Customer intelligence has been a staple in the contact center for years, guiding CSRs down the path toward higher upsell, cross-sell, and conversion rates. Traditionally, intelligence capabilities have been built into CRM systems as a means of improving customer service. These solutions were designed to gather vast amounts of data and make it available in a single, convenient location.
As this practice grew and analytics was added, these systems not only gathered the data from broader sources, but also began to extrapolate meaning from it and doing a job that only the more experienced representatives were capable of completing. It has been a boon to customer service effectiveness (and frankly, customers have come to expect it), but as a sales tool, in this context it's limited to a pool of existing customers.
The idea that valuable data can be collected, compiled, and acted on hasn't been lost on the savvy sales and marketing types, though. The beauty of standalone "intelligence" solutions is that data sets and sources can be customized to match changing needs and goals. Organizations stand to gain immensely from information regarding who, how, and where their products are being used (location-based marketing services like Groupon are great examples of companies that have done this well). This information represents varying levels of demand for a particular product or service, and sales and marketing strategies can be adapted accordingly to take advantage of potential revenue opportunities.
While the success of business/customer intelligence essentially relies on "the wisdom of the masses," the collective decision of any given group is a powerful thing that isn't taken lightly. It isn't a stretch for companies to change an entire product line or feature set because of trends in usage data.
In the software industry, for example, a support or customer service professional is able to determine how an application has been used by the customer and whether or not the open ticket was a result of user error or a software bug. In either case, support personnel can resolve the issue and close the ticket while this information is fed to development teams to ensure that future updates or versions of the program are better suited to the needs of its users-because happy customers are good customers.
The interconnectivity of digital data systems today is largely what has allowed intelligence technologies to make such a splash in the sales and marketing industries. Not only has it enabled greater efficiencies through automation, but the breadth of data sets available to the analytics engines is far greater than what any human being is capable of comprehending thanks to our "online, all the time" mentality today.
These two attributes are the keys to success for business intelligence-especially in service, sales, and marketing, where data sets are largely made up of customers and prospects. On a technical level, every step in these functions is affected. Data is generated at touchpoints across all channels (including self-service); stored electronically where it is instantly accessible at anytime; analyzed for behavioral and attitudinal patterns; summarized for business rules engines; and fed to users as situationally-relevant information and suggested courses of action with the highest probability of eliciting a desired response. It's a staggering amount of work that is done automatically, and not just for one contact but for thousands.
From a cost-saving perspective, automating these processes eliminates the time and labor that otherwise would be involved with doing them manually. In terms of effectiveness, the likelihood of human error tainting results during the process is all but eliminated. Going a step further, instead of simply having accurate data we now have "intelligent" systems that can draw upon that data for more meaningful-and successful-interactions on the front end, letting the user give their full attention to the subject. In short, business intelligence is a win-win.
For the software industry, business intelligence offers a new level of actual usage data not previously available. Consider the value in knowing, via user opt-in, precisely which features are used most, which aren't used at all, as well as when trial downloads expire. Product marketing can tailor the pipeline and enhancements specifically to meet actual usage, while sales is empowered with actionable data to convert the trial user to a paying customer. That level of intelligence can have just a great an impact on the bottom line.
I've seen customers achieve sales conversion rates upwards of 75 percent and top-line growth of 25 percent after empowering their employees with this kind of "intelligent" support. This of course depends largely on the makeup of their organization and if they have the internal capability to take action once the data is gleaned.
Business intelligence has come a long way since first emerging in the contact center. Its models and capabilities continue to expand and accommodate new uses, industries, and sources. As marketing moves more and more towards accountability within an organization, the need to back up all decisions with data will become the norm. Marketing departments that arm themselves with actionable data will be the ones best equipped to make decisions based largely on customer usage and insight.
How are you using intelligence to make data-driven decisions?
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