10 Must-Haves for Your Data Toolkit
Create a customer-first enterprise through unified data management.
Imagine a customer who is not only eager to buy a product or service, but has made that desire abundantly clear. Yet your sales team doesn’t move on the red-hot opportunity. The reason: that desire has only been expressed to the customer support department, which doesn’t share the information with sales.
As much as companies talk about the customer journey and gaining a total view of the customer, many struggle making use of the data that is required to do so. Less than a third of companies are driven by the customer experience (CX) today, according to a recent Forrester study. Companies that lack a comprehensive view of their customers generally lack an IT infrastructure that can break down data silos and create real-time customer profiles using all their CX data.
A better customer experience is possible. With the right data toolkit, companies can build CX applications. They can access, segment, and activate customers in real time to create better customer experiences. And they can enrich their understanding of customers with artificial intelligence (AI) and analytics.
For these experience-driven companies, the payoff is significant. Forrester found such companies increase customer lifetime value 1.6 times and revenue 1.4 times more than other companies.
But satisfying customers requires the right toolkit. Here are the 10 “must haves” for a data toolkit that will allow you to deliver personalized, consistent, connected, relevant, and effective customer experiences.
#1. A platform for activating and acting on customer data
A data toolkit begins with a CXM platform that can be tested and optimized to provide compelling customer experiences. To deliver experiences at scale, companies need a platform that provides real-time customer profiles, continuous intelligence, and an open and extensible architecture.
Only by bridging marketing and IT silos can meaning be found in disparate data sets, achieving a more holistic customer view without complicated integrations that require costly workarounds.
Marketers benefit from a toolkit that can stitch together, analyze, and activate data across channels. They gain a single version of the truth that lets them move swiftly with confidence.
As a result, information from different sources such as a customer’s purchase history, call center contacts, and offline channels can be liberated, ensuring the data that CIOs and marketers want to use is definitive, clear, and unambiguous. They can forget about fragmented data, complicated governance, and inconsistent campaigns, and instead focus on creating compelling customer experiences.
Both consumers and the online brands they interact with benefit from the transparent exchange of pseudonymous data elements — consumers receive personalized content, discounted product offers, and streamlined user experiences. Brands receive vital revenue streams supporting multiple online business models.
Adobe uses generally accepted best practices related to consumer privacy and opt-out procedures. You should make sure tools you use help support your ability to provide transparency and control to your consumers, while delivering personalized ads subject to the Online Behavioral Advertising (OBA) Self-Regulatory Principles and comply with your obligations under privacy regulations (such as delete access and delete requests).
#2. Role-based sharing to break down silos
A vast store of customer information is
For example, role-based access control (RBAC) functionality automatically alerts designated people about issues with data delivery. Viewing this information on an easy-to-understand dashboard lets them see dips or spikes that may signal a problem to address or an opportunity to take advantage of. Ensuring the right people have insights to take immediate action is critical to the customer experience.
#3. Data standardization and unification
From data warehouses to data lakes to multi-cloud environments, many companies are looking for a means to store and manage their data in one central repository.
The challenge is that all this data has been collected by different systems that speak a different language. They use different semantic rules to catalog the data and to classify the data. Therefore, even though they’re in the same place, they are not functionally together. From a functional perspective, they are still in silos. You need a solution that standardizes the data.
Unifying the data allows for onboarding of structured, semi-structured, and unstructured customer interaction data from virtually any channel, by a variety of manners including streaming, bulk ingestion API, native third-party connectivity, mobile SDK, and tag manager.
This has enormous practical applications. Consider a marketer that wants to get a list of every person over age 40 who has visited an automaker’s website for a potential campaign. Under normal circumstances, identifying that data can be a time-consuming, expensive chore. Standardized data can provide a way to run “what if” scenarios against all the information in a data lake without transforming the data, so the marketer can easily determine if there are enough viable leads to justify launching a campaign. This happens quickly and easily, giving marketers the flexibility to test ideas as fluidly as their imagination comes up with them.
Customer data is, after all, a first step to release the ingenuity and creativity of your people. It is not about the amount of data you have, but what you can learn from that data to satisfy your customers.
#4. Redefined infrastructure with shared goals
Even after standardizing their data, many companies struggle with making use of the information quickly enough. The traditional tech stack that companies have is not designed for low latency. Different systems are not designed to interact with one another in milliseconds.
Typically, the integrations that allow the systems to talk to one another are built in an ad hoc manner that is time-consuming, expensive, and not scalable. In this setup, systems need to communicate horizontally to share data from one place to the other, creating additional latency and data workflows. Because of poorly maintained API integrations, data often goes unused, growing outdated and unusable as specs change.
The solution is to build connectors (or APIs) directly into the systems. As a result, when System A needs to use data that was collected by System B to enrich a customer profile, it can retrieve the information without delays of needing to talk to different systems. The simplified workflows allow you to deliver personalized experiences at scale.
#5. Real-time, cloud-based solutions
In the digital world, every customer expects that the information they want will be available at or near real time.
Only a cloud-based, real-time solution enables you to deliver on customer expectation. You want to be able to build customer profiles using interactions with a brand through different devices and channels — such as web, mobile, CRM, and experience events.
Profiles can be based on a view of your customer today instead of two days ago. This gives you an up-to-the-minute understanding of their behavioral, transactional, financial, and operational data. The result is better targeting and better customer experiences.
#6. Artificial intelligence and machine-learning capabilities
The goal of any data scientist or marketer is to gain a greater understanding of the customer and drive customer intelligence in a more effective way. A key way to accomplish that is by using artificial intelligence (AI) and machine learning.
Advanced analytics can provide anomaly detection and automated alerts, which highlight and call out unusual events. The information can be immediately sent to specific stakeholders in the organization so they can take immediate action.
For example, an anomaly might indicate that one day 23% of orders came from a new campaign that represented 3% of total traffic. This is valuable information that can guide both the next and future steps. The automation takes the pressure off individuals to go through all the data that has been collected and processed, so they can gain insights faster and take action on them.
#7. Democratized systems and workflows
As advanced technology like AI becomes more critical to daily operations, it’s important that this technology be available to a wide range of people — from data scientists who can build their own machine-learning models to people who are new to the space and can benefit from out-of-the-box tools.
Machine-learning or other technology can make working with the data easier for people with all levels of expertise. Ideally, you don’t want users to spend time preparing data, extracting it from a data lake, and sending it to the machine-learning tool.
Each step of the process — building the model, training the model, running data through models, finding insights, and driving those insights back to a system of engagement — should be done more rapidly. Advanced technology can cut days or even weeks out of the process.
#8. Mechanisms to create and use unified profiles
Building a holistic, unified profile makes it easier to add in channels in the future — and that’s a major differentiator for any company that wants to deliver personalized campaigns both online and off. A solid unified customer profile or unified data model enables businesses to connect customers to these experiences more efficiently and in real time.
A single integrated customer profile allows you to access all digital interactions and offline transactions in a central location. It can include contact information, emails received, tracking logs, subscriptions, social engagements, support calls, and more. Such insights permit you to segment and target customers with precision, resulting in real-time, highly relevant interactions that resonate with specific customer segments.
By using unified profiles to provide customer data to all channels, lines of business including marketers can deliver the right experiences that keep customers engaged and then turn them into advocates for their brand.
#9. Analytics that confirm your data-driven experiences
Creating data-driven experiences is important, but it’s just as important to confirm that the experiences are meeting the customer’s expectations and causing them to take the desired actions.
You need to be able to instantly test and deliver personalized experiences to customers everywhere based on real-time updates to customer profiles. Analytics will confirm that you’re delivering data-driven experiences that result in sales, loyalty, and other signs of satisfied customers.
#10. Data-driven culture from the top down
Companies that are successful in customer experience focus become customer obsessed.
If you unite all your systems such as marketing, sales, and customer service, and streamline workflows, you’ll be able to discover insights that result in better experiences, higher value customers, and more long-term growth. By integrating data sources and tools with other best-in-class products, you can quickly take action and deliver world-class experiences.
Disconnected data is not simply a technology problem, however. Sharing data with a few stakeholders who archive the email and never take action is the epitome of a poor data culture.
When departments share data, that’s when you know you have a data-driven culture. Successful companies take a top-down approach that promotes collaboration over competition. These companies constantly improve their culture and put clear and structured processes in place to support that culture.
While people often speak about data integration, the most powerful integration is data and creativity. Company leaders must empower their people to use data to magnify efficiency, inspire innovation, and focus on the customer.
https://www.adobe.com/experience-cloud/use-cases/customer-intelligence.html