The Impact of Predictive Analytics on Customer Experience

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Predictive analytics prepares a marketer for the future, by extracting inputs from existing databases to envisage future outcomes and using these inputs to enhance the customer experience.

With organisations harnessing the power of technologies like Big Data, AI, and analytics, marketing has been continually evolving. Enterprises that leverage AI and machine learning have an edge in the consumer market, as they use comprehensive data to analyse their customer behavioural patterns and predict future interests.

Predictive analytics is a form of business intelligence technology that focuses on combining the existing data for patterns and using that data to make predictions on future outcomes or identify risks.

Think of it like a weather forecast for your business that predicts customer behavioural changes or any possible future situations to prepare you. Predictive analytics leads to higher engagement, increased customer retention, and higher lead generation, which ultimately results in increased sales.

The Impact of Predictive Analysis on Customer Experience

1. Customer Needs Forecasting

Predictive analysis is a powerful marketing tool used by several organisations to precisely forecast customers’ needs even before they have themselves made up their mind. The early identification of a customer’s needs allows brands to be more proactive; enabling them to curate their messages in a personalised manner, effectively recommending a product to the customer even before they realise they need it.

Also Read: Big Data, IoT, and AI Will Help Personalise Brand Experience

Predictive analysis has helped organisations create a reliable and satisfying customer experience journey for their target audience. Right from recommending a certain product based on their previous purchase, interests, viewed items to generating a sale, predictive analysis is the key driver in today’s modern digitalisation. It allows brands to address customers’ needs promptly and to exceed their expectations by providing valuable information and support.

2. Customer Churn Reduction

Marketers and retailers have worked for decades, to find ways to reduce customer churn – the percentage of the once-loyal customer who has stopped investing in a brand’s products or services. Customer retention has been one of the major agendas for marketing strategies and business models throughout the world, given it is far less expensive to retain an existing customer as compared to generating new leads and acquiring new ones.

According to Seongjoon Koo, Chief Data Officer, J.D. Power, “Predictive analysis can be used to identify the customers at the brink of a high churn level and help organisations take requisite actions by paying more attention to such customers in a personalised manner.”

3. Real-Time Marketing Bets

Personalised marketing is a powerful way to reach out to target customers, but it has to be based on data. Predictive analysis can run through data almost instantly and provide effective solutions in a short period. It helps organisations make real-time marketing bets that can be used to retain existing customers by providing them with a valuable service like a giveaway, personalised discount coupon customised offers.

Also Read: How Intent Analysis Can Help Programmatic Advertising

This doesn’t imply that one can always retain customers by offering free products or services. You have to make sure that these rewards are properly balanced without losing money. Customer retention is highly essential, but not at the total cost of the revenue of a company.

4. Faster Shipping Options

Predictive analysis helps brands build a complete customer experience that leaves customers content and satisfied with their entire customer journey. With an increasing number of customers demanding same-day or next-day delivery, predictive analysis helps retailers and shipping partners ensure that they provide reliable, on-time arrivals.

Predictive analytics can not only be used to identify a customer’s behavioural pattern or interests but to determine the optimal solutions for complex problems or situations that might arise in the future. For example, if a customer is staying in a remote area demands for next-day delivery, you can identify the ideal transportation routes beforehand to avoid any unforeseen costs or denying your customer. A happy and satisfied customer is the key to organic, real, word of mouth marketing that can help organisations acquire new customers within a short period.

5. Staffing Up or Down

Predictive analysis can help organisations anticipate high or low call volumes. Data collected from the website’s browsing pattern can indicate if a company needs to staff up or down. If there is a season end sale going on, it is expected that more customers will purchase products in a given period which might require extra staffing.

Having a strong internal support team can prove to be highly beneficial and convenient for brands, especially ecommerce. Staffing appropriately saves companies money by not paying people when there isn’t any significant amount of work for them and enhances customer experience by ensuring there are enough people to handle the incoming traffic during hectic times.

Top 3 Examples of Predictive Analytics Improving CX

Many organisations use predictive analytics as their core marketing strategy to retain existing customers and acquire new ones. Every brand uses it with a different end goal and purpose. Here are some of the most popular and well-known brands globally that use predictive analytics to stay ahead in the market:

Harley Davidson Targets Potential Customers With AI

The renowned motorcycle company uses predictive analysis to target potential customers, generate leads, and close sales. When customers are targeted directly, they can have a more personalised experience based on their interests that leads to higher satisfaction.

Harley Davidson uses an artificial intelligence program known as Albert to identify potential high-value customers that are ready to make a purchase. Once a list of all the target customers has been made, the sales department then may contact the customer directly and walk them through the sales process to make a purchasing decision.

Also Read: Build or Buy Your B2B Customer Data Platform?

Netflix Uses Data For Personalised Recommendations

Netflix is a big user of predictive analysis, and no list would be complete without mentioning it. Everything Netflix does, right from the shows that it creates to the movies that it promotes is a result of accumulated data over time using predictive analysis.

Netflix gathers a lot of information about their users such as demographics, watch history, ratings and preferences, which is then put into an AI-powered algorithm to predict what they might watch next. The AI predictions are almost always accurate. It was found that about 80 per cent of the total content that is watched on Netflix is due to the recommendations. Having such a robust predictive analysis system in place saves the company USD one billion a year in customer retention.

Sephora Recommends Suitable Products to Customers

Sephora is a Paris-based, French multi-million company which sells personal care and beauty products throughout the world via its stores and online portals. Finding new makeup and beauty products can be overwhelming at times for customers, but Sephora offers a combination of different technologies that help customers decide if they’re getting the right products for their skin and lifestyle.

Sephora’s data software creates personalised profiles for each customer based on their interests, previous purchases, and preferences. Artificial Intelligence then analyses that data to predict the products that customers might need or want to buy following their previous order. A list of customised suggested products is shown to the customers under the “Recommended for you” tab on its home page.

Sephora also uses targeted rewarding strategy for customers who are loyal to the brand by analysing how much they have spent in a year. A study found that 80 per cent of the customers are loyal to Sephora and are indeed rewarded accordingly.

Also Read: How to Put the Experience Back Into B2B Customer Experience

Some might undermine the value of having structured data in place, but it is quite essential for various industries and domains. Refined data and patterned algorithms are providing organisations of every stripe with vital information. Which that can improve efficiency, reduce costs, enhance market knowledge, reduce customer churn and take customer experience a notch higher.

If properly implemented, predictive analytics can provide a wide array of useful information that brands can leverage as per their needs. It is a versatile technology that comes with high potential and gamut of functionalities. In a nutshell, it depends on your planning and predictive analytics technique, how well you can benefit from it. Keep rehearsing and retrying to determine what works best for you.