7 Ways Your Marketing Data Can Reduce Unwanted Business Surprises
Q: Who likes surprises?
A: Most everybody does – as long as they are the “good” kind of surprises.
Q: Who likes surprises in business?
In life, the good kind of surprises can be cause to celebrate – while of course we hope and pray we will avoid the bad kind altogether. In the business world, however, surprises of any sort can be costly. Even an unexpected, dramatic upturn in business can put undue stress on a company’s operational resources. Not to mention what the kinds of surprises like inventory breakage, lost shipments, and inconvenienced customers can do to our bottom line.
While you’ll never be able eliminate all kinds of surprises from your business, there are ways to see further and more clearly into your company’s future. Like most companies, you are sitting on way more marketing data than you probably realize.
Most executives and managers are so busy with day-to-day concerns that they don’t take the time to inventory, compile, and analyze their marketing data for the purpose of anticipating the future. The result of ignoring your marketing data: missed opportunities and wasted resources.
Here are 7 ways to reduce unwanted business surprises by leveraging marketing data:
1. Customer segmentation and targeting for better conversions: differentiating your customers by key attributes tells you which prospects to target and how to develop effective messaging to reach them; this allows you to achieve more predictable – and stronger – response and conversion rates
2. ROI analysis to compare campaign performance: use metrics and math to determine the right mix of marketing modalities so that you have a better idea of how your decisions affect results.
3. Conjoint analysis for feature selection: determine which product features your users like most, and in what combination, allowing you to build and market better-selling products.
4. Data mining to unearth new insights about customer behavior: find patterns in your customers’ website usage, marketing response and purchasing habits so that you can take action to cater to what they want most.
5. Predictive modeling for cross-sell and upsell: determine which products are most likely to be sold with other products, then set up a recommendation engine to recommend those products to past and current customers.
6. Bayesian analysis for estimating campaign response rates by segment: figure out which customer segments have the highest campaign response rates, even if you have only small sample sizes for individual segments.
7. Geo-demographic analysis for ideal location selection: find the ideal locations for expanding your brick-and-mortar store or restaurant, based upon an analysis of the demographic and industrial attributes of your existing best locations.
Check out these 7 marketing analytical techniques and create for yourself a more predictable – and profitable – business life.
Talk to MindEcology about how to better leverage your marketing data with a custom analysis.