CMOs and marketing leaders struggle daily to answer the questions, “Why did this customer buy? What specific events and interactions led to a conversion?”
While it’s fairly easy to attribute the last touch to a purchase, that doesn’t give insight into the total buyer journey. Attributing which interactions and events happened when, and in which order, is difficult. And that’s where big data analytics comes in. Marketing leaders are turning to big data analytics to answer these challenging questions and to measure the effectiveness of their marketing efforts in a way that was impossible before.
Big data and advanced analytics initiatives have helped marketers create highly targeted campaigns with personalized messages. A McKinsey study found that marketers who employed data-driven personalization delivered five to eight times the ROI on marketing spend.
In fact, “In a recent survey about big data, 73 percent of respondents have either already invested or are planning to invest in big data projects over the next two years,” according to the Gartner Report, "The Impact of Big Data on Master Data Management and How to Survive It." “Organizations have a desire and appetite to address key business areas such as enhanced customer experience, targeted marketing and process efficiency.”
Below is an example of how one company capitalized on big data analytics for better target marketing.
Operational Challenges Held the Marketing Team Back
A CMO at a large financial services company wanted to boost marketing effectiveness by improving her ability to segment prospects and customers for more targeted offers. The goal was to improve conversion rates, reduce returned mail costs and gain more accurate marketing attribution. The marketing team’s goals were directly aligned with the business goals of leveraging data strategically to drive revenue and up-sell, improve customer experience and increase the number of products per household.
Unfortunately, she had many complex operational challenges: The company consisted of multiple distinct brands, with multiple products, distribution channels and marketing strategies. Its customer and prospect data was fragmented, inconsistent and spanned several CRM applications, dozens of legacy and operational systems, various marketing and campaign applications, and third-party data sources. Simple questions were hard to answer and the fragmentation prevented any true understanding of marketing attribution and marketing effectiveness.
The Impact on Lead Conversions and Customer Experience
Some of the questions the team couldn't answer, which directly impacted lead conversion and customer experience:
- Who are our leads/prospects? Any overlap with customers?
- How many customers do we have? Who are they? Which products do they already have?
- How many households do we have? Who lives in each household? Where are they located?
- What relationships exist between customers, their beneficiaries, prospects and sales people?
- How many prospects/customers overlap with other brands?
- Which customer segments are most profitable?
- Which customers are more likely to churn?
- How many new customers did we acquire last week?
- Which campaigns are quantifiably the most effective?
- Are we paying for prospect and lead lists more than once?
- How could the sales team leverage interaction and relationship data?
The Big Data Analytics Journey
The marketing and IT team partnered on a big data analytics initiative to do real time customer and prospect profiling on Hadoop.
While many big data projects begin by dumping all the data to be analyzed on the Hadoop platform, this team pursued a “data-first” approach. They focused on strategically managing their data so that the analyses would lead to meaningful and actionable outcomes. Below are the three key steps in their process:
Step 1: Centralize Data for Comprehensive and Trusted Analytics
To begin, the team had to centralize their fragmented and inconsistent data.
- Big data integration technology enabled the team to move and transform all customer, prospect, distributor and product data
- It also enabled them to move and transform all marketing campaign and solicitation history, all interaction data from sales, call center, weblogs and social, and all enrichment data from prospect lists and third party data providers, such as Acxiom, Epsilon, and Dun and Bradstreet
Step 2: Prepare and Master the Data
The next step was to feed, aggregate and secure all data on Hadoop.
- The team employed data quality technology to profile the aggregated data for greater insights before it standardized, cleansed and validated the data for accuracy. This included customer and prospect names, mailing addresses, phone numbers and email addresses.
- Big data relationship management technology created unique IDs for all prospects and customers in order to match and link all the related records together. This helped the marketing team identify relationships hidden within the data to reach a clear understanding of unique prospects, customers and which of those live in common households. Then, the data was appended with internal and external interaction data, campaign solicitation history, and web logs. It is further enriched with third-party data sources, demographics and external interactions.
This foundation of accurate, trusted and related enterprise data provided the marketing with a real time analytical index of individuals, households, touchpoints, products, sales distributions and the relationships between them.
Step 3: Delivering the Data
Next up: people needed a way to access the data. A self-service analytics environment served the marketing teams, data scientists and business analysts.
- With one centralized location for trusted enterprise data, analytics teams could use traditional business intelligence, visualization and predictive modeling tools to analyze event streams and marketing attribution models.
For the first time, the marketing team could strategically manage its customer and prospect data to answer the questions: “Why did this customer buy? What specific series of events and interactions resulted in a lead conversion with revenue?” This insight helped them build more refined segmentation profiles and target personas in order to continually improve the effectiveness of their marketing campaigns.
An additional benefit is that relationship data can be fed to campaign management systems, call center applications and CRM applications to improve lead conversions, sales close rates and customer experience across interaction points.
Data First Means Customer First
Marketing leaders working in complex organizations are embarking on big data analytics initiatives to optimize marketing effectiveness, drive competitive differentiation and improve customer experience. Marketing, in active partnership with IT organizations, is taking a “data first” approach to strategically managing their customer and prospect data. By fueling their sales, campaign management and analytical applications with clean, consistent and connected data, organizations can do better target marketing and continually improve the effectiveness of their marketing campaigns.