Unlocking the potential of predictive marketing can be a game-changer, but it’s not without its hurdles. Here we’ll explore the common challenges faced in predictive marketing and share strategies to conquer them, paving you the way for successful future campaigns.
Predictive Marketing Challenges: Requires Skilled Staff
Predictive targeting usually comes at a cost. Filtering through large databases of prospects to find useful qualitative data is no easy feat. This is even more difficult when you try to decipher your customers’ preferences when making purchases.
While there are some reasonably effective tools you can employ to gather this sort of data, it usually involves technical, qualified people. Experts who are capable of weeding out what is useful from what isn’t. Many low-level employees often lack the specialized skillset to manage this type of marketing campaign effectively.
The best way to spare yourself the headache and costly burden of trial and error is to hire professionals who possess the know-how and deliver results.
Predictive Marketing Challenges: Not Easily Executed
Although predictive marketing is becoming more effective, it relies on statistics, and stats always have room for error. This can be mitigated by analyzing the correct metrics and setting clear objectives from the start, so that you can allocate your valuable resources where it’s needed most.
On the flip side, when predictive targeting is done right, you get to leverage your sales possibilities to extents that your competitors most likely will never be able to achieve by the hand of conventional methods.
Predictive Marketing Challenges: Burdensome Volumes of Data
Before you decide to venture into the daunting world of user data, these are some of the tasks that should appear on your ‘to-do’ list:
- Data preparation
- Data cleansing
- Identifying crucial columns
- Detecting correlations and searching for patterns
- Acquiring vast knowledge of mathematical algorithms
- Choosing useful models
- Determining their suitability
- Validating the accuracy of the extracted data and its format
- Understanding the gathered results
- Funnelling fresh data to keep the model updated
- Addressing any possible data imbalances
- Constant model deployment and testing
- Integration with core applications
Luckily, there are expert solutions so that you don’t have to do any of this on your own.
Jump Those Hurdles
Do all these intricacies and hurdles that come with predictive marketing make it unfeasible? Not with the right people to assist you in every step of the way.