The opportunity to influence customers when they interact with your organization means you must be intelligently ready with information that’s relevant, timely, and available in all digital channels. Customers now expect highly personalized experiences, which forces marketers to look for ways to be more responsive and contextual.
If your marketing approach uses only push methods such as digital ads, blast emails, and branded apps, then chances are your results are trending downward. Consumers have come to greatly distrust push methods of marketing automation. They want value from their interactions with a brand.
Rather than campaign- or event-driven marketing, which focus more on the products and services the company wants customers to buy, advanced marketers use data and customer centricity to create real-time interactions based on customer needs at a specific time or around a specific topic. This approach switches the emphasis to the consumer.
Real-time interactions are customer initiated, relationship-driven, immediate interactions with a channel such as voice, app notification, web chat, or social media post. The relevant offer becomes highly effective, because it is more appropriate to the consumer’s need in that moment. The ability to serve these immediate needs requires real-time interaction management (RTIM) strategies.
Making the case for RTIM
What is RTIM? Let’s say you are driving in a snow storm and you get an alert from your weather app for nearby hotels that offer reduced rates for weary travelers. Or if you buy movie tickets online, you may be sent an email with discount coupons for popcorn at that theater for that evening. These offers have context, convenience, and meet individual needs in the exact moment the need arises.
A marketing campaign starts and stops. It has a beginning and an end—usually based on the timeframe of a product or service offer. RTIM, on the other hand, is about modeling offers around the profile of the individual throughout the customer lifecycle in perpetuity. RTIM is more responsive than proactive. At its core, RTIM possesses the ability to provide contextually relevant marketing to customers across digital channels and devices to where (and when) consumers are.
For example, if I create a golf reservation on a mobile app and get an offer for golf balls and other golf products offered through the app, I will be more inclined to purchase because the offer is done at the moment of interaction. An email follow-up later in the day is more passive than a mobile marketing offer redeemable at the time of purchase, and I’m less likely to respond because I’ve moved on to other things.
Real-time, relationship-driven interactions engage customers across channels with contextual relevance, insight, and systems of engagement to deliver the right marketing offer. It’s about much more than just technology and data analytics. To achieve RTIM, organizations must create a strategic framework first and choose the right provider platform to enable its investment.
A framework for RTIM
RTIM has become a reality, thanks to advances in artificial intelligence (AI), decision engines, customer analytics, omnichannel, and Big Data. Technologies such as AI are used for real-time decision engines to optimize customer offers. Big Data is used for multiple inputs such as purchase behavior, consumers demographics, and geographical implications like the weather. Customer analytics are used to make predictions on consumer behavior to aid in real-time decisioning. Omnichannel includes the various channels of interaction with the consumer and the coordinated strategy across all channels to ensure awareness of any offers to continue the conversation with the consumer in a knowledgeable and seamless manner.
There is a lot of technology behind RTIM, but that’s just part of the story. Functionally, here are some key concepts that support a strategic RTIM framework:
Identity: Understand your customers and their behavior while also identifying the different channels they may use. Create microsegments or individual treatment strategies based on customer needs, value, and behavior across all channels.
Context: Digital experiences must align with the context where users are interacting. What journey did a consumer take to interact with you (website, mobile app, voice)? What do you know about the consumer such as location or weather? Do you know if the consumer is at home, at the office, or in an airport? These factors form contextual understanding of your consumer.
Decisions: Recommendations, messages, and incentives are examples of decisions that must be personalized for the consumer, based on data. Business rules many decisions, but it’s important to learn what works and what doesn’t work for a given consumer, situation, or relevant need.
Offer: While making decisions to drive personalized offers, orchestrating the offer across various channels can impact how the offer is delivered (voice versus web versus mobile app). In addition, as a consumer goes from mobile app to website to social, how should the offer be positioned in the various channels?
Optimization: Every interaction, offer, and decision must be measured and fed back into the learning process to be evaluated in order to optimize for future interactions. This is a continuous process to feed recommendations from the RTIM platform to marketers so they can make changes to decisions and offers.
Planning for RTIM
RTIM requires an organization to look at its customer experience across channels. What messages and offers would you make per channel? How strong is your presence in the various channels? How well do you perform when interacting with consumers via email, SMS, mobile app, social, website, call center, face-to-face, or other channels?
Typical channels used in RTIM are digital. RTIM offers are found in mobile apps, web chats, call center interactions (associate and IVR), ATMs, and in e-commerce activity. However, all channels, including traditional phone, mail, and in-person, need to be considered to ensure all offers in all channels are coordinated and integrated.
RTIM is most often used in a consumer context, but there are use cases for RTIM in the B2B space, as well. One is to use AI to analyze sales calls or online meetings to look for patterns/language/sentiment and then to present scripts and suggestions in real time to the sales team during the call.
RTIM gains momentum
The market for RTIM has become a top technology trend, according to Forrester Research, because of its use of advanced analytics to address cross-channel customer experience. The RTIM market is growing largely due to the demand for better personalized customer experiences that marketers have grown to trust the RTIM providers with their investments.
The RTIM market will continue to gain the attention of marketers and customer experience professionals, and RTIM providers will continue to evolve and differentiate their capabilities.
The biggest roadblock to RTIM success is that few marketers take the time to craft the strategy, and instead lean on the technology. Defining the vision, a blueprint or roadmap for RTIM, and execution strategy need to be conducted first.
Marketers also find the amount and specificity of data used in RTIM programs to be hard to manage, which may lead to irrelevant offers that lack context for customer engagement. It’s critical to create a cross-functional team of data and customer experts to manage the art and science of RTIM.
Lastly, dependency on analytical models to predict consumer behavior can quickly fall out of currency. As needs and interests change, so must offers. To overcome this challenge, your RTIM must be able to adjust offers and interactions in real time.
The RTIM framework model has only recently come to light as organizations attempt to build a capability to enable the best and relevant offers. ?
