Do you happen to know this man?
His name is Dave Brailsford, a coach rendered famous for overhauling the British national Cycling Team and, in only 6 years, bringing his team to victory at the 2004 Olympic Games in Athens and starting a tidal wave sweeping the 2 subsequent Olympic gatherings. Being the first Briton to win The Tour de France in 2012 made a legend of the man.
What is the magic sauce behind this success story? His secret was to take a holistic approach (Nutrition, Training, Lifestyle even psychology), then break down the training process into elementary steps and seek small incremental improvement for each with an expectation for it to snowball.
Quite naturally, one can apply the same concept to Marketing by breaking the Customer Lifecycle into small "bits" and looking at incremental improvement at each stage.
To illustrate the point:
|Simplified Customer Lifecyle|
Each customer evolves over time. Let's look at a transition matrix with representative numbers of a medium-sized retailer:
Each line sums up to 100%. As an example, this matrix says that out of 100 loyal customers, 80 remain Loyal and 20 becomes at-risk
Now, let's do a projection to future dates using this transition matrix. In order to get a projection to 2 months we apply twice the Transition Matrix to the initial population; 3 time for 3 months, etc.
We do it twice:
- Under "normal conditions"
- Under "Brailsford conditions" that boosts the transition matrix by applying little 1% incremental improvement for each transition.
Here, the Brailsfold-inspired improvement is twofold: Getting more customers to Loyal as well as getting less at-risk or even lost.
The result is quite stunning. With our hypotheses, we get $560M of monthly demand vs. $467M, a 20% improvement! You will also notice that the company is much healthier by having a large share of its customer base as Loyal.
The bottom-line is that companies can look at quite significant results by launching multiple small initiatives across the Customer Lifecycle with intent of creating this snowball effect.
For those who want to check the code and rerun the analysis with their own numbers, it's here