For a long time, retail sales were driven by physical locations of stores, a well-stocked inventory assuring a range of products available in one place, the price of products and customer service. Traditionally, customer engagement or customer relationship management has not been very high on the retail industry’s priority. Retail sales largely remained reactive and seldom needed to proactively drive customers to their stores.
These days with so many online options for the customer, retailers need to focus more on customer experience and customer engagement. Predictive customer analytics can help retail businesses understand customer shopping habits, preferences and purchase behaviors. They can proactively remind them of offers, purchase schemes, discounts, interesting marketing or promotional events, without the customer having to visit the physical store. Such cognitive marketing can drive sales from existing customers rather than undertaking the task of looking for new customers.
Customer engagement solutions can also help retailers design a holistic customer experience at the different touch points, across channels and across various customer- facing systems. The payoff is better brand engagement affinity and more footfalls.
People don’t want to be forced do things the way their parents did – with cash books and long queues. As many more people go digital, there are a significant proportion of them looking to conduct their financial transactions online and on-device.
A recent survey of banking trends suggests that for global banking leaders the top priority was improving the quality of the customer journey and providing a positive customer experience. Most banks, for example, take a multi-channel service approach by enabling customers to conduct business with them online, over the phone, in a branch location, or using a mobile app. However, not only does this call for data integration across channels, it also calls for a streamlined, integrated, seamless and personalized omni- channel engagement experience.
Another consumer survey suggests that 75% of consumers expect a consistent experience wherever they engage. So the mandate for financial institutions is clear — a strong understanding of customers and their goals and preferences at every stage of the journey — whether they’re opening a small business checking account, filing a supplementary health insurance claim, paying off a home equity loan or cashing out ESPP shares.
Along with this is the ability to deliver the experience with a predominantly digital focus. This could include websites with web chats, bots and artificial intelligent engines answering queries and educating consumers on financial matters and helping them make decisions. The bottom line is leveraging personalization technology and cognitive technology to provide more relevant, engaging and effective digital experiences.
Traditionally, in the insurance sector, great customer experience was equivalent to prompt claim settlement. But now, the thought process of both consumers and insurers has changed, leading to a new emphasis on customer relationship management and a new way of looking at customer experience and customer engagement.
In this regard, life insurers are far ahead than commercial and specialty insurers. With the aid of digital technologies like connected homes and social media marketing, insurers are moving on from being conventional. They are investing time and effort researching and analyzing their customers and leveraging predictive customer analytics, artificial intelligence and machine learning to generate insights and create personalized campaigns.
In this new paradigm, the basic insurance operation model has been turned on its head with new models being introduced. The one common underlying factor in all of these new models is personalization. These engagement models are reaping success because insurers are able to more closely connect with their customers through personalization technology.
With cognitive marketing technology and predictive customer analytics, insurers can now offer discounts and proactive risk management services to customers sitting at home. Through social media interaction they can understand their customers’ lifestyles and through predictive modelling and predictive analytics they can accurately anticipate what offers would interest them and create customized coverage to suit their needs.
More than increased customers, insurance companies are experiencing an increase in customer retention. By suiting experiences and products personalized to the needs and preferences based on implicit customer behavior, insurance companies have been able to build brand loyalty.
Travel is one industry that can hugely gain from business intelligence and predictive customer analytics, from creating new products that customers can easily relate to, to segmentation based on customer behavior rather than demographics. Some of the areas in which predictive customer analytics can bring break through value include:
Travelers are increasingly searching for personalized experiences. With personalization technology and predictive customer analytics they can predict what customers want and adapt their offers accordingly. For example, this could be done by bringing together a flight, allotting extra baggage or hotel, while also slightly adapting the price dynamically and considering competitor offers in real-time.
Trip purpose has served as a segmentation criteria for travel firms. On the other hand, clustering algorithms allow the discovery of new kinds of behaviors; the ones that cannot be classified easily. This is a key feature of segmenting customers based on behavior.
Historically, forecasting engines worked with past bookings. However, what happens if an airline considered moving their 9:00AM flight to 8:00AM? Or how would the impact on demand be if the price of flight tickets increased by 10%? New generation predictive modelling and predictive insights can provide accurate predictions based on such dynamic “factors”.
Market and regulatory pressures are pushing the healthcare industry to explore and embrace innovations such as healthcare analytics for reducing costs, improving clinical outcomes and providing better healthcare quality.
In the healthcare industry, predictive customer analytics include evidence, recommendations and actions for each outcome and category.
The most obvious application of predictive customer analytics is in clinical event prediction through analytical algorithms, such as readmissions, length of stay, clustering of patient outcomes to historical cohorts at time of admit, mortality and the onset of cancer or heart failure.
Besides their application in the clinical setting, predictive modelling and analytics can also provide a variety of institution-level analytics such as cost-of-care analytics, drug pricing analytics, sales optimization and sales performance analytics.
However, such modeling and algorithmic output is only possible in a data-rich warehousing environment. The healthcare industry would need to not only invest in data ecosystems but also 'live' certain work flows to create a stable context for deriving rich client insights and predictions.