beauty products, a woman will take into consideration her skin type, color of her eyes and hair, any recurring problems like acne. These fixed characteristics, together with budgetary constraints and affinity for specific brands, can become the foundation for personalized recommendations.

This method was used in this AI fitness app, where predictive analytics was applied to create a fully customized experience.

Getting a new client can be five times costlier than retaining existing ones. This is a significant reason to invest in predictive analytics to reduce churn rates.

Clients don’t just stop using products out of the blue. They are either dissatisfied with the service or are already looking for alternatives. If a company can identify such behaviors as soon as possible, it can take corrective actions. A personal message, a discount code, or an invitation to try a brand extension could be all it takes to remedy the situation.

This is one of the features we already see on YouTube and Netflix. Based on our browsing history, the time spent watching certain shows, and even skipped episodes or songs, the recommendation engines are ready to suggest other movies or songs we are going to love. 

This is also a way to sell more by showing customers other items that could be nice additions to what they selected before. Fashion retailers use this strategy to sell complete looks instead of individual garments.

In a world where product manuals are obsolete and users don’t
read terms and conditions, the best support is the one available on-demand or even preventive. If the system can detect potential issues and prompt the user with hints or know-how on addressing them, that becomes much more useful than extensive documentation.

Preemptive user guidance means identifying the points where
errors are most likely to appear. Predictive analytics systems could detect when a user is likely to struggle with using a certain function, or if they input the wrong information.

To mitigate this, your company could look at existing records and create “walk-me” tours highlighting the most difficult steps of the process.

This is a bit far-fetched, but what if an algorithm knows what you are going to buy before you do, and gets the item ready to be shipped to you before you even order?

Predictive analytics of such level is not there yet, and potentially will never be, but this utopia is helping companies ship faster. This problem gets especially critical during busy times, such as Black Friday, or in remote areas where next-day delivery was not possible until now.

Dynamic pricing is no longer a new technique but the norm.
While some clients might say that it is abusive, in fact it is the best
real-life application of economic theory.

When demand rises, it is only necessary for the price to follow. Discounts are applied when stocks are moving too slowly or a client is interested in the product but its price is too high. In this scenario, predictive analytics can look at a client’s past purchases and determine the price they are willing and able to pay.

Although using big data solutions and predictive analytics in particular seems like the holy grail of marketing, there are a few limitations to consider. Some are strategic; others are related to the resources necessary.

First of all, a great customer experience, either online or offline, is mapped onto the customer journey. Every organization should first get this right, especially at the most critical touchpoints.

Once an organization has sorted this out, it’s time to check if it has enough data to build predictive models.

Since these tasks are challenges in themselves, this means that not every organization is ready for predictive analytics. It requires a certain degree of business maturity, in terms of both strategy and the existence of previous records of clients’ actions and preferences.