Our client is a food delivery startup. They wanted us to prove that happier customers correspond to higher Revenue. Which means, they wanted us to calculate the monetary impact (in terms of overall Revenue) of making a customer happier (say, by X%). In short,
We had to determine the effect of customer experience on food delivery in pecuniary terms.
It’s no secret that the start-up ecosystem is massively focused on Growth and Expansion. While, they do segment their customers internally based on behavioural data, they do not associate it with attitudinal data.
(Quick note- Behavioural data means spending habits whereas Attitudinal data refers to customer satisfaction scores)
Therefore, there were two main challenges.
First, we had to prove that happy customers correspond to higher Revenue.
Why? To inculcate customer centricity in the organisation, not merely as an altruistic practice but as something that has a significant impact on Profit.
Second, we had to identify priority areas that have the highest impact on recommendation, for each user segment.
Intuitively, everyone understands that high value customers should be kept happier. It’s common sense.
However, we provided our food delivery client with a rupee (monetary) value of how much extra Revenue happier customers will generate.
We proved that as the customer satisfaction score increases (say, from 7 to 8 to 9), so does the Revenue.
To put in simply,
FINDING 1- Promoters spend almost 39% more as compared to Detractors.
FINDING 2- While the reorder rate is similar for Promoters and Passives, it is significantly lower for Detractors
How did we do this?
Our first step was to create customer segments according to both behavioural and attitudinal data using various Clustering techniques.
Because of this, we were able to accurately pinpoint problems/issues specific to each segment.
For example, we were also to answer questions like-
Is slow Delivery Speed a more important issue for high value or low value customers? Are high value customers more likely to be unhappy with slow Delivery speed or is it equal for both?
The chart below shows the factors that influence customer satisfaction.
This allowed our client to craft a personalised food delivery experience, which customers always respond better to than impersonal or broad offerings.
What’s more, this gave our client granular, actionable data to improve services. Such as, if they improve one specific area (order speed etc), what impact it will have on reorder value.
So, we identified Priority areas that were generating Promoters and Detractors.
In conclusion, this helped our client deliver data-backed streamlined Customer Experience to delight customers and increase Revenue.
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