Understanding Your Customer Journey by Extending Adobe Analytics with Big Data

Transcription

1 SOLUTION BRIEF Understanding Your Customer Journey by Extending Adobe Analytics with Big Data Business Challenge Today s digital marketing teams are overwhelmed by the volume and variety of customer interaction data coming in from digital ads, websites, social media, mobile, s, call center calls, transaction data and many other sources. If you are a digital marketing expert, you most likely use Adobe Analytics (SiteCatalyst) to analyze customer behavior on your website, but the bigger challenge is to quickly combine and analyze all your customer interaction data across multiple systems to get a deeper understanding of your customer journey. You want to answer questions like what is the behavior of those prospects that buy certain products? or what are the campaigns that influence prospects to become customers. Adobe Analytics has provided high value to enterprises for several years. However, as the nature and volume of marketing data has evolved, the Adobe suite is having challenges in scaling and handling multiple data types. History of Adobe Analytics Over the last several years, Adobe has assembled a suite of acquired products into a solution set for customer and marketing analytics. These solutions include: Adobe Analytics (formerly SiteCatalyst) This product, formerly Omniture's Software-As-A-Service (SaaS) application, offers client-side web analytics. Adobe Data Workbench (formerly Insight) This solution is a multi-channel segmentation tool, intended to provide both client- and server-side analytics. Adobe Data Workbench was derived from Omniture s acquisition of Visual Sciences in 2000, which subsequently was acquired by Adobe. Insight for Retail A Visual Sciences Insight product geared towards multiple online and offline retail channels. Data Connectors (formerly Genesis) A third-party data integration tool, primarily developed to work with SiteCatalyst Adobe Analytics Premium Adobe Analytics + Adobe Data Workbench Adobe Analytics and Adobe Analytics Premium Challenges Data Volume: Adobe Analytics Premium is limited to 20 TB of data. Data Variety: Adobe Data Workbench is designed to handle structured data, where the format of incoming data is well known. However, the fastest growing categories of customer interaction data (social, mobile, , text, IVR, etc) is unstructured. Data Democratization: Adobe Analytics Premium is powerful, but complex to use by a broad set of users for simple queries. Executives are constrained to deliver self-service customer analytics, by the availability of scarce and expensive technical resources. 1

2 Discovering Patterns: Adobe Analytics Premium can perform ad-hoc data discovery, but is not designed to support more advanced data discovery such as identifying patterns of prospects that become customers or customers that churn. Complex Architecture: Adobe Analytics Premium architecture is complex and relies on a scarce group of existing specialized architects. Personally Identifiable Information (PII) compliance: Adobe Analytics Premium is only available as a subscription cloud offering, with no options for on-premise or private cloud. As such, it is difficult to deploy Adobe Insight in a way that is PII-compliant. So how does a marketer solve these challenges, extend their Adobe installation, and leverage all their available customer Big Data to drive ROI, customer acquisition, and campaign conversion? Big Data Analytics Solution Datameer s big data analytics platform, combined with Ignitio s deep web and digital analytics expertise, enables you to extend your Adobe Analytics and combine all customer interaction data, regardless of their format or volume. These capabilities enable deeper understanding of your customer journey across all customer interaction channels and across all divisions of your company. Figure 1: Shows how with Datameer you can combine all data types, regardless of volume and as far as multiple years. Unstructured Structured Digital Ads Marketing Automation Machine Data CRM Adobe Analytics Transaction Social Media Demographic Data Mobile Data EDW/DB FIGURE 1: Combine All Data Types and Volumes 2

3 Easily Combine All Customer Interaction Data Across All Channels Datameer is purpose-built for Hadoop and enables you to leverage all your data, from Adobe Analytics, digital ads, web, mobile data, campaigns, point-of-sale, transaction, CRM and more. This broad range of data ingestion capabilities, regardless of volume or timeline, enables your business users to quickly identify patterns in your customer journey. For example, Datameer shows you the behavior of leads that became customers, clearly showing the campaigns that influenced their conversion, and customer behavior patterns that signal churn. Figure 2: Demonstrates the behavior of prospects that became customers and the campaigns that influenced them the most. Clicked On Promotion Clicked On Digital Ad Visited Website Purchased with Credit Card Applied for Home Loan Purchased New TV Store Visit FIGURE 2: Understand Your Customer Buying Journey Across All Channels Self-Service: Empower Business Analysts to Get Insights in Hours Datameer s intuitive user interface is designed to put big data in the hands of business analysts. Now your business analysts can combine structured, semi-structured and unstructured data together. There is no longer a need to rely on IT to use complex ETL and data warehouses. Datameer s 60+ connectors enable business analysts to easily ingest data from all data sources, create unlimited data views, prepare the data using our visual data preparation tools and analyze them together for new insights in hours and days. Store & Access Your Data Either On-Premise or in a Private Cloud Datameer is available both as on-premise and as a managed private cloud, with built-in compliance and security architecture to ensure privacy of customer data. In addition, Hadoop also includes several layers of security and compliance, making it ideal for storage and compute of all customer behavior and transaction data. Enrich Your Data with Third Party Data With Datameer your digital marketing team can enrich your customer interaction data with third party data, such as cookie data, enabling you to precisely identify consumer segments and target each with personalized ads to increase conversion. 3

4 Find the Patterns in Your Data With Predictive Analytics With Datameer Smart Analytics, your business analyst can easily find the signal in all the noise of your data. Using data mining technologies, Datameer Smart Analytics adds analytic algorithms that automatically identifies patterns, relationships and correlations within any data with simple point-and-click. Smart Analytics includes four main functionality areas: Clustering Decision tree Column dependencies Recommendation engine Critical in helping users better understand their data, Smart Analytics is key in enabling you to better understand customer segmentation and guides further analysis based on patterns Datameer automatically detects in your data. Rather than relying on scarce data scientists, business users can use the simple, point-and-click functions of Datameer s Smart Analytics combined with the linear scalability and data flexibility of Hadoop to analyze datasets of any type and size. Clustering Using clustering (K-means algorithm) through a simple point and click dialog, users can automatically find groups within data based on specific data dimensions. With clustering, it is then simple to identify and address groups by customer type, text documents, products, patient records, click-path, behavior, purchasing patterns, etc. Decision Tree Datameer s decision trees automatically help you understand what combination of data attributes result in a desired outcome. Decision trees illustrate the strengths of relationships and dependencies within data and is often used to determine what common attributes influence outcomes such as disease risk, fraud risk, purchases and online signups. The structure of the decision tree reflects the structure that is possibly hidden in your data. Column Dependencies With a single click, column dependencies visually display the strength of relationship between attributes within any dataset. This helps users better understand the characteristics of their data and is often used to help target further analytics. Column dependencies can highlight relationships between job title and purchase amount, age and disease type, location and product selection, transaction type and frequency, and account age and product type, for example. 4

5 Recommendation Engine Datameer s recommendation engine automatically predicts a person s interest based on historical data from many users. Useful in increasing client engagement, recommending more relevant choices and increasing customer satisfaction, recommendations can for example, predict interest in music, products, applications, movies, documents and services. Case Studies A global financial services company: Accelerating customer acquisition by increasing ad conversion by 20% A global financial services company uses big data analytics to perform clickstream analysis on their digital advertisements. Their goal was to create more targeted ads that improve conversion and ensure the different business units were not over-targeting certain consumer segments. The company ingests four to five billion advertisement records per month across all business units to better understand ad exposure for each consumer segment. Using Datameer, they can ingest and process large amounts of data extremely quickly, as well as empower analysts and business managers to run everything from basic to sophisticated analytics without the need for programmers. For example, using customer segmentation analysis, they found that 60% of the overall company advertisement budget was focused on only 4% of consumer segments. Based on this analysis, they reallocated their budget to other consumer segments to increase ad conversion. Now they can trace 50-60% of the people who receive their ads based on cookies, and they have increased advertisement conversion 20% by creating more targeted ads for each consumer segment. A major credit card company: Lowering customer acquisition costs A major credit card company was spending millions of dollars on digital advertisements. Management wanted to create more relevant, targeted ads and increase digital advertisement conversion. This would require analyzing all their ad click stream data to gain a deeper understanding of high-value customers and using these insights to deliver more targeted campaigns that would boost wallet share. Using Datameer, the company correlated data on customer purchase histories, customer profiles, and customer behavior collected from social media sites indicating their personal interests. This data was then correlated with transaction histories and data on things that customers liked on Facebook to identify hidden patterns. These patterns enabled management to see that a large percentage of their high-value customers regularly watch the Food Network and shop at Whole Foods. Armed with this insight, the company was able to define a more personalized advertisement strategy to target people with certain profiles. This involved creating advertisements to run on the Food Network channel and special promotions for Whole Foods customers. The end result? They reduced their advertisement cost by $3.5 million per year, while improving conversion. 5

6 To learn more about big data analytics, visit Download Free Trial at San Francisco 1550 Bryant Street, Suite 490 San Francisco, CA USA Tel: Fax: New York 9 East 19 th Street, 5 th floor New York, NY USA Tel: Halle Datameer GmbH Große Ulrichstraße Halle (Saale), Germany Tel: Website Download free trial at datameer.com/datameer-trial.html 2015 Datameer, Inc. All rights reserved. Datameer is a trademark of Datameer, Inc. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. Other names may be trademarks of their respective owners.