A business may have 99 problems, but a lack of data isn’t one of them. All businesses collect data, and many have more than they can manage. The problem they have is a lack of insight.
For businesses today, there’s a major gap between pieces of information and analyses that can drive strategy. With so much to pull together and track, it is literally not humanly possible to stay on top of it all.
Why More Means Less
As a recent Forrester report noted, “Traditional, manual approaches to generating insights and orchestrating customer touchpoints can’t cope with the volume, velocity, and variability of the engagement data” generated in the modern customer’s journeys. As a result, most of the data collected is not even used for analytics.
Part of the problem may be due to data quality with information not being up to the standard required to be relied on. An Experian Data Management Benchmark Report found that, on average, 33% of organizations have inaccurate customer and prospect data.
When the data is distributed among silos because the different departments collect their own data but don’t bring it together, what you have is scattered pieces rather than a whole picture. The result is fragmented data that prevents businesses from tapping into the potential of analytics.
Why Customer Analytics Fall Short
This gap between the ideal of analytics and the reality of marketing is the theme of a Harvard Business Review analytic services report. While 85% count improved customer experiences among the benefits of real-time customer analytics and over half cite insight into their customers’ journey and “better collaboration between marketing, sales, service, and operations,” many consider themselves to fall short on those goals.
With today’s proliferation of touch points connecting customers to brands, there are more opportunities than ever to gather data on their behavior. But when that data is divided between the sales and marketing departments, it’s impossible to construct a complete 360-degree view of the customer.
A single view of the customer includes all the digital touch points related to ecommerce, social media interactions and emailed communication, as well as the interactions that occur offline in-person or on the phone. All those data points have to not only be integrated, but constantly updated to complete the data picture that provides the basis for analytics used in personalized marketing.
A lack of data integration doesn’t just result in missing components, but in duplicate data. As an Experian white paper noted, “Without proper data quality control systems in place, the same customer can appear multiple times, with nothing to identify them as the same person.”
That can lead to the same promotion being sent to a customer multiple times, even if they’ve already responded to the offer. Such half-baked targeting is not what marketers aim for in attempting to reap the benefits of AMB.
Even with all the data in place, you may not be able to pinpoint the dynamics of causality. While a simplistic level of analysis can find correlations in data, it takes more to find actual causal relationships. With so many touch points along the customer journey, last-click attribution can obscure where the credit is really due.
It might be that an event, blog, video or social post is what first captured the customer’s attention and set off the journey toward purchase. So how can you know which of your marketing tactics are really paying off?
Daunting as it all appears, it is possible to overcome the obstacles of data silos, poor-quality data and the failure to derive actionable insight. It’s a matter of setting your strategy and implementing the tools that will bridge the gap between data and analytics.
1. Suit your data to your goals rather than your goals to your data. To be able to make the most of your data and realize the full potential of analytics, you have to apply the habit that Stephen Covey called “Begin with the end in mind.” In this case, it means consider your business goals, and then set up your system to take in the data that relates to those goals. Many go about this backward, collecting data and then trying to find a use for it.
2. Set up your system to aggregate, deduplicate and automatically update. When you put in a system to get a single point of data, you have to ascertain that your data quality is good enough to form the basis for analytics. With the right infrastructure in place, you can enable an AI-empowered CDP to constantly update all the streams of pertinent customer data coming in from various departments and augmented by third-party data to provide a truly comprehensive view of the customer.
3. Use ID resolution. This gives your company the ability to match customer data across siloed systems and devices, using deterministic and probabilistic matching. This means customer records all stem from a single record. With AI in place, that ID will automatically update, which is essential to effective personalization because the situation of a customer doesn’t stay exactly the same from year to year or even from month to month.
4. Ascertain you have accurate attribution with AI. Machine learning is an essential tool for assessing marketing performance in a way that is really beyond human capability. As the Forrester report noted, “Management solutions use machine learning to determine attribution by ingesting, connecting, and analyzing data from a wide variety of sources, including paid/owned/earned media, social listening, web analytics, marketing automation, events, and content marketing.”
Businesses today have a wealth of data to tap into to derive value for their sales and marketing. The key to achieving that value is not just gathering all the data; it’s setting up an AI-powered system for analytics that delivers the right insight to the right department at the right time.
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