Driving revenue performance and applying data in order to influence/generate buying behavior throughout the customer life cycle is not new — it’s an ongoing goal. Real-time marketing, which relies on capturing data in real time, improves revenue performance and is the optimal way to drive 1:1 customer interaction throughout the customer life cycle. However, real-time is highly complex and costly, and its transactional nature is not directly applicable to many industries, products and services. As a result, it remains an elusive goal.
Then there is the Big Data problem. Data is exploding, with an increase of nearly 1,000 percent in the last five years from offline and online sources that are just waiting to be monetized, according to IDC. Compounding this challenge are social media and mobile communications that create new ways to buy products. Thankfully, cloud-based computing and associated technologies such as social media listening platforms, marketing automation systems, and sales force automation are making data more accessible and actionable.
In order to achieve its real-time goals, an organization needs to deal with the fundamental data issues it has today and then evolve toward Right-Time Revenue Optimization. Right-Time Revenue Optimization focuses on driving revenue performance at the right time in the customer life cycle using multichannel marketing.
Using Big Data Strategically
Multichannel marketing, at the right time, addresses the aggregation of customer behavior over time and takes a very deep, analytical approach to customer interactions, based on behaviors, purchase history and a multitude of market dynamics. The customer intelligence derived from these interactions guide an optimized revenue strategy that focuses on markets, accounts and buyers, in order to spur specific buying behavior throughout the customer life cycle.
By leveraging the data and continuing to accumulate a Big Data strategy that is applied to the customer life cycle, channels and customer intelligence, organizations can deliver more highly qualified leads, nurture opportunities into real sales faster, and ultimately create customer retention that increases the top and bottom lines.
Achieving Right-Time Revenue Optimization and a resulting effective multichannel marketing strategy requires the ability to use Big Data strategically. However, when you transform Big Data into customer intelligence and then leverage it across multichannel marketing, the opportunities to drive revenue throughout the customer life cycle become enormous. It also helps to identify and define what markets and customers represent the highest revenue and profitability potential, how to build more brand loyalty and tie that to profitability, and ultimately, how to find, win and keep more valuable customers.
However, this is no easy task. Achieving your revenue goals can be simplified by starting with customer intelligence and grounding your Right-Time Revenue Optimization strategies on what your customers, market and business tell you.
The issue isn’t whether customer intelligence is necessary; it’s what kind of customer intelligence do marketers need to effectively segment their markets? Defining the issue that way enables marketers to treat customer intelligence as an evolution, rather than a revolution, by
- treating existing data as customer intelligence;
- learning from it — e.g., what size customer or title groups are your best prospects; and
- improving upon it over time and filling in the gaps — even if it is basic firmographic information, e.g. revenue size, industry, titles.
Most marketers would be very surprised to learn they already have what they need to get started to create more sophisticated segmentation strategies. In fact, they need to unlock the power of customer intelligence in the form of their existing data.
Whether all of that data is readily accessible, inaccurate or complete is not the point — nor should it hinder you from beginning your customer intelligence journey. What is there is actionable today, right now. Even the simplest marketing campaigns produce customer interaction history that qualifies as customer intelligence. Some examples:
- What is the most popular message or offer to a particular type of contact?
- How many contacts, and their title and type of company, actually responded to the message or offer?
- Which channel was the most effective?
Those basic questions begin to paint a picture around customer intelligence that tells the marketer who is typically responding to their campaigns and how. What is the best way to engage them? Is this the best market segment? In this simple example, a marketer would form the beginning stages of segmenting, profiling, and interacting with customers in ways they want to be communicated with. Isn’t that one of the definitions of more effective segmentation?
After all, Big Data doesn’t come into your marketing environment from one source. It literally flows from hundreds of sources and includes purchase, customer service and pre-sales histories. Locked in the raw data about customer behavior are important insights about their interactions, which are significantly more actionable if an organization creates a marketing data mart that is integrated with a customer intelligence system.
The use of customer intelligence is a critical component to harnessing the competitive advantage of Big Data. While the benefits of Real-Time Marketing remain elusive and not always applicable, marketing professionals can optimize revenue and profitability for their organizations by realizing the use of customer intelligence to drive Right-Time Revenue Optimization today.