Your customers want you to provide compelling experiences when they engage with you. With a powerful analytics solution in your digital marketing arsenal, you can identify inflection points in the customer journey for personalized engagement.
Advancing the maturity of your analytics practice is a significant factor in making the transition from being a reactive business to one that’s driven by understanding and predicting customer behavior in relation to your business outcomes. These are the insights necessary to become an experience-driven business — one that customers love to engage with.
Your company can realize substantial material gains in customer lifetime value through the use and application of advanced analytics. According to Forrester, by 2021 firms that excel at leveraging data and analytics to drive insights at scale will earn total global annual revenues of $1.8 trillion.
Outdated analytics technology limits your ability to discover useful insights and deliver compelling customer experiences, ultimately hurting your organization’s revenue. Even though the idea of modernizing your analytics tool kit and training employees on new tools may seem daunting and not worth the disruption, it’s essential for success in today’s fragmented, omni-channel environment.
Moving past standard analytics.
Every organization needs a solid foundation of relevant customer data from which it can make decisions and automate action on analytical outputs in other systems of action — such as a DMP, campaign, or testing and targeting tool. It’s not just about collecting data, it’s what you do with that data that makes all the difference.
“Analytics is a means to an end — it should result in some kind of output or insight,” says Nate Smith, group product marketing manager at Adobe. “It’s a way of seeing and understanding patterns. It gives us insights into an audience, and those insights can be integrated into other customer-touching technologies for personalization and campaign management.”
Amber Thornton, product marketing manager with Adobe, agrees and sees a lack of know-how when it comes to using data. “Many people are using analytics as a data collection dump or a data filter,” she says. “They’re collecting some data and creating reports, but they aren’t doing anything with it.”
Part of the reason for this data dead-end is that many companies simply don’t have enough qualified employees to manage complicated analytics technology beyond storage and connectivity. So, it’s incumbent upon vendors to step in and close that gap by providing intuitive tools that are useful throughout the organization for self-serve data exploration, not just for data scientists. Additionally, by using analytics solutions that are powered by artificial intelligence you can make collecting, organizing, and deciphering data much simpler, and surface opportunities that would have remained hidden. Many brands are able to gain a competitive advantage when it comes to using data to create compelling customer experiences.
A roadmap for analytics maturity.
Becoming an experience business requires taking your analytics and data maturity to a higher level than most companies operate at today. This involves mapping out the steps you must take to integrate your data sources and then leverage that information in ways that impact your bottom line.
“It’s important to not get overwhelmed with large initiatives that have grand promise, but ultimately will fail in execution. Start small then begin to add additional data sets and analytics techniques to move up the maturity curve,” Smith says. “You can then move out of descriptive analytics into diagnostic analytics.”
Organizations that want to advance their analytics maturity must focus on four key areas:
Move from data reporting to data actioning. Many of the current tools on the market offer various analytics features and capabilities. While collecting data is good for reporting results, even better is the ability to know how to respond to those results.
“Many companies don’t really have a strategy when they start trying to implement some of these analytics,” Thornton says. “It’s kind of a ‘get as much data as possible and dump it into this pile, and then we’ll try to figure something out from it later,’ rather than approaching it from the perspective of ‘what questions are we trying to answer?’”
As businesses realize the power that data has to improve outcomes, and as they approach data analysis with strategic questions and goals, they then are putting their data to work. You must move from data collection and reporting to insight discovery and an understanding of why things happen — and then respond to those insights with clear actions.
Automate your actions. Once you can determine a desirable plan of action from your data analysis, why not automate it? With the amount of data that you can collect, you need to rely on artificial intelligence to analyze it, suggest actions, and then even automate those actions in real time and on a continual basis.
Adobe Sensei brings to life the artificial intelligence technology that is integrated into features across many of Adobe’s products. For example, in Adobe Analytics, Adobe Sensei powers features such as anomaly detection and contribution analysis that can automate expansive data science workflows. With this automation, you can quickly identify what’s driving any unusual behaviors in your data. The intelligent alerts feature then proactively identifies and notifies you of those anomalies.
“We had a customer that found a deactivated campaign code that was costing them nearly $2 million a day with anomaly detection,” Smith says. “They were able to run contribution analysis and fix it. That analysis took less than 30 minutes. Those are not just time-savers, that’s a real return on investment.”
Enhance your experience business. We’re now in the era of experiences, and analytics are at the core of becoming an experience business. According to Gartner, enterprises that are able to harness diverse customer data through analytics will win at providing more relevant customer experiences.
That customer data includes segmentation, cross-channel analysis, audience enrichment, and predictive analytics to help you understand your customers better. Modern intelligence in a business focused on experiences means transforming all your insights into actions that enhance the customer experience. If you use analytics as a means to drive action in designing customer experiences, you’ll be ahead of your competitors.
Analyze the entire journey. With automated tools, you can gain insights into how different customers are engaging with your brand across their entire journey — not just at specific stages or with specific campaigns. You can see how their engagement plays out across the customer journey and then use that information to drive connected experiences for your customers at every moment you interact with them. You’ll be able to see your customers holistically, build long-term and loyal relationships with them, delight people who are getting to know your brand, and understand the value of every customer touchpoint — whether in-store, en línea, and everywhere in between.
Growing pains vs. pain of the same.
Companies that are truly insight-driven are outperforming their competition.
Take leading PC manufacturer Lenovo, which uses advanced analytics to maximize its marketing effectiveness and to improve its customer experience. The company had a goal to increase conversions and customer satisfaction, and it needed reliable information about where to focus marketing efforts and investments to achieve those goals.
“The Adobe Analytics solution delivered the data quality essential for taking our business forward,” says Ashish Braganza, Lenovo’s senior manager of Global Business Intelligence.
The company exceeded its goals for ROI with the implementation by achieving a 12-fold return on investment in six months.
As Lenovo’s experience illustrates, using best-in-class analytics tools can empower companies to create compelling digital experiences. However, Smith says more businesses need to be less complacent with their current analytics solution and look for ways to evolve.
“With advanced analytics solutions, you’re going to be able to have a level of trust in your data that you’ve never had with your existing platform,” Smith says. “A lot of people get into this mentality of ‘I know it’s not perfect, but it’s probably good enough and the disruption or whatever internal effort it would take to actually change this is just not worth it.’ But it is worth it to move your practice up the maturity curve. As you go up each level from descriptive to diagnostic analytics, the returns are significant.”