To Be Customer-Centric, Brands Need Real-Time Analytics
Advanced analytics have become the most critical tool in crafting personalized customer experiences at scale. However, relying solely on data collected by traditional CRM systems is a thing of the past. Consumers are increasingly interacting and transacting across more online and offline channels — and to provide truly helpful experiences, marketers need to action on those different data sources in real-time.
There is plenty of evidence that personalization generates lifts of 10% to 25% in business performance. This is reflected in the phenomenon that has been dubbed the ‘Experience Gap’ — the fact that the cost of customer acquisition is by 25% per year, while customer lifetime value (LTV) has remained largely unchanged. If this trend continues, more brands will struggle and, ultimately, not survive in the long-term.
1. Real-Time Events Are Not Enough
Real-time data is vital to creating personalized experiences. Browsing behavior provides in-the-moment signals on a customer interest and intent for a product or service. This is extremely valuable information in itself. It is also very valuable to know in real-time where your customer is interacting with your organization — in-store, website, social media, etc. This tells you exactly which channels are, at a given moment, most likely to drive a positive response from each individual.
The problem is, many brands still rely solely on these individual events without taking into consideration a customer’s entire history with the brand. As a result, businesses end up delivering superficial experiences that are ineffective and have been shown to actually decrease customer value. The classic example? Following a customer around the internet with an ad or email about a product they already purchased in-store or via another channel.
The most relevant customer journeys rely on the entire customer history and layer in the contextual events, from engagement signals and transactions all the way to spending patterns over time. Context is still critical. But it becomes more valuable when it is combined with the full history of the customer’s interactions, allowing you to deliver a more helpful real-time experience.
2. Next-Level Personalization
While personalization has been the marketing buzzword for years, many brands still struggle to meet even the most basic customer expectations. Consumers are bombarded by different messages from the same brand that are both completely personalized and siloed by channel. The customer does not think in channels.
It requires message orchestration powered by real-time analytics to create truly compelling customer experience — something that is impossible to do with business intelligence and marketing tools.
3. The Four Dimensions of Customer Data
To make historical and contextual targeting a reality, marketers must be able to creatively mix and match datasets across four dimensions of first-party data, including:
- Demographic data. This includes data such as age, gender, and household preferences
- Full historical behavioral data. This includes all detailed customer interactions with your brand, including purchases, browsing, email, and even customer service carts. All these help predict future action.
- Real-time 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 intent and channel preference.
- Predictive scoring. When you unify all customer data into a single actionable place, you can then apply powerful machine learning techniques to provide predictions about the kind of content that is most likely to resonate at a given time and in a given channel.
Marketers have long struggled with accessing and actioning on their customer data, and now real-time experiences have added another layer of complexity. For these experiences to be at all relevant, they must be delivered exactly in the moment the customer is in interacting with the brand.
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 and real-time analytics, that data lives in disparate systems, from point of sale and enterprise data warehouses, to multiple CRM systems. Unfortunately, you cannot simply dump all that data to a data lake and be done with it. 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 load these lists across multiple, channel-specific tools.
What exactly are the capabilities a brand needs in order to deliver on the kinds of truly relevant experiences customers increasingly expect? You need to seamlessly connect data. For example, with real-time and historic customer data unified in a single actionable location, you are able to apply both human and artificial intelligence to create actionable insights and deliver truly impactful customer experiences.
This is accomplished through what Gartner has described as the “smart hub dumb spokes” approach, with a Customer Data Platform (CDP) serving as the smart hub. “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.
The struggle to unify data across multiple consumer touchpoints and channels is real, but brands now have way forward — one that will give marketers more insights and power than ever before.
Tamara Gruzbarg is an expert analytics executive who has led data initiatives across multiple industries including digital, publishing, finance, research, consulting & retail. Tamara joined ActionIQ as Head of Industry Insights after having been a client of the platform herself at multiple organizations. In this role, she works with clients to realize the value of the insights that live in their data and the power of ActionIQ to make data-driven marketing a reality. In her previous roles as SVP of Data, Analytics, and Consumer Insight at Meredith Corporation (formerly Time Inc.) and VP of CRM and Analytics at Stuart Weitzman (acquired by Coach Inc.), Tamara designed and implemented successful data-driven ML-powered solutions in areas of Customer Acquisition, Personalization, CRM and Campaign Management.