Imagine you are an umbrella manufacturer with most of your customers distributed across Seattle, New York City, and Los Angeles. In addition to demographic differences, behavioral differences exist as well: the amount of usage of the umbrellas, the preferred designs of the umbrellas, and what they want the umbrellas for. It is likely a one-size-fits-all solution will not work to increase the loyalty of your customers. How do you go about finding targeted solutions across the segments in your customer base that will enact positive change?
Hotspot analysis is the process of identifying a trend of performance indicators across various segments. In the case of the umbrella company, your customer segments could be the cities in which your umbrellas are sold. Using segmentation, you can break out loyalty across your customers in Seattle, NYC, and LA. Over the past four quarters, let’s say the overall changes in loyalty break out like this, where L stands for loyalty and V stands for vulnerability:
Seattle’s loyalty went up in Q1, but between Q2-Q4 the loyalty decreased, and vulnerability increased, indicating a negative trend in those customers. This suggests there is an underlying issue for the customers in Seattle that is causing them to be disenchanted with your umbrella company.
New York City had a lot of variability over the past year, with increases in loyalty and decreases in vulnerability in Q1/Q3 and decreases in loyalty and increases in vulnerability in Q2/Q4. This would suggest that there is no real trend happening in NYC, just normal expected variance in that customer base.
Finally, in Los Angeles, loyalty decreased and vulnerability increased in Q1, however, between Q2-Q4, loyalty increased and vulnerability decreased, indicating a positive trend in those customers. This suggests that your umbrella company is doing something well and retaining/increasing your share of spend in your customers.
Now, we want to identify the root causes of these swings in loyalty/vulnerability. Because it has demonstrated a trend in increasing vulnerability over the past three quarters, let’s take Seattle and assume that Seattle experienced an exceptionally wet year. These customers likely bought more umbrellas in Q1 than normal, but your production was able to handle the increase in the demand, leading to the increase in loyalty.
Then, the rain kept falling in Q2 and the Seattle customers kept demanding your umbrellas. However, your production capability was not able to keep up with the demand, which could be attributed to a shortage of umbrella handles from your supplier or other external factors that affect umbrella production. After ordering umbrellas from you, due to the restraints of your production line, customers had to wait 3 weeks to receive their umbrella. You have since been trying to play catch up but have not been able to meet the demand of the customers.
These problems would likely be reflected in overall scores in Logistics/Delivery. Making changes in this area would likely positively affect the overall loyalty of your customers in Seattle and thus affect your overall share of spend by your customers. For example, finding a new umbrella handle supplier or reducing the volume of orders you take in could be a solution to the production problem. Custom solutions to your segments will help prevent these changes from being felt across different segments while improving the loyalty of your target.
Companies or organizations with large customer bases sometimes have trouble enacting change to a specific segment of their customers without negatively affecting the rest. When making changes that could potentially affect the entire customer population, a one-size-fits all solution rarely works. Using Hotspot Analysis, targeted segmenting within your customer base gives you a better focus on the individual and allows you to provide a tailored solution to your customer that will not only increase your loyalty, but your bottom line.