How to Develop a Seamless Analytics-Maturity Plan
Tired of meaningless data that can’t be leveraged? Tired of not knowing your audience base? What if you could find your customers before they find you and target them with an optimized experience? It’s time to achieve predictive-marketing brilliance. Follow these three simple stages to develop a bulletproof analytics-maturity plan using Adobe Marketing Cloud services.
Stage 1: Collect, Connect, and Democratize
Descriptive analytics is focused on understanding past performance. The objective is to reduce the time to configure data collection. This stage provides the foundation for finding more time to focus on analysis and then being able to take action on insights. Getting set up with core services is your first step.
Once you have been provisioned with an Adobe Marketing Cloud organization ID, you will want to begin quickly and efficiently configuring your implementation and collecting data across channels and devices. Activation — powered by dynamic tag management (DTM) — is a foundational core service of the Adobe Marketing Cloud. DTM will reduce your dependency on information technology (IT) resources and improve the security of your data collection. As you deploy DTM, you will want to also implement the Adobe Marketing Cloud Visitor ID, which is part of the People core service. This visitor ID ensures that you are tracking individual customers across devices and across web experiences with a common identifier, and it is a prerequisite for taking action with your audiences and customers across Adobe Marketing solutions.
At Adobe Summit 2016, we announced the Device Co-op. This device graph can be used to leverage anonymized data about future prospects and current customers. This is particularly useful in generating information about unauthenticated users — those who are making purchases or browsing your site as guests. Participating in the Device Co-op will enable you to anonymously collaborate with the world’s most popular brands to recognize a familiar consumer behind an unfamiliar device. For example, if 10,000 unique devices have accessed your site, and there are two devices per individual, only 5,000 people have really engaged with your brand on that particular day.
Analysis Workspace allows you to democratize analytics. Dimensions, segments, or metrics can be selected, or curated, to publish to particular users or groups for self-serve analysis. This creates a sandbox in which users are able to answer their own questions concerning queries, requests, or questions. As a business owner, this means you’ll be less inundated with questions and better able to focus on more mature, higher-value analyses that enable you to develop a deeper understanding of your customers.
Stage 2: Analyze, Unify, and Compare
Using diagnostic analytics, you can effectively analyze the massive amount of data you’ve collected during Stage 1 to understand what significant changes are occurring in your data and to provide clear paths for data-driven optimization.
Anomaly Detection identifies statistical anomalies — separating “true” signals from “noise” as data changes over time, helping analysts unearth anomalous behaviors or patterns that may have otherwise gone unnoticed.
The Adobe Audience Manager feature provides enriched segmentation of both online and offline data, including demographic, firmographic, and psychographic data. The recently released Adobe Marketing Cloud Audiences (in Analytics) integrates these Audience Manager segments into Adobe Analytics, providing deeper, real-time insights regarding smaller-sized audiences that may have otherwise gone overlooked.
At Adobe Summit 2016 in Las Vegas, we announced a new feature, called Segment IQ, that is coming soon in Adobe Analytics. Using automated analysis, Segment IQ discovers the differences between your segments, uncovering significant characteristics that are driving your key performance indicators (KPIs). You’ll now be able to identify overlaps in your segments and use this insight to refine your segmentation strategy.
Stage 3: Enrich, Explain, and Uncover
Advanced diagnostic analytics is focused on enriching your understanding of the changes that have occurred in your data and rapidly gaining a deeper understanding of your customer.
Customer Attributes enables companies to combine enterprise-customer data — gender, age, loyalty level, lifetime value, etc. — with online behavioral data to obtain a deeper understanding of customer interactions. Uploaded numeric and text attributes form new metrics and dimensions (that are applied at the visitor level) and introduce powerful segmentation and targeting opportunities. When analyzed alongside the rest of your Adobe Analytics data, Customer Attributes provides a deeper, richer understanding of critical customer interactions and identifies and targets key micro-segments.
Contribution Analysis intelligently identifies hidden patterns or contributing factors for statistical anomalies in your data. By rapidly analyzing all of your Analytics data, it will save you countless hours searching for explanations to changes in metrics. Contribution Analysis leverages powerful machine learning designed to transform analysts and marketers into data scientists.
Audience Clustering objectively categorizes individuals into distinct, actionable personas based on similarities in product preference, geo-demographics, and behavioral attributes. Clustering helps analysts move beyond simple segmentation toward identifying groups (or clusters) of similar people who are very different from other groups (or clusters) of similar people.
Stage 4: Attribute, Predict, and Classify
Predictive analytics provides insights regarding what is likely to happen next with either your business or your customer along the journey to conversion. Now, you can proactively manage the future of your business as well as your customer.
To ensure impartiality through a data-driven approach, Algorithmic Attribution uses advanced statistics and machine learning to objectively determine the impact of each marketing touch along a customer’s journey toward conversion — leading to a better understanding of marketing-campaign effectiveness. By combining interactions across paid-media and brand-owned experiences, you gain a clearer understanding of the relative impact of each moment along the entire customer journey.
Propensity scoring lets you define customers based on their potentials for successful conversions or completions of specified events. It allows you to maximize the probable impact of efforts before executing a process or directing a campaign. Propensity Scoring ensures that you’re marketing to the most relevant audiences.
A Decision Tree is a set of predictive rules used to progressively classify customers or prospects based on their probabilities of completing a business outcome. Because decision-tree rules can be deployed, you can optimize experiences for both new prospects and existing customers in real-time.
Source: Adobe Blogs
Kalyan Banga200 Posts
I am Kalyan Banga, a Post Graduate in Business Analytics from Indian Institute of Management (IIM) Calcutta, a premier management institute, ranked best B-School in Asia in FT Masters management global rankings. I have spent 6 years in field of Analytics.