How Shadow Analytics Disrupts Intent Marketing: Analytics Corner
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These days, connecting marketing to a customer's intent is one of the holy grails of the space. Intent marketing —marketing that positions a product or service in response to measures of consumer intent — is a strategy leading to that goal. But shadow analytics — the practice of using alternative solutions to an organization's preferred software —i can derail the quest.
Intent marketing is an extension of the micro-moment philosophy — the idea that customers have certain questions or thoughts in mind about their needs prior to a purchase action. A series of micro-moments create a customer journey. Customers use digital channels — often multiple channels — to get to a product or service that addresses their need. Understanding these moments is the prerequisite for exploiting them. A retailer who cares about shopping cart activity, for example, can create content that highlights conveniences like easy in-store returns, free shipping, or other service features responsive to a customer's needs.
Metrics for the customer journey
Interest in customer journey metrics has been rising, causing analysts to turn to self-service solutions for quick answers. Self-service analytics means open source platforms that collect data and provide visualization for analysis. The rising interest of machine learning techniques such as decision trees, neural networks, and other applications has led to cloud platforms being included in an already broad range of self-service analytics choices, from Microsoft Azure, which can incorporate data from various sources, to Neo4j, an open source database that can graph data relationships.
Such availability of tools can rapidly lead to a wide variety of analytics initiatives within an enterprise. Cloud solutions are easy to try out, especially ones aimed at data mining. They allow users to experiment with software that they feel best aids an analysis, instead of software they feel is management-approved, but not immediately helpful.
While analysts may feel empowered by making their own choice of software, they can face added complexity when comparing results across teams. Intent marketing is based on considered assumptions about customer personas, but competing shadow analytics spproaches can generate to a lack of consensus when it comes to measurements and results.
Building more silos
Lack of communication can lead to a silo environment, with competing versions of best practices. Dynamic real-time data, but limited or infrequent collaboration, can mean losing sight of how to create engagement opportunities for those micro-moments.
In the case of intent marketing, poor models or planning can lead to oversaturation of customers with unwanted messages, or messages appearing on unsuitable platforms or channels. Poor placement of marketing messages is one of the banes of shadow analytics.
What do to about it
There are steps that marketing or marketing ops managers can take. Marketers should assess the alignment of operations dealing with customer intent. This requires a deeper examination than, for example, reviewing HTML content for a webpage — a traditional diagnostic exercise before the advent of chatbots, apps, and mobile devices.
However, correcting assumptions and practices shouldn't trigger an outright reinvention of business processes. Marketers should instead seek incremental process adjustments, particularly where predictive analytics are applied. Incremental changes can yield real solutions that match up to customer journey activity.
Keeping shadow analytics in check is vital for keeping a company consistently on track with customer journey activity. With customers discovering new ways to service their needs digitally, marketers should seek analytics that show how a brand can understand intent, and best iterate on it. Doing so brings analytics out of the shadows and into the ROI light.