95% of Martech Executives say AI, Machine Learning, and Real-Time 1:1 Marketing are crucial to their long-term success, but only 5% are using these technologies successfully today
When we’re on journeys, we often encounter decisive moments, and how fast we get accurate and relevant information and the decisions we subsequently make can have a huge impact on the outcome. Take, for example, one of your vacations. Your airline cancels a flight leaving you precious seconds to react and find other options before they evaporate; Or, if you’re upset with your wireless service and decide to reach out – calling to give them one last chance. Every case is different in terms of the people involved and the situational context, but timing and the flow of relevant information always plays a crucial role.
Time Is Of the Essence
If you’ve ever managed a project or worked with a project manager, one of the first things you learn is what’s called the triple constraints: the scope (quality), the cost, and the time (schedule) for a project. Project managers always manage a project using this idiom:
“No problem, I’ll deliver as long as I can control at least one of these 3 constraints. Deliver by Jan 1 you say. Fine it will cost X and we can deliver Y scope. Change the scope and the cost will go up. You want to hold costs to $50k, then we’ll deliver X scope by Jan 1. Change the scope and the date will slip.”
It seems lately in projects and commercial transactions we’re trying to break these laws. We want everything faster, cheaper, and more of it at higher quality. Why not, it sounds great. Although the quest for instant gratification isn’t new, it certainly has upped its game.
It’s obvious why we want things faster – because time is a scarce resource and finishing something can mean the difference between opportunity and obsolescence. Since we can’t pack more minutes into a day, we squeeze more tasks into limited time usually sacrificing scope / quality, and all in an attempt to push things to faster completion. With continual pressure to increase productivity levels, technology helps accomplish more with the same resources. Clearly, time is of the essence.
However, there is another reason time is so precious. Time is the enemy of information’s value to decision making. When something happens, information about it may be extremely valuable seconds after it occurs, yet through time that value decays (see Figure 1) at rates that are different depending on its importance to a given decision.
For example, when someone has a heart attack an enzyme called creatine kinase (CK) appears in the blood. Detecting the recurrence of that enzyme and passing that information quickly to a doctor could save that person’s life. Much later, that information may still be valuable in a post analysis of that patient’s situation, and years later, it could still prove valuable to researchers performing wider studies on congestive heart failure. Nonetheless, its value diminishes considerably over time, eventually relegated to but one data drop in a vast data lake, and ultimately some information even becomes a cost and liability with no corresponding value.
In CRM, real-time data can be of corresponding value and the context of the situation determines its value and decay curve. While on buying journeys, consumers come to decisive moments. When considering what consumers expect at those moments, brands must assume they are impatient, easily distracted, and likely to act suddenly. Once again, speed becomes paramount.
When Do Customer Interactions Need To Be Real-Time?
Honestly, there’s no such thing as real-time information. When an event occurs, time immediately elapses so, at best, a device can detect an event and pass that as information in near real-time to a decision maker. In some cases to be of optimal value, this information needs to reach the decision maker and the decision made – all in less than 1 second:
Given that understanding, what’s most important is that the information makes its way into a decision management system that can factor this into making a decision and in enough time to affect some other event that hasn’t yet occurred.
In our heart attack example, the presence of the enzyme CK is the initial event followed by some device that detects it, followed by that information passing to a decision maker who takes action to prevent a subsequent heart attack:
For CRM, a customer may be on the verge of switching service providers and the event may be one last contact with the existing provider:
Depending on the problem at hand, having real-time information and using it in a dynamic way may be critical to positively affecting customer experience and satisfaction.
In general, customers are less willing to wait for service or answers than ever before. When someone decides to research a potential purchase and browses a website, if it’s not responding nearly instantaneously, it’s likely the consumer will move on. Moreover, if the customer calls into a contact center and enters a long wait queue, again it’s likely the customer will balk. In other words, you literally have seconds to make the right impression and provide them with relevant content.
As we’ve just seen, the situations the events occur within, and how events relate to each other are crucial to understanding the full story. The circumstances that form the setting for one or more events are its context, and the data that comes from them contextual data.
Feeding this data into a decision engine can be essential so decision makers understand the full context of each event before making subsequent decisions.
Take another use case. Again, the event is a call coming into customer service. Here’s the setting for that event:
On a rainy day on May 22, 2017 at 4pm, while stuck in a traffic jam on 1-81 in southern VA, a certain customer (Mary) places a call to her credit card provider. Having just learned of late fees, she is not happy. She’s a loyal customer of 23 years who always pays on time and her aim is to get these fees removed.
Consider some of the contextual data in this scenario:
- Historical background of customer – e.g., family history, purchase history, engagement history – i.e., knowledge of this customer as an individual (23-year loyal customer – Mary)
- Date / Time (May 22, 2017 at 4pm Eastern)
- Location / Proximity (northbound on 1-81 at x lat / y long)
- Environmental conditions – e.g., weather / traffic (raining – stuck in traffic)
- Preferences (uses phone to call in)
- Emotional state (unhappy with fees; may be further agitated by weather & traffic conditions)
- Journey stage (just received bill and is inquiring about fees)
- Current intent / agenda (aims to get fees removed)
Think about how important each piece of information is to the scenario and how all of it, when factored together, forms a more complete picture. For instance, if the call started at 4pm and the conversation with Mary is still in progress at 4:30pm, it’s likely she’s antsy and probably tired from a long day. Now combine that with the weather, traffic conditions, journey stage, current intent, and historical background, and now it’s very likely her patience is wearing thin.
In short, situational context answers these questions about an event.
Brands that can factor in this kind of contextual information are more likely to address immediate sensitivities and handle customer interactions accordingly. Human agents, unlike artificial ones, have the potential for very high levels of emotional intelligence (EI) [i]but still need assistance compiling the information and making sense of it. So far, artificial intelligent (AI) bots aren’t that advanced in EI and humans have the edge in relating to people, applying soft skills, and making complex judgments. Thus, it’s crucial to realize the full situation when designing systems that will include both self-service options and escalation to humans, and when humans are involved, providing them with augmented information so they can serve customers better.
In this scenario, as with most others, the contextual information becomes practically useless the next day. Unless the systems involved can detect these events and the context in near real-time, and pass it on, the value of this information decays rapidly. Imagine the customer service person saying, “If you just call me back tomorrow morning Mary, my system will run an overnight batch job, and I’ll have a sentiment and value score for you so I can help you better” – said a successful customer service person never.
And systems can’t pre-calculate conditions or recommendations either, hoping that works. How would you rate a pizza delivery company if you ordered a pie, and delivery came in 15 minutes (a near real-time pizza order), but they cooked it the night before?
One Contextual Brain Making Real-Time Decisions
When businesses engage with customers – or vice versa – such as in the examples given, they’re expecting an ongoing conversation regardless of the initial channel. In some cases (such as a phone call), the exchange begins and ends on the same channel, with stimulus and response occurring dynamically and repeatedly. In other cases, such as a website visit or chat session, conversations may take longer, and experience periods of pause, delay, or even happen over multiple channels and sessions.
Unfortunately, many brands treat each interaction as an isolated transaction instead of viewing them as parts of a continuous conversation that builds toward a deeper relationship. When viewed from the latter lens, each interaction nudges the relationship in a positive or negative direction, in effect adding to a “Good Graces” balance, or subtracting from it (see Figure 2). Clearly, the goal of any customer-centric minded organization is to evaluate each interaction’s contribution, monitor this balance, and work to strengthen the relationship – improving it with each engagement.
This is more than a loyalty point balance, an NPS score, or a sentiment score. It’s more like a credibility score, measuring the overall standing your brand has with a given consumer.
Consumers reward companies with more business when they provide convenience and simplicity, can be trusted, and help improve their lives. Amazon is a prime (pun intended) example. Consider the Amazon Dash button, which really isn’t the revolution in buying experience originally promised, at least not yet. This is the internet-connected button you place, for example, on your washing machine, and you push to reorder detergent. However, many customers purchase the button (for $4.99, which comes with an equal credit upon the first purchase) but never use it. They just love Amazon, trust them (i.e., they have a high good graces balance), and go for their innovative ideas – and this idea smacks of convenience, simplicity, and real-time engagement.
Customers also keep score in terms of brands that just get it. What this means is buyers notice if they get superior, well-coordinated services, regardless of the channel, time, location, or representative involved. For a business to do this, it must have a culture that oozes of a customer-obsessed mentality and have systems that support its people in providing outstanding services at scale. If those systems are in silos (see Figure 3), each commanded by a different brain, achieving that goal becomes impossible.
Can Anyone (Or Any System) Do This Really Well?
The short answer is yes, companies should be able to do a decent job of this, but they aren’t. Amazingly, a DataStax commissioned Forrester survey of 206 organizations across four major countries revealed, “95% of organizations are currently unable to make sense of customer data, and struggle to gain real-time insights from this data.”
When agents serve customers, to provide great service, they need technological assistance just as a pilot needs instruments. Flight deck instruments must depict actual conditions, with little latency, or they’re useless. Finding out too late that your altitude is 2000 feet could spell disaster. When on chat or a call, consumers expect immediate responses to each question and have a threshold for the total time they’re willing to devote. In theory, whether the agent is a bot or a human makes little difference, what matters is whether the agent provides authentic empathy, good judgement, relevant responses, and resolves the situation quickly.
Today, offloading some of this to machines is possible (sensing conditions, detecting patterns, managing decisions, and triggering actions), but seemingly only with CRM rocket surgeons behind the curtains.
That shouldn’t be the case, and it’s holding back brands from providing exceptional customer experience. Some vendors, nevertheless, are working to change that and provide tooling that allows for real-time interaction management or RTIM[ii] controlled by business users. Though the right tooling alone won’t bring success, as firms must align data, organization, and processes also, choosing the wrong technology will introduce barriers and rigidity that will be difficult or impossible to overcome.
When considering the entirety of the data, insights, and technological solution needed, keep in mind that each vendor has evolved their platform from very different starting points. Some have cobbled together solutions by acquiring smaller firms and in fact don’t even have a single platform. Others have built out their solution, appending capabilities to legacy outbound and batch oriented architectures (often based on email campaign blasting), making it nearly impossible to handle enterprise real-time processing requirements. It’s likely you’ll hit a wall (see Figure 4) with these types of solutions in attempting to transform to real-time 1:1 contextual engagements.
Thus, look instead for help from partners that have extensive experience with real-time customer interactions and have built solutions from the ground up to handle the complexities of real-time data wrangling, dynamic analytics, and scaling for the instant gratification response times required by today’s customers.