Using Real Time Analytics
Authored by ActionIQ Team
#1 The Imperative for Real Time Analytics
Real time analytics have become a critical tool in crafting personalized customer experiences that are truly relevant. However, you cannot rely only on data collected by CRM systems anymore. Consumers interact with more and more channels and transactional systems every year. And to provide truly helpful experiences, you need to be able to draw actionable insights from all those different data sources.
Brands that are able to achieve this can expect to gain the lion’s share of the $1.7 trillion—that’s trillion with a ‘t’—that personalized experiences will generate, according to McKinsey’s Jason Heller. However, the window for acting on this opportunity is limited. Innovative brands will have captured much of that value within the next two years, and if you do not act, “you will be left behind by your competitors,” Heller says.
There is plenty of evidence that personalization can deliver significant value. Personalized marketing campaigns regularly generate lift of 10% to 25%. And maximizing campaign lift is especially urgent because of a phenomenon that has been dubbed the Experience Gap—i.e. the fact that the cost of customer acquisition is, by some estimates, rising as much as 25% per year, whereas the average lifetime customer value (LVC) has remained largely unchanged. Obviously, that is not a sustainable situation for any brand.
# 2 Next-Level Personalization
While personalization has been a marketing imperative for some time, the standards for what is a truly relevant, helpful customer experience are rising. Consumers are bombarded by messages that are in fact personalized by organisations, but only in a shallow way—i.e. based on a single trigger.
“Customers find themselves lost in today’s world—sifting through overwhelming amounts of information, adjusting to rapidly changing technology, struggling with relentless economic fragility, among other polarizing issues,” write Gartner analysts.
In other words, it takes more real time big data analytics than ever to create truly compelling customer experience—something that is harder and harder to do with a traditional data warehouse and business intelligence tools.
Fortunately, a new generation of tools—what Gartner calls smart hubs can unify all kinds of data sources (including streaming analytics), run real-time big data analytics to discover hidden opportunities, and create marketing campaigns that increase both ROI and total sales revenue.
#3 Real-Time Business Intelligence Is Not Enough
There is no doubt that stream processing, or real time, data is vital to marketing campaigns. A customer’s browser information, for example, can indicate exactly which product, or product category, they are currently interested in, and the exact moment when they are in shopping mode. This is, of course, extremely valuable information in delivering a relevant message.
It is also very valuable to know in real time how your customer is connecting to your organisation—in-store, via your website, via a mobile app or social media, etc. This too, can, tells you exactly which channels are, at a given moment, most likely to drive a positive response from customers.
The problem is, many brands still rely only on these kinds of real time triggers without taking into consideration a customer’s entire relation with the brand. As a result, businesses end up generating experiences that are superficial, ineffective and that can actually harm rather than help the brand. The classic example? Delivering a digital ad or email about a product that a customer has already purchased in-store or transactions via another channel.
Yes, realtime data from CRM systems are invaluable in delivering timely messages that reach the customer in the moment of consideration. However, realtime analytics now must also take into account historical customer data, as well as online user behavior, to deliver experiences that resonate.
RealTime Data + Historical Data = Highly Relevant Experiences
The most relevant customer journeys do not rely on a single behavior or demographic attribute. Rather, they identify preferences and opportunities based on historical data based on the customer’s interactions with your brand over time, from engagement signals and transactions all the way to spending patterns over time.
Obviously, “context” is still critical. That is, knowing when a customer is in buying mode (currently browsing your site or clicking your ads, etc.).
It is by combining the two—historical and real time contextual data—that you can truly deliver the right message to the right person at the right time, via the right channel.
#4 The four dimension of customer data
To make “behavioral and contextual targeting” possible, marketers must be able to creatively combine datasets across four dimensions of first-party data, including:
Demographic data. This includes data, like age, gender, and income.
Historic behavioral data. This includes past customer interactions with your brand, for example purchases transactions, past browsing, abandoned carts, etc. All these can help predict future action.
Realtime contextual data. This generally includes a customer’s digital behaviors—web browsing, emails they have opened and/or clicked through for more information, etc. These indicate specific product preferences, the fact that the customer is in shopping mode, and their channel of preference.
Predictive scoring. When you can unify all this data into a single database, analytics, including AI and machine language ML, can provide powerful predictions about the kind of content that is most likely to resonate at a given time and in a given channel.
#5 Traditional Barriers to Combining Historical Data and RealTime Insights
Marketers have long understood the value of these four kinds of data. However, they have faced serious challenges in accessing all that data in a usable form, and very rarely can they do so as fast as marketers would like. After all, response times must be very fast when you want to reach a customer with 1) the most relevant message possible, and 2) exactly in the moment they are in shopping mode.
So what is holding marketers back? Essentially it comes down to two things:
Siloed customer data. While brands collect more than enough data to fuel historic + realtime analytics, that data lives in multiple disparate systems, from transaction engines and enterprise data warehouse to multiple CRM systems. Unfortunately, you cannot simply move all that data to a data lake. All that data needs to be organized in a way that enables marketers to combine it in different ways—and each piece of behavioral data must be associated with an individual customer profile.
Ad-hoc, manual marketing operations.To make even a subset of customer data actionable, large enterprises still resort to ad-hoc, manual processes. Marketers request datasets and customer lists from IT, which can take days or weeks. They likely also rely on analytics experts to then model that data to discover insights and opportunities. Finally, they must coordinate campaigns across multiple, channel-specific teams and tools.
Here is how one Gartner analyst has put it: “Marketing teams in large enterprises became accustomed to the complexity of storing data in multiple places, having analytics systems separated from execution, the limitations of proprietary protocols and inflexible data models.”
However, these old limitations no longer need to hold you back.
#6 From raw data to real time insights and action
So what, exactly, are the capabilities a brand needs in order to deliver on the kinds of truly relevant experiences customers increasingly expect? Stepping back, it becomes clear that, while data or real-time analytics are critical, they are not enough on their own. You need to put them directly in the hands of marketers—and you must enable them to instantly orchestrate experiences and customer journeys across any channel, based on those insights.
In other words, you need to seamlessly connect data, marketers’s business acumen, systems of insight, and centralized execution of campaigns across all channels. Specifically, you need these four core capabilities:
- Data unification and holistic customer profiles. You must be able to gather all relevant online and offline customer behavioral data—including historical behaviors (more than just one, three, or six month’s worth). And you must be able to append that data to individual customer profiles, including data streams.
- Marketing self-service. Marketers need access to that unified data in a user-friendly way that makes sense for the way they do business. In this way, you empower them to formulate strategies and execute with truly data-driven insights.
- Predictive analytics. With real-time and historic customer data unified in one place unified, you are able to unleash AI and ML to deliver all kinds of truly impactful and actionable insights, as well as more traditional business intelligence.
- Omni-channel orchestration. As channels continue to proliferate, you need a centralized way to push the best possible message to the most appropriate channel at the moment of consideration. Siloed execution (i.e. having to coordinate execution across multiple, disconnected channels) can significantly slow down the process and water down the value of all the valuable streaming data that you are collecting about customer behaviors.
To help brands gain these capabilities, Gartner has made the case for a smart hub approach, for example a customer data platform (CDP).
“CDP’s seek to avoid that trap of using cheaper storage and processing, flexible data models, and productized vendor integrations, as marketers face new imperatives for cohesive cross-channel marketing, and look to scale and automation to drive ROI,” writes Gartner’s Benjamin Bloom.
#7 Smart Hubs in Action—Responsive Welcome Journeys
All this may sound abstract and a bit technical. So let’s consider some use cases that can really bring this approach all to life—and to demonstrate how valuable it can be in driving net-new revenue, LVC, and marketing’s return on marketing investment (ROI).
A great place to start is your welcome journey. Many innovative brands are still relying on a sort of fixed, one-size-fits all approach to the welcome journey that does not take into account the data—and the very clear behavioral signals—that customers provide over the course of that journey.
So let’s consider where things go awry, and how smart-hub solutions can solve the problem and make the customer journey truly relevant.
Deliver a truly relevant welcome message
To ensure that your initial welcome message is truly relevant to the recipient, you need to recognize whether the person who has just shared their email is truly a new customer. After all, they may have already shopped with you at a physical store—perhaps, for years. Or they may simply be opting back in to receive digital communications, and thus already have a relationship with your brand.
Unfortunately, siloed systems still prevent many brands from recognizing such customers and their relationship to the brand. And so businesses end up treating them like strangers, just as they are ready to deepen their relationship.
To avoid this, you need an advanced matching tool that can cleanse, match and synch personally identifiable information (PII) from any across any data source.
Personalize follow-up messages based on customer behaviors
Certainly you don’t want to send a follow-up message highlighting a product that a customer has just purchased. That is the kind of message that is unlikely to be relevant to a customer. Yet it remains a very common mistake that brands make during the welcome journey.
However, if you can capture and understand subsequent interactions that follow the initial message (e.g. online browsing, purchases, abandoned carts, etc.), you can create much more effective messages. To achieve this, you need the ability to automatically gather and unify cross-channel transactional data and historic behavioral data into a unified customer record.
Make post-purchase messages as relevant as possible
After a customer makes their first purchase, you have great information for crafting a great follow-up. However, you still need to take into account the customer’s post-purchase behavior, especially if they decide to return the product for any reason.
If you miss this important return transaction, you may end up asking for feedback about a purchase a customer has already decided does not work for them. However, a smart hub (and well-timed message) enables you to make a helpful recommendation for an alternative product or service.
Take advantage of replenishment opportunities
Replenishment campaigns, done right, can be extremely effective. However, this requires that marketers access and interact with purchase data reveals which products customers repeat-buy, and the cadence at which they do so. With these insights, you can deliver a message just as the customer is most likely to respond.
Again, this requires a solution like a smart hub that can capture cross-channel purchase behavior and enable marketers to quickly set up a campaign that can automatically detect and act on those insights.
#8 Creating Value of a Customer’s Entire Journey with Your Brand
The welcome journey is just one of the countless ways in which marketers can leverage real-time analytics and holistic customer data to drive value across the entire customer lifecycle, from acquisition and activation to repeat purchases and long-term retention.
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