The Learning Adopting Improving Performing (LAIP) model provides a new tool for categorising personal analytics metrics according to the maturity of the behaviour. This allows program managers to channel behaviour adoption appropriate to business priorities and the current status of the individual and cohort.

The model stems from a collaboration between  Ben Martin and myself when looking at metrics to encourage effective social selling practices.

Anyone creating a personal analytics program may find it a helpful tool when evaluating which metrics to include, when to include them and how to weight them.

The LAIP Model

In our LAIP model, maturity of a behaviour is evaluated along two axes:

• how established is the behaviour?  has it become a habit?
• how much value does the behaviour drive? is it worthwhile?

Based on these two axes we can create a boston matrix and into each quadrant we can categorise our metrics.

Learning

The player is learning the new behaviour and associated tools / processes.

Adoption

The player is seeking to create a regular habit around the new behaviour.

Improving

The player already has a habit but seeks to derive more value from the existing behaviour.

Performance

The player is seeking to achieve higher performance in the adopted behaviour.

Worked Example

Let’s apply this model in the context of an inside sales team looking to drive telephone calls off the back of cold (unsolicited) emails.

Say for example I have the following metrics which I am tracking for each of my sales reps:

• Total emails sent  (Learning)
• 20  emails sent per day (Adopting)
• Responses per email ratio  (Improving)
• Number of telephone calls arranged (Performing)

Number of cold emails sent is a  Learning metric because it is relatively simple. Sending out emails is a new behaviour and for now I just want to track the total number I’ve sent. This helps me as I get going with sending out those cold emails.

Once I’ve got the hang of sending emails I might want to tighten up the metric so I can be sure I adopt the behaviour I want which is to send 20 each day. So a ratio formula – number of cold emails / day with a goal (20) is a real Adopting metric. This helps me adopt the behaviour I want to achieve.

My Performing metric, in this case, has nothing to do with the underlying behaviour but all to do with the value I am hoping to achieve with my cold emailing behaviour – which is telephone calls with a real lead. So here my metric is number of telephone calls I’ve arranged.  Over time I can make this more sophisticated, perhaps calls per month, per week and so on.

Finally there is a chance that I develop my cold emailing behaviour but it isn’t driving the value that I want. In this case I need to consider anImproving metric – a ratio of email responses to those sent out. This looks at the quality of the emails in terms of who I sent them to and their content. An improving metric assumes that the behaviour is established but is not driving value.

Handling misfit metrics

Like any model, the LAIP model can only offer an approximate view on reality – inevitably there will be some metrics that seem to fit into more than one category or no category at all. The expectation in this case is that the manager will provide a “best fit” assessment when plotting metrics on the matrix.

Conclusion

Overall this model offers gamification gurus a way of categorising metrics, particularly useful in multi-metric scoring systems where scores from multiple behaviours are composited into a single score.

By categorising the metrics,  the program manager can ensure that the personal analytics dashboard is aligned to the current business goals for the individual or current cohort. The program manager does this by weighting and prioritising metrics within the overall score algorithm.