Not Just a Statement, It's a Problem
Millions of words and billions of hours have been vested for exploring the right means to boosting customer responsiveness to campaigns. Marketers are still relying on direct email advertising and phone calls to attract customer responsiveness and the outcome is far from expected.
The fundamental problem with these existing means is that they cannot take into account customers’ behavioral preference. You can reach out for a wide spectrum of potential audience but remain complete clueless whether the campaigned product is of any demand among the targeted audience. The final result is low conversion in return of exponential manual labor, time consuming processes and difficulty with managing the entire procedure within the allocated budget.
Why Campaign Analytics Matter Over Traditional Methods
Campaign analytics solves up these issues. A recent survey has shown that campaign planning as per the analytics results increases customer responsiveness significantly (up to 30%, sometimes even more). Campaign analysis provides only predictive results and allows marketers to develop statistical models, helping them with better understanding of customer purchasing behavior. It is with the help of analytics that marketers enjoy improved scope to prepare the right customer base along with the scope for better response to campaigns. The analytics models help you with keeping weeding customers away from the scope of target and take into account only those who are most likely to take an interest in those campaigns.
Questions That Are and Can Be Answered Only by Analytics
In a nutshell, analytics helps campaigns more responsive by helping businesses with specific answers to the following questions:
- Customers that campaigners must target
- The kind of products that customers would find attractive
- The kind of incentive should be offered that would be leading to customer conversion
- The right time for the campaign to be launched
The analytics should run on the existing data-pool, created by the business during years of their operation. Clearly, the database should be containing several information concerning customers’ personal data, the offers they opted for, purchasing information, prior campaign responsiveness etc. Findings of the analytics would reflect several aspects that in turn would help with pumping campaign response. Here follows the key ingredients that the results would reflect:
- Core customer behavioral pattern, in terms of purchasing preference
- How their purchasing preference fluctuates in different seasons
- The essential factors that are helping with generating better customer response rates
- Rapid creation of predictive statistical models, showing a customer-base that are most likely to respond
Quicker the Better
Brick-and-mortar retail businesses are at great benefit with the increasing usage of analytics while planning in-store campaigns. However, the pre-requisite is of course store automation. Sooner they get it done, quicker they start with creating the data-pool, making it easy for planners in running the analytics, receiving results, develop insight about targeted customer-base and plan promotional campaigns that will guarantee improved customer responsiveness.