Harnessing Analytics to Improve Multi-Channel Customer Interactions
In my last post, we established the importance of turning detailed audience data into true customer desire. To accomplish these lofty goals, organizations must embrace analytics in the era of big data.
We’ve all heard the big data definitions. But what does it really mean to marketers? Several years ago, Clive Humby, Principal at H&D Ventures in the UK, famously stated that “Data is the new oil. Data is just like crude. It’s valuable, but if unrefined it cannot really be used.” That still rings as true now as it did in 2006. The difference is that industry leaders are doing more than just refining their data; they are using it to generate sustainable business value.
Just like the technology made available 100 years ago to discover oil and process it into consumable products, big data technology is still in the early stages of transforming and revolutionizing the way enterprises consume and use data more effectively to market and drive their businesses.
There are three key areas of focus for companies to hone in on as they start their data-driven marketing journey:
- Capture & integrate data in whatever form
- Ability to process massive amounts of data
- Analyze in real-time to predict next action
- Filter out the noise and find the data of desire
- Experienced in applying analytics to real time marketing
- To understand how to effectively use disparate data sources
- Create automated process to drive the right actions
- The role of a forward-thinking CMO – using data to drive the right message to the right person at the right time for the right price.
Let’s dive into a few examples of how big data technology is being applied by industry leading data-driven marketers:
We’ll start with Trident Marketing.
Trident is a direct response marketing and sales firm for leading brands such as DirectTV and ADT Security, handling more than 4 million calls per year for its clients. They use advanced analytics to capture massive amounts of data to predict how customers respond to campaigns and predict which consumers are likely to cancel services.
Within a few seconds of an initiated call, their predictive system crunches (parses) 30 variables of customer data:- area code, ZIP code, credit risk, cell phone or land line, rents an apartment, owns a home, seasonal areas, etc. From those variables, they can estimate your probable churn rate. At that point, their customer service representative will strike a deal and based on that action, the caller will be routed to the “best sales representative on the floor.” This practice increased revenue by 1,000 percent in four years by making sales and marketing campaigns more targeted and effective. It also reduced churn rates from 4% to 1%.
Let’s also take a quick look Telerx, a similar story with a different twist:
Telerx is a contact center outsourcing provider that specializes in providing services to clients in the pharmaceuticals and consumer products sectors. By harnessing analytics technologies and adopting a more holistic approach to multi-channel customer interactions, Telerx has moved into a new space: consumer intelligence and research. The analysis of unstructured text data mined from social networks, calls center notes and near real-time transcriptions of customer phone calls enables Telerx to offer its clients revolutionary new insights into consumer behavior.
The results were as impressive as they are transformational.
Automated transcription and analysis of consumer phone calls reveals twice as many actionable consumer insights as analysis of social media or call center notes alone. In addition, embedding analytics services into its contact center offerings puts Telerx in a position to gain significant competitive advantage over rival service providers. Social media analysis helps to predict “hot topics,” enabling Telerx to set appropriate staffing levels and brief its contact center teams before customers start calling.
Clearly, knowing this information up front allows executives in marketing and customer service to make highly specific, smarter decisions.
In my next post, we’ll examine what’s possible with big data as marketers test and measure some exciting projects.