With most companies having made great strides in improving their customer experience (CX) across select digital touchpoints, industry leaders are now looking at how to scale experience across the customer function and entire business at large. Doing so requires organizations to become customer obsessed and put data at the center of all they do to better anticipate needs and provide personalized experiences when, where and how the customers want it.
According to our research, The Business of Experience, leaders who approach experience holistically see an increase in their profitability year-on-year by at least six times over their industry peers. They look at experience beyond touchpoints and as something to be embraced by the entire business to meet the liquid expectations of today’s customers. They are also twice as likely to say they can translate customer data into actions, something that is critical to becoming truly customer obsessed, which in turn creates brand-obsessed customers.
By using robust artificial intelligence (AI) and customer analytics solutions, businesses can deliver differentiated, truly contextual experiences that ultimately build stronger brand loyalty and drive growth.
What does it mean to be data driven?
Historically, when we talked digital customers, we thought of them in terms of email addresses and cookies—a digital trail of their browsing behaviors. Now, marketers can’t rely on cookies alone to know their consumers and measure performance, especially considering Google’s announcement to remove support for third-party cookies by default on its Chrome browser, following Apple (Safari) and Mozilla (Firefox). It’s even more critical that companies use data from diverse sources, create stronger data architectures, and build governance frameworks to truly understand who consumers are and, more importantly, what they desire from brands.
Successful businesses have likely formed a unified identity graph, or an understanding of each customer. They use AI models, analytics tools and techniques to bring together a wide range of previously siloed, structured and unstructured data sets, and merge them with data from ecosystem partners. It’s this convergence of transactional data, demographic data and psychographics that helps brands infer what customers are thinking and when they might make a purchasing decision. Pulled together in a responsible way that protects individuals’ privacy, the data paints a more complete picture—a Living Customer Profile—of every existing and potential customer.
Align your data to your business objectives
Once organizations have created these customer profiles, they need to figure out what insights they want to generate, levers to pull, hypotheses to test and experiments to run against their business objectives.
In an events-driven market landscape, this could mean driving incremental sales revenue by prompting shopping experiences based on social media activity. Or sending an email with tailored products and promotions based on historical purchases. Importantly, AI and machine learning (ML) tools can automate these touchpoints based on deep and even real-time customer insights at a scale never before possible—with the added benefit of continuously improving experiences over time. When an offer is extended and a customer responds, these signals create a benchmark from which businesses can automatically optimize and recommend their next offers and experiences.
Bring the data together for maximum impact
Take Accenture’s Consumer 360 (C360) Platform, the data engine that fuels Accenture’s AI-powered marketing solution and enables next best action and hyper-personalization capabilities. Accenture partners with key technology providers to securely bring together data into one architecture where Accenture can apply proprietary AI and ML algorithms to build rich, comprehensive consumer profiles. Models can then accurately predict what, when and how the consumer will buy next. By tapping into insights from data across Accenture’s multiple ecosystem partners instead of just a single provider, brands can expand their online audience and boost their shop-to-order conversions.
A global cosmetics manufacturer used C360 to double the number of customers with known identities in its database. As part of this effort, more than 35 data sources were centralized into a single cloud ecosystem and customer identities were created across multiple brands using secure data collection and analysis methods. This enabled the company to predict how customers will act and what kind of engagements will resonate with them.
In a typical journey, a customer might watch several YouTube videos on how to apply certain cosmetics. She may talk to her friends about these products on a social media platform. She’s probably spent time on the manufacturer’s website browsing product and pricing information and has searched multiple digital retail channels.
All this data feeds into the C360 engine to provide a comprehensive picture of the customer's preferences and motivations behind why she’s engaging with the brand and category. With those insights, the manufacturer can predict with a high degree of accuracy when she will make her next purchase and where, as well as what type of product she's likely to buy, and then engage her with relevant offers and promotions for other products or services she may enjoy.
By aggregating, activating upon and optimizing insights from such customers, the company was able to increase its engagement by 25% and increase average revenue earned per visit by 10%.
Know your customer to grow your business
With the right combination of data, AI capabilities, analytics tools and partnerships, companies across industries—from retail and telecom to financial services and the auto industry—can know their customers at a granular level. This opens up companies to a myriad opportunities to create and deliver experiences that will strengthen customer loyalty and drive new business growth—and ultimately provide the kind of competitive differentiation all companies strive for today.