Analytics is like rocket fuel for businesses. With deeper knowledge of the customer at your fingertips, you can dramatically accelerate interpreting and serving their needs, while making your interactions with those buyers even more relevant and meaningful.
Marketers need to step up the quality of their interactions with far more discerning, impatient, and demanding customers, or risk losing their attention, business, and loyalty with irrelevant, annoying, or poorly timed offers.
Guest article by Mikko Jarva, CTO, Comptel
If we view customer engagement as a puzzle, we can envision predictive and real-time analytics as its two major pieces – without both, the full picture is not complete. Yes, they do different things: Predictive analytics rely on more static, pre-identified, historic customer information to deduce future outcomes, while real-time analytics examine contextual and interactive data to create a snapshot of a customer’s immediate needs and situation.
Some marketers believe one form of analytics is better than the other, but the truth is that while each has its strengths and weaknesses, together, they give us the best route by which to achieve our end-goal: effective customer engagement.
Predictive Analytics Inform, But Don’t Initiate, Future Actions
Predictive analytics generally refers to the data-driven methodology in which the likelihood of specific future events is estimated, based on historical evidence and patterns drawn from similar past incidents.
Nearly every business collects customer data, and the real strength of predictive analytics lies in its ability to support granular analysis of this information to reveal revenue risks and opportunities across the business. With data on product usage and consumption, you could determine if a certain customer is a prime candidate for a cross- or up-sell. In customer service, hidden relationships between data could reveal the likelihood of customer churn or surface potential retention risks.
For a more specific example, think of the advantages mobile service providers gain by analyzing customer purchase history. If Customer A recently upgraded from a 3G-capable phone 4G, a provider could infer that the switch to a more feature-rich smartphone may encourage this customer to purchase more data-driven applications in the future.
With historical evidence on the purchasing habits of other similar customers who made comparable upgrades in the past, the provider could make an even more knowledgeable prediction of Customer A’s future needs. All of this insight informs how the provider should market to, and serve, Customer A.
Of course, predictions don’t automatically solve business problems – they simply forecast opportunities and risks. And it could take considerable time and effort to draw these insights, making them ineffective for immediate action. Historical data is old data by definition, and it loses relevancy with each idle moment.
Real-Time Analytics Increase Relevancy
That’s where real-time analytics comes in. When marketers have access to immediate, personalized customer behavioral data, they can apply insights right away to support highly targeted, contextual marketing offers.
A real-time analytics approach acknowledges that data achieves its peak point of value and relevancy at the exact moment it is created or acquired. Let’s say, for example, that aforementioned mobile service provider is tracking Customer A’s mobile data usage in real time. With this view, the company would be able to determine the exact minute Customer A will reach his or her data cap. Now, the provider can instantly spin up a pay-as-you-go offer to help the customer extend service without any interruptions.
Similar real-time marketing opportunities exist across industries, and they allow you to address customers’ most urgent and immediate needs. Real-time analytics will be an especially valuable capability for companies developing solutions for the Internet of Things (IoT). Connected devices will create new data every second, and savvy businesses can act on that data instantly to drive better marketing and customer support initiatives.
The demands for real-time analytics and predictive analytics are different. For real-time analytics, you need to be able to access raw data at its source, process it instantly, embed a certain level of intelligent analysis, and trigger actions right away. For predictive analytics, your analytics platform must incorporate automated machine learning capabilities to achieve flexible and cost-effective historical data analysis. As the system automatically gains richer customer intelligence, it drives flexible, data-driven decisions now and in the future.
It might sound like a futuristic proposition, but it’s a natural evolution of Big Data into intelligent, fast data.
Achieving Balance Between Predictive and Real-Time Analytics
The most effective customer engagement is both targeted and contextual at once. This is why a multi-faceted analytics platform needs to leverage both historical and real-time data to drive relevant marketing actions.
Predictive analytics enable better targeting. Real-time analytics capture the value of context. Together, they form a more complete picture of your customers, which in turn enriches your understanding of their needs, and empowers you to improve your customer engagement approach. It’s about seeing both the forest and the trees.
Mikko Jarva is CTO at Comptel, a telecommunication solutions provider that enables the delivery of digital and communications services to more than two billion people, and cares for more than 20 percent of all mobile usage data.